Constraint Based Reconstruction and Analysis (COBRA) is a framework within the field of Systems Biology which aims to understand cellular metabolism through the analysis of large scale metabolic models. These models are based on meticulously curated reconstructions of all chemical reactions in an organism. Instead of attempting to predict the exact state of the biological system, COBRA describes the physiological constraints that the system must satisfy and studies the range of solutions satisfying these constraints. Monte Carlo Sampling is one of the COBRA methods used to study how biological properties are distributed over the entire solution space. A set of randomly distributed solutions is generated and serves as a proxy for the entire space. Various aspects of Monte Carlo Sampling in Systems Biology are illustrated : 1) Monte Carlo Sampling has been used historically (Chapter 1), 2) A faster and more efficient procedure for generating Monte Carlo Samples is developed (Chapter 2); 3) Carbon 13 tracing experiments are an important tool for measuring reaction rates through a network. Monte Carlo Sampling was used to optimize the choice of label and explain and measure the dimensionality of output data (Chapter 3); 4) It is possible to incorporate the thermodynamic "loop-law'' into many COBRA methods including sampling (Chapter 4). Additionally two software projects are presented which assist in analyzing COBRA models : 1) the BiGG knowledgebase of reconstructions (Chapter 5) and 2) the COBRA Matlab toolbox v. 2.0 (Chapter 6). These two projects make COBRA methods available to the scientific community

Background

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.

Description

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.

Conclusions

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 http://bigg.ucsd.edu.

Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host-pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host-pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host-pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host-pathogen network and were able to describe three distinct pathological states. Integrated host-pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated.

Abstract Background Carbon-13 (13C) analysis is a commonly used method for estimating reaction rates in biochemical networks. The choice of carbon labeling pattern is an important consideration when designing these experiments. We present a novel Monte Carlo algorithm for finding the optimal substrate input label for a particular experimental objective (flux or flux ratio). Unlike previous work, this method does not require assumption of the flux distribution beforehand. Results Using a large E. coli isotopomer model, different commercially available substrate labeling patterns were tested computationally for their ability to determine reaction fluxes. The choice of optimal labeled substrate was found to be dependent upon the desired experimental objective. Many commercially available labels are predicted to be outperformed by complex labeling patterns. Based on Monte Carlo Sampling, the dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of 13C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed. Conclusions While 13C analysis is a useful tool in systems biology, high redundancy in measurements limits the information that can be obtained from each experiment. It is however possible to compute potential limitations before an experiment is run and predict whether, and to what degree, the rate of each reaction can be resolved.