Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth

<p>Abstract</p> <p>Background</p> <p>Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known <it>in vivo</it> phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the <it>in silico</it> capability in predicting <it>in vivo</it> phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes.</p> <p>Results</p> <p>Here we employed a systematic and iterative process, designated as Reconciling <it>In silico/in vivo</it> mutaNt Growth (RING), to settle discrepancies between <it>in silico</it> prediction and <it>in vivo</it> observations to a newly reconstructed genome-scale metabolic model of the fission yeast, <it>Schizosaccharomyces pombe</it>, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of <it>S. pombe</it>. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype).</p> <p>Conclusion</p> <p>This study presents validation and refinement of a newly reconstructed metabolic model of the yeast <it>S. pombe</it>, through improving the metabolic model’s predictive capabilities by reconciling the <it>in silico</it> predicted growth phenotypes of single-gene knockout mutants, with experimental <it>in vivo</it> growth data.</p

Combining Genomics, Metabolome Analysis, and Biochemical Modelling to Understand Metabolic Networks

Now that complete genome sequences are available for a variety of organisms, the elucidation of gene functions involved in metabolism necessarily includes a better understanding of cellular responses upon mutations on all levels of gene products, mRNA, proteins, and metabolites. Such progress is essential since the observable properties of organisms – the phenotypes – are produced by the genotype in juxtaposition with the environment. Whereas much has been done to make mRNA and protein profiling possible, considerably less effort has been put into profiling the end products of gene expression, metabolites. To date, analytical approaches have been aimed primarily at the accurate quantification of a number of pre-defined target metabolites, or at producing fingerprints of metabolic changes without individually determining metabolite identities. Neither of these approaches allows the formation of an in-depth understanding of the biochemical behaviour within metabolic networks. Yet, by carefully choosing protocols for sample preparation and analytical techniques, a number of chemically different classes of compounds can be quantified simultaneously to enable such understanding. In this review, the terms describing various metabolite-oriented approaches are given, and the differences among these approaches are outlined. Metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting are considered. For each approach, a number of examples are given, and potential applications are discussed

What Can Causal Networks Tell Us about Metabolic Pathways?

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits

The compositional and evolutionary logic of metabolism

Metabolism displays striking and robust regularities in the forms of modularity and hierarchy, whose composition may be compactly described. This renders metabolic architecture comprehensible as a system, and suggests the order in which layers of that system emerged. Metabolism also serves as the foundation in other hierarchies, at least up to cellular integration including bioenergetics and molecular replication, and trophic ecology. The recapitulation of patterns first seen in metabolism, in these higher levels, suggests metabolism as a source of causation or constraint on many forms of organization in the biosphere. We identify as modules widely reused subsets of chemicals, reactions, or functions, each with a conserved internal structure. At the small molecule substrate level, module boundaries are generally associated with the most complex reaction mechanisms and the most conserved enzymes. Cofactors form a structurally and functionally distinctive control layer over the small-molecule substrate. Complex cofactors are often used at module boundaries of the substrate level, while simpler ones participate in widely used reactions. Cofactor functions thus act as "keys" that incorporate classes of organic reactions within biochemistry. The same modules that organize the compositional diversity of metabolism are argued to have governed long-term evolution. Early evolution of core metabolism, especially carbon-fixation, appears to have required few innovations among a small number of conserved modules, to produce adaptations to simple biogeochemical changes of environment. We demonstrate these features of metabolism at several levels of hierarchy, beginning with the small-molecule substrate and network architecture, continuing with cofactors and key conserved reactions, and culminating in the aggregation of multiple diverse physical and biochemical processes in cells.Comment: 56 pages, 28 figure

Dispensability of Escherichia coli's latent pathways

Gene-knockout experiments on single-cell organisms have established that expression of a substantial fraction of genes is not needed for optimal growth. This problem acquired a new dimension with the recent discovery that environmental and genetic perturbations of the bacterium Escherichia coli are followed by the temporary activation of a large number of latent metabolic pathways, which suggests the hypothesis that temporarily activated reactions impact growth and hence facilitate adaptation in the presence of perturbations. Here we test this hypothesis computationally and find, surprisingly, that the availability of latent pathways consistently offers no growth advantage, and tends in fact to inhibit growth after genetic perturbations. This is shown to be true even for latent pathways with a known function in alternate conditions, thus extending the significance of this adverse effect beyond apparently nonessential genes. These findings raise the possibility that latent pathway activation is in fact derivative of another, potentially suboptimal, adaptive response

Studies of molecular mechanisms integrating carbon metabolism and growth in plants

Plants use light energy, carbon dioxide and water to produce sugars and other carbohydrates, which serve as stored energy reserves and as building blocks for biosynthetic reactions. Supply of light is variable and plants have evolved means to adjust their growth and development accordingly. An increasing body of evidence suggests that the basic mechanisms for sensing and signaling energy availability in eukaryotes are evolutionary conserved and thus shared between plants, animals and fungi. I have used different experimental approaches that take advantage of findings from other eukaryotes in studying carbon and energy metabolism in plants. In the first part, I developed a novel screening procedure in yeast aimed at isolating cDNAs from other organisms encoding proteins with a possible function in sugar sensing or signaling. The feasibility of the method was confirmed by the cloning of a cDNA from Arabidopsis thaliana encoding a new F-box protein named AtGrh1, which is related to the yeast Grr1 protein that is involved in glucose repression. In the second part of the study, plant homologues of key components in the yeast glucose repression pathway were cloned and characterized in the moss Physcomitrella patens, in which gene function can be studied by gene targeting. We first cloned PpHXK1 which was shown to encode a chloroplast localized hexokinase representing a previously overlooked class of plant hexokinases with an N-terminal chloroplast transit peptide. Significantly, PpHxk1 is the major hexokinase in Physcomitrella, accounting for 80% of the glucose phosphorylating activity. A knockout mutant deleted for PpHXK1 exhibits a complex phenotype affecting growth, development and sensitivities to plant hormones. I also cloned and characterized two closely related Physcomitrella genes, PpSNF1a and PpSNF1b, encoding type 1 Snf1-related kinases. A double knockout mutant for these genes was viable even though it lacks detectable Snf1-like kinase activity. The mutant suffers from pleiotropic phenotypes which may reflect a constitutive high energy growth mode. Significantly, the double mutant requires constant high light and is therefore unable to grow in a normal day/night light cycle. These findings are consistent with the proposed role of the Snf1-related kinases as energy gauges which are needed to recognize and respond to low energy conditions

Modeling the complex dynamics of enzyme-pathway coevolution.

Peer reviewedPostprin