BioMog: A Computational Framework for the De Novo Generation or Modification of Essential Biomass Components

The success of genome-scale metabolic modeling is contingent on a model''s ability to accurately predict growth and metabolic behaviors. To date, little focus has been directed towards developing systematic methods of proposing, modifying and interrogating an organism''s biomass requirements that are used in constraint-based models. To address this gap, the biomass modification and generation (BioMog) framework was created and used to generate lists of biomass components de novo, as well as to modify predefined biomass component lists, for models of Escherichia coli (iJO1366) and of Shewanella oneidensis (iSO783) from high-throughput growth phenotype and fitness datasets. BioMog''s de novo biomass component lists included, either implicitly or explicitly, up to seventy percent of the components included in the predefined biomass equations, and the resulting de novo biomass equations outperformed the predefined biomass equations at qualitatively predicting mutant growth phenotypes by up to five percent. Additionally, the BioMog procedure can quantify how many experiments support or refute a particular metabolite''s essentiality to a cell, and it facilitates the determination of inconsistent experiments and inaccurate reaction and/or gene to reaction associations. To further interrogate metabolite essentiality, the BioMog framework includes an experiment generation algorithm that allows for the design of experiments to test whether a metabolite is essential. Using BioMog, we correct experimental results relating to the essentiality of thyA gene in E. coli, as well as perform knockout experiments supporting the essentiality of protoheme. With these capabilities, BioMog can be a valuable resource for analyzing growth phenotyping data and component of a model developer''s toolbox.     

Comparative Analysis of Yeast Metabolic Network Models Highlights Progress,Opportunities for Metabolic Reconstruction

We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.     

A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium,iPS189

With a genome size of ∼580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.     

Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated resource allocation models, such as genome-scale models of metabolism and gene expression (ME-models), have the ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we apply the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME-model mostly agree with the standard biomass objective function used in models of metabolism alone (M-models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of peroxyl scavenging acids in the proteins used to sustain aerobic growth; (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.     

Decaprenylphosphoryl-β-D-ribose 2'-epimerase, the target of benzothiazinones and dinitrobenzamides, is an essential enzyme in Mycobacterium smegmatis

Background

The unique cell wall of bacteria of the suborder Corynebacterineae is essential for the growth and survival of significant human pathogens including Mycobacterium tuberculosis and Mycobacterium leprae. Drug resistance in mycobacteria is an increasingly common development, making identification of new antimicrobials a priority. Recent studies have revealed potent anti-mycobacterial compounds, the benzothiazinones and dinitrobenzamides, active against DprE1, a subunit of decaprenylphosphoribose 2′ epimerase which forms decaprenylphosphoryl arabinose, the arabinose donor for mycobacterial cell wall biosynthesis. Despite the exploitation of Mycobacterium smegmatis in the identification of DprE1 as the target of these new antimicrobials and its use in the exploration of mechanisms of resistance, the essentiality of DprE1 in this species has never been examined. Indeed, direct experimental evidence of the essentiality of DprE1 has not been obtained in any species of mycobacterium.

Methodology/Principal Findings

In this study we constructed a conditional gene knockout strain targeting the ortholog of dprE1 in M. smegmatis, MSMEG_6382. Disruption of the chromosomal copy of MSMEG_6382 was only possible in the presence of a plasmid-encoded copy of MSMEG_6382. Curing of this “rescue” plasmid from the bacterial population resulted in a cessation of growth, demonstrating gene essentiality.

Conclusions/Significance

This study provides the first direct experimental evidence for the essentiality of DprE1 in mycobacteria. The essentiality of DprE1 in M. smegmatis, combined with its conservation in all sequenced mycobacterial genomes, suggests that decaprenylphosphoryl arabinose synthesis is essential in all mycobacteria. Our findings indicate a lack of redundancy in decaprenylphosphoryl arabinose synthesis in M. smegmatis, despite the relatively large coding capacity of this species, and suggest that no alternative arabinose donors for cell wall biosynthesis exist. Overall, this study further validates DprE1 as a promising target for new anti-mycobacterial drugs.     

Insect Cell-Derived Cofactors Become Fully Functional after Proteinase K and Heat Treatment for High-Fidelity Amplification of Glycosylphosphatidylinositol-Anchored Recombinant Scrapie and BSE Prion Proteins

The central event in prion infection is the conformational conversion of host-encoded cellular prion protein (PrP^C) into the pathogenic isoform (PrP^Sc). Diverse mammalian species possess the cofactors required for in vitro replication of PrP^Sc by protein-misfolding cyclic amplification (PMCA), but lower organisms, such as bacteria, yeasts, and insects, reportedly lack the essential cofactors. Various cellular components, such as RNA, lipids, and other identified cofactor molecules, are commonly distributed in both eukaryotes and prokaryotes, but the reasons for the absence of cofactor activity in lower organisms remain to be elucidated. Previously, we reported that brain-derived factors were necessary for the in vitro replication of glycosylphosphatidylinositol-anchored baculovirus-derived recombinant PrP (Bac-PrP). Here, we demonstrate that following protease digestion and heat treatment, insect cell lysates had the functional cofactor activity required for Bac-PrP replication by PMCA. Mammalian PrP^Sc seeds and Bac-PrP^Sc generated by PMCA using Bac-PrP and insect cell-derived cofactors showed similar pathogenicity and produced very similar lesions in the brains of inoculated mice. These results suggested that the essential cofactors required for the high-fidelity replication of mammalian PrP^Sc were present in the insect cells but that the cofactor activity was masked or inhibited in the native state. We suggest that not only RNA, but also DNA, are the key components of PMCA, although other cellular factors were necessary for the expression of the cofactor activity of nucleic acids. PMCA using only insect cell-derived substances (iPMCA) was highly useful for the ultrasensitive detection of PrP^Sc of some prion strains.     

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings and discovered that phylogeny is not the sole predictor of metabolic similarity. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.     

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.     

An energetic profile of Corynebacterium glutamicum underpinned by measured biomass yield on ATP

The supply and usage of energetic cofactors in metabolism is a central concern for systems metabolic engineering, particularly in case of energy intensive products. One of the most important parameters for systems wide balancing of energetic cofactors is the ATP requirement for biomass formation Y_ATP/Biomass. Despite its fundamental importance, Y_ATP/Biomass values for non-fermentative organisms are still rough estimates deduced from theoretical considerations. For the first time, we present an approach for the experimental determination of Y_ATP/Biomass using comparative ¹³C metabolic flux analysis (¹³C MFA) of a wild type strain and an ATP synthase knockout mutant. We show that the energetic profile of a cell can then be deduced from a genome wide stoichiometric model and experimental maintenance data. Particularly, the contributions of substrate level phosphorylation (SLP) and electron transport phosphorylation (ETP) to ATP generation become available which enables the overall energetic efficiency of a cell to be characterized. As a model organism, the industrial platform organism Corynebacterium glutamicum is used. C. glutamicum uses a respiratory type of energy metabolism, implying that ATP can be synthesized either by SLP or by ETP with the membrane-bound F₁F_O-ATP synthase using the proton motive force (pmf) as driving force. The presence of two terminal oxidases, which differ in their proton translocation efficiency by a factor of three, further complicates energy balancing for this organism. By integration of experimental data and network models, we show that in the wild type SLP and ETP contribute equally to ATP generation. Thus, the role of ETP in respiring bacteria may have been overrated in the past. Remarkably, in the genome wide setting 65% of the pmf is actually not used for ATP synthesis. However, it turns out that, compared to other organisms C. glutamicum still uses its energy budget rather efficiently.     

Identification of essential alphaproteobacterial genes reveals operational variability in conserved developmental and cell cycle systems

The cell cycle of Caulobacter crescentus is controlled by a complex signalling network that co‐ordinates events. Genome sequencing has revealed many C. crescentus cell cycle genes are conserved in other Alphaproteobacteria, but it is not clear to what extent their function is conserved. As many cell cycle regulatory genes are essential in C. crescentus, the essential genes of two Alphaproteobacteria, Agrobacterium tumefaciens (Rhizobiales) and Brevundimonas subvibrioides (Caulobacterales), were elucidated to identify changes in cell cycle protein function over different phylogenetic distances as demonstrated by changes in essentiality. The results show the majority of conserved essential genes are involved in critical cell cycle processes. Changes in component essentiality reflect major changes in lifestyle, such as divisome components in A. tumefaciens resulting from that organism's different growth pattern. Larger variability of essentiality was observed in cell cycle regulators, suggesting regulatory mechanisms are more customizable than the processes they regulate. Examples include variability in the essentiality of divJ and divK spatial cell cycle regulators, and non‐essentiality of the highly conserved and usually essential DNA methyltransferase CcrM. These results show that while essential cell functions are conserved across varying genetic distance, much of a given organism's essential gene pool is specific to that organism.     

Combining Functional and Structural Genomics to Sample the Essential Burkholderia Structome

Background

The genus Burkholderia includes pathogenic gram-negative bacteria that cause melioidosis, glanders, and pulmonary infections of patients with cancer and cystic fibrosis. Drug resistance has made development of new antimicrobials critical. Many approaches to discovering new antimicrobials, such as structure-based drug design and whole cell phenotypic screens followed by lead refinement, require high-resolution structures of proteins essential to the parasite.

Methodology/Principal Findings

We experimentally identified 406 putative essential genes in B. thailandensis, a low-virulence species phylogenetically similar to B. pseudomallei, the causative agent of melioidosis, using saturation-level transposon mutagenesis and next-generation sequencing (Tn-seq). We selected 315 protein products of these genes based on structure-determination criteria, such as excluding very large and/or integral membrane proteins, and entered them into the Seattle Structural Genomics Center for Infection Disease (SSGCID) structure determination pipeline. To maximize structural coverage of these targets, we applied an “ortholog rescue” strategy for those producing insoluble or difficult to crystallize proteins, resulting in the addition of 387 orthologs (or paralogs) from seven other Burkholderia species into the SSGCID pipeline. This structural genomics approach yielded structures from 31 putative essential targets from B. thailandensis, and 25 orthologs from other Burkholderia species, yielding an overall structural coverage for 49 of the 406 essential gene families, with a total of 88 depositions into the Protein Data Bank. Of these, 25 proteins have properties of a potential antimicrobial drug target i.e., no close human homolog, part of an essential metabolic pathway, and a deep binding pocket. We describe the structures of several potential drug targets in detail.

Conclusions/Significance

This collection of structures, solubility and experimental essentiality data provides a resource for development of drugs against infections and diseases caused by Burkholderia. All expression clones and proteins created in this study are freely available by request.     

Comparative trace element and organic growth factor requirements of five hansenula species

A comparative response of specific trace elements and organic growth factors for the growth of five Hansenula species (H. anomala, H. beijerinckii, H. ciferrii, H. polymorpha and H. sydowiorum) has been studied. Out of twenty three trace elements tested, Fe, Zn, Mn and Cu were found to be essential for the growth of all yeast species studied here, whereas the rest of the elements exhibited variable essentiality. From fifteen organic growth factors tested, thiamine, biotin, pyridoxine and inositol are the most commonly required growth factors by the yeasts, whereas the rest of the organic growth factors showed variable essentiality. All species of yeasts investigated required different concentrations of trace elements and organic growth factors for their optimum growth. Concentrations higher than the optimum have been found to be inhibitory for the growth of all the yeasts studied.     

Building the blueprint of life

With recent breakthroughs in experimental microbiology making it possible to synthesize and implant an entire genome to create a living cell, the challenge of constructing a working blueprint for the first truly minimal synthetic organism is more important than ever. Here we review the significant progress made in the design and creation of a minimal organism. We discuss how comparative genomes, gene essentiality data, naturally small genomes, and metabolic modeling are all being applied to produce a catalogue of the biological functions essential for life. We compare the minimal gene sets from three published sources with functions identified in 13 existing gene essentiality datasets. We examine how genome-scale metabolic models have been applied to design a minimal metabolism for growth in simple and complex media. Additionally, we survey the progress of efforts to construct a minimal organism, either through implementation of combinatorial deletions in Bacillus subtilis and Escherichia coli or through the synthesis and implantation of synthetic genomes.     

Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.     

Metabolite-centric approaches for the discovery of antibacterials using genome-scale metabolic networks

Development of genome-scale metabolic models and various constraints-based flux analyses have enabled more sophisticated examination of metabolism. Recently reported metabolite essentiality studies are also based on the constraints-based modeling, but approaches metabolism from a metabolite-centric perspective, providing synthetic lethal combination of reactions and clues for the rational discovery of antibacterials. In this study, metabolite essentiality analysis was applied to the genome-scale metabolic models of four microorganisms: Escherichia coli, Helicobacter pylori, Mycobacterium tuberculosis and Staphylococcus aureus. Furthermore, chokepoints, metabolites surrounded by enzymes that uniquely consume and/or produce them, were also calculated based on the network properties of the above organisms. A systematic drug targeting strategy was developed by combining information from these two methods. Final drug target metabolites are presented and examined with knowledge from the literature.     

In silico identification of potential drug targets in swine pathogen Haemophilus parasuis

Gram-negative bacterium Haemophilus parasuis has recently become one of the most important etiological agents causing serious systemic disease (Gl?sser??s disease) in pigs. Antibiotic therapy has played a crucial role in the treatment of this disease. Antibiotic resistance observed from the clinical isolates of this pathogen urges us to discover novel drug targets for antimicrobial agents. In this study, we used a combined strategy including exploration of the gene essentiality and comparison of metabolic pathways to infer drug targets of H. parasuis. We identified 931 gene products essential for bacterial growth according to the DEG database. One hundred and ninety-nine enzyme-coding genes were found in the genome of H. parasuis but were absent in that of the swine host. Lastly, we determined 117 enzymes exhibiting essentiality and specificity to H. parasuis as a candidate set of drug targets. Comparison of metabolic pathways between the pathogen and host showed that 25 targeting enzymes belonged to nine unique pathways of the pathogen. The profile of promising targets identified in our study will provide a useful basis for developing more effective antibiotics against the severe swine disease caused by H. parasuis.     

Why boron?

It is now more than 80 years since boron was convincingly demonstrated to be essential for normal growth of higher plants. However, its biochemical role is not well understood at the moment. Several recent reviews propose that B is implicated in three main processes: keeping cell wall structure, maintaining membrane function, and supporting metabolic activities. However, in the absence of conclusive evidence, the primary role of boron in plants remains elusive. Besides plants, growth of specific bacteria, such as heterocystous cyanobacteria and the recently reported actinomycetes of the genus Frankia, requires B, particularly for the stability of the envelopes that control the access of the nitrogenase-poisoning oxygen when they grow under N₂-fixing conditions. Likewise, a role for B for animal embryogenesis and other developmental processes is being established. Finally, a new feature of the role of boron comes from signaling mechanisms for communication among bacteria and among legumes and rhizobia leading to N₂-fixing symbiosis, and it is possible that new roles for B, based on its special chemistry and its interaction with Ca would appear in the world of signal transduction pathways. In conclusion, the diversity of roles played by B might indicate that either the micronutrient is involved in numerous processes or that its deficiency has a pleiotropic effect. The arising question is why such an element? Since all of the roles clearly established for B are related to its capacity to form diester bridges between cis-hydroxyl-containing molecules, we propose that the main reason for B essentiality is the stabilization of molecules with cis-diol groups turning them effective, irrespectively of their function.     

Whole-genome metabolic model of <Emphasis Type="Italic">Trichoderma reesei</Emphasis> built by comparative reconstruction

Background

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism’s metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.

Results

A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.

Conclusions

The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.

     

Stoichiometric model and flux balance analysis for a mixed culture of Leptospirillum ferriphilum and Ferroplasma acidiphilum

The oxidation process of sulfide minerals in natural environments is achieved by microbial communities from the Archaea and Bacteria domains. A metabolic reconstruction of two dominant species, Leptospirillum ferriphilum and Ferroplasma acidiphilum, which are always found together as a mixed culture in this natural environments, was made. The metabolic model, composed of 152 internal reactions and 29 transport reactions, describes the main interactions between these species, assuming that both use ferrous iron as energy source, and F. acidiphilum takes advantage of the organic compounds secreted by L. ferriphilum for chemomixotrophic growth. A first metabolic model for a mixed culture used in bacterial leaching is proposed in this article, which pretends to represent the characteristics of the mixed culture in a simplified manner. It was evaluated with experimental data through flux balance analysis (FBA) using as objective function the maximization of biomass. The growth yields on ferrous iron obtained for each microorganism are consistent with experimental data, and the flux distribution obtained allows understanding of the metabolic capabilities of both microorganisms growing together in a bioleaching process. The model was used to simulate the growth of F. acidiphilum on different substrates, to determine in silico which compounds maximize cell growth, and which are essential. Knockout simulations were carried out for L. ferriphilum and F. acidiphilum metabolic models, predicting key enzymes of central metabolism. The results of this analysis are consistent with experimental data from literature, showing a robust behavior of the metabolic model. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 31:307–315, 2015