40 results found
Brito AF, Pinney JW, 2017, Protein–Protein Interactions in Virus–Host Systems, Frontiers in Microbiology, Vol: 8, ISSN: 1664-302X
Liberal R, Lisowska BK, Leak DJ, et al., 2015, PathwayBooster: a tool to support the curation of metabolic pathways, BMC BIOINFORMATICS, Vol: 16, ISSN: 1471-2105
Zurauskiene J, Kirk P, Thorne T, et al., 2014, Derivative processes for modelling metabolic fluxes, Bioinformatics, Vol: 30, Pages: 1892-1898, ISSN: 1367-4803
Motivation: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations.Results: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli.
Bryant WA, Faruqi AA, Pinney JW, 2013, Analysis of Metabolic Evolution in Bacteria Using Whole-Genome Metabolic Models, JOURNAL OF COMPUTATIONAL BIOLOGY, Vol: 20, Pages: 755-764, ISSN: 1066-5277
Williams KJ, Bryant WA, Jenkins VA, et al., 2013, Deciphering the response of Mycobacterium smegmatis to nitrogen stress using bipartite active modules, BMC Genomics, Vol: 14, ISSN: 1471-2164
BackgroundThe ability to adapt to environments with fluctuating nutrient availability is vital for bacterial survival. Although essential for growth, few nitrogen metabolism genes have been identified or fully characterised in mycobacteria and nitrogen stress survival mechanisms are unknown.ResultsA global transcriptional analysis of the mycobacterial response to nitrogen stress, showed a significant change in the differential expression of 16% of the Mycobacterium smegmatis genome. Gene expression changes were mapped onto the metabolic network using Active Modules for Bipartite Networks (AMBIENT) to identify metabolic pathways showing coordinated transcriptional responses to the stress. AMBIENT revealed several key features of the metabolic response not identified by KEGG enrichment alone. Down regulated reactions were associated with the general reduction in cellular metabolism as a consequence of reduced growth rate. Up-regulated modules highlighted metabolic changes in nitrogen assimilation and scavenging, as well as reactions involved in hydrogen peroxide metabolism, carbon scavenging and energy generation.ConclusionsApplication of an Active Modules algorithm to transcriptomic data identified key metabolic reactions and pathways altered in response to nitrogen stress, which are central to survival under nitrogen limiting environments.
Liberal R, Pinney JW, 2013, Simple topological properties predict functional misannotations in a metabolic network., Bioinformatics, Vol: 29, Pages: i154-i161
MOTIVATION: Misannotation in sequence databases is an important obstacle for automated tools for gene function annotation, which rely extensively on comparison with sequences with known function. To improve current annotations and prevent future propagation of errors, sequence-independent tools are, therefore, needed to assist in the identification of misannotated gene products. In the case of enzymatic functions, each functional assignment implies the existence of a reaction within the organism's metabolic network; a first approximation to a genome-scale metabolic model can be obtained directly from an automated genome annotation. Any obvious problems in the network, such as dead end or disconnected reactions, can, therefore, be strong indications of misannotation. RESULTS: We demonstrate that a machine-learning approach using only network topological features can successfully predict the validity of enzyme annotations. The predictions are tested at three different levels. A random forest using topological features of the metabolic network and trained on curated sets of correct and incorrect enzyme assignments was found to have an accuracy of up to 86% in 5-fold cross-validation experiments. Further cross-validation against unseen enzyme superfamilies indicates that this classifier can successfully extrapolate beyond the classes of enzyme present in the training data. The random forest model was applied to several automated genome annotations, achieving an accuracy of ~60% in most cases when validated against recent genome-scale metabolic models. We also observe that when applied to draft metabolic networks for multiple species, a clear negative correlation is observed between predicted annotation quality and phylogenetic distance to the major model organism for biochemistry (Escherichia coli for prokaryotes and Homo sapiens for eukaryotes). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Bryant WA, Sternberg MJE, Pinney JW, 2013, AMBIENT: Active Modules for Bipartite Networks - using high-throughput transcriptomic data to dissect metabolic response, BMC SYSTEMS BIOLOGY, Vol: 7, ISSN: 1752-0509
Ames RM, Macpherson JI, Pinney JW, et al., 2013, Modular biological function is most effectively captured by combining molecular interaction data types., PLoS One, Vol: 8
Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the comple
Sheng X, Huvet M, Pinney J, et al., 2012, Evolutionary Characteristics of Bacterial Two-Component Systems, EVOLUTIONARY SYSTEMS BIOLOGY, Vol: 751, Pages: 121-137, ISSN: 0065-2598
Pinney JW, 2011, Host-pathogen systems biology, Handbook of Statistical Systems Biology, Editors: Stumpf, Balding, Girolami, Publisher: Wiley, Pages: 451-466, ISBN: 9781119970613
Coletta A, Pinney JW, Weiss Solis DY, et al., 2010, Low-complexity regions within protein sequences have position-dependent roles., BMC Systems Biology, Vol: 4
BackgroundRegions of protein sequences with biased amino acid composition (so-called Low-Complexity Regions (LCRs)) are abundant in the protein universe. A number of studies have revealed that i) these regions show significant divergence across protein families; ii) the genetic mechanisms from which they arise lends them remarkable degrees of compositional plasticity. They have therefore proved difficult to compare using conventional sequence analysis techniques, and functions remain to be elucidated for most of them. Here we undertake a systematic investigation of LCRs in order to explore their possible functional significance, placed in the particular context of Protein-Protein Interaction (PPI) networks and Gene Ontology (GO)-term analysis.ResultsIn keeping with previous results, we found that LCR-containing proteins tend to have more binding partners across different PPI networks than proteins that have no LCRs. More specifically, our study suggests i) that LCRs are preferentially positioned towards the protein sequence extremities and, in contrast with centrally-located LCRs, such terminal LCRs show a correlation between their lengths and degrees of connectivity, and ii) that centrally-located LCRs are enriched with transcription-related GO terms, while terminal LCRs are enriched with translation and stress response-related terms.ConclusionsOur results suggest not only that LCRs may be involved in flexible binding associated with specific functions, but also that their positions within a sequence may be important in determining both their binding properties and their biological roles.
Dickerson JE, Pinney JW, Robertson DL, 2010, The biological context of HIV-1 host interactions reveals subtle insights into a system hijack, BMC Systems Biology, Vol: 4
MacPherson JI, Dickerson JE, Pinney JW, et al., 2010, Patterns of HIV-1 protein interaction identify perturbed host-cellular subsystems, PLoS Computational Biology
Huvet M, Toni T, Sheng X, et al., 2010, The evolution of the Phage shock protein (Psp) response system: interplay between protein function, genomic organization and system function., Mol Biol Evol
Ratmann O, Wiuf C, Pinney JW, 2009, From evidence to inference: probing the evolution of protein interaction networks, HFSP Journal, Vol: 3, Pages: 290-306
The evolutionary mechanisms by which protein interaction networks grow and change are beginning to be appreciated as a major factor shaping their present-day structures and properties. Starting with a consideration of the biases and errors inherent in our current views of these networks, we discuss the dangers of constructing evolutionary arguments from naïve analyses of network topology. We argue that progress in understanding the processes of network evolution is only possible when hypotheses are formulated as plausible evolutionary models and compared against the observed data within the framework of probabilistic modeling. The value of such models is expected to be greatly enhanced as they incorporate more of the details of the biophysical properties of interacting proteins, gene phylogeny, and measurement error and as more advanced methodologies emerge for model comparison and the inference of ancestral network states.
Knight CG, Pinney JW, 2009, Making the right connections: biological networks in the light of evolution., Bioessays, Vol: 31, Pages: 1080-1090
Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein-protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre-stage. We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves.
Humphries JD, Byron A, Bass MD, et al., 2009, Proteomic analysis of integrin-associated complexes identifies RCC2 as a dual regulator of Rac1 and Arf6., Sci Signal, Vol: 2
The binding of integrin adhesion receptors to their extracellular matrix ligands controls cell morphology, movement, survival, and differentiation in various developmental, homeostatic, and disease processes. Here, we report a methodology to isolate complexes associated with integrin adhesion receptors, which, like other receptor-associated signaling complexes, have been refractory to proteomic analysis. Quantitative, comparative analyses of the proteomes of two receptor-ligand pairs, alpha(4)beta(1)-vascular cell adhesion molecule-1 and alpha(5)beta(1)-fibronectin, defined both core and receptor-specific components. Regulator of chromosome condensation-2 (RCC2) was detected in the alpha(5)beta(1)-fibronectin signaling network at an intersection between the Rac1 and adenosine 5'-diphosphate ribosylation factor 6 (Arf6) subnetworks. RCC2 knockdown enhanced fibronectin-induced activation of both Rac1 and Arf6 and accelerated cell spreading, suggesting that RCC2 limits the signaling required for membrane protrusion and delivery. Dysregulation of Rac1 and Arf6 function by RCC2 knockdown also abolished persistent migration along fibronectin fibers, indicating a functional role for RCC2 in directional cell movement. This proteomics workflow now opens the way to further dissection and systems-level analyses of adhesion signaling.
Huxley-Jones J, Pinney JW, Archer J, et al., 2009, Back to basics - how the evolution fo the extracellular matrix underpinned vertebrate evolution, INTERNATIONAL JOURNAL OF EXPERIMENTAL PATHOLOGY, Vol: 90, Pages: 95-100, ISSN: 0959-9673
Pinney JW, Dickerson JE, Fu W, et al., 2009, HIV-host interactions: a map of viral perturbation of the host system, AIDS, Vol: 23, Pages: 549-554, ISSN: 0269-9370
Pinney JW, Stumpf MPH, 2009, Evolving proteins at Darwin's bicentenary., Genome Biol, Vol: 10
A report of the Biochemical Society/Wellcome Trust meeting 'Protein Evolution - Sequences, Structures and Systems', Hinxton, UK, 26-27 January 2009.
Gaskell EA, Smith JE, Pinney JW, et al., 2009, A unique dual activity amino acid hydroxylase in Toxoplasma gondii., PLoS One, Vol: 4
The genome of the protozoan parasite Toxoplasma gondii was found to contain two genes encoding tyrosine hydroxylase; that produces L-DOPA. The encoded enzymes metabolize phenylalanine as well as tyrosine with substrate preference for tyrosine. Thus the enzymes catabolize phenylalanine to tyrosine and tyrosine to L-DOPA. The catalytic domain descriptive of this class of enzymes is conserved with the parasite enzyme and exhibits similar kinetic properties to metazoan tyrosine hydroxylases, but contains a unique N-terminal extension with a signal sequence motif. One of the genes, TgAaaH1, is constitutively expressed while the other gene, TgAaaH2, is induced during formation of the bradyzoites of the cyst stages of the life cycle. This is the first description of an aromatic amino acid hydroxylase in an apicomplexan parasite. Extensive searching of apicomplexan genome sequences revealed an ortholog in Neospora caninum but not in Eimeria, Cryptosporidium, Theileria, or Plasmodium. Possible role(s) of these bi-functional enzymes during host infection are discussed.
MacPherson JI, Pinney JW, Robertson DL, 2009, JNets: exploring networks by integrating annotation., BMC Bioinformatics, Vol: 10, ISSN: 1471-2105
BACKGROUND: A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction. RESULTS: JNets was designed as a network visualization tool that incorporates annotation to explore the underlying features of interaction networks. The software is available as an application and a configurable applet that can provide a flexible and dynamic online interface to many types of network data. As a case study, we use JNets to investigate approved drug targets present within the HIV-1 Human protein interaction network. Our software highlights the intricate influence that HIV-1 has on the host immune response. CONCLUSION: JNets is a software tool that allows interaction networks to be visualized and studied remotely, from within a standard web page. Therefore, using this free software, network data can be presented in an enhanced, interactive format. More information about JNets is available at http://www.manchester.ac.uk/bioinformatics/jnets.
Hakes L, Pinney JW, Robertson DL, et al., 2008, Protein-protein interaction networks and biology - what's the connection?, Nat Biotechnol, Vol: 26, Pages: 69-72
Archer J, Pinney JW, Fan J, et al., 2008, Identifying the important HIV-1 recombination breakpoints., PLoS Comput Biol, Vol: 4
Recombinant HIV-1 genomes contribute significantly to the diversity of variants within the HIV/AIDS pandemic. It is assumed that some of these mosaic genomes may have novel properties that have led to their prevalence, particularly in the case of the circulating recombinant forms (CRFs). In regions of the HIV-1 genome where recombination has a tendency to convey a selective advantage to the virus, we predict that the distribution of breakpoints--the identifiable boundaries that delimit the mosaic structure--will deviate from the underlying null distribution. To test this hypothesis, we generate a probabilistic model of HIV-1 copy-choice recombination and compare the predicted breakpoint distribution to the distribution from the HIV/AIDS pandemic. Across much of the HIV-1 genome, we find that the observed frequencies of inter-subtype recombination are predicted accurately by our model. This observation strongly indicates that in these regions a probabilistic model, dependent on local sequence identity, is sufficient to explain breakpoint locations. In regions where there is a significant over- (either side of the env gene) or under- (short regions within gag, pol, and most of env) representation of breakpoints, we infer natural selection to be influencing the recombination pattern. The paucity of recombination breakpoints within most of the envelope gene indicates that recombinants generated in this region are less likely to be successful. The breakpoints at a higher frequency than predicted by our model are approximately at either side of env, indicating increased selection for these recombinants as a consequence of this region, or at least part of it, having a tendency to be recombined as an entire unit. Our findings thus provide the first clear indication of the existence of a specific portion of the genome that deviates from a probabilistic null model for recombination. This suggests that, despite the wide diversity of recombinant forms seen in the viral populati
Bland ND, Pinney JW, Thomas JE, et al., 2008, Bioinformatic analysis of the neprilysin (M13) family of peptidases reveals complex evolutionary and functional relationships., BMC Evol Biol, Vol: 8
BACKGROUND: The neprilysin (M13) family of endopeptidases are zinc-metalloenzymes, the majority of which are type II integral membrane proteins. The best characterised of this family is neprilysin, which has important roles in inactivating signalling peptides involved in modulating neuronal activity, blood pressure and the immune system. Other family members include the endothelin converting enzymes (ECE-1 and ECE-2), which are responsible for the final step in the synthesis of potent vasoconstrictor endothelins. The ECEs, as well as neprilysin, are considered valuable therapeutic targets for treating cardiovascular disease. Other members of the M13 family have not been functionally characterised, but are also likely to have biological roles regulating peptide signalling. The recent sequencing of animal genomes has greatly increased the number of M13 family members in protein databases, information which can be used to reveal evolutionary relationships and to gain insight into conserved biological roles. RESULTS: The phylogenetic analysis successfully resolved vertebrate M13 peptidases into seven classes, one of which appears to be specific to mammals, and insect genes into five functional classes and a series of expansions, which may include inactive peptidases. Nematode genes primarily resolved into groups containing no other taxa, bar the two nematode genes associated with Drosophila DmeNEP1 and DmeNEP4. This analysis reconstructed only one relationship between chordate and invertebrate clusters, that of the ECE sub-group and the DmeNEP3 related genes. Analysis of amino acid utilisation in the active site of M13 peptidases reveals a basis for their biochemical properties. A relatively invariant S1' subsite gives the majority of M13 peptidases their strong preference for hydrophobic residues in P1' position. The greater variation in the S2' subsite may be instrumental in determining the specificity of M13 peptidases for their substrates and thus allows M13 peptida
Eales JM, Pinney JW, Stevens RD, et al., 2008, Methodology capture: discriminating between the "best" and the rest of community practice., BMC Bioinformatics, Vol: 9
BACKGROUND: The methodologies we use both enable and help define our research. However, as experimental complexity has increased the choice of appropriate methodologies has become an increasingly difficult task. This makes it difficult to keep track of available bioinformatics software, let alone the most suitable protocols in a specific research area. To remedy this we present an approach for capturing methodology from literature in order to identify and, thus, define best practice within a field. RESULTS: Our approach is to implement data extraction techniques on the full-text of scientific articles to obtain the set of experimental protocols used by an entire scientific discipline, molecular phylogenetics. Our methodology for identifying methodologies could in principle be applied to any scientific discipline, whether or not computer-based. We find a number of issues related to the nature of best practice, as opposed to community practice. We find that there is much heterogeneity in the use of molecular phylogenetic methods and software, some of which is related to poor specification of protocols. We also find that phylogenetic practice exhibits field-specific tendencies that have increased through time, despite the generic nature of the available software. We used the practice of highly published and widely collaborative researchers ("expert" researchers) to analyse the influence of authority on community practice. We find expert authors exhibit patterns of practice common to their field and therefore act as useful field-specific practice indicators. CONCLUSION: We have identified a structured community of phylogenetic researchers performing analyses that are customary in their own local community and significantly different from those in other areas. Best practice information can help to bridge such subtle differences by increasing communication of protocols to a wider audience. We propose that the practice of expert authors from the field of evolutio
Ptak RG, Fu W, Sanders-Beer BE, et al., 2008, Cataloguing the HIV type 1 human protein interaction network., AIDS Res Hum Retroviruses, Vol: 24, Pages: 1497-1502
Although many interactions between HIV-1 and human proteins have been reported in the scientific literature, no publicly accessible source for efficiently reviewing this information was available. Therefore, a project was initiated in an attempt to catalogue all published interactions between HIV-1 and human proteins. HIV-related articles in PubMed were used to develop a database containing names, Entrez GeneIDs, and RefSeq protein accession numbers of interacting proteins. Furthermore, brief descriptions of the interactions, PubMed identification numbers of articles describing the interactions, and keywords for searching the interactions were incorporated. Over 100,000 articles were reviewed, resulting in the identification of 1448 human proteins that interact with HIV-1 comprising 2589 unique HIV-1-to-human protein interactions. Preliminary analysis of the extracted data indicates 32% were direct physical interactions (e.g., binding) and 68% were indirect interactions (e.g., upregulation through activation of signaling pathways). Interestingly, 37% of human proteins in the database were found to interact with more than one HIV-1 protein. For example, the signaling protein mitogen-activated protein kinase 1 has a surprising range of interactions with 10 different HIV-1 proteins. Moreover, large numbers of interactions were published for the HIV-1 regulatory protein Tat and envelope proteins: 30% and 33% of total interactions identified, respectively. The database is accessible at http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/ and is cross-linked to other National Center for Biotechnology Information databases and programs via Entrez Gene. This database represents a unique and continuously updated scientific resource for understanding HIV-1 replication and pathogenesis to assist in accelerating the development of effective therapeutic and vaccine interventions.
Adamo A, Pinney JW, Kunova A, et al., 2008, Heat stress enhances the accumulation of polyadenylated mitochondrial transcripts in Arabidopsis thaliana., PLoS ONE, Vol: 3
BACKGROUND: Polyadenylation of RNA has a decisive influence on RNA stability. Depending on the organisms or subcellular compartment, it either enhances transcript stability or targets RNAs for degradation. In plant mitochondria, polyadenylation promotes RNA degradation, and polyadenylated mitochondrial transcripts are therefore widely considered to be rare and unstable. We followed up a surprising observation that a large number of mitochondrial transcripts are detectable in microarray experiments that used poly(A)-specific RNA probes, and that these transcript levels are significantly enhanced after heat treatment. METHODOLOGY/PRINCIPAL FINDINGS: As the Columbia genome contains a complete set of mitochondrial genes, we had to identify polymorphisms to differentiate between nuclear and mitochondrial copies of a mitochondrial transcript. We found that the affected transcripts were uncapped transcripts of mitochondrial origin, which were polyadenylated at multiple sites within their 3'region. Heat-induced enhancement of these transcripts was quickly restored during a short recovery period. CONCLUSIONS/SIGNIFICANCE: Our results show that polyadenylated transcripts of mitochondrial origin are more stable than previously suggested, and that their steady-state levels can even be significantly enhanced under certain conditions. As many microarrays contain mitochondrial probes, due to the frequent transfer of mitochondrial genes into the genome, these effects need to be considered when interpreting microarray data.
Pinney JW, Amoutzias GD, Rattray M, et al., 2007, Reconstruction of ancestral protein interaction networks for the bZIP transcription factors., Proc Natl Acad Sci U S A, Vol: 104, Pages: 20449-20453
As whole-genome protein-protein interaction datasets become available for a wide range of species, evolutionary biologists have the opportunity to address some of the unanswered questions surrounding the evolution of these complex systems. Protein interaction networks from divergent organisms may be compared to investigate how gene duplication, deletion, and rewiring processes have shaped the evolution of their contemporary structures. However, current approaches for comparing observed networks from multiple species lack the phylogenetic context necessary to reconstruct the evolutionary history of a network. Here we show how probabilistic modeling can provide a platform for the quantitative analysis of multiple protein interaction networks. We apply this technique to the reconstruction of ancestral networks for the bZIP family of transcription factors and find that excellent agreement is obtained with an alternative sequence-based method for the prediction of leucine zipper interactions. Further analysis shows our probabilistic method to be significantly more robust to the presence of noise in the observed network data than a simple parsimony-based approach. In addition, the integration of evidence over multiple species means that the same method may be used to improve the quality of noisy interaction data for extant species. The ancestral states of a protein interaction network have been reconstructed here by using an explicit probabilistic model of network evolution. We anticipate that this model will form the basis of more general methods for probing the evolutionary history of biochemical networks.
Pinney JW, Papp B, Hyland C, et al., 2007, Metabolic reconstruction and analysis for parasite genomes., Trends Parasitol, Vol: 23, Pages: 548-554
With the completion of sequencing projects for several parasite genomes, efforts are ongoing to make sense of this mass of information in terms of the gene products encoded and their interactions in the growth, development and survival of parasites. The emerging science of systems biology aims to explain the complex relationship between genotype and phenotype by using network models. One area in which this approach has been particularly successful is in the modeling of metabolism. With an accurate picture of the set of metabolic reactions encoded in a genome, it is now possible to identify enzymes or transporters that might be viable targets for new drugs. Because these predictions greatly depend on the quality and completeness of the genome annotation, there are substantial efforts in the scientific community to increase the numbers of metabolic enzymes identified. In this review, we discuss the opportunities for using metabolic reconstruction and analysis tools in parasitology research, and their applications to protozoan parasites.
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