RESUMEN
Phosphate is essential for all living systems, serving as a building block of genetic and metabolic machinery. However, it is unclear how phosphate could have assumed these central roles on primordial Earth, given its poor geochemical accessibility. We used systems biology approaches to explore the alternative hypothesis that a protometabolism could have emerged prior to the incorporation of phosphate. Surprisingly, we identified a cryptic phosphate-independent core metabolism producible from simple prebiotic compounds. This network is predicted to support the biosynthesis of a broad category of key biomolecules. Its enrichment for enzymes utilizing iron-sulfur clusters, and the fact that thermodynamic bottlenecks are more readily overcome by thioester rather than phosphate couplings, suggest that this network may constitute a "metabolic fossil" of an early phosphate-free nonenzymatic biochemistry. Our results corroborate and expand previous proposals that a putative thioester-based metabolism could have predated the incorporation of phosphate and an RNA-based genetic system. PAPERCLIP.
Asunto(s)
Simulación por Computador , Redes y Vías Metabólicas , Fosfatos/metabolismo , Nucleótidos de Adenina/química , Algoritmos , Coenzima A , Coenzimas , Origen de la Vida , Fosfatos/química , TermodinámicaRESUMEN
SignificanceMetabolism relies on a small class of molecules (coenzymes) that serve as universal donors and acceptors of key chemical groups and electrons. Although metabolic networks crucially depend on structurally redundant coenzymes [e.g., NAD(H) and NADP(H)] associated with different enzymes, the criteria that led to the emergence of this redundancy remain poorly understood. Our combination of modeling and structural and sequence analysis indicates that coenzyme redundancy may not be essential for metabolism but could rather constitute an evolved strategy promoting efficient usage of enzymes when biochemical reactions are near equilibrium. Our work suggests that early metabolism may have operated with fewer coenzymes and that adaptation for metabolic efficiency may have driven the rise of coenzyme diversity in living systems.
Asunto(s)
Coenzimas , NAD , Coenzimas/metabolismo , NAD/metabolismo , NADP/metabolismoRESUMEN
Heterotrophic marine bacteria utilize and recycle dissolved organic matter (DOM), impacting biogeochemical cycles. It is currently unclear to what extent distinct DOM components can be used by different heterotrophic clades. Here, we ask how a natural microbial community from the Eastern Mediterranean Sea (EMS) responds to different molecular classes of DOM (peptides, amino acids, amino sugars, disaccharides, monosaccharides, and organic acids) comprising much of the biomass of living organisms. Bulk bacterial activity increased after 24 h for all treatments relative to the control, while glucose and ATP uptake decreased or remained unchanged. Moreover, while the per-cell uptake rate of glucose and ATP decreased, that of Leucin significantly increased for amino acids, reflecting their importance as common metabolic currencies in the marine environment. Pseudoalteromonadaceae dominated the peptides treatment, while different Vibrionaceae strains became dominant in response to amino acids and amino sugars. Marinomonadaceae grew well on organic acids, and Alteromonadaseae on disaccharides. A comparison with a recent laboratory-based study reveals similar peptide preferences for Pseudoalteromonadaceae, while Alteromonadaceae, for example, grew well in the lab on many substrates but dominated in seawater samples only when disaccharides were added. We further demonstrate a potential correlation between the genetic capacity for degrading amino sugars and the dominance of specific clades in these treatments. These results highlight the diversity in DOM utilization among heterotrophic bacteria and complexities in the response of natural communities. IMPORTANCE: A major goal of microbial ecology is to predict the dynamics of natural communities based on the identity of the organisms, their physiological traits, and their genomes. Our results show that several clades of heterotrophic bacteria each grow in response to one or more specific classes of organic matter. For some clades, but not others, growth in a complex community is similar to that of isolated strains in laboratory monoculture. Additionally, by measuring how the entire community responds to various classes of organic matter, we show that these results are ecologically relevant, and propose that some of these resources are utilized through common uptake pathways. Tracing the path between different resources to the specific microbes that utilize them, and identifying commonalities and differences between different natural communities and between them and lab cultures, is an important step toward understanding microbial community dynamics and predicting how communities will respond to perturbations.
Asunto(s)
Bacterias , Procesos Heterotróficos , Agua de Mar , Agua de Mar/microbiología , Bacterias/clasificación , Bacterias/metabolismo , Bacterias/genética , Bacterias/aislamiento & purificación , Mar Mediterráneo , Microbiota , Aminoácidos/metabolismo , Compuestos Orgánicos/metabolismoRESUMEN
Environmental microbiome engineering is emerging as a potential avenue for climate change mitigation. In this process, microbial inocula are introduced to natural microbial communities to tune activities that regulate the long-term stabilization of carbon in ecosystems. In this review, we outline the process of environmental engineering and synthesize key considerations about ecosystem functions to target, means of sourcing microorganisms, strategies for designing microbial inocula, methods to deliver inocula, and the factors that enable inocula to establish within a resident community and modify an ecosystem function target. Recent work, enabled by high-throughput technologies and modeling approaches, indicate that microbial inocula designed from the top-down, particularly through directed evolution, may generally have a higher chance of establishing within existing microbial communities than other historical approaches to microbiome engineering. We address outstanding questions about the determinants of inocula establishment and provide suggestions for further research about the possibilities and challenges of environmental microbiome engineering as a tool to combat climate change.
Asunto(s)
Ecosistema , Microbiota , Bacterias , Cambio Climático , Microbiota/fisiología , CarbonoRESUMEN
Redox biochemistry plays a key role in the transduction of chemical energy in living systems. However, the compounds observed in metabolic redox reactions are a minuscule fraction of chemical space. It is not clear whether compounds that ended up being selected as metabolites display specific properties that distinguish them from nonbiological compounds. Here, we introduce a systematic approach for comparing the chemical space of all possible redox states of linear-chain carbon molecules to the corresponding metabolites that appear in biology. Using cheminformatics and quantum chemistry, we analyze the physicochemical and thermodynamic properties of the biological and nonbiological compounds. We find that, among all compounds, aldose sugars have the highest possible number of redox connections to other molecules. Metabolites are enriched in carboxylic acid functional groups and depleted of ketones and aldehydes and have higher solubility than nonbiological compounds. Upon constructing the energy landscape for the full chemical space as a function of pH and electron-donor potential, we find that metabolites tend to have lower Gibbs energies than nonbiological molecules. Finally, we generate Pourbaix phase diagrams that serve as a thermodynamic atlas to indicate which compounds are energy minima in redox chemical space across a set of pH values and electron-donor potentials. While escape from thermodynamic equilibrium toward kinetically driven states is a hallmark of life and its origin, we envision that a deeper quantitative understanding of the environment-dependent thermodynamic landscape of putative prebiotic molecules will provide a crucial reference for future origins-of-life models.
Asunto(s)
Quimioinformática/métodos , Simulación de Dinámica Molecular , Azúcares/química , Aldehídos/química , Conformación de Carbohidratos , Ácidos Carboxílicos/química , Cetonas/química , Oxidación-ReducciónRESUMEN
Cellular populations assume an incredible variety of shapes ranging from circular molds to irregular tumors. While we understand many of the mechanisms responsible for these spatial patterns, little is known about how the shape of a population influences its ecology and evolution. Here, we investigate this relationship in the context of microbial colonies grown on hard agar plates. This a well-studied system that exhibits a transition from smooth circular disks to more irregular and rugged shapes as either the nutrient concentration or cellular motility is decreased. Starting from a mechanistic model of colony growth, we identify two dimensionless quantities that determine how morphology and genetic diversity of the population depend on the model parameters. Our simulations further reveal that population dynamics cannot be accurately described by the commonly-used surface growth models. Instead, one has to explicitly account for the emergent growth instabilities and demographic fluctuations. Overall, our work links together environmental conditions, colony morphology, and evolution. This link is essential for a rational design of concrete, biophysical perturbations to steer evolution in the desired direction.
Asunto(s)
Variación Genética , Agar , Movimiento CelularRESUMEN
Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.
Asunto(s)
Redes y Vías Metabólicas , Modelos Biológicos , AlgoritmosRESUMEN
In the oceans and seas, environmental conditions change over multiple temporal and spatial scales. Here, we ask what factors affect the bacterial community structure across time, depth and size fraction during six seasonal cruises (2 years) in the ultra-oligotrophic Eastern Mediterranean Sea. The bacterial community varied most between size fractions (free-living (FL) vs. particle-associated), followed by depth and finally season. The FL community was taxonomically richer and more stable than the particle-associated (PA) one, which was characterized by recurrent 'blooms' of heterotrophic bacteria such as Alteromonas and Ralstonia. The heterotrophic FL and PA communities were also correlated with different environmental parameters: the FL population correlated with depth and phytoplankton, whereas PA bacteria were correlated primarily with the time of sampling. A significant part of the variability in community structure could, however, not be explained by the measured parameters. The metabolic potential of the PA community, predicted from 16S rRNA amplicon data using PICRUSt, was enriched in pathways associated with the degradation and utilization of biological macromolecules, as well as plastics, other petroleum products and herbicides. The FL community was enriched in predicted pathways for the metabolism of inositol phosphate, a potential phosphorus source, and of polycyclic aromatic hydrocarbons.
Asunto(s)
Bacterias , Petróleo , Bacterias/genética , Mar Mediterráneo , Fitoplancton , ARN Ribosómico 16S/genéticaRESUMEN
The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.
Asunto(s)
Redes y Vías Metabólicas , Consorcios Microbianos/fisiología , Modelos Biológicos , Programas Informáticos , Biología Computacional , Gráficos por Computador , Simulación por Computador , Humanos , Microbiota/fisiología , Biología de SistemasRESUMEN
Global regulators that bind strategic metabolites allow bacteria to adapt rapidly to dynamic environments by coordinating the expression of many genes. We report an approach for determining gene regulation hierarchy using the regulon of the Bacillus subtilis global regulatory protein CodY as proof of principle. In theory, this approach can be used to measure the dynamics of any bacterial transcriptional regulatory network that is affected by interaction with a ligand. In B. subtilis, CodY controls dozens of genes, but the threshold activities of CodY required to regulate each gene are unknown. We hypothesized that targets of CodY are differentially regulated based on varying affinity for the protein's many binding sites. We used RNA sequencing to determine the transcription profiles of B. subtilis strains expressing mutant CodY proteins with different levels of residual activity. In parallel, we quantified intracellular metabolites connected to central metabolism. Strains producing CodY variants F71Y, R61K, and R61H retained varying degrees of partial activity relative to the WT protein, leading to gene-specific, differential alterations in transcript abundance for the 223 identified members of the CodY regulon. Using liquid chromatography coupled to MS, we detected significant increases in branched-chain amino acids and intermediates of arginine, proline, and glutamate metabolism, as well as decreases in pyruvate and glycerate as CodY activity decreased. We conclude that a spectrum of CodY activities leads to programmed regulation of gene expression and an apparent rerouting of carbon and nitrogen metabolism, suggesting that during changes in nutrient availability, CodY prioritizes the expression of specific pathways.
Asunto(s)
Bacillus subtilis/genética , Proteínas Bacterianas/genética , Regulación Bacteriana de la Expresión Génica , Factores de Transcripción/genética , Arginina/biosíntesis , Bacillus subtilis/metabolismo , Proteínas Bacterianas/metabolismo , Carbono/metabolismo , Escherichia coli/genética , Escherichia coli/metabolismo , Ácido Glutámico/biosíntesis , Ligandos , Análisis de Secuencia de ARN , Transaminasas/metabolismo , Factores de Transcripción/metabolismoRESUMEN
Stoichiometric models of metabolism, such as flux balance analysis (FBA), are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks. Here we revisit the central assumption of FBA, i.e. that intracellular metabolites are at steady state, and show that deviations from flux balance (i.e. flux imbalances) are informative of some features of in vivo metabolite concentrations. Mathematically, the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization, the dual problem, and its corresponding variables, known as shadow prices. First, using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations, we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites. We next hypothesize that metabolites which are limiting for growth (and thus have very negative shadow price) cannot vary dramatically in an uncontrolled way, and must respond rapidly to perturbations. Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations, we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price. Finally, we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods. In particular, we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model. In this case, shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data. In general, these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell.
Asunto(s)
Redes y Vías Metabólicas , Metabolómica/métodos , Modelos Biológicos , Biología de Sistemas/métodos , Algoritmos , Escherichia coli/genética , Escherichia coli/metabolismo , Perfilación de la Expresión Génica , Espacio Intracelular/metabolismo , Estadísticas no ParamétricasRESUMEN
The capacity of microorganisms to respond to variable external conditions requires a coordination of environment-sensing mechanisms and decision-making regulatory circuits. Here, we seek to understand the interplay between these two processes by combining high-throughput measurement of time-dependent mRNA profiles with a novel computational approach that searches for key genetic triggers of transcriptional changes. Our approach helped us understand the regulatory strategies of a respiratorily versatile bacterium with promising bioenergy and bioremediation applications, Shewanella oneidensis, in minimal and rich media. By comparing expression profiles across these two conditions, we unveiled components of the transcriptional program that depend mainly on the growth phase. Conversely, by integrating our time-dependent data with a previously available large compendium of static perturbation responses, we identified transcriptional changes that cannot be explained solely by internal network dynamics, but are rather triggered by specific genes acting as key mediators of an environment-dependent response. These transcriptional triggers include known and novel regulators that respond to carbon, nitrogen and oxygen limitation. Our analysis suggests a sequence of physiological responses, including a coupling between nitrogen depletion and glycogen storage, partially recapitulated through dynamic flux balance analysis, and experimentally confirmed by metabolite measurements. Our approach is broadly applicable to other systems.
Asunto(s)
Regulación Bacteriana de la Expresión Génica , Shewanella/crecimiento & desarrollo , Shewanella/genética , Transcripción Genética , Algoritmos , Antibacterianos/farmacología , Medios de Cultivo , Escherichia coli/efectos de los fármacos , Perfilación de la Expresión Génica , Fenotipo , Shewanella/metabolismoRESUMEN
An epistatic interaction between two genes occurs when the phenotypic impact of one gene depends on another gene, often exposing a functional association between them. Due to experimental scalability and to evolutionary significance, abundant work has been focused on studying how epistasis affects cellular growth rate, most notably in yeast. However, epistasis likely influences many different phenotypes, affecting our capacity to understand cellular functions, biochemical networks adaptation, and genetic diseases. Despite its broad significance, the extent and nature of epistasis relative to different phenotypes remain fundamentally unexplored. Here we use genome-scale metabolic network modeling to investigate the extent and properties of epistatic interactions relative to multiple phenotypes. Specifically, using an experimentally refined stoichiometric model for Saccharomyces cerevisiae, we computed a three-dimensional matrix of epistatic interactions between any two enzyme gene deletions, with respect to all metabolic flux phenotypes. We found that the total number of epistatic interactions between enzymes increases rapidly as phenotypes are added, plateauing at approximately 80 phenotypes, to an overall connectivity that is roughly 8-fold larger than the one observed relative to growth alone. Looking at interactions across all phenotypes, we found that gene pairs interact incoherently relative to different phenotypes, i.e. antagonistically relative to some phenotypes and synergistically relative to others. Specific deletion-deletion-phenotype triplets can be explained metabolically, suggesting a highly informative role of multi-phenotype epistasis in mapping cellular functions. Finally, we found that genes involved in many interactions across multiple phenotypes are more highly expressed, evolve slower, and tend to be associated with diseases, indicating that the importance of genes is hidden in their total phenotypic impact. Our predictions indicate a pervasiveness of nonlinear effects in how genetic perturbations affect multiple metabolic phenotypes. The approaches and results reported could influence future efforts in understanding metabolic diseases and the role of biochemical regulation in the cell.
Asunto(s)
Epistasis Genética , Redes y Vías Metabólicas/genética , Saccharomyces cerevisiae/genética , Evolución Biológica , Mapeo Cromosómico , Biología Computacional , Enfermedad/genética , Humanos , Modelos Genéticos , Mutación , FenotipoRESUMEN
Epistatic interactions, manifested in the effects of mutations on the phenotypes caused by other mutations, may help uncover the functional organization of complex biological networks. Here, we studied system-level epistatic interactions by computing growth phenotypes of all single and double knockouts of 890 metabolic genes in Saccharomyces cerevisiae, using the framework of flux balance analysis. A new scale for epistasis identified a distinctive trimodal distribution of these epistatic effects, allowing gene pairs to be classified as buffering, aggravating or noninteracting. We found that the ensuing epistatic interaction network could be organized hierarchically into function-enriched modules that interact with each other 'monochromatically' (i.e., with purely aggravating or purely buffering epistatic links). This property extends the concept of epistasis from single genes to functional units and provides a new definition of biological modularity, which emphasizes interactions between, rather than within, functional modules. Our approach can be used to infer functional gene modules from purely phenotypic epistasis measurements.
Asunto(s)
Epistasis Genética , Saccharomyces cerevisiae/genética , Algoritmos , Genética de Población , Cinética , Mutación , Saccharomyces cerevisiae/metabolismoRESUMEN
Metabolism is the complex network of chemical reactions occurring within every cell and organism, maintaining life, mediating ecosystem processes and affecting Earth's climate. Experiments and models of microbial metabolism often focus on one specific scale, overlooking the connectivity between molecules, cells and ecosystems. Here we highlight quantitative metabolic principles that exhibit commonalities across scales, which we argue could help to achieve an integrated perspective on microbial life. Mass, electron and energy balance provide quantitative constraints on their flow within metabolic networks, organisms and ecosystems, shaping how each responds to its environment. The mechanisms underlying these flows, such as enzyme-substrate interactions, often involve encounter and handling stages that are represented by equations similar to those for cells and resources, or predators and prey. We propose that these formal similarities reflect shared principles and discuss how their investigation through experiments and models may contribute to a common language for studying microbial metabolism across scales.
Asunto(s)
Bacterias , Ecosistema , Redes y Vías Metabólicas , Bacterias/metabolismo , Bacterias/genética , Metabolismo Energético , Modelos BiológicosRESUMEN
Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.
Asunto(s)
Microbiota , Bacterias/metabolismo , Bacterias/clasificación , Bacterias/genética , Modelos BiológicosRESUMEN
Background: Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. Results: We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. Conclusions: MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.
RESUMEN
The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.
Asunto(s)
Algoritmos , FenotipoRESUMEN
Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such "history-dependent" sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.
Asunto(s)
Metabolismo/fisiología , Modelos Biológicos , Biología de Sistemas/métodos , Acetatos/metabolismo , Algoritmos , Cinética , Metabolismo/genética , Ácido Pirúvico/metabolismo , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Shewanella/genética , Shewanella/metabolismo , Shewanella/fisiologíaRESUMEN
COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.