Linking Bistable Dynamics to Metabolic P Systems

Bistability, or more generally multistability, is an important recurring theme in biological systems. In particular, the discovery of bistability in signal pathways of genetic networks, prompts strong interest in understanding both the design and function of these networks. Therefore, modelling these systems is crucial to understand their behaviors, and also to analyze and identify characteristics that would otherwise be di cult to realize. Although di erent classes of models have been used to study bistable dynamics, there is a lag in the development of models for bistable systems starting from experimental data. This is due to the lack of detailed knowledge of biochemical reactions and kinetic rates. In this work, we propose a procedure to develop, starting from observed dynamics, Metabolic P models for multistable processes. As a case study, a mathematical model of the Schl ogel's dynamics, which represents an example of a chemical reaction system that exhibits bistability, is inferred starting from observed stochastic bistable dynamics. Since, recent experiments indicate that noise plays an important role in the switching of bistable systems, the success of this work suggests that this approach is a very promising one for studying dynamics and role of noise in biological systems, such as, for example, genetic regulatory networks

Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO2 Levels.

Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean's primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom's metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and shared metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum's response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. IMPORTANCE Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum's metabolism

The Discovery of Initial Fluxes of Metabolic P Systems

A central issue in systems biology is the study of efficient methods to infer fluxes of biological reactions starting from experimental data. Among the different techniques proposed in the last years, in the theory of Metabolic P systems Log-Gain principles have been introduced, which prove to be helpful for deducing biological fluxes from temporal series of observed dynamics. However, crucial tasks remain to be performed for a complete suitable application of these principles. In particular the algebraic systems introduced by the Log-Gain principles require the knowledge of the initial fluxes associated with a set of biochemical reactions. In this paper we propose an algorithm for estimating initial fluxes, which is tested in two case studies

Toward a Self-replicating Metabolic P System

This work concerns the synthesis of a "minimal cell' by means of a P system, which is a distributed rewriting system inspired by the structure and the functioning of the biological cell. Specifically, we aim to define a dynamical system which exhibits a steady metabolic evolution, resulting in self-maintenance and self-reproduction. Metabolic P systems represent a class of P systems particularly promising to model a minimal cell in discrete terms, since they have already successfully modeled several metabolisms. The main further step is thus to find a simple way to obtain Metabolic P system self-replication. This paper deals with ideas presented at the BWMC11 (held in Seville, Feb 2011) and opens a new trend in membrane computing, based on computational synthetic biology oriented applications of P systems modeling. The framework is here outlined, and some problems to tackle the synthesis of a minimal cell are discussed. Moreover, an overview of literature and a list of appealing research directions is given, along with several references

Difference in the distribution pattern of substrate enzymes in the metabolic network of Escherichia coli, according to chaperonin requirement

<p>Abstract</p> <p>Background</p> <p>Chaperonins are important in living systems because they play a role in the folding of proteins. Earlier comprehensive analyses identified substrate proteins for which folding requires the chaperonin GroEL/GroES (GroE) in <it>Escherichia coli</it>, and they revealed that many chaperonin substrates are metabolic enzymes. This result implies the importance of chaperonins in metabolism. However, the relationship between chaperonins and metabolism is still unclear.</p> <p>Results</p> <p>We investigated the distribution of chaperonin substrate enzymes in the metabolic network using network analysis techniques as a first step towards revealing this relationship, and found that as chaperonin requirement increases, substrate enzymes are more laterally distributed in the metabolic. In addition, comparative genome analysis showed that the chaperonin-dependent substrates were less conserved, suggesting that these substrates were acquired later on in evolutionary history.</p> <p>Conclusions</p> <p>This result implies the expansion of metabolic networks due to this chaperonin, and it supports the existing hypothesis of acceleration of evolution by chaperonins. The distribution of chaperonin substrate enzymes in the metabolic network is inexplicable because it does not seem to be associated with individual protein features such as protein abundance, which has been observed characteristically in chaperonin substrates in previous works. However, it becomes clear by considering this expansion process due to chaperonin. This finding provides new insights into metabolic evolution and the roles of chaperonins in living systems.</p

Environmental variability and modularity of bacterial metabolic networks

<p>Abstract</p> <p>Background</p> <p>Biological systems are often modular: they can be decomposed into nearly-independent structural units that perform specific functions. The evolutionary origin of modularity is a subject of much current interest. Recent theory suggests that modularity can be enhanced when the environment changes over time. However, this theory has not yet been tested using biological data.</p> <p>Results</p> <p>To address this, we studied the relation between environmental variability and modularity in a natural and well-studied system, the metabolic networks of bacteria. We classified 117 bacterial species according to the degree of variability in their natural habitat. We find that metabolic networks of organisms in variable environments are significantly more modular than networks of organisms that evolved under more constant conditions.</p> <p>Conclusion</p> <p>This study supports the view that variability in the natural habitat of an organism promotes modularity in its metabolic network and perhaps in other biological systems.</p

A systematic investigation of Escherichia coli central carbon metabolism in response to superoxide stress

<p>Abstract</p> <p>Background</p> <p>The cellular responses of bacteria to superoxide stress can be used to model adaptation to severe environmental changes. Superoxide stress promotes the excessive production of reactive oxygen species (ROS) that have detrimental effects on cell metabolic and other physiological activities. To antagonize such effects, the cell needs to regulate a range of metabolic reactions in a coordinated way, so that coherent metabolic responses are generated by the cellular metabolic reaction network as a whole. In the present study, we have used a quantitative metabolic flux analysis approach, together with measurement of gene expression and activity of key enzymes, to investigate changes in central carbon metabolism that occur in <it>Escherichia coli </it>in response to paraquat-induced superoxide stress. The cellular regulatory mechanisms involved in the observed global flux changes are discussed.</p> <p>Results</p> <p>Flux analysis based on nuclear magnetic resonance (NMR) and mass spectroscopy (MS) measurements and computation provided quantitative results on the metabolic fluxes redistribution of the <it>E. coli </it>central carbon network under paraquat-induced oxidative stress. The metabolic fluxes of the glycolytic pathway were redirected to the pentose phosphate pathway (PP pathway). The production of acetate increased significantly, the fluxes associated with the TCA cycle decreased, and the fluxes in the glyoxylate shunt increased in response to oxidative stress. These global flux changes resulted in an increased ratio of NADPH:NADH and in the accumulation of α-ketoglutarate.</p> <p>Conclusions</p> <p>Metabolic flux analysis provided a quantitative and global picture of responses of the <it>E. coli </it>central carbon metabolic network to oxidative stress. Systematic adjustments of cellular physiological state clearly occurred in response to changes in metabolic fluxes induced by oxidative stress. Quantitative flux analysis therefore could reveal the physiological state of the cell at the systems level and is a useful complement to molecular systems approaches, such as proteomics and transcription analyses.</p

PathCase-SB architecture and database design

<p>Abstract</p> <p>Background</p> <p>Integration of metabolic pathways resources and regulatory metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation in metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis are desirable and intellectually challenging computational tasks.</p> <p>Description</p> <p>PathCase Systems Biology (PathCase-SB) is built and released. The PathCase-SB database provides data and API for multiple user interfaces and software tools. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate data of selected biological data sources on the web (currently, BioModels database and KEGG), and to provide more powerful and/or new capabilities via the new web-based integrative framework. This paper describes architecture and database design issues encountered in PathCase-SB's design and implementation, and presents the current design of PathCase-SB's architecture and database.</p> <p>Conclusions</p> <p>PathCase-SB architecture and database provide a highly extensible and scalable environment with easy and fast (real-time) access to the data in the database. PathCase-SB itself is already being used by researchers across the world.</p

A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype

&lt;p&gt;Background The study of metabolism has attracted much attention during the last years due to its relevance in various diseases. The advance in metabolomics platforms allows us to detect an increasing number of metabolites in abnormal high/low concentration in a disease phenotype. Finding a mechanistic interpretation for these alterations is important to understand pathophysiological processes, however it is not an easy task. The availability of genome scale metabolic networks and Systems Biology techniques open new avenues to address this question.&lt;/p&gt; &lt;p&gt;Results In this article we present a novel mathematical framework to find enzymes whose malfunction explains the accumulation/depletion of a given metabolite in a disease phenotype. Our approach is based on a recently introduced pathway concept termed Carbon Flux Paths (CFPs), which extends classical topological definition by including network stoichiometry. Using CFPs, we determine the Connectivity Curve of an altered metabolite, which allows us to quantify changes in its pathway structure when a certain enzyme is removed. The influence of enzyme removal is then ranked and used to explain the accumulation/depletion of such metabolite. For illustration, we center our study in the accumulation of two metabolites (L-Cystine and Homocysteine) found in high concentration in the brain of patients with mental disorders. Our results were discussed based on literature and found a good agreement with previously reported mechanisms. In addition, we hypothesize a novel role of several enzymes for the accumulation of these metabolites, which opens new strategies to understand the metabolic processes underlying these diseases.&lt;/p&gt; &lt;p&gt;Conclusions With personalized medicine on the horizon, metabolomic platforms are providing us with a vast amount of experimental data for a number of complex diseases. Our approach provides a novel apparatus to rationally investigate and understand metabolite alterations under disease phenotypes. This work contributes to the development of Systems Medicine, whose objective is to answer clinical questions based on theoretical methods and high-throughput “omics” data.&lt;/p&gt

BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions

<p>Abstract</p> <p>Background</p> <p>Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use.</p> <p>Description</p> <p>We describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.</p> <p>Conclusions</p> <p>BiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at <url>http://bigg.ucsd.edu</url>.</p