Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox.
ABSTRACT The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.
SourceAvailable from: Lars Lewejohann[Show abstract] [Hide abstract]
ABSTRACT: In mammals, new neurons are generated throughout the entire lifespan in two restricted areas of the brain, the dentate gyrus (DG) of the hippocampus and the subventricular zone (SVZ)-olfactory bulb (OB) system. In both regions newborn neurons display unique properties that clearly distinguish them from mature neurons. Enhanced excitability and increased synaptic plasticity enables them to add specific properties to information processing by modulating the existing local circuitry of already established mature neurons. Hippocampal neurogenesis has been suggested to play a role in spatial-navigation learning, spatial memory, and spatial pattern separation. Cumulative evidences implicate that adult-born OB neurons contribute to learning processes and odor memory. We recently demonstrated that the cell fate determinant TRIM32 is upregulated in differentiating neuroblasts of the SVZ-OB system in the adult mouse brain. The absence of TRIM32 leads to increased progenitor cell proliferation and less cell death. Both effects accumulate in an overproduction of adult-generated OB neurons. Here, we present novel data from behavioral studies showing that such an enhancement of OB neurogenesis not necessarily leads to increased olfactory performance but in contrast even results in impaired olfactory capabilities. In addition, we show at the cellular level that TRIM32 protein levels increase during differentiation of neural stem cells (NSCs). At the molecular level, several metabolic intermediates that are connected to glycolysis, glycine, or cysteine metabolism are deregulated in TRIM32 knockout mice brain tissue. These metabolomics pathways are directly or indirectly linked to anxiety or depression like behavior. In summary, our study provides comprehensive data on how the impairment of neurogenesis caused by the loss of the cell fate determinant TRIM32 causes a decrease of olfactory performance as well as a deregulation of metabolomic pathways that are linked to mood disorders.Frontiers in Cellular Neuroscience 01/2015; 9:75. DOI:10.3389/fncel.2015.00075 · 4.18 Impact Factor
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ABSTRACT: Lignocellulosic biomass is an attractive sustainable carbon source for fermentative production of bioethanol. In this context, use of microbial consortia consisting of substrate-selective microbes is advantageous as it eliminates the negative impacts of glucose catabolite repression. In this study, a detailed in silico analysis of bioethanol production from glucose-xylose mixtures of various compositions by coculture fermentation of xylose-selective Escherichia coli strain ZSC113 and glucose-selective wild-type Saccharomyces cerevisiae is presented. Dynamic flux balance models based on available genome-scale metabolic networks of the microorganisms have been used to analyze bioethanol production and the maximization of ethanol productivity is addressed by computing optimal aerobic-anaerobic switching times. A set of genetic engineering strategies for ethanol overproduction by E. coli strain ZSC113 have been evaluated for their efficiency in the context of batch coculture process. Finally, simulations are carried out to determine the pairs of genetically modified E. coli strain ZSC113 and S. cerevisiae that significantly enhance ethanol productivity in batch coculture fermentation.02/2015; 2015:1-11. DOI:10.1155/2015/238082
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ABSTRACT: Background Flux balance analysis is traditionally implemented to identify the maximum theoretical flux for some specified reaction and a single distribution of flux values for all the reactions present which achieve this maximum value. However it is well known that the uncertainty in reaction networks due to branches, cycles and experimental errors results in a large number of combinations of internal reaction fluxes which can achieve the same optimal flux value. Results In this work, we have modified the applied linear objective of flux balance analysis to include a poling penalty function, which pushes each new set of reaction fluxes away from previous solutions generated. Repeated poling-based flux balance analysis generates a sample of different solutions (a characteristic set), which represents all the possible functionality of the reaction network. Compared to existing sampling methods, for the purpose of generating a relatively “small” characteristic set, our new method is shown to obtain a higher coverage than competing methods under most conditions. The influence of the linear objective function on the sampling (the linear bias) constrains optimisation results to a subspace of optimal solutions all producing the same maximal fluxes. Visualisation of reaction fluxes plotted against each other in 2 dimensions with and without the linear bias indicates the existence of correlations between fluxes. This method of sampling is applied to the organism Actinobacillus succinogenes for the production of succinic acid from glycerol. Conclusions A new method of sampling for the generation of different flux distributions (sets of individual fluxes satisfying constraints on the steady-state mass balances of intermediates) has been developed using a relatively simple modification of flux balance analysis to include a poling penalty function inside the resulting optimisation objective function. This new methodology can achieve a high coverage of the possible flux space and can be used with and without linear bias to show optimal versus sub-optimal solution spaces. Basic analysis of the Actinobacillus succinogenes system using sampling shows that in order to achieve the maximal succinic acid production CO2 must be taken into the system. Solutions involving release of CO2 all give sub-optimal succinic acid production. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0476-5) contains supplementary material, which is available to authorized users.BMC Bioinformatics 12/2015; 16(1). DOI:10.1186/s12859-015-0476-5 · 2.67 Impact Factor