Building In Silico Analysis Platform Of Constraint-based Metabolic Models For Microorganisms

Since comes the post-genomic era, it is of urgent need to interpret the deluge of various omics data in a systematic way. As cellular functions rely on the coordinated activity of multiple gene products and environmental factors, understanding the interrelatedness and connectivity of these elements can help us understand life as a whole, not just the sum of its parts.Systems biology is the study of a complex biological system, viewed as an integrated and interacting network of genes, proteins and metabolites, which give rise to life. Instead of analyzing individual components or aspects of the complex biological system, systems biology focuses on all the information from different levels of the system. Because any functions performed by a system is the outcome of all interacting parts in the system.One area of active research in this area has focused on microbial metabolism. Genomic information, coupled with biochemical and strain-specific information, has been used to reconstruct whole-cell metabolic networks for sequenced organisms. And the constraint-based modeling approach is widely used to integrate all the information mentioned above into a mathematical model. At present it is the only methodology by which genome-scale models have been constructed.Most of other Systems biology modeling approaches, such as kinetic model, graphic theory, logical and stochastic approaches, requiring detail kinetic and concentration information of enzymes and various cofactors, which are very difficult to obtain. The constraint-based framework uses much fewer parameters. In this paper, this approach is described.The constraint-based modeling approach is based on the assumption that organisms exist in particular environments that typically have scarce resources, and over time, the probability that the fit will survive is higher compared with the probability of survival of the less fit. To be fit for survival, a myriad of constraints must be satisfied, which limits the range of available phenotypes. The constraints include physico-chemical constraints (such as, mass conservation), thermodynamic constraints (irreversibility of a reaction) and capacity constraints (such as, maximum uptake rate of a transporter), etc. which can be represented mathematically.Mass balance is utilized in terms of the flux and the stoichiometry of each reaction, S ?v=0. S is the stoichiometric matrix containing the stoichiometry of all reactions in the network. S is an m×nmatrix where m corresponds to the number of metabolites and n is the number of reactions or fluxes taking place within the network. The entry S ij defines the stoichiometry of reactant i in reaction j . Let v be a vector such that v j defines the flux through reaction j .Thermodynamic constraints and capacity constraints limit the range of each flux within the network, v_min < v_j max. For irreversible reactions, v min =0. Specific upper limits v maxthat are based on enzyme capacity measurements, which are generally imposed on reactions.Based on these constraints, the solution of the problem is limited in a closed space called convex. To obtain the only solution or limited solutions, the objective function is needed. The objective function represents probable physiological function, and is defined in context of the studied organism. It can aim to maximize biomass production, minimize or maximize ATP production, minimize nutrient uptake rate or any estimated functions.Biomass production maximization has been shown to be consistent with experimental data. Biomass production is essentially the production of molecules that are needed for growth. It is comprised a set of molecules in specific concentrations, such as amino acids, RNA, DNA, lipids and fatty acids, etc. Hence our problem can be formulated as a LP problem in the following manner:Then the optimal is got by using simplex algorithm.This paper's main job is building computer environment for model implements under Linux/Perl. A set of toolkits were installed and described, such as libSBML, GLPK, Math-GLPK-Solve, Math-MatrixSparse. And a user model called sbml2flux is completed. It provides functions such as parsing metabolic models in sbml format, extracting important information for building in silico models, constructing stoichiometry involving all metabolic reactions and formulating LP problem and obtaining the optimal.In this paper, the model is built for E.coli metabolic network which involves 77 core metabolic reactions and 63 metabolites. Using the model, some simulations are implemented. For each metabolite in the reactant list of the objective function, the original coefficient was multiplying by a positive value from 0 to 3 with an incremental 0.01 step by step; in each step the optimal cell growth was calculated by the modified objective function; so the trend of optimal growth rate with single biomass composition can be illustrated. From the charts, we can directly conclude that the relative change of the coefficient of ATP has the strongest impact on the optimal growth, while f6p, g3p, e4p, and g6p have little; H₂O has no impact at all because it can exchange with environment freely; Others such as nadph and pyr lie between them. Another noteworthy point is that the increase of ATP leads to a decrease of optimal growth.This procedure provides a direct quantitative view of to which degree the coefficient in biomass composition can influence the optimal growth, which should be an essential part in evaluating the constraint-based model reconstruction.Based on this analysis platform, we can construct metablic models for other microorganisms.