Ecn 211 Filer Homework 4 Inequality

Abstract

Background

Identifying domains in protein sequences is an important step in protein structural and functional annotation. Existing domain recognition methods typically evaluate each domain prediction independently of the rest. However, the majority of proteins are multidomain, and pairwise domain co-occurrences are highly specific and non-transitive.

Results

Here, we demonstrate how to exploit domain co-occurrence to boost weak domain predictions that appear in previously observed combinations, while penalizing higher confidence domains if such combinations have never been observed. Our framework, Domain Prediction Using Context (dPUC), incorporates pairwise "context" scores between domains, along with traditional domain scores and thresholds, and improves domain prediction across a variety of organisms from bacteria to protozoa and metazoa. Among the genomes we tested, dPUC is most successful at improving predictions for the poorly-annotated malaria parasite Plasmodium falciparum, for which over 38% of the genome is currently unannotated. Our approach enables high-confidence annotations in this organism and the identification of orthologs to many core machinery proteins conserved in all eukaryotes, including those involved in ribosomal assembly and other RNA processing events, which surprisingly had not been previously known.

Conclusions

Overall, our results demonstrate that this new context-based approach will provide significant improvements in domain and function prediction, especially for poorly understood genomes for which the need for additional annotations is greatest. Source code for the algorithm is available under a GPL open source license at http://compbio.cs.princeton.edu/dpuc/. Pre-computed results for our test organisms and a web server are also available at that location.

Background

Protein domains are fundamental units of protein structure, function, and evolution. As a result, domain prediction is an important first step in the annotation of protein sequences [1]. Enhancements in domain identification improve protein annotations, as domains are often associated with specific cellular functions, and novel domain predictions can either newly predict or further refine functional predictions [2,3]. Furthermore, some domains are known to be associated with structures and thus their identification can be used for inferring protein structure [4,5]. Domain predictions are also the starting point for a range of more sophisticated analyses, including comparative genomics of domain families in diverse organisms [6-8], studies of the evolution of protein and domain structure and function [9-11], prediction of protein-protein interactions [12-15] and identification of complex evolutionary relationships [16].

The majority of proteins contain more than one domain [17]. Domains occur in different combinations, and the domain composition of multidomain proteins is critical for their specialized functions. Domains do not form random combinations, and indeed a limited fraction of domain pairs and triplets are highly recurrent [18]. While the mechanisms that lead to new domain combinations have been extensively explored and the analysis of observed domain combinations has received significant recent attention (reviewed in [19]), this information has not yet been widely used for domain prediction.

Here, we exploit the tendency of certain domains to co-occur with each other in order to improve domain identification. While domains within any given sequence are typically identified by considering each domain family individually, domain co-occurrence or "context" is useful in detecting weak sequence similarity [20]. In particular, two domain families that frequently co-occur provide "positive context", and for a given sequence, if these domains are identified with low confidence individually, their weak signal can be amplified. Similarly, domain family pairs that have never been observed provide "negative context" and their occurrences can be penalized (but not necessarily eliminated), thereby preventing unnatural combinations of low scoring predictions and limiting false predictions.

We have developed a novel graph-theoretic framework that combines individual domain scores with pairwise scores derived from domain co-occurrence statistics, in order to find a set of domains that maximize an overall score. Our approach, dPUC (Domain Prediction Using Context), uses Pfam [21] profile hidden Markov models (HMMs) [22] to score domains individually, along with a novel log-odds scoring system that captures the propensity of pairs of domains to be found in the same sequence. While we have developed our approach using Pfam, alternate libraries of domain profiles (e.g., SMART [23], Superfamily [4], or CDD [5]), as well as different pairwise context scoring schemes, can be readily incorporated.

We test dPUC via rigorous benchmarks on eight organisms, ranging from bacteria (Escherichia coli and Mycobacterium tuberculosis), to protozoa (yeast and Plasmodium species) and metazoa (human, fly, worm). We present the first large-scale demonstration that incorporating domain context improves domain predictions in organisms across the evolutionary spectrum. Overall, dPUC gains up to 11% more domains at noise rates comparable to the Standard Pfam's, and outperforms the recently-published method CODD (Co-Occurent Domain Discovery) which also incorporates domain context [24]. Further analysis demonstrates that dPUC's performance improvements are due in part to penalization of negative context as well as allowing context between repeated domains. Importantly, we also find that dPUC does not require much additional time beyond that necessary for Pfam to initially identify domains.

We have found that dPUC is particularly effective at improving domain predictions for the genome of the poorly annotated malaria parasite Plasmodium falciparum. For this parasite, we perform further testing and show that dPUC's predictions are consistent among orthologs in closely related Plasmodium species. Moreover, we have used dPUC to annotate Plasmodium proteins and have newly identified proteins taking part in core processes such as ribosomal assembly and other RNA processing events. Overall, our findings in Plasmodium and other genomes suggest that domain identification can be significantly improved by incorporating context, particularly for organisms with poorly understood genomes.

Methods

Data

Pfam database

Pfam 23 was downloaded from the website (http://pfam.sanger.ac.uk/). A list of nesting families was extracted from Pfam-A.seed, defining a set of "allowed overlaps" consisting of domains with overlapping amino acid ranges within a protein sequence. The domain architecture of a protein is defined as its ordered list of domains, including repeats. We parsed the Pfam-A.full file to obtain the complete domain assignments to Uniprot 12.5 sequences, and thereby the domain architectures used to compute domain context scores (described below).

Proteomes

We used the proteomes of several model organisms for our testing, along with those of several parasites of medical interest. The proteomes of E. coli, M. tuberculosis, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens, were downloaded from Uniprot [25] 15.8 (15.10 for M. tuberculosis), by obtaining all sequences with the organism taxon numbers 83333, 1773, 4932, 6239, 7227, and 9606, respectively, the keyword "complete proteome" (keyword 181), and not including isoforms. The proteomes of the Plasmodium species P. falciparum [26], and P. vivax [27] were downloaded from PlasmoDB 6.0 [28], and sequences containing internal stop codons were removed. Consistency of predictions on the Plasmodium species were further tested by analysis on P. knowlesi [29], P. chabaudi, P. berghei, and P. yoelii, which were also downloaded from PlasmoDB.

Approach

The dPUC model

For a given protein sequence, let P be a set of candidate domains (which we obtain by setting a permissive threshold on the HMMER domain E-values). For each domain i in P, let Hi be the domain score of i, let Ti be the domain score threshold for the family of domain i, and for each pair of domains i and j, let Cij be the context score between i and j (see dPUC context scores below). Let D P be a subset of domains, and then for each domain i we define its score with respect to this set of domains as

Our goal is to find the subset of domains D P that maximizes the "total score" Σi D Si,D, constrained so that D only contains "allowed overlaps" and each domain i in D satisfies the domain threshold Si,D ≥ 0. Note that without context scores (that is, Cij = 0 i,j), the last inequality is reduced to the standard definition of a domain threshold. Our framework can be illustrated from a graph-theoretic point of view (Figure ​1).

Figure 1

Illustration of the dPUC framework using Pfam to identify initial domains. A. We gather candidate domain predictions using Pfam with a permissive threshold. Domains are arranged in the x-axis by their amino acid coordinates, but the y-axis arrangement...

Formulation using ILP

We solve our combinatorial optimization problem with integer linear programming (ILP). In particular, we define a 0/1 variable xi for each domain i, and a 0/1 variable xij for each pair domains i and j. If variable xi is set to 1, this indicates that domain i is included in our final set of predictions. Similarly, if variable xij is set to 1, this indicates that the pair of domains i and j are in our final set of predictions. The score of each domain with respect to the other chosen domains is given by

Our goal is to maximize

where TF* is a Pfam-specific sequence threshold (see Additional File 1 "dPUC implementation details"). Though a theoretically difficult problem, we solve this ILP via a two pronged approach based on domain elimination and using lp_solve 5.5.0.14 [30]. See Additional File 1 Supplementary Methods for further information on solving the ILP and relevant details about the Pfam curated and domain-specific "gathering" thresholds, including the treatment of combined local and glocal domains.

dPUC context scores

We derive pairwise log-odds context scores using Pfam architectures found in Uniprot. These architectures are filtered to remove those that occurred in only one sequence, since they are more likely to be erroneous. Let eip be the number of domains of family i in protein p, and let ep be the total number of domains in protein p. We obtain "normalized pair counts" cij as follows:

where the sum over proteins p goes only over multidomain proteins, as single domain proteins would have zero denominators. Note that the normalization given above divides the pairs from each protein by the total number of domains of that protein (minus one), to compensate for inflated counts due to proteins with many domains. While there are many possible normalization schemes, our scheme keeps domains from counting themselves, and each protein p contributes ep counts to all cij in total. These counts are turned into probabilities by setting

where c = Σij cij, α = 1 is the regularization parameter (to handle the zero count case), and n is the number of domain families observed in the architectures (≤ 10,340 families in Pfam 23 due to architecture filtering). By construction, Σij pij = 1. The null model probabilities are

since random domains appear approximately uniformly using E-value thresholds. The context scores are

with b = 2, to match the HMMER2 bit scores. From these equations we can derive that unobserved pairs (pairs with cij = 0) are always penalized, since they are assigned the same large negative score of -logb[1 + c/α]. Finally, observed pairs with negative scores are instead set to zero. This way, observed pairs are never penalized. For Pfam 23, only 5 out of the 15,929 observed pairs had negative scores that were subsequently set to zero. We also experimented with other log-odds scoring schemes (see Additional File 1 Supplementary Results); however, most schemes did not significantly change the performance of the overall approach.

Empirical analysis of dPUC runtimes

Since each protein sequence is a separate problem, the problem can be parallelized over proteins. We considered the 25,047 proteins of E. coli, M. tuberculosis, S. cerevisiae, and P. falciparum, and ran each problem on a 2.66 GHz Intel processor with 8 GB RAM. Runtime is measured in wall clock time. The Standard Pfam (the bulk of which is HMMER processing) runs in 96.4 ± 41.4 seconds per protein (mean ± standard deviation), while the dPUC overhead (that is, not including HMMER) runs at an additional 0.0229 ± 0.865 s/protein. However, the runtime distributions have very long tails, as can be seen in Additional File 1 Figure S1. Nevertheless, we find that in 95% of the cases, the dPUC optimal solution is obtained within 0.015 s, and in 99.94% of the cases, the optimal solution is found within 1 s.

Other approaches

Baseline methods

We tested three non-context methods that serve as natural baselines for dPUC. The first is the "Standard Pfam", which uses their curated and domain-specific "gathering" (GA) thresholds (details in Additional File 1 "Pfam relevant details"). Since the Standard Pfam produces a single data point, we created the "Pfam Extended GA" method, in which the GA thresholds are shifted by constant amounts (details in Additional File 1 "Pfam Extended GA thresholds"), allowing us to explore a range of noise cutoffs. Lastly, the "Pfam E-value" method uses domain E-value thresholds instead of the GA thresholds.

CODD context method

To contrast with dPUC, we implemented two simple context approaches that filter candidate domains. The first is based on CODD, which incorporates positive context information whereby a low scoring domain can be predicted based on co-occurrence with a higher scoring domain [24]. Given a network of positive context domain pairs, a set G of Pfam predictions that pass the gathering thresholds with no disallowed overlaps, and a set of candidate predictions D, this filter finds the set of final predictions P as follows. First we initialize P = G. For each domain d in D sorted ascending by E-value, we transfer d to P if d has positive context with any domain in G and d does not have disallowed overlaps with any domains in P. The second approach is a novel double positive and negative filter of our creation that we call nCODD, and is described in the Additional File 1 Supplementary Methods.

Testing

We developed two approaches for assessing the performance of dPUC.

Estimated FDR

In our first test, we compare the number of predictions from different methods (i.e., Standard Pfam, dPUC, and CODD) on real and shuffled protein sequences. The key idea is that domain predictions on shuffled sequences arise by chance alone, whereas predictions on real sequences give us the total number of predictions (true or false), and their ratio approximates the false discovery rate. We shuffle the residues of each protein separately, thereby preserving the amino acid composition of each sequence as well as the length distribution over each proteome. For the context methods (dPUC, CODD, nCODD), we run them on the real sequence concatenated to its shuffled sequence, but only count the number of predicted domains on the shuffled portion of the sequence. Therefore, we count the random predictions that might have been rewarded by positive context not only with other random predictions, but also with potentially real domains from the real sequence (Figure ​2A). The shuffling is performed 20 times for each sequence.

Figure 2

dPUC predicts more domains over a range of FDRs. A. Illustration of the FDR estimation procedure. For each original protein sequence, we make predictions on it and on twenty shuffled sequences concatenated to the original sequence, to allow "real" domains...

We estimate the false discovery rate (FDR) as follows. Let A be the number of predictions per shuffled protein, and let R be the number of predictions per real protein. Then FDR = A/R, which is a common approximation of the FDR [31]. To calculate the FDR of the new dPUC domains only, let An and Rn be the quantities defined above when we use negative context only, and A and R when we use the E-value threshold of interest for candidate domains. The difference of the data corresponds almost entirely to the novel domains, ignoring the effect of the initial negative context elimination:

This benchmark is only appropriate for methods in which the orientation of the domains is unimportant, as it is for the methods tested here. We note that the Markov model of Coin et al. [20] is sensitive to the orientation of domains, so its performance cannot be measured by this benchmark; we also note that an implementation of this program is not available online. See Additional File 1 "Estimated FDR details" for further details on estimating the FDR.

Ortholog coherence scores

In our second test, we measure how often domains are predicted across orthologs. The key assumption is that real domains are very likely to be present in orthologs, whereas false domains are very unlikely to be. Therefore, the average "ortholog coherence" score is inversely related to the amount of spurious predictions. We chose the Plasmodium species' proteins because their sequence divergence is large enough to discard "coherent" false predictions due to high sequence similarity, yet the sequences are similar enough for orthologs to be identified easily, and domain architectures are largely conserved. Moreover, as we show below, our approach performs well in improving domain identifications in these species.

We computed the orthologous groups of six Plasmodium species using OrthoMCL 1.4 [32]. We obtained 5582 orthologous groups (32,250 proteins). We eliminated orthologous groups with more than 13 proteins to avoid constructing large alignments and to ignore the well-studied large paralogous families that are characteristic of Plasmodium species (including PfEMP1, RIFIN, STEVOR, in P. falciparum and VIR, YIR, KIR, and CIR in the other species, which may bias our results). This left us with 5523 groups with a total of 30,065 proteins. Each group was aligned with T-Coffee 8.14 [33], using the M-Coffee special mode which combines the alignments of T-Coffee, ProbCons 1.12 [34], and Muscle 3.6 [35]. The score of a domain is the fraction of times we observed overlapping domains (after mapping to the alignment) of identical family in the orthologs. The score of a method is the average domain score over all proteins (Figure ​3A).

Figure 3

dPUC predicts more domains over a range of Ortholog Coherence scores on Plasmodium species. A. Illustration of scores. Domain predictions are made on hypothetical aligned orthologs and in-paralogs (Pf1, Pf2, Pv1, and Pc1). Color denotes domain family....

Results

We chose eight diverse and representative organisms to test our method, including human, four model organisms, D. melanogaster, C. elegans, S. cerevisiae, E. coli, and several pathogens including the eukaryotic human malaria parasites P. falciparum and P. vivax, and the prokaryotic parasite M. tuberculosis. We test our method separately on each of these organisms since they have different and sometimes extreme biases in protein lengths, amino acid compositions, amino acid coverage by domains, and domain family content (Additional File 1 Figure S2).

dPUC improves Pfam predictions across all tested organisms and across a range of FDRs

For a given method (e.g., Standard Pfam, dPUC, or CODD), we estimate its FDR as the ratio of the number of predictions made on shuffled sequences (when concatenated to real sequences, in the case of context methods), to the number of predictions made on the real sequences only. This concatenation approach allows noise in the shuffled sequence to be boosted by potentially real domains via context (Figure ​2A). We test dPUC by varying the HMMER E-value threshold on its candidate domains. dPUC consistently enhances the performance of Standard Pfam across organisms and over the entire range of FDRs tested (Figure ​2B). The Standard Pfam produces a single data point, and notably, its FDR is non-zero for all organisms. To explore Pfam's tradeoff between coverage and false positives, we vary the threshold to the HMMER E-value; this leads to fewer predictions on real sequences for the same FDR, showing that curation of Pfam thresholds has added value. We also vary the Pfam "gathering" thresholds by shifting them uniformly for all domain families, which performs better than E-value thresholds in all organisms except in M. tuberculosis, suggesting the Pfam curated domain thresholds are less appropriate for this diverged and compositionally biased (GC-rich) organism than they are for model organisms. Note that even when limiting the dPUC candidate domains to those predicted by the Standard Pfam (effectively when setting E ≤ 0.001, the leftmost datapoint of the dPUC curves), we see a sharp decrease in dPUC's FDR relative to the Standard Pfam; this improved performance can be directly attributed to the removal of false predictions using negative context scores.

The FDR as described above applies to the entire set of predictions. For dPUC with low HMMER E-value thresholds on the candidate domains, the FDR is the net effect of removing domains from the Standard Pfam through negative context, as well as adding new domains with positive context. Since negative context alone reduces the FDR of the predictions, the FDR of the new domains must be larger than the FDR of the whole. For dPUC with E ≤ 1, the FDR of the new domains only (see Methods) varies between 0.8-2%, depending on the organism (see Additional File 1 Table S1).

dPUC outperforms simple filters incorporating context

We implemented an alternative context method, using filters, in which context scores are not defined. The first filter emulates the method CODD [24], in which candidate domains pass if they co-occurred with the domains that pass the Pfam gathering thresholds. Interestingly, the performance of CODD is similar to non-context methods in some organisms, and especially at high HMMER E-values thresholds (Figure ​2B). We tested CODD using the published CODD positive context network, which notably lacks context between domains of the same family, and additionally removes observed domain pairs that do not occur more often than expected from the hypergeometric distribution. We note, however, that using the CODD filter with the dPUC network of positive context domain pairs improves the predictions compared to using this more limited CODD network (Additional File 1 Figure S3). Our previous analysis suggested that a negative filter (to mirror our negative scores) was necessary to enhance the FDR, and indeed, our approach of a double positive and negative filter, nCODD (see Additional File 1 Supplementary Methods), has better performance than CODD (Additional File 1 Figure S3). Nevertheless, nCODD falls behind dPUC in all organisms tested, with the exception of D. melanogaster and H. sapiens, in which performance is similar to dPUC's; this could be due to the overall better annotations of these two model organisms. In P. falciparum, nCODD is too aggressive and removes predictions from the real sequences as well as from the shuffled sequences, shifting the entire curve downward relative to dPUC. These results suggest that there is added value in our setup of scores and thresholds, compared to these simpler filter formulations.

Performance of chosen context scores

Our context scores have a specific form (log-odds, see Methods) in which a pair score is logarithmically proportional to how often the domain family pair is observed in Standard Pfam. Although these log-odds scores seem naturally compatible with the HMMER log-odds scores, it is not certain that this is the form the scores should have. To test the importance of these scores, we shuffled the positive scores (three times), which resulted in decreased performance compared to our original context scores, but better performance than that of non-context methods (data not shown). Therefore, carefully choosing values for our scores is important, and our form, which weighs the evidence of co-occurrence per domain family pair, performs better than random scores from the same distribution. Additional score parameter variations are discussed in Additional File 1 "dPUC Pfam parameter robustness".

dPUC increases Pfam coverage

We choose a threshold of E ≤ 1 to identify the candidate domains, which corresponds to an overall FDR of 0.03-0.2% across organisms; that is, this is within an order of magnitude of the Standard Pfam FDR of 0.01-0.09% (Additional File 1 Table S1). We then calculate the dPUC net percent improvements compared to the Standard Pfam (Table ​1). The number of domains increases by 4-11%, with a trend roughly inverse to the level of initial coverage in each organism (Additional File 1 Figure S2D). Unique domain families increase by 3-6%, while repeated families increase at the higher rates of 12-40%. Amino acid coverage improves by 2-8% relative to the amino acids covered by the Standard Pfam. However, most new predictions appear in proteins that have Standard Pfam domains, since there are smaller increases in protein coverage (0.1-1.8%). Overall, the two Plasmodium species attain the highest increases in coverage, but even the best-annotated model organism, E. coli, experiences increases in coverage under all metrics (Table ​1).

Table 1

dPUC increases domain predictions and amino acid coverage

dPUC leads to additional functional annotations

We used the MultiPfam2GO procedure [3] to obtain GO annotations from our domain predictions. This program uses a probabilistic approach to determine how sets of domains imply GO terms. We ran this procedure on the Standard Pfam and dPUC predictions, and filtered the results so that only the most specific GO terms remained (by removing all ancestors using all GO relationships).

In total, 2.5-7.8% of proteins had new or modified GO terms, depending on the organism. As we observed before with Pfam coverage, the two Plasmodium species attained the largest increases in GO terms. We present the summary of our data in Table ​2 (detailed counts are in Additional File 1 Table S2). The vast majority of the original GO terms from Standard Pfam (over 97%) are preserved by dPUC with E ≤ 1. Additionally, we obtain a 1.2-4.0% increase in GO terms that are completely new, and a further 0.4-0.9% in GO terms that are more specific than previously existing GO terms. Our procedure also results in a negligible 0.2-0.7% of GO terms in dPUC becoming less specific than their Standard Pfam counterparts, and 0.3-1.4% GO terms being deleted. Similarly, most proteins with GO terms from Standard Pfam are unchanged by dPUC, but 1.8-5.0% of proteins have new or more specific GO terms, while only 0.3-1.3% of proteins have fewer or less specific GO terms. Finally, 0.3-1.5% of proteins experience both increased and decreased specificity of GO terms.

Table 2

dPUC predictions lead to novel or more specific Gene Ontology terms on proteins

As expected, inspection reveals that most GO terms that are deleted or become less specific are a consequence of either domain replacement brought upon by positive context (which are usually accompanied by new GO terms) or domain removal due to negative context. In either case, we expect the new domain predictions to be more accurate, and the resulting removal of GO terms is welcomed. Interestingly, the addition of domains can also lead to GO term loss, which is a consequence of the MultiPfam2GO probabilistic model combined with incomplete training data (a detailed example is presented in Additional File 1 "Novel domain predictions may lead to GO term deletions with MultiPfam2GO").

Domain coherence is enhanced across Plasmodium orthologs

We chose to focus on the Plasmodium parasites, the causative agents of malaria, due to their wide impact on human health, and also because our method showed the largest improvements in these organisms. A simple test of prediction quality is to ask if domains are predicted in orthologous sequences as well, since orthology information is not exploited by our method. Importantly, ortholog co-prediction is expected to be low for false predictions and high for real domains, providing us with an alternative measure of noise that does not depend on statistical simulations.

We defined an "ortholog coherence" score between 0 and 1, namely, the average fraction of times a domain is predicted in orthologous proteins (Figure ​3A). We looked at the proteins of six Plasmodium species (P. falciparum, P. vivax, P. knowlesi, P. chabaudi, P. berghei, and P. yoelii) with orthologs or in-paralogs in these organisms as predicted by OrthoMCL, and plotted these scores against their number of domain predictions per protein for the same methods tested earlier (Figure ​3B, Additional File 1 Figure S4). These coherence scores may be artificially low due to artefacts in the alignments or gene models (that is, exons might be missing in some orthologs), but on average all methods should be affected equally. Using dPUC with E ≤ 1 increases domain predictions by 11% at practically the same ortholog coherence (a 0.87% decrease) as the Standard Pfam. Coherence scores recapitulate our conclusions derived independently from our FDR analysis, displaying an increase in domain predictions over a wide range of ortholog coherence thresholds.

Novel P. falciparum annotations

Careful manual analysis of novel domain predictions on the P. falciparum proteome led to the reannotation of 55 proteins, either due to novel Pfam domains that had not been predicted before (Table ​3) or due to novel Pfam domains confirmed by other domain databases (Table ​4). Our discoveries include the identification of orthologs to many core machinery proteins conserved in all eukaryotes, including those involved in ribosomal assembly and other RNA processing events, which surprisingly had not been previously known (full details in the Additional File 1 "New annotations on P. falciparum"). Three predictions find direct support in the literature. PF11_0086, the predicted "poly(A)-binding protein-interacting protein 1" PAIP1, has a strong yeast 2-hybrid interaction with PFL1170w [36], the annotated "poly(A)-binding protein" PABP1 homolog in P. falciparum. Additionally, the two DEAD-box helicases PFE1390w and MAL8P1.19, predicted to be the orthologs of ABSTRAKT and DBP10 respectively, agree with the assignments of a recently published curated list of P. falciparum helicases [37]. Such new functional annotations can serve as starting points for developing new therapeutic intervention strategies. Our suggestions have been submitted to PlasmoDB [28] as community annotations.

Table 3

Completely novel P. falciparum dPUC predictions lead to refined protein annotations

Table 4

Additional P. falciparum dPUC predictions lead to refined protein annotations

Discussion

We have shown that domain identification can be greatly improved across a diverse set of organisms by exploiting domain co-occurrence information. Our method is more successful at increasing domains in genomes with lower domain coverage (Table ​1 and Additional File 1 Figure S2D) and shows greatest improvements in the least annotated of the organisms we tested, the malaria parasites.

To our knowledge, there has been limited prior work examining the utility and systematic use of domain context for domain prediction [20], and to date such approaches have not been widely adopted, nor are they publicly available. Coin et al. developed a Markov model framework to score the dependence between domains in a sequence. However, their work presents two versions of the Markov model, each of which has opposing limitations: the first-order Markov model has few parameters, but its predictive power is limited; alternatively, their kth-order Markov model has more power (k = 5 gives twice as many predictions as k = 1), at the cost of an exponential (in k) increase in parameters, which become difficult to estimate accurately and increase the computational requirements of the approach. In contrast, dPUC allows interactions between all domains, as opposed to just the previous k domains, while keeping the parameter space small, simplifying parameter estimation and reducing our program's memory usage. Furthermore, the source code accompanying our work should facilitate further improvements in context-based identification of domains.

Recently, a method was published (CODD) that uses a list of "favored" domain pairs to predict lower-scoring domains in P. falciparum if high scoring domains that preferentially appear with them are found in the same sequence [24]. We have shown that dPUC significantly outperforms such a filter (Figure ​2 and Figure ​3), as it predicts many more domains at any fixed FDR. There are several important methodological contributions of dPUC that together explain this performance enhancement. First, our method penalizes (but does not necessarily eliminate) domain pairs that have never been observed before, whereas CODD uses only information about favored domain pairs. Second, our method uses domain family pair-specific log-odds scores and thresholds, while CODD treats all favored domain family pairs equally. Third, our method allows weak domains to boost each other in the absence of strong domains without any special treatment, while CODD has to be run separately to allow candidate domains to predict each other, and with different E-value thresholds since this is much more prone to false predictions. Fourth, our problem is combinatorial in nature, allowing domains with low E-values to overcome higher E-value candidates if the context is more favorable, while CODD simply prioritizes candidate domains by E-value. Fifth, CODD uses a limited positive context network that does not include repeating domains and eliminates pairs that do not have small enough p-values derived from the hypergeometric distribution. Although our benchmarks show that negative context and our more complete dPUC positive context networks are critical differences (Additional File 1 Figure S3 and Figure S4), these features alone do not explain dPUC's higher performance, suggesting that the other differences also play an important role. The importance of negative context is consistent with the fact that domain context is usually not transitive. In particular, we find that if a Pfam domain A co-occurs with domains B in at least one sequence in Uniprot and it co-occurs with domain C in at least one sequence, then domains B and C co-occur in at least one sequence only 15.7% of the time; however, domain pairs that do not co-occur can readily be predicted by methods that only reward favored domain pairs.

Other works, such as AIDAN [38] and a similar work [39], have also used domain context to refine domain predictions in a manner complementary to the work discussed here. In particular, while our method learns parameters from domain databases and uses these to make predictions on each protein sequence independently, AIDAN clusters sequences based on the original domain predictions, and refines these predictions by performing domain architecture alignments and evaluating sequence comparisons within these clusters when domains are missing or are mismatched. Further, the thresholds used by AIDAN are tuned so less than 0.05% of the new domain assignments are errors. The AIDAN approach can be used as a second step in any domain prediction pipeline, including our own, to further refine predictions by directly taking the sequence similarity of related proteins into account, and this is a promising avenue for future work.

Domain context is independent of the information that HMM scores capture, since an HMM score only uses the sequence the domain encompasses, ignoring the surrounding sequence that contains the rest of the domains, while domain context allows these other domains to affect each other. These two information sources do not overlap. In other words, HMMs capture so-called "vertical" information [40] while domain context captures "horizontal" information. This is similar to advances in remote homology detection due to the incorporation of secondary structure predictions in addition to HMM/profile information [40-42]. Fuzzy HMMs [43] have been proposed to model positional dependence within a domain, but currently these predictions are less tractable compared to classical HMM algorithms. We believe that incorporating horizontal information in HMM analysis will become more common, as it is evidently an important means for boosting subtle sequence signals.

We have implemented a log-odds system to score the co-occurrence of domain pairs. Indeed, log-odds scores are widespread in bioinformatics, and our scoring system is inspired by the log-odds scores used in local sequence alignment (LSA) [44]. We borrowed the concept that optimal scores ought to be log ratios of the "target" frequencies versus the "background" frequencies. Broadly speaking, there are many similarities in both problems, including that they score pairs and that the searches are both "local" (LSA seeks the best subsequences, while dPUC seeks the best subset of domains). However, an important assumption of the theory behind LSA is that different amino acid pairs are uncorrelated, whereas domain pair scores in dPUC are correlated, since solutions must be connected as a full clique (see the solution in Figure ​1B). Our framework does not handle these correlations, and while we have demonstrated empirically that log-odds scores perform remarkably well, we expect that a better theoretical foundation for our problem (which might explicitly relate arbitrary scores to statistical parameters) will lead to scores that yield better performance. Other changes in the dPUC scoring system may also lead to improvements; for example, differentiating between the two orderings of domain pairs is a promising avenue for future research, as it has been shown that many domain pairs occur in a single order [18].

While we applied dPUC to Pfam using HMMER2, there are other tools available which could be used, including SAM [45] and the new HMMER3 [21], as well as other domain databases, like SMART, Superfamily, and CDD. More broadly, the dPUC framework could be used to improve prediction of other protein sequence features, including signal sequences and transmembrane domains, both of which co-occur with a limited number of domains. The same underlying mathematical framework may also be applicable in other diverse settings, for example, in uncovering cis-regulatory modules, as transcription factors often work cooperatively to regulate genes. We have shown that dPUC significantly improves predictions over other models that score domains independently. We anticipate that our framework will be useful for incorporating context with other DNA, RNA, or protein sequence features and will find its greatest utility in the annotation of newly sequenced genomes from highly diverged organisms.

Conclusions

Common domain identification methods typically consider each domain separately, and they have limited applicability when the similarity between the query sequence and known domains is very low, as is the case for poorly understood genomes. Here we significantly improved domain predictions by exploiting the tendency for domains to co-occur in specific combinations. We developed an approach based on the observation that weak domain predictions are better supported if they appear in previously-observed combinations, while domain combinations that have never been observed are less likely to be valid. Our method improved domain predictions in all organisms tested, including the best known model organisms. The biggest improvements were seen for the divergent organism Plasmodium falciparum, the parasitic agent of malaria, for which much of the core cellular machinery remains unidentified. Overall, our approach is likely to be most useful for poorly understood genomes where the need for additional annotations is arguably the greatest.

Authors' contributions

AO and MS conceived and designed the approach and experiments. AO implemented the approach and performed the experiments. All authors analyzed the data, wrote, read and approved the final manuscript.

Supplementary Material

Additional file 1:

Supplementary information. PDF file (17 pages) that includes all the supplementary methods, results, figures and tables.

Click here for file(793K, PDF)

References

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  • Schug J, Diskin S, Mazzarelli J, Brunk BP, Stoeckert CJ. Predicting Gene Ontology Functions from ProDom and CDD Protein Domains. Genome Res. 2002;12:648–655. doi: 10.1101/gr.222902.[PMC free article][PubMed][Cross Ref]
  • Forslund K, Sonnhammer ELL. Predicting protein function from domain content. Bioinformatics. 2008;24:1681–1687. doi: 10.1093/bioinformatics/btn312.[PubMed][Cross Ref]
  • Wilson D, Pethica R, Zhou Y, Talbot C, Vogel C, Madera M, Chothia C, Gough J. SUPERFAMILY--sophisticated comparative genomics, data mining, visualization and phylogeny. Nucl Acids Res. 2009;37:D380–386. doi: 10.1093/nar/gkn762.[PMC free article][PubMed][Cross Ref]
  • Marchler-Bauer A, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, Fong JH, Geer LY, Geer RC, Gonzales NR, Gwadz M, He S, Hurwitz DI, Jackson JD, Ke Z, Lanczycki CJ, Liebert CA, Liu C, Lu F, Lu S, Marchler GH, Mullokandov M, Song JS, Tasneem A, Thanki N, Yamashita RA, Zhang D, Zhang N, Bryant SH. CDD: specific functional annotation with the Conserved Domain Database. Nucl Acids Res. 2009;37:D205–210. doi: 10.1093/nar/gkn845.[PMC free article][PubMed][Cross Ref]
  • Ye Y, Godzik A. Comparative Analysis of Protein Domain Organization. Genome Res. 2004;14:343–353. doi: 10.1101/gr.1610504.[PMC free article][PubMed][Cross Ref]
  • Coulson RMR, Hall N, Ouzounis C. Comparative Genomics of Transcriptional Control in the Human Malaria Parasite Plasmodium falciparum. Genome Res. 2004;14:1548–1554. doi: 10.1101/gr.2218604.[PMC free article][PubMed][Cross Ref]
  • Iyer LM, Anantharaman V, Wolf MY, Aravind L. Comparative genomics of transcription factors and chromatin proteins in parasitic protists and other eukaryotes. Int J Parasitol. 2008;38:1–31. doi: 10.1016/j.ijpara.2007.07.018.[PubMed][Cross Ref]
  • Przytycka T, Davis G, Song N, Durand D. Graph Theoretical Insights into Dollo Parsimony and Evolution of Multidomain Proteins. J Comput Biol. 2006;13:351–363. doi: 10.1089/cmb.2006.13.351.[PMC free article][PubMed][Cross Ref]
  • Fong JH, Geer LY, Panchenko AR, Bryant SH. Modeling the Evolution of Protein Domain Architectures Using Maximum Parsimony. J Mol Biol. 2007;366:307–315. doi: 10.1016/j.jmb.2006.11.017.[PMC free article][PubMed][Cross Ref]
  • Weiner J, Moore A, Bornberg-Bauer E. Just how versatile are domains? BMC Evol Biol. 2008;8:285. doi: 10.1186/1471-2148-8-285.[PMC free article][PubMed][Cross Ref]
  • Sprinzak E, Margalit H. Correlated sequence-signatures as markers of protein-protein interaction. J Mol Biol. 2001;311:681–692. doi: 10.1006/jmbi.2001.4920.[PubMed][Cross Ref]
  • Deng M, Mehta S, Sun F, Chen T. Inferring Domain-Domain Interactions From Protein-Protein Interactions. Genome Res. 2002;12:1540–1548. doi: 10.1101/gr.153002.[PMC free article][PubMed][Cross Ref]
  • Guimarães K, Jothi R, Zotenko E, Przytycka T. Predicting domain-domain interactions using a parsimony approach. Genome Biol. 2006;7:R104.[PMC free article][PubMed]
  • Kanaan SP, Huang C, Wuchty S, Chen DZ, Izaguirre JA. Inferring Protein-Protein Interactions from Multiple Protein Domain Combinations. Method Mol Biol. 2009;541:43–59. full_text. [PubMed]
  • Song N, Joseph JM, Davis GB, Durand D. Sequence Similarity Network Reveals Common Ancestry of Multidomain Proteins. PLoS Comput Biol. 2008;4:e1000063. doi: 10.1371/journal.pcbi.1000063.[PMC free article][PubMed][Cross Ref]
  • Liu J, Rost B. CHOP: parsing proteins into structural domains. Nucl Acids Res. 2004;32:W569–W571. doi: 10.1093/nar/gkh481.[PMC free article][PubMed][Cross Ref]
  • Vogel C, Berzuini C, Bashton M, Gough J, Teichmann SA. Supra-domains: Evolutionary Units Larger than Single Protein Domains. J Mol Biol. 2004;336:809–823. doi: 10.1016/j.jmb.2003.12.026.

Economics, People and the Environment

Instructor: Paul Gottlieb

Course number: 11:373:101 (SCL, 21C Certified)

Syllabus

Normally offered: Fall & Spring

Prerequisites and other registration information: Not open to students who have completed a term of microeconomics or macroeconomics, and not open to Environmental and Business Economics majors.

Format: Lecture and Recitation. Individual recitation sections meet once per week for a 55-minute recitation period. All sections meet together twice a week for lecture in an 80-minute class period.

Description: Please visit http://pgottlieb.rutgers.edu/epe.htm for a course overview. This is an introductory economics course that fulfills the economic systems portion of the Cook College Area V (Human Behavior, Economic Systems and Political Processes) graduation requirements. Because it does not serve as a pre-requisite for more advanced economics courses, it is generally not appropriate for students who are planning to major in any program that requires additional economics courses.

Examinations: One midterm exam given in a lecture period and one final exam given during exam period.

Other requirements: Many of the course materials, including required readings are available only on Sakai, where most assignments will be submitted. Students are required to obtain an iClicker transmitter, which is used in many of the lectures. There will be four quizzes given in the recitation periods. Other assignments include three short (2 to 3 paragraphs) written exercises based on required readings, several in-class recitation exercises, and numerous (roughly weekly) very short written assignments in which students are asked to raise a question or make a comment related to one of the reading assignments. Attendance is expected in both lecture and recitation.

Course specific learning goals:

  1. To enhance your ability, as educated members of society, to make informed and realistic assessments of the economic dimensions of public policy issues.
  2. To develop an economically-informed analytical way of thinking, while also recognizing some of the important limitations to this type of analysis.
  3. To develop and enhance critical thinking skills.
  4. To enhance your ability to extract meaning from written material.

School of Arts and Sciences learning goals:

21C-b. Analyze a contemporary global issue from a multidisciplinary perspective

Social and historical i. Explain and be able to assess the relationship among assumptions, method, evidence, arguments, and theory in social and historical analysis.

SC-n. Apply concepts about human and social behavior to particular questions and situations.

School of Environmental and Biological Sciences learning goals: Learning goal in economic analysis is pending

Additional Information: None

Principles and Applications of Microeconomics

Instructor:John Italia

Course number: 11:373:121 (SCL, ECN Certified)

Syllabus

Normally offered: Fall

Prerequisites and other registration information: Open to Cook College majors (others by permission).

Format: Lecture and discussion; two 80-minute periods.

Description: This unique approach to introductory microeconomics is designed to explore and analyze current applications of economic principles illustrated through real-world examples. Concepts introduced include supply and demand, marginal costs and benefits, utility, opportunity costs, breakeven analysis, price floors, price supports, markets, competition, monopoly, and pure competition. The course is intended to be thoroughly interactive and entry level while providing a strong foundation for intermediate courses in microeconomics. The first segment of this course introduces critical economic concepts and relates those concepts to consumer behavior in the marketplace. The second part of this course explores business costs and develops methods that will allow participants to analyze costs of their own or some other business. In part three of the course, students are introduced to market models of industrial organization exploring competition in its various forms and the government constraints that regulate each of the competitive models. The last section of the course explores market failure, externalities and the economics of taxation.

Examinations: There are two hourly exams and a final (cumulative) comprised entirely of multiple choice questions. Four quizzes also factor in to the participant's final grade.

Other requirements: Attendance to this course is mandatory and participation is recognized and rewarded. Students are encouraged to routinely engage in discussion surrounding concepts and applications being presented in each lecture.

Additional Information: In addition to building an excellent foundation for advanced courses in economics, this course is also designed to help students acquire marketable analysis and critical thinking skills, which are attractive to employers.

Principles and Applications of Microeconomics

This course covers all the materials that are covered in the introduction to microeconomics courses.


Instructor: Isaac Vellangany

Course number: 11:373:121 (Satisfies Learning Goals ECN and SCL)

Course Name: Principles and Applications of Microeconomics.

Normally offered: Fall, Spring.

Syllabus Prerequisites and other registration information:  Open to 373, equivalent to 01:220:101 and 21:220:101.Basic knowledge in calculus will enhance your understanding of the concepts in marginal principles, but not required for registration.

Format: Two 80 minutes lecture per week.

Course Description: By the end of the course, students should be able to combine abstract concepts with formal analytical tools in order to understand how consumers and producers make optimal choices, and how these choices affect real market outcomes. The course covers three main topics: how factor market and product markets work, how government regulations and intervention affect markets, and how firms make decisions about production to optimize firm’s objectives.

LEARNING OUTCOMES   

1. Demonstrate an understanding of the basic economic issue of scarcity.  
2 Analyze production possibilities of firms and countries, the sources of their comparative advantages, and gains from trade   
3. Calculate elasticities (price, Income and cross) and its relevance to understanding markets.  
4. Utilize the concepts of equity, efficiency, and market failure to analyze and evaluate government policies such as price floors and ceilings, tax policy, environmental policy, etc.   
5. Demonstrate an understanding of government regulation in a market economy.  

Assessment Metrics: There are two midterm exams (60% of the grade) and a semi-cumulative final (25% of the grade) comprised entirely of multiple choice questions. Current event based homework assignments and attendance contribute 15% of the grade


For further info contact the undergraduate program director of the department at etavernier@aesop.rutgers.edu or vellangany@aesop.rutgers.edu

Principles and Applications of Microeconomics

Instructor: Edward Lipman, Jr.

Course number: 11:373:121:02 (SCL, ECN Certified)

Syllabus

Normally offered: Spring

Prerequisites and other registration information: Open to Cook College majors (others by permission).

Format: Lecture and discussion; two 80-minute periods.

Description: This unique and innovative approach to viewing Principles and Applications of Microeconomics within the context of agricultural activities is designed to explore and analyze current applications of economic principles. Concepts to be reviewed include marginal benefits, costs, utility, opportunity costs, breakeven analysis, price floors, price supports, price, markets, competition, monopoly, pure competition, and more. The first unit of this course introduces critical economic concepts and relates those concepts to behavior of consumers in the marketplace. The second unit of this course explores business costs and develops methods and practices that will allow participants to cost out or analyze costs of their own or some other business. In part three of the course, students are introduced to market models of industrial organization exploring competition in its various forms and the government constraints, which regulate each of the competitive models explored. This course is intended to be thoroughly interactive and entry level.

Examinations: There are two hourly exams and a final (cumulative) comprised entirely of multiple choice questions.

Course Learning Goals

1. To demonstrate an understanding of the concepts of scarcity and opportunity cost and the use of marginal analysis to evaluate tradeoffs and make decisions
2. To demonstrate the ability to apply basic constrained optimization techniques to choices made by households, firms, and government
3. To demonstrate an understanding of how supply and demand interact to determine prices, allocate resources, and the optimal decision-making that underlies market outcomes
4. To build an understanding of consumer behavior, utility maximization and sensitivity to price changes.
5. To identify, analyze and explain the choices faced by producers about pricing and output across various market structures
6. To describe the role of public policy intervention in cases where markets fail to perform optimally by evaluating the impact of externalities and tax policies

Other requirements: Based on the simple premise that "you can't win it if you are not in it" attendance to this course is mandatory and is taken regularly. In-class participation is recognized and rewarded. Students are encouraged and expected to routinely engage in discussion surrounding concepts and applications being presented in each lecture.

Additional Information: None

Principles and Applications of Microeconomics

Instructor: Majid R. Sani

Course number: 11:373:121:03 (SCL, ECN Certified)

Syllabus

Normally offered: Summer

Prerequisites and other registration information: Open to Cook College majors (others by permission).

Format: Lecture and discussion; two 80-minute periods.

Description: This unique approach to introductory microeconomics is designed to explore and analyze current applications of economic principles illustrated through real-world examples. Concepts introduced include supply and demand, marginal costs and benefits, utility, opportunity costs, breakeven analysis, price floors, price supports, markets, competition, monopoly, and pure competition. The course is intended to be thoroughly interactive and entry level while providing a strong foundation for intermediate courses in microeconomics. The first segment of this course introduces critical economic concepts and relates those concepts to consumer behavior in the marketplace. The second part of this course explores business costs and develops methods that will allow participants to analyze costs of their own or some other business. In part three of the course, students are introduced to market models of industrial organization exploring competition in its various forms and the government constraints that regulate each of the competitive models. The last section of the course explores market failure, externalities and the economics of taxation.

Examinations: There are two hourly exams and a final (cumulative) comprised entirely of multiple choice questions. Four quizzes also factor in to the participant's final grade.

Other requirements: Attendance to this course is mandatory and participation is recognized and rewarded. Students are encouraged to routinely engage in discussion surrounding concepts and applications being presented in each lecture.

Additional Information: In addition to building an excellent foundation for advanced courses in economics, this course is also designed to help students acquire marketable analysis and critical thinking skills, which are attractive to employers.

Principles and Applications of Macroeconomics

Instructor: Majid R. Sani

Course number: 11:373:122 (SCL, ECN Certified)

Syllabus

Normally offered: Fall, Spring and Summer

Prerequisites and other registration information: Open to Cook College majors (others by permission).

Format: Lecture and discussion; two 80-minute periods.

Course Description:  This is an introductory course in macroeconomics. Macroeconomics may be defined as a "level of economic analysis concerned with the activity of the entire economy and interactions among large sectors of it."

Learning Objectives:
Basic economic concepts including opportunity costs, scarcity,
positive and normative
economics
How to analyze production possibilities of firms and countries,
the sources of their
comparative advantages, and gains from trade
How to use the supply and demand model to understand how markets
work.
How to measuring GDP and Economic Growth.
Monitoring Cycles, Jobs, and the Price Level.
Economic Growth
Finance, saving, and Investment
Money, the Price Level, and Inflation
Aggregate Supply and Aggregate demand.
Expenditure Multipliers: The Keynesian Model.
U.S. Inflation, Unemployment, and Business Cycles
Fiscal Policy.
Monetary Policy.

Corporate Citizenship and Social Responsibility

Instructor: Isaac Vellangany

Course number: 11:373:201

Course Name: Corporate citizenship and Social Responsibility (CCSR)

Syllabus

Normally offered: Fall (in class), Winter and Summer (online).

Format: Two 80 minutes lecture per week and online case study discussion

Prerequisites and other registration information:  Open to 373, equivalent to 01:220:101 and 21:220:101.Basic knowledge in calculus will enhance your understanding of the concepts in marginal principles, but not required for registration.

Course Description: In this course, we will investigate some of the ethical issues facing businesses including acceptable risk, workers right, economic sustainability and inclusivity, outsourcing, whistle blowing and ethical issues in marketing. In a way to understand these issues is to reflect upon our own ethical thinking by raising questions like: what moral philosophies and theories direct our ethical behavior and business practices? Do we apply moral theories and standards proactively in decision making, and not after the fact rationalization of our action? Is there an ethical framework at the organization level to adhere to? How do we differentiate between being ethical and legal? All these quires require critical thinking and this course aims to equip students to achieve this goal. 

Course Learning Objectives (SCL)
LEARNING OUTCOMES  
1. Identify and analyze ethical conflicts and social responsibility issues involving different stakeholders
2 Develop viable alternatives and make effective decisions relating to business ethics and social responsibility.
3. Demonstrate competency in the underlying ethical theories (Schools of Moral Philosophy) such as deontology, teleology, virtue ethics, etc.
4.  Test the strengths and weaknesses of various moral beliefs and ethical arguments relevant to business practices. 
5. Reinforce personal sense of compassion and fairness in the context of your current or future professional roles.
6. Evaluate your own perception of ethical leadership.

Assessment Metrics: There are two midterm exams (50% of the grade) and a semi-cumulative final (30% of the grade) comprised entirely of multiple choice questions. Final case study presentation and paper contributes 20% of the grade.

Sustainability Decision Tools

Instructor: Dr. Serpil Guran and Dr. Paul Gottlieb

Course number: 11:373:202

Syllabus

Normally offered: Spring (online)

Prerequisites and other registration information:  Open to 373, equivalent to 01:220:101 and 21:220:101.Basic knowledge in calculus will enhance your understanding of the concepts in marginal principles, but not required for registration.

Course Description:This course is designed for students from any major (or no major) and at any stage of their undergraduate careers. The course is inter-disciplinary which makes it attractive to students in the natural sciences, social sciences, business, and engineering. It is hoped that such a diverse group will contribute important perspectives to class discussions

LEARNING OUTCOMES  
By the end of the course students will be able to:

  • Define the practical and theoretical aspects of “sustainability,” which has been an influential organizing principle for public and private-sector decision-making for more than 25 years.
  • Integrate sustainability thinking into their individual consumption decisions and those they will make on behalf of future employers in the private, public, and nonprofit sectors.
  • Identify and give examples of the primary sustainability decision tools, life-cycle analysis and carbon footprint analysis.
  • Apply the theoretical foundation gained to careers such as corporate sustainability officer, green supply chain manager, environmental policy analyst, energy or materials accountant/auditor, etc.
  • Describe the new emerging Food- Energy- Water “FEW” concept that is essential to achieve United Nations’ 2030 Sustainability Goals and creation of resilient communities

Assessment Metrics: Each week, your instructors will assign readings or other materials, some drawn from the two required paperbacks and others posted on eCollege.  Students will be required to submit a one-page paper at the end of most weeks, typically in response to a question from the instructors. These one-pagers will not be shared with other students, enabling the instructors to evaluate individual performance.  Meanwhile, the entire class will engage in an asynchronous, open, guided discussion of the readings. These discussions will be open (approximately) until the following week’s paper is due.
Two exams will be administered throughout the semester.  Students will also be responsible for a final life cycle analysis project.  Given the time constraints of a single semester, this project is not likely to require the collection of original data.

Small Business Essentials

Instructor: Peter Renzulli, CPA

Course number: 11:373:205

Syllabus

Normally offered: Spring

Prerequisites and other registration information:  Microeconomics course and Pre-calculus recommended

Course Description:This course focuses on the design and management of all aspects of a business, such as bookkeeping, inventory, customer experience, pricing, marketing, and benchmarking.  This course is designed for non-business majors

Learning Goals:

This course is designed for non-business majors. By the end of the course, students will be able to:

1. Prepare and maintain a complete set of accounting books, including the preparation basic accrual financial statements.

2. Analyze and interpret different strategies of purchasing inventory using the merchandising method of accounting as well as:
a. Recognize different inventory methods such as LIFO, FIFO and Average.
b. Discuss the impacts of proper and improper purchasing of inventory on profitability and cash flow.

3. Determine the pros and cons of owning a business based on life style choices and finances.

4. Design a business strategy based on: a. Understanding different customer experiences b. Analyzing the 4 P’s (product, people, process and price) of managing a business.

5. Validate business strategies from four different and specific approaches:
a. Operational approach
i. Design processes to run a business
ii. Discuss attributes of the right people to hire in the business
b. Marketing approach
i. Formulate branding and social media strategies to deliver the business strategy
ii. Build an networking plan to market the business
c. Financial approach
i. Evaluate different benchmarking methods to measure the business
ii. Understand and evaluate DuPont Formula as a benchmarking strategy.
d. Understand economic theory and its impact on business design and strategy.

Assessment Metrics: 

Business Decision Computer Tools

Instructor: Edwin Robinson

Course number: 11:373:210

Syllabus

Normally offered: Fall& Spring

Prerequisites and other registration information: Open to 373 majors who have successfully completed an introductory level microeconomics course (e.g., 11:373:121 or 01:220:102). Knowledge of marketing and management might also be helpful.

Format: One single-period lecture and one double-period computer laboratory weekly.

Description: Business Decision Computer Tools is an applied economics computer course using spreadsheets, statistical software, databases, graphics, and presentation tools to design, analyze, solve, and communicate business and economic problems. The course also looks at web based topics found in business and the role of ethics in technology. We will use predominantly Microsoft Excel XP and Access XP. SAS and SPSS, statistical software packages, will be reviewed. Other programs we will utilize are MS Project and Dreamweaver MX.

Examinations: Two hourlies given in the class periods, each worth 15% of the final grade.

Other requirements: Attendance (lectures and labs) is mandatory and is 10% of the final grade. Ten required individual decision problems will be due throughout the semester. Problems will be evaluated on correctness, as well as creativity and uniqueness in the way the results are presented. All late problems will be marked down (10 percent) on a daily basis. Grade for all 10 problems is 60% of the final grade.

Additional Information: The above information is being provided to give potential students a general idea about the course. Specific details may change from semester to semester, and will be provided by the instructor in the course syllabus

Applied Mathematical Concepts in Ag Economics I

11:373:211

Syllabus

Instructor: Isaac Vellangany

Course number: 11:373:211
Course Name: Applied Mathematical Concepts for Agricultural Economics (4 credits). Fulfills calculus requirement for EBE major.
Normally offered:  Fall semester.
Format: Two 80 minutes lecture per week and 80 minutes recitation per week.
Prerequisites and other registration information:  No prerequisites required for registration. Open to SEBS, SAS, and RBS.
Course Description Mathematics is increasingly important in terms of the expression and communication of ideas in economics and business. A basic knowledge of mathematics is indispensable for understanding almost all fields of economics. Yes, it is a math course and some of you may struggle to master the material. This should not, however, dissuade you from taking up the challenge. In order to facilitate your understanding of the subject matter, we will have recitation sessions and you are expected to make full use of this opportunity.
Course LearningLEARNING OUTCOMES  
1. Explain information presented in mathematical forms (e.g., equations, graphs, diagrams, tables, and words).
2. Apply exponential and logarithmic functions to analyze growth, interest compounding and investment appraisal.
3. Demonstrate understanding of and ability to explain the economic applications of differentiation, and use it to formulate economic problems, including marginal utilities, elasticity, marginal cost/ benefit, marginal product of labor/capital.
4. Utilize matrix algebra techniques to find the unknowns: Cramer’s rule, Inverse Methods and Gauss-Markov elimination procedure: use graphic calculator.
5. Apply linear programming principles and applications to solve real world problems: use solver in excel spreadsheet.
6. Understand and use these differential calculus techniques to solve problems in economics, such as profit maximization, cost minimization or utility optimization Objectives.

Assessment Metrics: There are two midterm exams (50% of the grade) and a semi-cumulative final (25% of the grade) comprised entirely of problem set questions. Five homework assignments contribute 25% of the grade

Application of Statistics to Economics


Instructor: Michael Camasso

Course Number:       11:373:215

Syllabus

Normally offered:     Fall

Prerequisites and other registration information: 01:640:115

Format: Lecture, Discussion, Computer Applications; two 80 minute period

Description:   Statistics provides many of the quantitative techniques that are essential to sound decision making in business and industry.  Economists, risk assessment specialists, and business analysts use statistics to help them address issues of personnel and firm performance, profitability, labor and capital costs, quality control and general operations management.  In this introductory course the focus will be on the practical applications of statistical analysis to real world problems that face professionals in today’s business and social science worlds.  While mathematical explanation will be minimized, the student will nonetheless receive a thorough introduction to the interplay between probability and statistics and the reinforcing roles played by descriptive and inferential statistics.  The course will consist of lectures, weekly readings, problem sets and two examinations.  In addition, students will learn how to apply statistics to real world data using SPSS for Windows.

Examination: There will be two examinations, a midterm and a final.  Both will test the student’s ability to use the concepts and methods in the course to identify relationships, interpret results and draw conclusions.  The midterm will account for 25 percent of the student’s grade while the final will comprise 30 percent.

Other requirements:  Students will be responsible for completing 3 problem sets. These problems will allow students to become familiar with the computer lab, computer applications with SPSS, and the use of computer software to answer questions in statistics. The problem sets will account for 45 percent of the student’s grade.

Additional Information:  mcamasso@rci.rutgers.edu

 

SUSTAINABLE FOOD POLICY FOR DEVELOPING COUNTRIES

Course Number: 11:373:218

Syllabus

Instructor: Isaac Vellangany

Normally offered:  Winter and summer (online).

Prerequisites and other registration information:  Open to SEBS Juniors. Introduction to Macroeconomics is required for registration

Course Description: This course is interdisciplinary and provides an overview of the food problem in the developing world, examines the social, political and economic reasons for population growth, poverty, malnutrition, food insecurity, underemployment, under development and gender inequality, and the role of agriculture in economic development. The course has three parts. Part I provides a global perspective of economic development and introduces a conceptual framework to analyze it. The framework includes both theories and empirical methods of economic development. Part II provides an in-depth look into critical domestic issues such as poverty, inequality, population growth and control, healthcare, education, human capital, agriculture, urbanization and rural-urban migration in the developing world. Part III extends the discussion to international and macroeconomic issues such as agricultural trade, the role of international institutions in promoting growth and protecting the environment.

Course Learning Objectives
LEARNING OUTCOMES  
1. Examine general country profile of Asian and Latin American countries, and the profile of the African continent from a historical perspective.
2. Understand the methodology behind the calculation of the Human Development Index (HDI), Head count ratio (HCR), Poverty Gap Ratio (PGR), Income Gap Ratio (IGR), Foster-Greer-Thorbecke (FGT) measures, Gini-coefficient and Gender Development Index (GDI).
3. Analyze agricultural production and rural development, food consumption and nutrition, food insecurity, food safety and socio-economic policy and planning that address existing and expected future problems related to the global, national and local food system.
4. Learn to analyze the root causes of poverty and its relation to demography, inequality, malnutrition, illiteracy, unemployment and rural/urban migration.
5. Evaluate trade-related aspects of agriculture and the role of WTO and FAO in fostering free trade among and between Developed countries (DC) and Less developed countries (LDCs).

Assessment Metrics: There are two midterm exams (50% of the grade) and a semi-cumulative final (30% of the grade) comprised entirely of multiple choice questions. Final case study paper and online discussion contributes 20% of the grade.

Introduction to Marketing

Instructor: Edward Lipman, Jr.

Course number: 11:373:231

Syllabus

Normally offered: Fall

Prerequisites and other registration information: 11:373:121 or 01:220:102

Format: Lecture and discussion with frequent small group interaction; two 80-minute periods.

Description: It has been said by many that any one with good ideas can be successful in the business world today. However, only 20 percent of the businesses that are created survive their first 5 years. This course helps explain that good ideas and good products are not enough to survive in the marketplace today. Honoring the fundamental principles of marketing, including pricing, product development, promoting and distributing of business products are absolutely essential to ensure the long-term viability of an organization. This course explores and applies marketing concepts and how they fit within the context of agricultural products and their distribution. Part one explores the marketing landscape in which all marketing decisions are made, including the development of relationships with customers, the creation of a target market, how to segment your markets into measurable and successful subcomponents, and an exploration of why consumers behave the way they do. Discussions surrounding service, quality, customers, and relationship building are explored with specific reference to the creation, pricing, promotion, and distribution of agricultural commodities. The second part of the course explores the importance of the pricing function and promotional activities in the marketing mix. Concepts like demand elasticity, sales promotion and public relations are discussed. The third part of the course explores distribution strategies, i.e., how to get a product out to the customer in the most efficient and least costly way. Supply chain theory and the science of retailing, wholesaling, and direct marketing are discussed. Included here are discussions regarding the growing industry we call "e-commerce" and ways to use the worldwide web for expanded marketing opportunities for all agri related organizations. While this course is conceptually based, students spend a great deal of energy analyzing appropriate marketing opportunities, courses of action, and creating original marketing strategies.

Examinations: There are three hourly examinations worth 20 percent each, a final examination worth 40 percent which is cumulative comprised of multiple-choice questions.

Learning Goals:
The course aims to achieve the attainment of learning goals through these specific objectives.
By the end of the course students will be able to: 1. Explain the fundamental principles of marketing • Price • Product • Distribution • Promotion
2. Analyze and identify the marketing decisions surrounding product, distribution, promotion and pricing
3. Develop/identify the elements of a marketing plan, the branding or promotion of a product
4. Discuss and establish a position on ethics-related issues in marketing

Other requirements: As this course builds on previous lectures, classroom attendance is mandatory and in-class participation and collaboration is recognized and rewarded.

Additional Information:

Introduction to Marketing

Instructor: Edmund M. Tavernier

Course number: 11:373:231
Syllabus (Summer)


Normally offered: Summer

Prerequisites and other registration information: 11:373:121 or 01:220:102

Format: Online instruction with mandatory participation in discussions and group activities.

Description: This course examines the fundamental principles of marketing, including pricing, product development, and the promotion and distribution of products that are absolutely essential to the long-term viability of an organization. The course explores the landscape in which marketing decisions are made, including the development of relationships with customers, the creation of a target market, how to segment your markets into measurable and successful subcomponents, and an exploration of why consumers behave the way they do. Discussions surrounding service, quality, customers, and relationship building are explored with specific reference to the creation, pricing, promotion, and distribution of products. The course also explores the importance of the pricing function and promotional activities in the marketing mix. Concepts such as demand elasticity, sales promotion and public relations are discussed. Marketing channels and supply chain management concepts are also examined along with digital marketing and social networking, and the power of branding.

Examinations: There are five 55-minute exams that account for 50% of the grade, a group project worth 30% of the grade and 4 discussion topics that account for 20% of the grade.  The exams are comprised of multiple-choice questions.

Learning Goals:
The course aims to achieve the attainment of learning goals through these specific objectives.
By the end of the course students will be able to:
1. Explain the fundamental principles of marketing • Price • Product • Distribution • Promotion
2. Analyze and identify the marketing decisions surrounding product, distribution, promotion and pricing
3. Develop/identify the elements of a marketing plan, the branding or promotion of a product
4. Discuss and establish a position on ethics-related issues in marketing

Other requirements: It is extremely difficult to do well in this course without participating in the discussions.  Participation and collaboration is recognized and rewarded.

Additional Information: I reserve the right to change the format of the course if I believe that such change enhances student learning.

Introduction to Marketing

Instructor: Edmund M. Tavernier

Course number: 11:373:231

Syllabus (Winter)
Normally offered: Winter

Prerequisites and other registration information: 11:373:121 or 01:220:102

Format: Online instruction with mandatory participation in discussions and group activities.


Description: This course examines the fundamental principles of marketing, including pricing, product development, and the promotion and distribution of products that are absolutely essential to the long-term viability of an organization. The course explores the landscape in which marketing decisions are made, including the development of relationships with customers, the creation of a target market, how to segment your markets into measurable and successful subcomponents, and an exploration of why consumers behave the way they do. Discussions surrounding service, quality, customers, and relationship building are explored with specific reference to the creation, pricing, promotion, and distribution of products. The course also explores the importance of the pricing function and promotional activities in the marketing mix. Concepts such as demand elasticity, sales promotion and public relations are discussed. Marketing channels and supply chain management concepts are also examined along with digital marketing and social networking, and the power of branding.

Examinations: There are five 55-minute exams that account for 50% of the grade, a group project worth 30% of the grade and 4 discussion topics that account for 20% of the grade.  The exams are comprised of multiple-choice questions.

Learning Goals:
The course aims to achieve the attainment of learning goals through these specific objectives.
By the end of the course students will be able to:
1. Explain the fundamental principles of marketing • Price • Product • Distribution • Promotion
2. Analyze and identify the marketing decisions surrounding product, distribution, promotion and pricing
3. Develop/identify the elements of a marketing plan, the branding or promotion of a product
4. Discuss and establish a position on ethics-related issues in marketing

Other requirements: It is extremely difficult to do well in this course without participating in the discussions.  Participation and collaboration is recognized and rewarded.

Additional Information: I reserve the right to change the format of the course if I believe that such change enhances student learning.

Introduction to Management

Instructor: Dr. Brian J. Schilling

Course number: 11:373:241

Syllabus

Normally offered: Fall

Prerequisites and other registration information: Open to all majors who have successfully completed an introduction to microeconomics course (e.g., 11:373:121 or 01:220:102) or Economics, People and the Environment (11:373:101).

Format: Lecture and discussion; class exercises.

Description: General applications of basic concepts, functions, and tools of management that contribute to success and improve individual performances in decision-making and other situations and problems in the field of management. The course is divided into two parts. In Part I, the class discusses management as an open system, quality issues, planning, decision-making, and organizing. In Part II, the class discusses motivation, teamwork, communication, and controlling issues. Throughout the course, issues are discussed to try and enable a win-win solution to problems and focuses on how to prevent problems (proactive) instead of solving problems (reactive).

Examinations: Two hourly exams, quizzes, a cumulative final exam.  Examinations comprises short answer, multiple choice, true/false, and problem solving questions.

Other requirements: Class is interactive.  Students have weekly group interaction where management issues are discussed. Class participation is expected (one cannot participate without being present).

Additional Information:

Introduction to Management

Instructor: John Italia

Course number: 11:373:241:01

Syllabus

Normally offered: Spring

Prerequisites and other registration information: Open to all majors who have successfully completed an introduction to microeconomics course (e.g., 11:373:121 or 01:220:102) or Economics, People and the Environment (11:373:101).

Format: Lecture and discussion with weekly small group activities; one three hour class with the first 2 ½ hours being lecture and discussion and the last ½ hour being the small group activity.

Description: General applications of basic concepts, functions, and tools of management that contribute to success and improve individual performances in decision-making and other situations and problems in the field of management. The course is divided into two parts. In Part I, the class discusses management as an open system, quality issues, planning, decision-making, and organizing. In Part II, the class discusses motivation, teamwork, communication, and controlling issues. Throughout the course, issues are discussed to try and enable a win-win solution to problems and focuses on how to prevent problems (proactive) instead of solving problems (reactive).

Examinations: Midterm and Final Exam. The Midterm is short answers and problem solving. The cumulative final exam is short answer and essay.

Other requirements: Students have weekly group interaction where management issues are discussed. Class participation is expected (one cannot participate without being present). A management paper (three page minimum) is required which is completed as part of your group project. A class presentation (four-minutes) is required using PowerPoint which is completed as part of your group project.

Additional Information: If you desire additional information, you may click here to view a recent syllabus. Please remember, however, that the instructor may make changes in it for the upcoming semester.

Introduction to Management

Instructor: Kristin Peacock and Larry Jaffe

Course number: 11:373:241:02

Syllabus

Normally offered: Fall, Spring, & Summer

Prerequisites and other registration information: open to all Rutgers University and Cook College students other than Environmental & Business Economics (373) Majors at Cook College who have completed successfully 11:373:101 OR 11:373:121 OR 01:220:102. Non-Cook College Rutgers students, should check with their respective colleges/departments to determine if ecourse credits will be accepted for major requirements and/or graduation requirements.

Format: During the course you will participate in online discussions. Near the end of the course you will be working with team members of a group to submit a group paper and PowerPoint presentation making recommendations for your business (based on concepts/models/theories presented in this course). You will work with your group to illustrate your recommendations in a logical format (as though you were giving the PowerPoint presentation to other managers in your business). During the last week of the semester (week 14) you will hand in a group paper and PowerPoint presentation based on concepts/models/theories presented in this course.

Description: This on line “introduction to management” course covers general applications of basic concepts, functions, and tools of management that contribute to success and improve individual performances in decision-making and other situations and problems in the field of management. The course is divided into two parts. In Part I, the class discusses management as an open system, quality issues, planning, decision-making, and organizing. In Part II, the class discusses staffing, motivation, teamwork, communication, and controlling issues. The second half of the course includes a group project working with other students on a business management plan. Throughout the course, issues are discussed to try and enable a win-win solution to problems and focuses on how to prevent problems (proactive) instead of solving problems (reactive).

Examinations: Regular quizzes.

Learning Goals:
1. To familiarize you with a core set of business management concepts while exploring real world examples.
2. To develop your ability to apply business management concepts, models, tools and theories.
3. To develop your ability to communicate effectively and encourage professional presentation skills.
4. To build marketable analytical and critical thinking skills that are attractive to employers.

Other requirements: To help promote a win-win environment in and out of the class to enhance the understanding and application of business management concepts, models, and theories. The instructor will periodically announce any changes and or additions to the topics that will be covered in the upcoming weeks.

Additional Information: This is an e-course.

Introduction to Management

Instructor: Isaac Vellangany

Course number: 11:373:241

Syllabus

Economics of Production

(3 credits)

Instructor: Majid Sani

Course number: 11:373:321

Syllabus

Normally offered: Fall

Prerequisites and other registration information: Open to majors who have successfully completed Calculus I (01:640:135)

Format: : Lecture; two 80-minute class periodsText Book: Operation Management, By J. Heizer and B. Render The Eight edition; Publisher: Prentice Hall. Dhillon, P.S. Economics of Production. Of which Chapters 1-4: Available in my mailbox (ask DAFRE Secretary) to make copies.

Description: This is an applied intermediate microeconomics course that focuses on Economics of Production and its application in agricultural and business management. The Economics of Production is looked at from the inside as concerns production, cost, profitability, and competitive strategy considerations, and it is examined from the outside as concerns the influences of market demand, competition, market structure, and resource supply. The course uses examples and applications to bridge the gap between theory and practice. The course also gives a good grounding in the terminology of Operations Management and an overall perspective of Operations Management within the context of a business organization.

Examinations: There may be three exams: Two Mid terms and one final exam that will consist multiple choice questions and/or short problems sets or case studies. Exact dates for the mid term exams will be announced in class at least two weeks before the exam. Final exam is scheduled according to Rutgers University schedule. Mid term exam will cover all materials and concepts up to the class before the exam. Mid term exam II will cover materials between the fort exam and the class before the exam. The final exam will cover all materials but will be weighted heavily towards post mid term period. Six individual assignments are also scheduled for the course. All assignments must be electronically sent to my email address.

Other requirements: Students are expected to attend class regularly. Late arrivals/early departures are very disruptive to other students and will not be tolerated. Portable appliances such as phones and beepers are also disruptive to the class and should be disengaged. Students are also expected to read and understand the course syllabus and the chapters of the Production Economics and Operations management.

Additional Information: If you desire additional information, contact the instructor at (732) 932-1966, ext. 3115 or email to Onyango@seop.rutgers.edu.

Public Policy Toward the Food Industry

Course number: 11:373:323

Instructor: Isaac Vellangany

Syllabus

Normally offered:  Spring (in class) summer (online)

Prerequisites and other registration information:  Open to SEBS.

Course Description: This course is based on the premise that a rational and desirable goal for any society is to develop and maintain a food system that promotes health, protects the environment, is sustainable, and supports the livelihoods of its participants. Further, This course deals with how governments—particularly that of the United States—design and implement policies and programs to foster social goals such as ensuring a sufficient, nutritionally adequate, safe, affordable, and sustainable food supply.  It examines why and how governments do or do not decide to set policies; reviews how stakeholders in the food system become involved in and influence policy development; identifies the social, cultural, economic, and political factors that influence stakeholder and government positions on policy issues; and describes the ways in which these factors promote or act as barriers to policies aimed at promoting public health, agricultural sustainability, and the environment.

Course Learning Objectives
LEARNING OUTCOMES  
1. Define what is meant by policy, and explain how policies differ from programs (farm policy vs farm program)
2. Describe the principal areas of domestic and international nutrition, food, and agriculture policy and the most important current issues related to those policy areas. (Trade and centers of Influence)
3 Identify the government agencies primarily responsible for each area of food and nutrition policy, explain their roles, and describe their principal policy goals and methods for achieving them (Federal Departments and Independent Agencies).
4. Explain what is meant by “food system,” the policy and political issues raised by this term, and the principal stakeholder groups and positions on food system issues (food security, food safety, malnutrition and obesity).
5. Identify the ways in which social, cultural, economic, commercial, and institutional factors promote or act as barriers to the design and implementation of agriculture, food, and nutrition policies and programs, and the ways in which these policies and programs affect health SNAP, WIC…farm bill.

Assessment Metrics: There are two midterm exams (50% of the grade) and a semi-cumulative final (30% of the grade) comprised entirely of multiple choice questions. Final case study presentation and paper contributes 20% of the grade.

For further info contact the undergraduate program director of the department at etavernier@aesop.rutgers.edu or vellangany@aesop.rutgers.edu

Economics of the Food Marketing System

3 credits

Course number: 11:373:331

Instructor: Sajib Bhuyan

Syllabus

Normally offered: Spring

Prerequisites and other registration information: Open to E&BE (373) majors and others who have successfully completed an introduction to microeconomics course (e.g., 11:373:121 or 01:220:102) and Agribusiness Marketing (11:373:231) or equivalent.

Format: Class meetings will consist of lectures, discussions, and video presentations. Active student participation is essential in all aspects of this class. Lectures, videos, and discussions will be used to integrate the topics covered in the course, to explain and amplify information contained in the reading assignments, and to present supplementary material. Students are strongly encouraged to participate actively, and are responsible for the material in reading assignments.; two 80 minute periods.

Description: Assuming that you are present, that you are a participant in discussion, that you speak to the instructor, and that you do the reading and the work required........ this course will enable you to understand how the U.S. food marketing system is organized and how the market participants (i.e., farmers, processors, wholesalers, retailers, and food services) behave; you will also learn about cost, price, and product management strategies as well as how consumer demand and government/public policies shape this dynamic market system.

Examinations: There are 3 in-class tests, including a final examination to assess student understanding and progress. Each test may consist of both multiple-choice and short essay-type questions.

Other requirements: Exam dates are given on the first day of class that students can plan accordingly. In addition, there are home work assignments and quizzes throughout the semester. Quiz with the lowest score is dropped from a student’s final grade. Each student also required to work, either individually or in a group, on a research paper, i.e., term paper, throughout the semester and must submit it at the end of the semester in order to pass the course. The goal of the term paper is to put together the concepts and topics learned in the classroom (i.e., about marketing channels and channel dynamics, marketing management strategies, and industry structure and performance) into use in the form of a research paper.

Additional Information: If you need additional information, e-mail Dr. Bhuyan at Bhuyan@aesop.rutgers.edu. A link to a generic version of the course outline could be found at http://aesop.rutgers.edu/~bhuyan.

Energy Economics and Policy

Course Number:11:373:335 (3 credits)

Prerequisites: 11:373:121 or 01:220:102

Professor: Gal Hochman

Syllabus

Normally offered: Spring

Prerequisites and other registration information: Open to those who have successfully completed an introduction to microeconomics course (e.g., 11:373:121 or 01:220:102), other prerequisites may apply as indicated in the Schedule of Classes.

Format: Two 80-minute lecture periods per week.

Description: The course addresses energy-related policies (related to the transportation, power, heat, and electricity) and examines objectives that can be stated in economic terms. The course investigates the most common economic and environmental causes of energy policy problems and evaluate the efficacy of the economic policy used to correct these problems in market, as well as non-market, economies. The course also extends the concepts discussed in class to understand renewable and alternative energy contexts, and develop and analyze new policies to address new concerns. 

Examinations: There are two 80-minute exams and a term paper plus presentation. Exams challenge students to communicate economic concepts verbally, graphically, and using basic analytical methods, as does the term paper and the oral presentation.

Other requirements: Weekly five-minute quizzes challenge students to communicate economic concepts verbally, graphically, and using basic analytical methods

Management: Human Systems Development

(3 credits)

Course number: 11:373:341

Professor: Kenneth Genco, Sr.

Syllabus

Normally offered: Fall & Spring

Prerequisites and other registration information: Open to 373 & 709 majors (others by permission) who have successfully completed an introduction to microeconomic course (e.g., 11:373:121 or 01:220:102) or Economics, People and the Environment (11:373:101).

Format: Lecture and discussion with occasional small group activities; two 80-minute periods.Required Text: Robbins, Stephen P., Managing Today!, Prentice Hall, 2000

Description: The course focuses on the development of human/interpersonal skills and the application of the behavioral and managerial sciences to promote ways individuals, groups and organizations may work together more effectively to achieve common goals and mutual success. In Part I we consider issues and opportunities in "the changing world of work" and review the historical roots of modern management practice. In Part II our attention turns to "leading and empowering people" through understanding the basics of human behavior; perceptions, belief systems and memory and its effect on the management of human systems; development of interpersonal skill including the nature, functions and process of communication and conflict management; motivation concepts, processes and approaches to increasing performance effectiveness; basic and contemporary issues in leadership including understanding groups and the development of effective teams; the challenges associated with leading and managing in the global environment; managing human resources including staffing, evaluation and compensation; and, the management of change. In Part III, the focus shifts to values, ethics, and personal responsibility and personal development. Throughout the course a major emphasis is the notion of self-management, the habits of highly effective people, and the development of a personal mission statement.

Examinations: There are two hourly exams and a final (non-cumulative) comprised of approximately three-fourths multiple-choice items and one-fourth short answer/essay type questions. 35% of each exam will be from portions of the text that may not be discussed explicitly in class.

Other requirements: The course encourages deep introspection and the application of the principles and concepts to one's daily life and self-management, especially as they relate to one's personal and professional development. To facilitate this students are required to develop a personal mission statement (paper). Class participation is expected (one cannot participate without being present).
The above information is being provided to give potential students a general idea about the course. Specific details may change from semester to semester, and will be provided by the instructor in the course syllabus

Additional Information: Please remember, however, that the instructor may make changes in the syllabus for the upcoming semester.

Business Finance I

Course number: 11:373:351

Syllabus

Normally offered: Fall & Spring

Prerequisites and other registration information: Open to 373 majors as well as those outside of 373 major (consult your program advisor if your program will allow you to take this course). Pre-requisites include Introduction to Microeconomics (11:373:121 or 01:220:102), and Statistics I (01:960:211) or Introductory Statistics for Business (01:960:285), and Principles of Accounting I (33:010:272 or 33:010:273).

Format: The class will be designed such that classroom participation (attendance as well as active participation), individual study, and preparation outside classroom are necessary for learning and performing well in the exams. Class lecture will focus the median student and cover the major points of each topic. However, unless otherwise told, students are required to read and understand the entire chapter. It is expected that students will read all materials thoroughly and work relevant problems from the end of the chapter and sample exam questions. 2. There will be no graded homework assignments. At the end of each topic, I will assign several problems. Solving these problems and understanding the solutions is essential for performing well on the exams. I strongly suggest coming to office hours or making an appointment to meet with me if you do not understand how to solve all the assigned problems.

Description: The course is designed to provide students a general overview of the fundamentals of corporate finance. Corporate finance is the process of identifying and financing investment projects. These are the primary responsibilities of a financial manager and this course provides students with the fundamental tools of performing these two activities. The course objective to answer three questions:

1. What do financial managers mean by value?
2. How is value calculated for various types of financial instruments and investment projects?
3. How is value sensitive to factors like time and risk?

Examinations: There will be three exams: two mid-term exams, and one final. Exams are cumulative. Mid-term I: February 28th Mid-term II: April 4th Final: See final schedule: May 5th (12:00-3:00) Overall course grade will be based on individual performance on the three exams. Each Midterm is worth 30% of the grade and the Final is worth 40%. • There will be no make-up exam.

Other requirements: The course is a requirement for 373 major and as such it is organized with 373 majors being the primary audience. It emphasizes utilization of concepts and techniques learned in the class for analyzing and solving practical problems. Hence, students are expected to engage in high level of thinking, independent analysis and problem solving, and drawing inferences based on given set of information, and through these activities, develop and master decision-making skills

Additional Information: Instructor: James Eaves Office: Room 211 in the Cook Office Building Phone: 732-932-9155 x221 Email: eaves@aesop.rutgers.edu Office Hours: Tuesday and Wednesday from 10:00-12:00 or by appointment. Please remember that syllabus for the course may be revised/updated by the instructor for the upcoming Fall semester.

Economics of Futures Markets

Instructor: Penny Carlson

Course number: 11:373:352

Syllabus

Normally offered: Fall

Prerequisites and other registration information: Open to juniors and seniors (others by permission if space permits) who have successfully completed an introduction to microeconomic course (e.g., 11:373:121 or 01:220:102) or Economics, People and the Environment (11:373:101).

Format: Lecture and discussion; two 80 minute periods.

Description: The purpose of the course is to first introduce students to basic investment instruments and then develop a more solid understanding of futures markets and related tools such as options on futures. Students will learn the basics about trading both stocks and futures. Topics include learning about order types, technical and fundamental analysis for stock and futures, using futures and options to hedge cash or physical positions and using futures and options as a tool to facilitate planning in the physical market place. Other topics on futures include, examination of factors that affect cash and futures prices (the basis), margin requirements and procedures, regulations, and spread/arbitrage trading. The Internet will be used extensively as an information source. See the most recent syllabus for a detailed listing of topics that will be covered.

Examinations: There is one hourly exam and a final (non-cumulative) comprised of multiple-choice, short answer/essay type questions and mathematical problems.

Other requirements: A group project is required. See the web address for details. Students are required to trade on an online simulated stock trading

Additional Information: Grading: Two Exams (30% each)
Group Project (30%)
Stock Market Game & Paper Trading (10%)
Attendance is Mandatory

Personal Finance

Instructor: Barbara O'Neil

Course number: 11:373:353

Syllabus

Normally offered: Fall

Prerequisites and other registration information: • 373 Major or Permission of Instructor • 11:373:121 or 11:373:101 or 01:220:102 • Junior/senior standing

Format: Lecture and discussion with small group activities, in-class student presentations, and required hands-on learning assignments and synthesis papers about personal finance books.

Description: Recent research studies describe a dismal financial situation for many individuals and families. The U.S. personal savings rate is lower than in any other industrialized nation and few people have adequately prepared to achieve their financial goals. In addition, the average U.S. household carries a $9,312 credit card balance and over 1.6 million bankruptcies were filed annually during the past few years. Among the largest increases in filings have been those by young adults in their 20s. More young people today file bankruptcy than graduate from college. Many times, household debt problems start with credit cards in college. Starting post-college life with a poor credit history can tie up future income and result in the loss of housing, employment opportunities, and favorable low-cost credit terms. This course will teach personal financial management skills with a focus on maximizing the awesome power of compound interest by investing at an early age and repaying debt promptly to avoid interest charges that can stretch out for decades. Specific topics that will be covered include: an overview of the financial planning process, goal-setting, cash flow management, financial statements and ratios, income taxes, banking, credit and debt, identity theft, predatory lending, insurance, the time value of money and compound interest, investing, renting and home buying, and retirement planning. The course is designed to encourage the personal application of financial planning concepts and will include assignments that foster critical thinking skills and analyses of financial data. Students will be able to directly apply what they have learned to their lives in order to become successful financially. There will be seven (7) short assignments, each worth 10% of a student’s grade, and a midterm (15%) and a non-cumulative final (15%). There will be three assigned books, The Wealthy Barber, The Ultimate Credit Handbook, and The Automatic Millionaire, that can be purchased inexpensively online. Purchase of the textbook, Personal Finance (8th Edition), by Garman & Forgue is highly recommended. Copies of the instructor’s biography, syllabus, PowerPoint presentations, exam review questions, and assignments are available online at http://rci.rutgers.edu/~boneill/.

Examinations: a midterm (15%) and a non-cumulative final (15%)

Other requirements: Poor attendance will result in the lowering of student grades (a half a grade for every three unexcused absences). There will also be two optional extra credit opportunities for students to raise their grade.

Additional Information: Professor O’Neill at oneill@aesop.rutgers.edu

Land Economics

Course number: 11:373:361

Instructor: Majid Sani

Syllabus

Normally offered: Fall

Prerequisites and other registration information: 11:373:121 or 01:220:102

Format: Lecture reinforced with four exercises

Description: The course objective is to identify the factors that affect land use. This is approached from a three-fold framework - physical, economic and institutional - with a focus on the economic framework. The valuation of real property, including cash-flow analysis and public policy, are emphasized. The course is divided into nine units including: (1) introduction (scope, content, and the problems dealt with; defining the threefold framework; the role of land in society); (2) the supply of land, with a focus on the fixed location aspect of land including the legal description of real property, the current land uses, and shift in land uses over time; (3) the demand for land including socio/demographic trends; (4) land resource requirements and how they are estimated; (5) the economic framework including land rent, the valuation of real property, and the real estate market. Cash flow analysis is emphasized in unit 5 along with soil conservation, the number one environmental issue associated with the land resources (6) the institutional framework (the role of formal and informal controls over the use of land); (7) property rights and the legal dimension; (8) public policy controls over land; and (9) land resource policy covering the history of the policy eras with a focus on the public domain.

Examinations: There are two hourlies and a final with objective testing via multiple choice questions.

Other requirements: Four exercises are used to reinforce some of the basic concepts covered in class along with a real estate valuation problem focusing on estimating fair market value via income capitalization and the sales comparison approach.

Additional Information:

Natural Resource Economics

Instructor: Majid Sani

Course number: 11:373:362 (SCL, GVT Certified)

Syllabus

Normally offered: Fall

Prerequisites and other registration information: 11:373:121 or 01:220:102

Format: Lecture

Description: The basic objective is the development of analytical skills in terms of identifying economic problems and proposing a solution (policy development) selected from a series of alternatives. By defining the problem in detail, the most appropriate public policy can be more easily identified. The course is divided into eight units: (1) introduction; (2) role of natural resources in society; (3) social/economic trends; (4) resource allocation in the market place; (5) the nature of goods and services; (6) role of property rights; (7) public policy for directing natural resource use; and (8) the time dimension of natural resource use. Cash-flow analysis is also emphasized in the latter section. Lectures and reading assignments supplemented by exercises are utilized. This course is taught at the upper division undergraduate level with the assumption that students have some familiarity with current social/political/economic aspects of natural resources. Emphasis is placed on market/nonmarket (private vs. public) decision-making structure regarding resource use along with policy implications. Concepts are stressed and the course is oriented around broad issues rather than particular resource problems. Breadth, rather than depth, of subject matter is emphasized.

Examinations: There are two hourlies and a final with objective testing via multiple-choice questions.

Other requirements: Four exercises are used to reinforce selected concepts covered in class. Cash flow analysis via benefit/cost analysis is emphasized.

Natural Resource Economics

Instructor: Edmund M. Tavernier

Course number: 11:373:362 (SCL, GVT Certified)

Syllabus (Summer)
Normally offered:Summer


Prerequisites and other registration information: 11:373:121 or 01:220:102

Format: Online instruction with mandatory participation in discussions and group activities.

Description: This course is taught at the upper division undergraduate level with the assumption that students have some familiarity with current social/political/economic aspects of natural resources. The basic objective of the course is to explore a variety of problems at the nexus of economics and environmental systems.  In so doing, the course provides students with the analytical skills needed to identify natural resources-driven economic problems, and the capacity to propose policy solutions to address them. Emphasis is placed on market/nonmarket (private vs. public) decision-making structure regarding resource use along with policy implications. The course examines the economic basis for pollution problems and policies such as pollution taxation designed to solve them.  The course also examines common-pool natural resources and the optimal rates of renewable and non-renewable resource extraction. Policy-driven markets such as cap and trade are also explored and evaluated.

Examinations: The final grade will be assessed based on the following: exams -60%; participation in online discussions – 20%; homework – 20%.  There may be opportunities to earn points from extra credit assignments.

Other requirements: It is extremely difficult to do well in this course if you do not participate in the online discussions.  Participation and collaboration is recognized and rewarded.

Additional Information: I reserve the right to change the format of this course if I believe that doing so will enhance student learning.

Natural Resource Economics

Instructor: Edmund M. Tavernier

Course number: 11:373:362 (SCL, GVT Certified)
Syllabus (Winter)

Normally offered: Winter

Prerequisites and other registration information: 11:373:121 or 01:220:102

Format: Online instruction with mandatory participation in discussions and group activities.

Description: This course is taught at the upper division undergraduate level with the assumption that students have some familiarity with current social/political/economic aspects of natural resources. The basic objective of the course is to explore a variety of problems at the nexus of economics and environmental systems.  In so doing, the course provides students with the analytical skills needed to identify natural resources-driven economic problems, and the capacity to propose policy solutions to address them. Emphasis is placed on market/nonmarket (private vs. public) decision-making structure regarding resource use along with policy implications. The course examines the economic basis for pollution problems and policies such as pollution taxation designed to solve them.  The course also examines common-pool natural resources and the optimal rates of renewable and non-renewable resource extraction. Policy-driven markets such as cap and trade are also explored and evaluated.


Examinations: The final grade will be assessed based on the following: exams -60%; participation in online discussions – 20%; homework – 20%.  There may be opportunities to earn points from extra credit assignments.

Other requirements: It is extremely difficult to do well in this course if you do not participate in the online discussions.  Participation and collaboration is recognized and rewarded.

Additional Information: I reserve the right to change the format of this course if I believe that doing so will enhance student learning.

Natural Resource Economics

Instructor: Majid Sani

Course number: 11:373:362

Syllabus

Normally offered: Fall

Environmental Economics

Course number: 11:373:363 (3 cr.)

Instructor: Gal Hochman

Syllabus

Normally offered: Spring

Prerequisites and other registration information: Open to those who have successfully completed an introduction to microeconomics course (e.g., 11:373:121 or 01:220:102), other prerequisites may apply as indicated in the Schedule of Classes.

Format: Two 80-minute lecture periods per week.

Description: The course addresses environmental and natural resource decisions (including conservation, development, preservation, and restoration) and examines objectives that can be stated in economic terms; these include objectives of individuals, groups, nations, and groups of nations. The course investigates the most common economic causes of environmental and resource policy problems and assesses the efficacy of major economic policy instruments used to correct these problems in market economies. The economic logic developed can be applied to environmental and resource issues at scales ranging from individual users and development projects to national income accounts, national policies, and international agreements, and (if you need to) develop and analyze new policies.

Examinations: There are two 80-minute exams and a term paper plus presentation. Exams challenge students to communicate economic concepts verbally, graphically, and using basic analytical methods, as does the term paper and the oral presentation.

Other requirements: Weekly five-minute quizzes challenge students to communicate economic concepts verbally, graphically, and using basic analytical methods.

Additional Information:

Food, Health and Safety Policy

Instructor: Dr. Isaac Vellangany

Course number: 11:373:371

Syllabus

Normally offered:  summer (online)

Prerequisites and other registration information:  Open to all majors who have successfully completed an introduction to microeconomic course (e.g., 11:373:121 or 01:220:102) or Economics, People and the Environment (11:373:101).

Course Description. This course provides an overview of food safety practices and principles emphasizing the role of food safety in public health. Emphasis is placed on the leading causes of foodborne illness and their associated food groups. Biological, chemical, and physical threats are discussed. Additional topics cover consumer concerns regarding the food supply such as genetically modified organisms, pesticides and other issues. The role of regulatory agencies and food safety education are also discussed.

Course Learning Objectives
LEARNING OUTCOMES  
1. Describe the principal areas of domestic and international food and nutrition policy—e.g., food assistance, dietary guidance and education, nutrition surveillance and monitoring, agricultural support, food industry regulation, food safety regulation, food and nutrition research, international food trade, and international food aid—and the most important current issues related to those policy areas.
2. Understand that nearly all foodborne disease can be prevented with proper handling and be able to describe the proper handling required for different food groups and recognize the role of time, temperature, cross contamination, and personal hygiene in food safety
3. Learn many of the organisms responsible for foodborne illness and understand factors that promote their growth
4. Identify the governmental agencies primarily responsible for each area of food and nutrition policy, explain their roles, and describe their principal policy goals, objectives, and methods
5. Discuss consumer concerns including GMOs, pesticides, bio- and agro-terrorism and the role of public health.
6. Evaluate the impacts Patient Protection and Affordable care Act (Obama Care) in promoting preventive health measures and ensuring affordable health care system in the United states.

Assessment Metrics: There are two midterm exams (50% of the grade) and a semi-cumulative final (30% of the grade) comprised entirely of multiple choice questions. Final case study paper that trace the recent “Food Outbreak News” and the food recall for food safety issues.

Additional Information: If you desire additional information, you may click here to view a recent syllabus . Please remember, however, that the instructors may make changes in it for the upcoming semester.

Global Marketing

Instructor: Prof. Ramu Govindasamy

Course number: 11:373:402

Syllabus

Normally offered: Spring

Prerequisites and other registration information: Open to E&BE (or others by permission) majors who have successfully completed (1) Principles & Applications of Microeconomics (11:373:121) and (2) Agribusiness Marketing 11:373:231.

Format: Lecture and discussion with occasional small group activities; two 80-minute periods.

Description: Global Marketing is an upper-level, managerially oriented course that addresses these challenges with three primary objectives: 
1) expose students to the different sociocultural, economic, and geopolitical environments in which global marketing strategies and programs are formulated and implemented; 
2) examine the cumulative impact of changes in these environments on marketing opportunities and threats; 
3) help develop relevant management skills for planning and expanding activities in global markets. We will read articles from The Wall Street Journal and The New York Times covering current events in the global marketplace to relate breaking news to the classroom experience. By the end of the semester you will have gained knowledge of marketing theory, practice, and application. You will also understand how global marketers create knowledge in the form of perceived value for their customers.

Examinations: There are two exams comprised of multiple-choice items and short answer/essay type questions, quizzes, homework assignments, and a marketing plan presentation.

Other requirements: The class will be divided into sub groups. Each sub group will be responsible for developing marketing plans for pre-selected companies consisting of the following topics: Executive summary, Export Readiness, Food and Agricultural Outlook, International Marketing Analysis, Select Market Entry Strategy, Promotional Strategies, Goals and Budgets, Conclusions. Each group is expected to make a presentation of their in marketing plan at the end of the semester to a group of audience including company representatives

Additional Information: Since the course includes a "hands-on approach" involving working in teams in the development of a marketing plan, the course has been approved as one means of fulfilling the college's experience-based education requirement.

Innovation and Entrepreneurship

Course number:11:373:403

Instructor: Dr. Isaac Vellangany PhD

Syllabus

Normally offered: Fall

Prerequisites and other registration information: Open to all students with (11:373:121) OR 01:220:102) AND (11:373:231).

Format: lecture, case studies, and some guest speakers; two eighty minute class periods. Along with the three exams (two midterm and a final) there will be final group paper and presentation on an assigned topic.
Course objectives: It is widely recognized that technological development is a key determinant of competitiveness. Development of the ability to absorb and adapt technology forms the core of technological capability building and technology management endeavors at the firm level. Policies that facilitate such capability building are crucial for growth of a nation. The main objective of the course is to expose students to key dimensions of innovation and technology policy and also highlight the role of other macro-economic policies in building technological/innovative capabilities in the country.  These capabilities are primarily to be built at the level of the firm as the course assumes that a typical business enterprise is at the core of the system. At the end of  the course, students will be able to: (i) understand the process of generation of innovation and its diffusion in a systemic framework; (ii) better appreciate the various components of an innovation policy and especially to distinguish between financial and non-financial instruments and their impact; (iii) understand the importance of policies to increase the supply of technically trained human resource for R&D and other innovation activities; (iv) be cognizant of the existence of new innovation indicators such as innovation surveys and also understand the limitations of replicating such efforts in developing countries; and (v) recognize the growth of high technology industries in both developed and developing countries.

Project: The group project is designed to enable students to utilize the knowledge gained in the course and apply it to a specific innovation and gauge its impact on society. The presentation of the project will normally take place during the first week of December and the final paper is due by the end of the second week of December.

Examination: There will be two midterm exams and a final exam.  The final exam will take place immediately after the presentation to allow time for the group to complete the final paper.

Grade distribution: Midterms 50%, final 30% and final paper presentation 20%.

Other Requirements: You are required to attend one seminar during the course conducted by the Center for Innovative Ventures of Emerging Technologies (CIVET) at Rutgers University.
For further info contact the undergraduate program director of the department at etavernier@aesop.rutgers.edu or vellangany@aesop.rutgers.edu

Bioscience Policy

Instructor: Carl Pray

Course number: 11:373:404 

Syllabus

Normally offered: Spring

Prerequisites and other registration information: Open to those who have successfully completed an introduction to microeconomics course (e.g., 11:373:121 or 01:220:102).

Format: One meeting per week for two periods.  There will be lectures by the instructor, speakers from industry and presentations by students.

Description: Advances in biology will be the basis of growth in some of key industries of the 21st Century. New Jersey biotech industries and Rutgers University are among the leaders in biological sciences and biotechnology research. This course will examine the impact of government investments in science and the impact of technology policies on research and technology development by industries which are based on the biological sciences – Pharmaceuticals, Food, Agriculture, and Biofuel. It will also look at how government policies shape the impact of science on these industries and on economic growth and welfare of the citizens of United States and the World.
Learning Goals: At the end of the course students will be able to:
1. Identify industries based on biology.
2. Utilize economic concepts to analyze government policies
3. Develop skills at finding reliable information in libraries and on the web.
4. Demonstrate improved research and writing skills through writing policy briefs.
5. Develop a set of policies that will increase sustainable growth of the biotechnology industries and ensure the safety of consumers of biotechnology products.

Examinations: There will be two 80-minute midterm exams

Other requirements: Group projects which will focus on how policies influence the evolution of a bio-based industry such as the pharmaceutical industry, the food industry or agricultural biotechnology industry and will require two class presentations and two short policy papers.

Additional Information: For more information contact Carl Pray at pray@aesop.rutgers.edu.

Demand and Price Analysis

Instructor: Majid Sani

Course number: 11:373:422

Syllabus

Normally offered: Spring & Summer

Prerequisites and other registration information: Open to those who have successfully completed an introduction to microeconomics course (e.g., 11:373:121 or 01:220:102), other prerequisites may apply as indicated in the Schedule of Classes.

Format: Two 80-minute lecture periods per week.

Description: By the end of this course, you should be able to: 1. Apply economic principles to help solve real-world business management decisions. 2. Make sound product pricing decisions based on your understanding of the demand for a firm's products and the preferences of consumers. 3. Maximize profits and meet production targets at least cost, based on your understanding of a firm's production processes and costs. 4. Diagnose key market structures, and use your knowledge of the strategic behavior of competitors to improve pricing and other business management decisions. 5. Recognize the effects of government regulations on firms, and develop management strategies that succeed in regulated business environments. 6. Extend the concepts we discuss in class to help you understand new business management situations, and develop and analyze new management strategies.

Examinations: There are two 80-minute exams and a comprehensive three-hour final exam. Exams challenge students to communicate economic concepts verbally, graphically, and using basic analytical methods.

Other requirements: Weekly five-minute quizzes challenge students to communicate economic concepts verbally, graphically, and using basic analytical methods.

Additional Information:

Econometric Applications

Instructor: M. Camasso

Course number: 11:373:425

Syllabus

Normally offered: Fall

Prerequisites and other registration information: 11:373:121 or 01:220:102; 01:220:103; 01:960:211 or 01:960:285; 01:640:135 or 01:640:151

Format: Lecture, Discussion, Computer Applications; two 80 minute period

Description: Econometrics is a set of conceptual and statistical tools that can help you find the answers to important economic questions. Economists have used econometric methods to answer such questions as: Does raising the minimum wage lead to higher rates of unemployment among unskilled workers? Can the type of college you attend (public/private) predict your returns to education (future earnings)? Are public school class sizes related to scores on academic achievement tests? Will providing unemployment compensation to farm workers lower worker productivity? Can abortion rates among poorer women be used to forecast future crime rates? Do “shotgun weddings” reduce the number of children born in poverty? The methods taught in this course have been used to answer these questions and a great many others as well.
The course will focus on the application of econometrics through class lectures, discussion, and “hands-on” computer applications and problem sets. The techniques of linear regression will be emphasized but the course will also cover some statistical alternatives when regression assumptions are not appropriate for the problem at hand.

Examinations: There will be two examinations, a midterm and a final. Both will test the student’s ability to use the concepts and methods in the course to identify relationships, interpret results and draw conclusions. The midterm will account for 30 percent of the student’s grade while the final will comprise 40 percent.

Other requirements: Students will be responsible for completing 5 problem sets. These problems will allow students to become familiar with the computer lab, computer applications with SAS and STATA, and the use of computer software to answer questions with econometrics. The problem sets will account for 30 percent of the student’s grade.

Additional Information: mcamasso@rci.rutgers.edu

International Trade policy

Course number: 11:373:431 (21C Certified)

Syllabus

Normally offered: Fall and Summer

Prerequisites and other registration information: Open to Juniors and Seniors of E&BE (others by permission) majors who have successfully completed 11:373:101 or 11:373:121 or 01:220:102.

Format: : Lecture and discussion with occasional small group activities; two 80-minute class periods.

Description: : International Trade Policy focuses on the nature of trade in agricultural products, trade policies and practices of import and export nations, agricultural policies of regional trading blocs, trade negotiations and current developments in agricultural trade and trade policy. Trade policies are examined in the context of globalization and an economic rationale is provided for the severe tension between the marketplace and social groups. The course also examines the role of exchange rates in trade policy.

Examinations: The exams for International Trade Policy comprise of a 10-page paper addressing a trade policy issue in the news and frequent short quizzes (2 to 4 true and false questions) to evaluate students’ understanding of the trade policy concepts taught in the lectures.

Other requirements: : The class will be divided into subgroups to facilitate the small group activities. Students in those groups are expected to work collaboratively on a paper of relevance to international trade. Class participation and attendance is highly regarded and will be rewarded appropriately.

Additional Information: : Please remember that the instructor may make changes to the syllabus and class format, as appropriate to enhance the student’s learning experience.

Business Finance II

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