Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale

BackgroundMembranes play a crucial role in cellular functions. Membranes provide a physical barrier, control the trafficking of substances entering and leaving the cell, and are a major determinant of cellular ultra-structure. In addition, components embedded within the membrane participate in cell signaling, energy transduction, and other critical cellular functions. All these processes must share the limited space in the membrane; thus it represents a notable constraint on cellular functions. Membrane- and location-based processes have not yet been reconstructed and explicitly integrated into genome-scale models.ResultsThe recent genome-scale model of metabolism and protein expression in Escherichia coli (called a ME-model) computes the complete composition of the proteome required to perform whole cell functions. Here we expand the ME-model to include (1) a reconstruction of protein translocation pathways, (2) assignment of all cellular proteins to one of four compartments (cytoplasm, inner membrane, periplasm, and outer membrane) and a translocation pathway, (3) experimentally determined translocase catalytic and porin diffusion rates, and (4) a novel membrane constraint that reflects cell morphology. Comparison of computations performed with this expanded ME-model, named iJL1678-ME, against available experimental data reveals that the model accurately describes translocation pathway expression and the functional proteome by compartmentalized mass.ConclusioniJL1678-ME enables the computation of cellular phenotypes through an integrated computation of proteome composition, abundance, and activity in four cellular compartments (cytoplasm, periplasm, inner and outer membrane). Reconstruction and validation of the model has demonstrated that the iJL1678-ME is capable of capturing the functional content of membranes, cellular compartment-specific composition, and that it can be utilized to examine the effect of perturbing an expanded set of network components. iJL1678-ME takes a notable step towards the inclusion of cellular ultra-structure in genome-scale models

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

Proteomic and transcriptomic data from wild-type and laboratory-evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.In laboratory-evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non-functional pathways.Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity

COBRApy: COnstraints-Based Reconstruction and Analysis for Python

Abstract Background COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Results Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. Conclusion COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. Availability http://opencobra.sourceforge.net

An experimentally-supported genome-scale metabolic network reconstruction for <it>Yersinia pestis </it>CO92

<p>Abstract</p> <p>Background</p> <p><it>Yersinia pestis </it>is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in <it>Y. pestis </it>displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas.</p> <p>Results</p> <p>Here we report a genome-scale reconstruction and mathematical model of metabolism for <it>Y. pestis </it>CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain.</p> <p>Conclusions</p> <p><it>Y. pestis </it>continues to be a dangerous threat to human health during modern times. The <it>Y. pestis </it>genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an <it>in silico </it>platform with which to investigate the metabolism of this important human pathogen.</p

White and gray matter brain development in children and young adults with phenylketonuria.

Phenylketonuria (PKU) is a recessive disorder characterized by disruption in the metabolism of the amino acid phenylalanine (Phe). Prior research indicates that individuals with PKU have substantial white matter (WM) compromise. Much less is known about gray matter (GM) in PKU, but a small body of research suggests volumetric differences compared to controls. To date, developmental trajectories of GM structure in individuals with PKU have not been examined, nor have trajectories of WM and GM been examined within a single study. To address this gap in the literature, we compared longitudinal brain development over a three-year period in individuals with PKU (n = 35; 18 male) and typically-developing controls (n = 71; 35 male) aged 7-21 years. Using diffusion tensor imaging (DTI) and structural magnetic resonance imaging (MRI), we observed whole-brain and regional WM differences between individuals with PKU and controls, which were often exacerbated with increasing age. In marked contrast with trajectories of WM development, trajectories of GM development did not differ between individuals with PKU and controls, indicating that neuropathology in PKU is more prominent in WM than GM. Within individuals with PKU, mediation analyses revealed that whole-brain mean diffusivity (MD) and regional MD in the corpus callosum and centrum semiovale mediated the relationship between dietary treatment compliance (i.e., Phe control) and executive abilities, suggesting a plausible neurobiological mechanism by which Phe control may influence cognitive outcomes. Our findings clarify the specificity, timing, and cognitive consequences of whole-brain and regional WM pathology, with implications for treatment and research in PKU