Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major

Genomes of Endotrypanum monterogeii from Panama and Zelonia costaricensis from Brazil: Expansion of Multigene Families in Leishmaniinae Parasites That Are Close Relatives of Leishmania spp

The Leishmaniinae subfamily of the Trypanosomatidae contains both genus Zelonia (monoxenous) and Endotrypanum (dixenous). They are amongst the nearest known relatives of Leishmania, which comprises many human pathogens widespread in the developing world. These closely related lineages are models for the genomic biology of monoxenous and dixenous parasites. Herein, we used comparative genomics to identify the orthologous groups (OGs) shared among 26 Leishmaniinae species to investigate gene family expansion/contraction and applied two phylogenomic approaches to confirm relationships within the subfamily. The Endotrypanum monterogeii and Zelonia costaricensis genomes were assembled, with sizes of 29.9 Mb and 38.0 Mb and 9.711 and 12.201 predicted protein-coding genes, respectively. The genome of E. monterogeii displayed a higher number of multicopy cell surface protein families, including glycoprotein 63 and glycoprotein 46, compared to Leishmania spp. The genome of Z. costaricensis presents expansions of BT1 and amino acid transporters and proteins containing leucine-rich repeat domains, as well as a loss of ABC-type transporters. In total, 415 and 85 lineage-specific OGs were identified in Z. costaricensis and E. monterogeii. The evolutionary relationships within the subfamily were confirmed using the supermatrix (3384 protein-coding genes) and supertree methods. Overall, this study showed new expansions of multigene families in monoxenous and dixenous parasites of the subfamily Leishmaniinae.

Recent Advances in Chemotherapeutics for Leishmaniasis: Importance of the Cellular Biochemistry of the Parasite and Its Molecular Interaction with the Host

Leishmaniasis, a category 1 neglected protozoan disease caused by a kinetoplastid pathogen called Leishmania, is transmitted through dipteran insect vectors (phlebotomine, sand flies) in three main clinical forms: fatal visceral leishmaniasis, self-healing cutaneous leishmaniasis, and mucocutaneous leishmaniasis. Generic pentavalent antimonials have long been the drug of choice against leishmaniasis; however, their success is plagued with limitations such as drug resistance and severe side effects, which makes them redundant as frontline therapy for endemic visceral leishmaniasis. Alternative therapeutic regimens based on amphotericin B, miltefosine, and paromomycin have also been approved. Due to the unavailability of human vaccines, first-line chemotherapies such as pentavalent antimonials, pentamidine, and amphotericin B are the only options to treat infected individuals. The higher toxicity, adverse effects, and perceived cost of these pharmaceutics, coupled with the emergence of parasite resistance and disease relapse, makes it urgent to identify new, rationalized drug targets for the improvement in disease management and palliative care for patients. This has become an emergent need and more relevant due to the lack of information on validated molecular resistance markers for the monitoring and surveillance of changes in drug sensitivity and resistance. The present study reviewed the recent advances in chemotherapeutic regimens by targeting novel drugs using several strategies including bioinformatics to gain new insight into leishmaniasis. Leishmania has unique enzymes and biochemical pathways that are distinct from those of its mammalian hosts. In light of the limited number of available antileishmanial drugs, the identification of novel drug targets and studying the molecular and cellular aspects of these drugs in the parasite and its host is critical to design specific inhibitors targeting and controlling the parasite. The biochemical characterization of unique Leishmania-specific enzymes can be used as tools to read through possible drug targets. In this review, we discuss relevant metabolic pathways and novel drugs that are unique, essential, and linked to the survival of the parasite based on bioinformatics and cellular and biochemical analyses.

Rapid-SL identifies synthetic lethal sets with an arbitrary cardinality

The multidrug resistance of numerous pathogenic microorganisms is a serious challenge that raises global healthcare concerns. Multi-target medications and combinatorial therapeutics are much more effective than single-target drugs due to their synergistic impact on the systematic activities of microorganisms. Designing efficient combinatorial therapeutics can benefit from identification of synthetic lethals (SLs). An SL is a set of non-essential targets (i.e., reactions or genes) that prevent the proliferation of a microorganism when they are "knocked out" simultaneously. To facilitate the identification of SLs, we introduce Rapid-SL, a new multimodal implementation of the Fast-SL method, using the depth-first search algorithm. The advantages of Rapid-SL over Fast-SL include: (a) the enumeration of all SLs that have an arbitrary cardinality, (b) a shorter runtime due to search space reduction, (c) embarrassingly parallel computations, and (d) the targeted identification of SLs. Targeted identification is important because the enumeration of higher order SLs demands the examination of too many reaction sets. Accordingly, we present specific applications of Rapid-SL for the efficient targeted identification of SLs. In particular, we found up to 67% of all quadruple SLs by investigating about 1% of the search space. Furthermore, 307 sextuples, 476 septuples, and over 9000 octuples are found for Escherichia coli genome-scale model, iAF1260.

Carbohydrate metabolism in trypanosomatids: New insights revealing novel complexity, diversity and species-unique features

The human pathogenic trypanosomatid species collectively called the "TriTryp parasites" - Trypanosoma brucei, Trypanosoma cruzi and Leishmania spp. - have complex life cycles, with each of these parasitic protists residing in a different niche during their successive developmental stages where they encounter diverse nutrients. Consequently, they adapt their metabolic network accordingly. Yet, throughout the life cycles, carbohydrate metabolism - involving the glycolytic, gluconeogenic and pentose-phosphate pathways - always plays a central role in the biology of these parasites, whether the available carbon and free energy sources are saccharides, amino acids or lipids. In this paper, we provide an updated review of the carbohydrate metabolism of the TriTryps, highlighting new data about this metabolic network, the interconnection of its pathways and the compartmentalisation of its enzymes within glycosomes, cytosol and mitochondrion. Differences in the expression of the branches of the metabolic network between the successive life-cycle stages of each of these parasitic trypanosomatids are discussed, as well as differences between them. Recent structural and kinetic studies have revealed unique regulatory mechanisms for some of the network's key enzymes with important species-specific variations. Furthermore, reports of multiple post-translational modifications of trypanosomal glycolytic enzymes suggest that additional mechanisms for stage- and/or environmental cues that regulate activity are operational in the parasites. The detailed comparison of the carbohydrate metabolism of the TriTryps has thus revealed multiple differences and a greater complexity, including for the reduced metabolic network in bloodstream-form T. brucei, than previously appreciated. Although these parasites are related, share many cytological and metabolic features and are grouped within a single taxonomic family, the differences highlighted in this review reflect their separate evolutionary tracks from a common ancestor to the extant organisms. These differences are indicative of their adaptation to the different insect vectors and niches occupied in their mammalian hosts.

Perspectives From Systems Biology to Improve Knowledge of Leishmania Drug Resistance

Neglected Tropical Diseases include a broad range of pathogens, hosts, and vectors, which represent evolving complex systems. Leishmaniasis, caused by different Leishmania species and transmitted to humans by sandflies, are among such diseases. Leishmania and other Trypanosomatidae display some peculiar features, which make them a complex system to study. Leishmaniasis chemotherapy is limited due to high toxicity of available drugs, long-term treatment protocols, and occurrence of drug resistant parasite strains. Systems biology studies the interactions and behavior of complex biological processes and may improve knowledge of Leishmania drug resistance. System-level studies to understand Leishmania biology have been challenging mainly because of its unusual molecular features. Networks integrating the biochemical and biological pathways involved in drug resistance have been reported in literature. Antioxidant defense enzymes have been identified as potential drug targets against leishmaniasis. These and other biomarkers might be studied from the perspective of systems biology and systems parasitology opening new frontiers for drug development and treatment of leishmaniasis and other diseases. Our main goals include: 1) Summarize current advances in Leishmania research focused on chemotherapy and drug resistance. 2) Share our viewpoint on the application of systems biology to Leishmania studies. 3) Provide insights and directions for future investigation.

Potential therapeutic targets shared between leishmaniasis and cancer

The association of leishmaniasis and malignancies in human and animal models has been highlighted in recent years. The misdiagnosis of coexistence of leishmaniasis and cancer and the use of common drugs in the treatment of such diseases prompt us to further survey the molecular biology of Leishmania parasites and cancer cells. The information regarding common expressed proteins, as possible therapeutic targets, in Leishmania parasites and cancer cells is scarce. Therefore, the current study reviews proteins, and investigates the regulation and functions of several key proteins in Leishmania parasites and cancer cells. The up- and down-regulations of such proteins were mostly related to survival, development, pathogenicity, metabolic pathways and vital signalling in Leishmania parasites and cancer cells. The presence of common expressed proteins in Leishmania parasites and cancer cells reveals valuable information regarding the possible shared mechanisms of pathogenicity and opportunities for therapeutic targeting in leishmaniasis and cancers in the future.

An insight to flux-balance analysis for biochemical networks

Systems biology is one of the integrated ways to study biological systems and is more favourable than the earlier used approaches. It includes metabolic pathway analysis, modelling, and regulatory as well as signal transduction for getting insights into cellular behaviour. Among the various techniques of modelling, simulation, analysis of networks and pathways, flux-based analysis (FBA) has been recognised because of its extensibility as well as simplicity. It is widely accepted because it is not like a mechanistic simulation which depends on accurate kinetic data. The study of fluxes through the network is informative and can give insights even in the absence of kinetic data. FBA is one of the widely used tools to study biochemical networks and needs information of reaction stoichiometry, growth requirements, specific measurement parameters of the biological system, in particular the reconstruction of the metabolic network for the genome-scale, many of which have already been built previously. It defines the boundaries of flux distributions which are possible and achievable with a defined set of genes. This review article gives an insight into FBA, from the extension of flux balancing to mathematical representation followed by a discussion about the formulation of flux-balance analysis problems, defining constraints for the stoichiometry of the pathways and the tools that can be used in FBA such as FASIMA, COBRA toolbox, and OptFlux. It also includes broader areas in terms of applications which can be covered by FBA as well as the queries which can be addressed through FBA.

Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy

Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly for organisms with limited experimental data. The objective is the development of a new ML pipeline to help in the annotation of essential genes of less explored disease-causing organisms for which minimal experimental data is available. The proposed strategy combines unsupervised feature selection technique, dimension reduction using the Kamada-Kawai algorithm, and semi-supervised ML algorithm employing Laplacian Support Vector Machine (LapSVM) for prediction of essential and non-essential genes from genome-scale metabolic networks using very limited labeled dataset. A novel scoring technique, Semi-Supervised Model Selection Score, equivalent to area under the ROC curve (auROC), has been proposed for the selection of the best model when supervised performance metrics calculation is difficult due to lack of data. The unsupervised feature selection followed by dimension reduction helped to observe a distinct circular pattern in the clustering of essential and non-essential genes. LapSVM then created a curve that dissected this circle for the classification and prediction of essential genes with high accuracy (auROC > 0.85) even with 1% labeled data for model training. After successful validation of this ML pipeline on both Eukaryotes and Prokaryotes that show high accuracy even when the labeled dataset is very limited, this strategy is used for the prediction of essential genes of organisms with inadequate experimentally known data, such as Leishmania sp. Using a graph-based semi-supervised machine learning scheme, a novel integrative approach has been proposed for essential gene prediction that shows universality in application to both Prokaryotes and Eukaryotes with limited labeled data. The essential genes predicted using the pipeline provide an important lead for the prediction of gene essentiality and identification of novel therapeutic targets for antibiotic and vaccine development against disease-causing parasites.

Construction and Validation of a Genome-Scale Metabolic Network of Thermotoga sp. Strain RQ7

Thermotoga are anaerobic hyperthermophiles that have a deep lineage to the last universal ancestor and produce biological hydrogen gas accompanying cell growth. In recent years, systems-level approaches have been used to elucidate their metabolic capacities, by integrating mathematical modeling and experimental results. To assist biochemical engineering studies of T. sp. strain RQ7, this work aims at building a metabolic model of the bacterium that quantitatively simulates its metabolism at the genome scale. The constructed model, RQ7_iJG408, consists of 408 genes, 692 reactions, and 538 metabolites. Constraint-based flux balance analyses were used to simulate cell growth in both the complex and defined media. Quantitative comparison of the predicted and measured growth rates resulted in good agreements. This model serves as a foundation for an integrated biochemical description of T. sp. strain RQ7. It is a useful tool in designing growth media, identifying metabolic engineering strategies, and exploiting the physiological potentials of this biotechnologically significant organism.

Genome-scale metabolic models highlight stage-specific differences in essential metabolic pathways in Trypanosoma cruzi

Chagas disease is a neglected tropical disease and a leading cause of heart failure in Latin America caused by a protozoan called Trypanosoma cruzi. This parasite presents a complex multi-stage life cycle. Anti-Chagas drugs currently available are limited to benznidazole and nifurtimox, both with severe side effects. Thus, there is a need for alternative and more efficient drugs. Genome-scale metabolic models (GEMs) can accurately predict metabolic capabilities and aid in drug discovery in metabolic genes. This work developed an extended GEM, hereafter referred to as iIS312, of the published and validated T. cruzi core metabolism model. From iIS312, we then built three stage-specific models through transcriptomics data integration, and showed that epimastigotes present the most active metabolism among the stages (see S1-S4 GEMs). Stage-specific models predicted significant metabolic differences among stages, including variations in flux distribution in core metabolism. Moreover, the gene essentiality predictions suggest potential drug targets, among which some have been previously proven lethal, including glutamate dehydrogenase, glucokinase and hexokinase. To validate the models, we measured the activity of enzymes in the core metabolism of the parasite at different stages, and showed the results were consistent with model predictions. Our results represent a potential step forward towards the improvement of Chagas disease treatment. To our knowledge, these stage-specific models are the first GEMs built for the stages Amastigote and Trypomastigote. This work is also the first to present an in silico GEM comparison among different stages in the T. cruzi life cycle.

Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia-present in many filariae-which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

In silico Metabolic Pathway Analysis Identifying Target Against Leishmaniasis - A Kinetic Modeling Approach

The protozoan Leishmania donovani, from trypanosomatids family is a deadly human pathogen responsible for causing Visceral Leishmaniasis. Unavailability of proper treatment in the developing countries has served as a major threat to the people. The absence of vaccines has made treatment possibilities to rely solely over chemotherapy. Also, reduced drug efficacy due to emerging resistant strains magnifies the threat. Despite years of formulations for an effective drug therapy, complexity of the disease is also unfortunately increasing. Absence of potential drug targets has worsened the scenario. Therefore exploring new therapeutic approach is a priority for the scientific community to combat the disease. One of the most reliable ways to alter the adversities of the infection is finding new biological targets for designing potential drugs. An era of computational biology allows identifying targets, assisting experimental studies. It includes sorting the parasite’s metabolic pathways that pins out proteins essential for its survival. We have directed our study towards a computational methodology for determining targets against L. donovani from the “purine salvage” pathway. This is a mainstay pathway towards the maintenance of purine amounts in the parasitic pool of nutrients proving to be mandatory for its survival. This study represents an integration of metabolic pathway and Protein-Protein Interactions analysis. It consists of incorporating the available experimental data to the theoretical methods with a prospective to develop a kinetic model of Purine salvage pathway. Simulation data revealed the time course mechanism of the enzymes involved in the synthesis of the metabolites. Modeling of the metabolic pathway helped in marking of crucial enzymes. Additionally, the PPI analysis of the pathway assisted in building a static interaction network for the proteins. Topological analysis of the PPI network through centrality measures (MCC and Closeness) detected targets found common with Dynamic Modeling. Therefore our analysis reveals the enzymes ADSL (Adenylosuccinate lyase) and IMPDH (Inosine-5′-monophosphate dehydrogenase) to be important having a central role in the modeled network based on PPI and kinetic modeling techniques. Further the available three dimensional structure of the enzyme “ADSL” aided towards the search for potential inhibitors against the protein. Hence, the study presented the significance of integrating methods to identify key proteins which might be putative targets against the treatment of Visceral Leishmaniasis and their potential inhibitors.

Multiscale Process Modelling in Translational Systems Biology of Leishmania major: A Holistic view

Present work aims to utilize systems biology and molecular modelling approach to understand the inhibition kinetics of Leishmania major GLO I and identifying potential hit followed by their validation through in vitro and animal studies. Simulation of GLO I inhibition has shown to affect reaction fluxes of almost all reactions in the model that led to increased production of various AGEs and free radicals. Further, in vitro testing of C1 and C2, selected through molecular modelling revealed remarkable morphological alterations like size reduction, membrane blebbing and loss in motility of the parasite, however, only C1 showed better antileishmanial activity. Additionally, C1 showed apoptosis mediated leishmanicidal activity (apoptosis-like cell death) along with cell-cycle arrest at sub-G0/G1 phase and exhibited potent anti-leishmanial effect against intracellular amastigotes. Furthermore, decrease in parasite load was also observed in C1 treated BALB/c female mice. Our results indicate that C1 has healing effect in infected mice and effectively reduced the parasitic burden. Hence, we suggest C1 as a lead molecule which on further modification, may be used to develop novel therapeutics against Leishmaniasis.

In silico disease model: from simple networks to complex diseases

In this chapter we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given.

Metabolomic Profile of BALB/c Macrophages Infected with Leishmania amazonensis: Deciphering L-Arginine Metabolism

Background: Leishmaniases are neglected tropical diseases that are caused by Leishmania, being endemic worldwide. L-arginine is an essential amino acid that is required for polyamines production on mammal cells. During Leishmania infection of macrophages, L-arginine is used by host and parasite arginase to produce polyamines, leading to parasite survival; or, by nitric oxide synthase 2 to produce nitric oxide leading to parasite killing. Here, we determined the metabolomic profile of BALB/c macrophages that were infected with L. amazonensis wild type or with L. amazonensis arginase knockout, correlating the regulation of L-arginine metabolism from both host and parasite. Methods: The metabolites of infected macrophages were analyzed by capillary electrophoresis coupled with mass spectrometry (CE-MS). The metabolic fingerprints analysis provided the dual profile from the host and parasite. Results: We observed increased levels of proline, glutamic acid, glutamine, L-arginine, ornithine, and putrescine in infected-L. amazonensis wild type macrophages, which indicated that this infection induces the polyamine production. Despite this, we observed reduced levels of ornithine, proline, and trypanothione in infected-L. amazonensis arginase knockout macrophages, indicating that this infection reduces the polyamine production. Conclusions: The metabolome fingerprint indicated that Leishmania infection alters the L-arginine/polyamines/trypanothione metabolism inside the host cell and the parasite arginase impacts on L-arginine metabolism and polyamine production, defining the infection fate.

Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania

Background: The integration of computational and mathematical approaches is used to provide a key insight into the biological systems. Through systems biology approaches we seek to find detailed and more robust information on Leishmanial metabolic network. Forman/Forman-Ricci curvature measures were applied to identify important nodes in the network(s). This was followed by flux balance analysis (FBA) to decipher important drug targets. Results: Our results revealed several key high curvature nodes (metabolites) belonging to common yet crucial metabolic networks, thus, maintaining the integrity of the network which signifies its robustness. Further analysis revealed the presence of some of these metabolites, MGO, in redox metabolism of the parasite. Being a component in the glyoxalase pathway and highly cytotoxic, we further attempted to study the outcome of the deletion of the key enzyme (GLOI) mainly involved in the neutralization of MGO by utilizing FBA. The model and the objective function kept as simple as possible demonstrated an interesting emergent behavior. The non-functional GLOI in the model contributed to "zero" flux which signifies the key role of GLOI as a rate limiting enzyme. This has led to several fold increase production of MGO, thereby, causing an increased level of MGO•- generation. Conclusions: The integrated computational approaches have deciphered GLOI as a potential target both from curvature measures as well as FBA which could further be explored for kinetic modeling by implying various redox-dependent constraints on the model. Furthermore, a constraint-based FBA on a larger model could further be explored to get broader picture to understand the exact underlying mechanisms. Designing various in vitro experimental perspectives could churn the therapeutic importance of GLOI.

An immunoproteomic approach to identifying immunoreactive proteins in Leishmania infantum amastigotes using sera of dogs infected with canine visceral leishmaniasis

Visceral leishmaniasis (VL), the most severe form of leishmaniasis, is caused by Leishmania donovani and Leishmania infantum. The infected dogs with canine visceral leishmaniasis (CVL) are important reservoirs for VL in humans, so the diagnosis, treatment and vaccination of the infected dogs will ultimately decrease the rate of human VL. Proteomics and immunoproteomics techniques have facilitated the introduction of novel drug, vaccine and diagnostic targets. Our immunoproteomic study was conducted to identify new immunoreactive proteins in amastigote form of L. infantum. The strain of L. infantum (MCAN/IR/07/Moheb-gh) was obtained from CVL-infected dogs. J774 macrophage cells were infected with the L. infantum promastigotes. The infected macrophages were ruptured, and pure amastigotes were extracted from the macrophages. After protein extraction, two-dimensional gel electrophoresis was employed for protein separation followed by Western blotting. Western blotting was performed, using symptomatic and asymptomatic sera of the infected dogs with CVL. Thirteen repeatable immunoreactive spots were identified by Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Some, including prohibitin, ornithine aminotransferase, annexin A4, and apolipoprotein A-I, have been critically involved in metabolic pathways, survival, and pathogenicity of Leishmania parasites. Further investigations are required to confirm our identified immunoreactive proteins as a biomarker for CVL.

Parasite Sensing of Host Nutrients and Environmental Cues

Parasites undergo complex life cycles that comprise a wide variety of cellular differentiation events in different host compartments and transmission across multiple hosts. As parasites depend on host resources, it is not surprising they have developed efficient mechanisms to sense alterations and adapt to the available resources in a wide range of environments. Here we provide an overview of the nutritional needs of different parasites throughout their diverse life stages and highlight recent insights into strategies that both hosts and parasites have developed to meet these nutritional requirements needed for defense, survival, and replication. These studies will provide the foundation for a systems-level understanding of host-parasite interactions, which will require the integration of molecular, epidemiologic, and mechanistic data and the application of interdisciplinary approaches to model parasite regulatory networks that are triggered by alterations in host resources.

Efflux pumps and antimicrobial resistance: Paradoxical components in systems genomics

Efflux pumps play a major role in the increasing antimicrobial resistance rendering a large number of drugs of no use. Large numbers of pathogens are becoming multidrug resistant due to inadequate dosage and use of the existing antimicrobials. This leads to the need for identifying new efflux pump inhibitors. Design of novel targeted therapies using inherent complexity involved in the biological network modeling has gained increasing importance in recent times. The predictive approaches should be used to determine antimicrobial activities with high pathogen specificity and microbicidal potency. Antimicrobial peptides, which are part of our innate immune system, have the ability to respond to infections and have gained much attention in making resistant strain sensitive to existing drugs. In this review paper, we outline evidences linking host-directed therapy with the efflux pump activity to infectious disease.

An in silico approach in identification of drug targets in Leishmania: A subtractive genomic and metabolic simulation analysis

Leishmaniasis is one of the major health issue in developing countries. The current therapeutic regimen for this disease is less effective with lot of adverse effects thereby warranting an urgent need to develop not only new and selective drug candidates but also identification of effective drug targets. Here we present subtractive genomics procedure for identification of putative drug targets in Leishmania. Comprehensive druggability analysis has been carried out in the current work for identified metabolic pathways and drug targets. We also demonstrate effective metabolic simulation methodology to pinpoint putative drug targets in threonine biosynthesis pathway. Metabolic simulation data from the current study indicate that decreasing flux through homoserine kinase reaction can be considered as a good therapeutic opportunity. The data from current study is expected to show new avenue for designing experimental strategies in search of anti-leishmanial agents.

Evolutionary Perspectives of Genotype-Phenotype Factors in Leishmania Metabolism

The sandfly midgut and the human macrophage phagolysosome provide antagonistic metabolic niches for the endoparasite Leishmania to survive and populate. Although these environments fluctuate across developmental stages, the relative changes in both these environments across parasite generations might remain gradual. Such environmental restrictions might endow parasite metabolism with a choice of specific genotypic and phenotypic factors that can constrain enzyme evolution for successful adaptation to the host. With respect to the available cellular information for Leishmania species, for the first time, we measure the relative contribution of eight inter-correlated predictors related to codon usage, GC content, gene expression, gene length, multi-functionality, and flux-coupling potential of an enzyme on the evolutionary rates of singleton metabolic genes and further compare their effects across three Leishmania species. Our analysis reveals that codon adaptation, multi-functionality, and flux-coupling potential of an enzyme are independent contributors of enzyme evolutionary rates, which can together explain a large variation in enzyme evolutionary rates across species. We also hypothesize that a species-specific occurrence of duplicated genes in novel subcellular locations can create new flux routes through certain singleton flux-coupled enzymes, thereby constraining their evolution. A cross-species comparison revealed both common and species-specific genes whose evolutionary divergence was constrained by multiple independent factors. Out of these, previously known pharmacological targets and virulence factors in Leishmania were identified, suggesting their evolutionary reasons for being important survival factors to the parasite. All these results provide a fundamental understanding of the factors underlying adaptive strategies of the parasite, which can be further targeted.

System pharmacogenomics application in infectious diseases

The new era in systems pharmacology has revolutionized the human biology. Its applicability, precise treatment, adequate response and safety measures fit into all the paradigm of medical/clinical practice. The importance of mathematical models in understanding the disease pathology and epideomology is now being realized. The advent of high-throughput technologies and the emergence of systems biology have resulted in the creation of systems pharmacogenomics and the focus is now on personalized medicine. However, there are some regulatory issues that need to be addresssed; are we ready for this universal adoption? This article details some of the infectious disease pharmacogenomics to the developments in this area.

A systematic reconstruction and constraint-based analysis of Leishmania donovani metabolic network: identification of potential antileishmanial drug targets

Visceral leishmaniasis, a lethal parasitic disease, is caused by the protozoan parasite Leishmania donovani. The absence of an effective vaccine, drug toxicity and parasite resistance necessitates the identification of novel drug targets. Reconstruction of genome-scale metabolic models and their simulation has been established as an important tool for systems-level understanding of a microorganism's metabolism. In this work, amalgamating the tools and techniques of computational systems biology with rigorous manual curation, a constraint-based metabolic model for Leishmania donovani BPK282A1 has been developed. New functional annotations for 18 formerly hypothetical or erroneously annotated genes (encountered during iterative refinement of the model) have been proposed. Further, to formulate an accurate biomass objective function, experimental determination of previously uncharacterized biomass constituents was performed. The developed model is a highly compartmentalized metabolic model, comprising 1159 reactions, 1135 metabolites and 604 genes. The model exhibited around 76% accuracy for the prediction of experimental phenotypes of gene knockout studies and drug inhibition assays. Employing in silico gene knockout studies, we identified 28 essential genes with negligible sequence identity to the human proteins. Moreover, by dissecting the functional interdependencies of metabolic pathways, 70 synthetic lethal pairs were identified. Finally, in order to delineate stage-specific metabolism, gene-expression data of the amastigote stage residing in human macrophages were integrated into the model. By comparing the flux distribution, we illustrated the stage-specific differences in metabolism and environmental conditions that are in good agreement with the experimental findings. The developed model can serve as a highly enriched knowledgebase of legacy data and an important tool for generating experimentally verifiable hypotheses.

Analysis of Piscirickettsia salmonis Metabolism Using Genome-Scale Reconstruction, Modeling, and Testing

Piscirickettsia salmonis is an intracellular bacterial fish pathogen that causes piscirickettsiosis, a disease with highly adverse impact in the Chilean salmon farming industry. The development of effective treatment and control methods for piscireckttsiosis is still a challenge. To meet it the number of studies on P. salmonis has grown in the last couple of years but many aspects of the pathogen's biology are still poorly understood. Studies on its metabolism are scarce and only recently a metabolic model for reference strain LF-89 was developed. We present a new genome-scale model for P. salmonis LF-89 with more than twice as many genes as in the previous model and incorporating specific elements of the fish pathogen metabolism. Comparative analysis with models of different bacterial pathogens revealed a lower flexibility in P. salmonis metabolic network. Through constraint-based analysis, we determined essential metabolites required for its growth and showed that it can benefit from different carbon sources tested experimentally in new defined media. We also built an additional model for strain A1-15972, and together with an analysis of P. salmonis pangenome, we identified metabolic features that differentiate two main species clades. Both models constitute a knowledge-base for P. salmonis metabolism and can be used to guide the efficient culture of the pathogen and the identification of specific drug targets.

In silico Tools for Target Identification and Drug Molecular Docking in Leishmania

Neglected tropical diseases represent a significant health burden in large parts of the world. Drug discovery is currently a key bottleneck in the pipeline of these diseases. In this chapter, the in silico approaches used for the processes involved in drug discovery, identification and validation of druggable Leishmania targets, and design and optimisation of new anti-leishmanial drugs are discussed. We also provide a general view of the different computational tools that can be employed in pursuit of this aim, along with the most interesting cases found in the literature.

Revealing the mystery of metabolic adaptations using a genome scale model of Leishmania infantum

Human macrophage phagolysosome and sandfly midgut provide antagonistic ecological niches for Leishmania parasites to survive and proliferate. Parasites optimize their metabolism to utilize the available inadequate resources by adapting to those environments. Lately, a number of metabolomics studies have revived the interest to understand metabolic strategies utilized by the Leishmania parasite for optimal survival within its hosts. For the first time, we propose a reconstructed genome-scale metabolic model for Leishmania infantum JPCM5, the analyses of which not only captures observations reported by metabolomics studies in other Leishmania species but also divulges novel features of the L. infantum metabolome. Our results indicate that Leishmania metabolism is organized in such a way that the parasite can select appropriate alternatives to compensate for limited external substrates. A dynamic non-essential amino acid motif exists within the network that promotes a restricted redistribution of resources to yield required essential metabolites. Further, subcellular compartments regulate this metabolic re-routing by reinforcing the physiological coupling of specific reactions. This unique metabolic organization is robust against accidental errors and provides a wide array of choices for the parasite to achieve optimal survival.

Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance

Background

Malaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug, artemisinin. Our limited understanding of Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms.

Results

Here, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites, including folate and polyamines. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistant parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood.

Conclusion

Using this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3905-1) contains supplementary material, which is available to authorized users.

Lessons from the genomes and transcriptomes of filarial nematodes

Human filarial infections are a leading cause of morbidity in the developing world. While a small arsenal of drugs exists to treat these infections, there remains a tremendous need for the development of additional interventions. Recent genome sequences and transcriptome analyses of filarial nematodes have provided novel biological insight and allowed for the prediction of novel drug targets as well as potential vaccine candidates. In this review, we discuss the currently available data, insights gained into the metabolism of these organisms, and how the filaria field can move forward by leveraging these data.

The genome of Onchocerca volvulus, agent of river blindness

Human onchocerciasis is a serious neglected tropical disease caused by the filarial nematode Onchocerca volvulus that can lead to blindness and chronic disability. Control of the disease relies largely on mass administration of a single drug, and the development of new drugs and vaccines depends on a better knowledge of parasite biology. Here, we describe the chromosomes of O. volvulus and its Wolbachia endosymbiont. We provide the highest-quality sequence assembly for any parasitic nematode to date, giving a glimpse into the evolution of filarial parasite chromosomes and proteomes. This resource was used to investigate gene families with key functions that could be potentially exploited as targets for future drugs. Using metabolic reconstruction of the nematode and its endosymbiont, we identified enzymes that are likely to be essential for O. volvulus viability. In addition, we have generated a list of proteins that could be targeted by Federal-Drug-Agency-approved but repurposed drugs, providing starting points for anti-onchocerciasis drug development.

Pathogenesis--Thoughts from the Front Line

The field of bacterial pathogenesis has advanced dramatically in the last decade. High throughput molecular technologies have empowered scientists as never before. However, there remain some limitations, misconceptions and ambiguities in the field that may bedevil even the experienced investigator. Here, I consider some of the unanswered questions that are not readily tractable to even the most powerful technology.

LmSmdB: an integrated database for metabolic and gene regulatory network in Leishmania major and Schistosoma mansoni

A database that integrates all the information required for biological processing is essential to be stored in one platform. We have attempted to create one such integrated database that can be a one stop shop for the essential features required to fetch valuable result. LmSmdB (L. major and S. mansoni database) is an integrated database that accounts for the biological networks and regulatory pathways computationally determined by integrating the knowledge of the genome sequences of the mentioned organisms. It is the first database of its kind that has together with the network designing showed the simulation pattern of the product. This database intends to create a comprehensive canopy for the regulation of lipid metabolism reaction in the parasite by integrating the transcription factors, regulatory genes and the protein products controlled by the transcription factors and hence operating the metabolism at genetic level.

Metabolomics-Based Study of Logarithmic and Stationary Phases of Promastigotes in Leishmania major by 1H NMR Spectroscopy

Background:

Cutaneous leishmaniasis is one of the most important parasitic diseases in humans. In this disease, one of the responsible organisms is Leishmania major, which is transmitted by sandfly vector. There are specific differences in biochemical profiles and metabolite pathways in logarithmic and stationary phases of Leishmania parasites. In the present study, ¹H NMR spectroscopy was used to examine the metabolites outliers in the logarithmic and stationary phases of promastigotes in L. major to enlighten more about the transmission mechanism in metacyclogenesis of L. major.

Methods:

Promastigote was cultured, logarithmic and stationary phases were separated by the peanut agglutinin, and cell metabolites were extracted. ¹H NMR spectroscopy was applied, and outliers were analyzed using principal component analysis.

Results:

The most altered metabolites in stationary and logarithmic phases were limited to citraconic acid, isopropylmalic acid, L-leucine, ornithine, caprylic acid, capric acid, and acetic acid.

Conclusion:

¹H NMR spectroscopy could play an important role in the characterization of metabolites in biochemical pathways during a metacyclogenesis process. These metabolites and their pathways can help in exploiting a transmission mechanism in metacyclogenesis, and outcoming data might be used in the metabolic network reconstruction of L. major modeling.

Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods

Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.

Dissecting Leishmania infantum Energy Metabolism - A Systems Perspective

Leishmania infantum, causative agent of visceral leishmaniasis in humans, illustrates a complex lifecycle pertaining to two extreme environments, namely, the gut of the sandfly vector and human macrophages. Leishmania is capable of dynamically adapting and tactically switching between these critically hostile situations. The possible metabolic routes ventured by the parasite to achieve this exceptional adaptation to its varying environments are still poorly understood. In this study, we present an extensively reconstructed energy metabolism network of Leishmania infantum as an attempt to identify certain strategic metabolic routes preferred by the parasite to optimize its survival in such dynamic environments. The reconstructed network consists of 142 genes encoding for enzymes performing 237 reactions distributed across five distinct model compartments. We annotated the subcellular locations of different enzymes and their reactions on the basis of strong literature evidence and sequence-based detection of cellular localization signal within a protein sequence. To explore the diverse features of parasite metabolism the metabolic network was implemented and analyzed as a constraint-based model. Using a systems-based approach, we also put forth an extensive set of lethal reaction knockouts; some of which were validated using published data on Leishmania species. Performing a robustness analysis, the model was rigorously validated and tested for the secretion of overflow metabolites specific to Leishmania under varying extracellular oxygen uptake rate. Further, the fate of important non-essential amino acids in L. infantum metabolism was investigated. Stage-specific scenarios of L. infantum energy metabolism were incorporated in the model and key metabolic differences were outlined. Analysis of the model revealed the essentiality of glucose uptake, succinate fermentation, glutamate biosynthesis and an active TCA cycle as driving forces for parasite energy metabolism and its optimal growth. Finally, through our in silico knockout analysis, we could identify possible therapeutic targets that provide experimentally testable hypotheses.

Systems analysis of host-parasite interactions

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug‐resistant parasites necessitates that the research community take an active role in understanding host–parasite infection biology in order to develop improved therapeutics. Recent advances in next‐generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host–parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high‐throughput ‐omic data will undoubtedly generate extraordinary insight into host–parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host–parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies. WIREs Syst Biol Med 2015, 7:381–400. doi: 10.1002/wsbm.1311

For further resources related to this article, please visit the WIREs website.

An argument for mechanism-based statistical inference in cancer

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.

Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis

Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair.

Use of (13)C stable isotope labelling for pathway and metabolic flux analysis in Leishmania parasites

This protocol describes the combined use of metabolite profiling and stable isotope labelling to define pathways of central carbon metabolism in the protozoa parasite, Leishmania mexicana. Parasite stages are cultivated in standard or completely defined media and then rapidly transferred to chemically equivalent media containing a single (13)C-labelled nutrient. The incorporation of label can be followed over time or after establishment of isotopic equilibrium by harvesting parasites with rapid metabolic quenching. (13)C enrichment of multiple intracellular polar and apolar (lipidic) metabolites can be quantified using gas chromatography-mass spectrometry (GC-MS), while the uptake and secretion of (13)C-labelled metabolites can be measured by (13)C-NMR. Analysis of the mass isotopomer distribution of key metabolites provides information on pathway structure, while analysis of labelling kinetics can be used to infer metabolic fluxes. This protocol is exemplified using L. mexicana labelled with (13)C-U-glucose. The method can be used to measure perturbations in parasite metabolism induced by drug inhibition or genetic manipulation of enzyme levels and is broadly applicable to any cultured parasite stages.

Essential gene identification and drug target prioritization in Leishmania species

Leishmaniasis is one of the neglected tropical diseases (NTDs), mainly affecting impoverished communities and having varied ranges of pathogenicity according to the diverse spectrum of clinical manifestations. It is endemic in many countries and poses major challenges to healthcare systems in developing countries. Despite the fact that most of the current mono and combination therapies are found to be failures, clear perception of gene essentiality for parasite survival are now desideratum to identify potential biochemical targets through selection. Here we used the metabolic network of L. major, to perform a comprehensive set of in silico deletion mutants and have systematically recognized a clearly defined set of essential proteins by combining several essential criteria. In this paper we summarize the efforts to prioritize potential drug targets up to a five-fold enrichment compared with a random selection.

TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei

The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.

A novel nutritional predictor links microbial fastidiousness with lowered ubiquity, growth rate, and cooperativeness

Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leverage the recent availability of thousands of automatically generated genome-scale metabolic models to develop a predictor of microbial minimal medium requirements, which we apply to thousands of species to study the relationship between their nutritional requirements and their ecological and genomic traits. We first show that nutritional requirements are more similar among species that co-habit many ecological niches. We then reveal three fundamental characteristics of microbial fastidiousness (i.e., complex and specific nutritional requirements): (1) more fastidious microorganisms tend to be more ecologically limited; (2) fastidiousness is positively associated with smaller genomes and smaller metabolic networks; and (3) more fastidious species grow more slowly and have less ability to cooperate with other species than more metabolically versatile organisms. These associations reflect the adaptation of fastidious microorganisms to unique niches with few cohabitating species. They also explain how non-fastidious species inhabit many ecological niches with high abundance rates. Taken together, these results advance our understanding microbial nutrition on a large scale, by presenting new nutrition-related associations that govern the distribution of microorganisms in nature.

Understanding microbial nutrition is critical for understanding microbial life, and thus has a major influence in many areas of biology. In recent years, the traditional methods of studying microbial nutrition, which rely on culturing bacteria and assessing their nutritional needs through extensive experiments, have been augmented by the development of genome-scale metabolic models, which enable in-depth analysis and prediction of nutrition for a few well-studied organisms. Recently, a pipeline was developed for generating genome-scale metabolic models automatically (the SEED). Here, we leverage models built from this pipeline in order to develop a novel predictor of microbial minimal medium requirements, which we then apply broadly for thousands of microbes across the tree of life. We first show that nutritional requirements are more similar among microorganisms that co-habit many ecological niches. We then use our medium predictions to examine the fastidiousness of organisms (i.e., their need for complex/specific media), and suggest an explanation for certain observed features of microbial abundance patterns. This study is one of the first to leverage genome-scale models on a large (>1000 species) scale, and sets the potential for a new host of strategies for understanding microbial nutrition and ecology in the future.

Functional metabolic map of Faecalibacterium prausnitzii, a beneficial human gut microbe

The human gut microbiota plays a central role in human well-being and disease. In this study, we present an integrated, iterative approach of computational modeling, in vitro experiments, metabolomics, and genomic analysis to accelerate the identification of metabolic capabilities for poorly characterized (anaerobic) microorganisms. We demonstrate this approach for the beneficial human gut microbe Faecalibacterium prausnitzii strain A2-165. We generated an automated draft reconstruction, which we curated against the limited biochemical data. This reconstruction modeling was used to develop in silico and in vitro a chemically defined medium (CDM), which was validated experimentally. Subsequent metabolomic analysis of the spent medium for growth on CDM was performed. We refined our metabolic reconstruction according to in vitro observed metabolite consumption and secretion and propose improvements to the current genome annotation of F. prausnitzii A2-165. We then used the reconstruction to systematically characterize its metabolic properties. Novel carbon source utilization capabilities and inabilities were predicted based on metabolic modeling and validated experimentally. This study resulted in a functional metabolic map of F. prausnitzii, which is available for further applications. The presented workflow can be readily extended to other poorly characterized and uncharacterized organisms to yield novel biochemical insights about the target organism.

Mycobacterium tuberculosis H37Rv: In Silico Drug Targets Identification by Metabolic Pathways Analysis

Mycobacterium tuberculosis (Mtb) is a pathogenic bacteria species in the genus Mycobacterium and the causative agent of most cases of tuberculosis. Tuberculosis (TB) is the leading cause of death in the world from a bacterial infectious disease. This antibiotic resistance strain lead to development of the new antibiotics or drug molecules which can kill or suppress the growth of Mycobacterium tuberculosis. We have performed an in silico comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen Mycobacterium tuberculosis (H37Rv). Novel efforts in developing drugs that target the intracellular metabolism of M. tuberculosis often focus on metabolic pathways that are specific to M. tuberculosis. We have identified five unique pathways for Mycobacterium tuberculosis having a number of 60 enzymes, which are nonhomologous to Homo sapiens protein sequences, and among them there were 55 enzymes, which are nonhomologous to Homo sapiens protein sequences. These enzymes were also found to be essential for survival of the Mycobacterium tuberculosis according to the DEG database. Further, the functional analysis using Uniprot showed involvement of all the unique enzymes in the different cellular components.

MC3: a steady-state model and constraint consistency checker for biochemical networks

Background

Stoichiometric models provide a structural framework for analyzing steady-state cellular behavior. Models are developed either through augmentations of existing models or more recently through automatic reconstruction tools. There is currently no standardized practice or method for validating the properties of a model before placing it in the public domain. Considerable effort is often required to understand a model’s inconsistencies before its reuse within new research efforts.

Results

We present a review of common issues in stoichiometric models typically uncovered during pathway analysis and constraint-based optimization, and we detail succinct and efficient ways to find them. We present MC³, Model and Constraint Consistency Checker, a computational tool that can be used for two purposes: (a) identifying potential connectivity and topological issues for a given stoichiometric matrix, S, and (b) flagging issues that arise during constraint-based optimization. The MC³ tool includes three distinct checking components. The first examines the results of computing the basis for the null space for Sv = 0; the second uses connectivity analysis; and the third utilizes Flux Variability Analysis. MC³ takes as input a stoichiometric matrix and flux constraints, and generates a report summarizing issues.

Conclusions

We report the results of applying MC³ to published models for several systems including Escherichia coli, an adipocyte cell, a Chinese Hamster Ovary cell, and Leishmania major. Several issues with no prior documentation are identified. MC³ provides a standalone MATLAB-based comprehensive tool for model validation, a task currently performed either ad hoc or implemented in part within other computational tools.