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

Mitochondria Restrict Growth of the Intracellular Parasite Toxoplasma gondii by Limiting Its Uptake of Fatty Acids

How intracellular pathogens acquire essential non-diffusible host metabolites and whether the host cell counteracts the siphoning of these nutrients by its invaders are open questions. Here we show that host mitochondria fuse during infection by the intracellular parasite Toxoplasma gondii to limit its uptake of fatty acids (FAs). A combination of genetics and imaging of FA trafficking indicates that Toxoplasma infection triggers lipophagy, the autophagy of host lipid droplets (LDs), to secure cellular FAs essential for its proliferation. Indeed, Toxoplasma FA siphoning and growth are reduced in host cells genetically deficient for autophagy or triglyceride depots. Conversely, Toxoplasma FA uptake and proliferation are increased in host cells lacking mitochondrial fusion, required for efficient mitochondrial FA oxidation, or where mitochondrial FA oxidation is pharmacologically inhibited. Thus, mitochondrial fusion can be regarded as a cellular defense mechanism against intracellular parasites, by limiting Toxoplasma access to host nutrients liberated by lipophagy.

Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting

Several antimalarial drugs exist, but differences between life cycle stages among malaria species pose challenges for developing more effective therapies. To understand the diversity among stages and species, we reconstructed genome-scale metabolic models (GeMMs) of metabolism for five life cycle stages and five species of Plasmodium spanning the blood, transmission, and mosquito stages. The stage-specific models of Plasmodium falciparum uncovered stage-dependent changes in central carbon metabolism and predicted potential targets that could affect several life cycle stages. The species-specific models further highlight differences between experimental animal models and the human-infecting species. Comparisons between human- and rodent-infecting species revealed differences in thiamine (vitamin B1), choline, and pantothenate (vitamin B5) metabolism. Thus, we show that genome-scale analysis of multiple stages and species of Plasmodium can prioritize potential drug targets that could be both anti-malarials and transmission blocking agents, in addition to guiding translation from non-human experimental disease models.

Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite. Plasmodial infections are difficult to treat for a myriad of reasons, but the ability of the organism to remain latent in hosts and the complex life cycles greatly contributed to the difficulty in treat malaria. Genome-scale metabolic models (GeMMs) enable hierarchical integration of disparate data types into a framework amenable to computational simulations enabling deeper mechanistic insights from high-throughput data measurements. In this study, GeMMs of multiple Plasmodium species are used to study metabolic similarities and differences across the Plasmodium genus. In silico gene-knock out simulations across species and stages uncovered functional metabolic differences between human- and rodent-infecting species as well as across the parasite’s life-cycle stages. These findings may help identify drug regimens that are more effective in targeting human-infecting species across multiple stages of the organism.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models

Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

Reduced models are used commonly to understand the metabolism of organisms and to integrate experimental data for many different studies such as physiology, fluxomics and metabolomics. Without consistent or clear criteria on how these reduced models are actually developed, it is difficult to ensure that they reflect the detailed knowledge that is kept in genome scale metabolic network models (GEMs). The redGEM algorithm presented here allows us to systematically develop consistently reduced metabolic models from their genome-scale counterparts. We applied redGEM for the construction of a core model for E. coli central carbon metabolism. We constructed the core model irJO1366 based on the latest genome-scale E. coli metabolic reconstruction (iJO1366). irJO1366 contains the central carbon pathways and other immediate pathways that must be connected to them for consistency with the iJO1366. irJO1366 can be used to understand metabolism of the organism and also to provide guidance for metabolic engineering purposes. The algorithm is also designed to be modular so that heterologous reactions or pathways can be appended to the core model akin to a “plug-and-play”, synthetic biology approach. The algorithm is applicable to any compartmentalized or non-compartmentalized GEM.

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models.

Stoichiometric models have been used in the area of metabolic engineering and systems biology for many decades. The early examples of these models include simplified ad hoc built metabolic pathways, and biomass compositions. The development of genome scale models (GEMs) brought a standard to metabolic network modeling. However, the vast amount of detailed biochemistry in GEMs makes it necessary to develop methods to manage the complexity in them. In this study, we developed lumpGEM, a tool that can systematically identify subnetworks from metabolic networks that can perform certain tasks, such as biosynthesis of a biomass building block and any other target metabolite. By generating alternative subnetworks, lumpGEM also accounts for the redundancy in metabolic networks. We applied lumpGEM on latest E. coli GEM iJO1366 and identified subnetworks/lumped reactions for every biomass building block defined in its biomass formulation. We also compared the results from lumpGEM with existing knowledge in the literature. The lumped reactions generated by lumpGEM can be used to generate consistently reduced metabolic network models.

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.

Genomics of apicomplexan parasites

The increasing prevalence of infections involving intracellular apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium (the causative agents of malaria, toxoplasmosis, and cryptosporidiosis, respectively) represent a significant global healthcare burden. Despite their significance, few treatments are available; a situation that is likely to deteriorate with the emergence of new resistant strains of parasites. To lay the foundation for programs of drug discovery and vaccine development, genome sequences for many of these organisms have been generated, together with large-scale expression and proteomic datasets. Comparative analyses of these datasets are beginning to identify the molecular innovations supporting both conserved processes mediating fundamental roles in parasite survival and persistence, as well as lineage-specific adaptations associated with divergent life-cycle strategies. The challenge is how best to exploit these data to derive insights into parasite virulence and identify those genes representing the most amenable targets. In this review, we outline genomic datasets currently available for apicomplexans and discuss biological insights that have emerged as a consequence of their analysis. Of particular interest are systems-based resources, focusing on areas of metabolism and host invasion that are opening up opportunities for discovering new therapeutic targets.

Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks

Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa) of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention.

Almost half of the world population is at risk of infection by malaria parasites. The rise in drug-resistant parasites requires better understanding and targeting of their metabolism. In this study, we present a genome-scale metabolic reconstruction (iPfa) of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Our results support and extend the available experimental evidence on the essential genes and nutritional requirements of this organism. Besides, we identify metabolites that give rise to thermodynamic bottlenecks and suggest substrate channeling. Overall, these results provide novel insight into the metabolism of P. falciparum and may guide experimental studies to develop a better characterization of the parasite metabolism and the identification of antimalarial drug targets.

Toxoplasma growth in vitro is dependent on exogenous tyrosine and is independent of AAH2 even in tyrosine-limiting conditions

Toxoplasma gondii is an obligate intracellular parasite capable of infecting virtually all nucleated cell types in almost all warm-blooded animals. Interestingly, Toxoplasma has a relatively full repertoire of amino acid biosynthetic machinery, perhaps reflecting its broad host range and, consequently, its need to adapt to a wide array of amino acid resources. Although Toxoplasma has been shown to be auxotrophic for tryptophan and arginine, it has not previously been determined if Toxoplasma is also auxotrophic for tyrosine. Toxoplasma tachyzoites and bradyzoites were recently found to express an amino acid hydroxylase (AAH2) that is capable of synthesizing tyrosine and dihydroxyphenylalanine (DOPA) from phenylalanine; however, the role of AAH2 in tachyzoite and bradyzoite infection has not yet been identified. To determine if Toxoplasma requires exogenous tyrosine for growth, we performed growth assays on tachyzoites and bradyzoites in nutrient-rich media titrated with varying amounts of tyrosine. We found that Toxoplasma tachyzoites form significantly smaller plaques in tyrosine-limiting media in a dose-dependent manner and that this phenotype is not affected by deletion of TgAAH2. To determine if bradyzoites require exogenous tyrosine for growth, we induced differentiation from tachyzoites in vitro in tyrosine-limiting media and found that replication and vacuole number are all decreased in tyrosine-deficient media. Importantly, culture of confluent human fibroblasts in tyrosine-deficient media does not affect their viability, indicating that, at least in vitro, the need for tyrosine is at the level of Toxoplasma, not the host cell supporting its growth.

Cationic amino acid transporters play key roles in the survival and transmission of apicomplexan parasites

Apicomplexans are obligate intracellular parasites that scavenge essential nutrients from their hosts via transporter proteins on their plasma membrane. The identities of the transporters that mediate amino acid uptake into apicomplexans are unknown. Here we demonstrate that members of an apicomplexan-specific protein family-the Novel Putative Transporters (NPTs)-play key roles in the uptake of cationic amino acids. We show that an NPT from Toxoplasma gondii (TgNPT1) is a selective arginine transporter that is essential for parasite survival and virulence. We also demonstrate that a homologue of TgNPT1 from the malaria parasite Plasmodium berghei (PbNPT1), shown previously to be essential for the sexual gametocyte stage of the parasite, is a cationic amino acid transporter. This reveals a role for cationic amino acid scavenging in gametocyte biology. Our study demonstrates a critical role for amino acid transporters in the survival, virulence and life cycle progression of these parasites.

Dual Identification and Analysis of Differentially Expressed Transcripts of Porcine PK-15 Cells and Toxoplasma gondii during in vitro Infection

Toxoplasma gondii is responsible for causing toxoplasmosis, one of the most prevalent zoonotic parasitoses worldwide. The mechanisms that mediate T. gondii infection of pigs (the most common source of human infection) and renal tissues are still unknown. To identify the critical alterations that take place in the transcriptome of both porcine kidney (PK-15) cells and T. gondii following infection, infected cell samples were collected at 1, 3, 6, 9, 12, 18, and 24 h post infection and RNA-Seq data were acquired using Illumina Deep Sequencing. Differential Expression of Genes (DEGs) analysis was performed to study the concomitant gene-specific temporal patterns of induction of mRNA expression of PK-15 cells and T. gondii. High sequence coverage enabled us to thoroughly characterize T. gondii transcriptome and identify the activated molecular pathways in host cells. More than 6G clean bases/sample, including >40 million clean reads were obtained. These were aligned to the reference genome of T. gondii and wild boar (Sus scrofa). DEGs involved in metabolic activities of T. gondii showed time-dependent down-regulation. However, DEGs involved in immune or disease related pathways of PK-15 cells peaked at 6 h PI, and were highly enriched as evidenced by KEGG analysis. Protein-protein interaction analysis revealed that TGME49_120110 (PCNA), TGME49_049180 (DHFR-TS), TGME49_055320, and TGME49_002300 (ITPase) are the four hub genes with most interactions with T. gondii at the onset of infection. These results reveal altered profiles of gene expressed by PK-15 cells and T. gondii during infection and provide the groundwork for future virulence studies to uncover the mechanisms of T. gondii interaction with porcine renal tissue by functional analysis of these DEGs.

Proteomic Profiling of Mouse Liver following Acute Toxoplasma gondii Infection

Toxoplasma gondii remains a global public health problem. However, its pathophysiology is still not-completely understood particularly the impact of infection on host liver metabolism. We performed iTRAQ-based proteomic analysis to evaluate early liver protein responses in BALB/c mice following infection with T. gondii PYS strain (genotype ToxoDB#9) infection. Our data revealed modification of protein expression in key metabolic pathways, as indicated by the upregulation of immune response and downregulation of mitochondrial respiratory chain, and the metabolism of fatty acids, lipids and xenobiotics. T. gondii seems to hijack host PPAR signaling pathway to downregulate the metabolism of fatty acids, lipids and energy in the liver. The metabolism of over 400 substances was affected by the downregulation of genes involved in xenobiotic metabolism. The top 10 transcription factors used by upregulated genes were Stat2, Stat1, Irf2, Irf1, Sp2, Egr1, Stat3, Klf4, Elf1 and Gabpa, while the top 10 transcription factors of downregulated genes were Hnf4A, Ewsr1, Fli1, Hnf4g, Nr2f1, Pparg, Rxra, Hnf1A, Foxa1 and Foxo1. These findings indicate global reprogramming of the metabolism of the mouse liver after acute T. gondii infection. Functional characterization of the altered proteins may enhance understanding of the host responses to T. gondii infection and lead to the identification of new therapeutic targets.

Tracing amino acid exchange during host-pathogen interaction by combined stable-isotope time-resolved Raman spectral imaging

This study investigates the temporal and spatial interchange of the aromatic amino acid phenylalanine (Phe) between human retinal pigment epithelial cell line (ARPE-19) and tachyzoites of the apicomplexan protozoan parasite Toxoplasma gondii (T. gondii). Stable isotope labelling by amino acids in cell culture (SILAC) is combined with Raman micro-spectroscopy to selectively monitor the incorporation of deuterium-labelled Phe into proteins in individual live tachyzoites. Our results show a very rapid uptake of l-Phe(D8) by the intracellular growing parasite. T. gondii tachyzoites are capable of extracting l-Phe(D8) from host cells as soon as it invades the cell. l-Phe(D8) from the host cell completely replaces the l-Phe within T. gondii tachyzoites 7-9 hours after infection. A quantitative model based on Raman spectra allowed an estimation of the exchange rate of Phe as 0.5-1.6 × 10(4) molecules/s. On the other hand, extracellular tachyzoites were not able to consume l-Phe(D8) after 24 hours of infection. These findings further our understanding of the amino acid trafficking between host cells and this strictly intracellular parasite. In particular, this study highlights new aspects of the metabolism of amino acid Phe operative during the interaction between T. gondii and its host cell.

Metabolomic Profiling of Mice Serum during Toxoplasmosis Progression Using Liquid Chromatography-Mass Spectrometry

Better understanding of the molecular changes associated with disease is essential for identifying new routes to improved therapeutics and diagnostic tests. The aim of this study was to investigate the dynamic changes in the metabolic profile of mouse sera during T. gondii infection. We carried out untargeted metabolomic analysis of sera collected from female BALB/c mice experimentally infected with the T. gondii Pru strain (Genotype II). Serum samples were collected at 7, 14 and 21 day post infection (DPI) from infected and control mice and were subjected to liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS)-based global metabolomics analysis. Multivariate statistical analysis identified 79 differentially expressed metabolites in ESI+ mode and 74 in ESI− mode in sera of T. gondii-infected mice compared to the control mice. Further principal component analysis (PCA) and partial least squares-discrimination analysis (PLS-DA) identified 19 dysregulated metabolites (5 in ESI+ mode and 14 in ESI− mode) related to the metabolism of amino acids and energy metabolism. The potential utility of these metabolites as diagnostic biomarkers was validated through receiver operating characteristic (ROC) curve analysis. These findings provide putative metabolite biomarkers for future study and allow for hypothesis generation about the pathophysiology of toxoplasmosis.

A Kinetic Platform to Determine the Fate of Hydrogen Peroxide in Escherichia coli

Hydrogen peroxide (H2O2) is used by phagocytic cells of the innate immune response to kill engulfed bacteria. H2O2 diffuses freely into bacteria, where it can wreak havoc on sensitive biomolecules if it is not rapidly detoxified. Accordingly, bacteria have evolved numerous systems to defend themselves against H2O2, and the importance of these systems to pathogenesis has been substantiated by the many bacteria that require them to establish or sustain infections. The kinetic competition for H2O2 within bacteria is complex, which suggests that quantitative models will improve interpretation and prediction of network behavior. To date, such models have been of limited scope, and this inspired us to construct a quantitative, systems-level model of H2O2 detoxification in Escherichia coli that includes detoxification enzymes, H2O2-dependent transcriptional regulation, enzyme degradation, the Fenton reaction and damage caused by •OH, oxidation of biomolecules by H2O2, and repair processes. After using an iterative computational and experimental procedure to train the model, we leveraged it to predict how H2O2 detoxification would change in response to an environmental perturbation that pathogens encounter within host phagosomes, carbon source deprivation, which leads to translational inhibition and limited availability of NADH. We found that the model accurately predicted that NADH depletion would delay clearance at low H2O2 concentrations and that detoxification at higher concentrations would resemble that of carbon-replete conditions. These results suggest that protein synthesis during bolus H2O2 stress does not affect clearance dynamics and that access to catabolites only matters at low H2O2 concentrations. We anticipate that this model will serve as a computational tool for the quantitative exploration and dissection of oxidative stress in bacteria, and that the model and methods used to develop it will provide important templates for the generation of comparable models for other bacterial species.

Metabolic Cooperation of Glucose and Glutamine Is Essential for the Lytic Cycle of Obligate Intracellular Parasite Toxoplasma gondii

Toxoplasma gondii is a widespread protozoan parasite infecting nearly all warm-blooded organisms. Asexual reproduction of the parasite within its host cells is achieved by consecutive lytic cycles, which necessitates biogenesis of significant energy and biomass. Here we show that glucose and glutamine are the two major physiologically important nutrients used for the synthesis of macromolecules (ATP, nucleic acid, proteins, and lipids) in T. gondii, and either of them is sufficient to ensure the parasite survival. The parasite can counteract genetic ablation of its glucose transporter by increasing the flux of glutamine-derived carbon through the tricarboxylic acid cycle and by concurrently activating gluconeogenesis, which guarantee a continued biogenesis of ATP and biomass for host-cell invasion and parasite replication, respectively. In accord, a pharmacological inhibition of glutaminolysis or oxidative phosphorylation arrests the lytic cycle of the glycolysis-deficient mutant, which is primarily a consequence of impaired invasion due to depletion of ATP. Unexpectedly, however, intracellular parasites continue to proliferate, albeit slower, notwithstanding a simultaneous deprivation of glucose and glutamine. A growth defect in the glycolysis-impaired mutant is caused by a compromised synthesis of lipids, which cannot be counterbalanced by glutamine but can be restored by acetate. Consistently, supplementation of parasite cultures with exogenous acetate can amend the lytic cycle of the glucose transport mutant. Such plasticity in the parasite's carbon flux enables a growth-and-survival trade-off in assorted nutrient milieus, which may underlie the promiscuous survival of T. gondii tachyzoites in diverse host cells. Our results also indicate a convergence of parasite metabolism with cancer cells.

Apicomplexan Energy Metabolism: Carbon Source Promiscuity and the Quiescence Hyperbole

The nature of energy metabolism in apicomplexan parasites has been closely investigated in the recent years. Studies in Plasmodium spp. and Toxoplasma gondii in particular have revealed that these parasites are able to employ enzymes in non-traditional ways, while utilizing multiple anaplerotic routes into a canonical tricarboxylic acid (TCA) cycle to satisfy their energy requirements. Importantly, some life stages of these parasites previously considered to be metabolically quiescent are, in fact, active and able to adapt their carbon source utilization to survive. We compare energy metabolism across the life cycle of malaria parasites and consider how this varies in other apicomplexans and related organisms, while discussing how this can be exploited for therapeutic intervention in these diseases.

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.