Analysis Of Genome-scale Models Research Articles

Inspecting the Solution Space of Genome-Scale Metabolic Models.

Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

Stochastic Simulations of Cellular Processes in Bacteria

Stochastic expression of genes produces heterogeneity in clonal populations of bacteria grown under identical conditions. We analyze the stochastic reaction-diffusion dynamics of selected biochemical pathways in Escherichia coli to show how individual cells vary expression of a set of genes in response to an environmental signal. The whole cells simulated under in vivo conditions include ribosomes, DNA, and large protein complexes, which take up 30-50% of the cell volume and are placed according to data from cryoelectron tomography and proteomics. using GPU processors, we simulate the dynamics for an entire cell cycle and compare the mRNA/protein distributions to those observed in single molecule experiments. We show how such distributions can be used to derive additional kinetic parameters and integrate effects of cell-to-cell variations into flux balance analysis of genome scale models of metabolic networks. The distribution of growth rates calculated for a colony of bacteria are analyzed and correlated to changes in fluxes through the metabolic network.With the availability of high-performance computing, simulations are poised to allow integration of data from structural, single-molecule, and biochemical studies into coherent computational models of cells and cellular processes. Here the calculations are performed with our Lattice Microbes software. Animation of reaction trajectories involving millions of particles is facilitated using a plugin to the VMD visualization and analysis program.“Noise contributions in an inducible genetic switch: A whole cell simulation study”, E. Roberts, A. Magis, J. Ortiz, W. Baumeister, and Z. Luthey-Schulten, Plos Comput. Biol. 7(3), e1002010 March (2011).“Determining the stability of genetic switches: Explicitly accounting for mRNA noise”M. Assaf, E. Roberts, and Z. Luthey- Schulten, Phys.Rev.Lett. 24, 248102, (2011).“Lattice Microbes: high-performance stochastic simulation method for the reaction-diffusion master equation”, E. Roberts, J. Stone, Z. Luthey-Schulten, J. Comp. Chem, (2012, in press).