An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli.

Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high-throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under-represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems

A kinetic core model of the glucose-stimulated insulin secretion network of pancreatic β cells

The construction and characterization of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic β cells is described. The model consists of 44 enzymatic reactions, 59 metabolic state variables, and 272 parameters. It integrates five subsystems: glycolysis, the TCA cycle, the respiratory chain, NADH shuttles, and the pyruvate cycle. It also takes into account compartmentalization of the reactions in the cytoplasm and mitochondrial matrix. The model shows expected behavior in its outputs, including the response of ATP production to starting glucose concentration and the induction of oscillations of metabolite concentrations in the glycolytic pathway and in ATP and ADP concentrations. Identification of choke points and parameter sensitivity analysis indicate that the glycolytic pathway, and to a lesser extent the TCA cycle, are critical to the proper behavior of the system, while parameters in other components such as the respiratory chain are less critical. Notably, however, sensitivity analysis identifies the first reactions of nonglycolytic pathways as being important for the behavior of the system. The model is robust to deletion of malic enzyme activity, which is absent in mouse pancreatic β cells. The model represents a step toward the construction of a model with species-specific parameters that can be used to understand mouse models of diabetes and the relationship of these mouse models to the human disease state. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00335-007-9011-y) contains supplementary material, which is available to authorized users

Targeted Metabolomics for Clinical Biomarker Discovery in Multifactorial Diseases

Diseases & disorder

Modeling Mitochondrial Bioenergetics with Integrated Volume Dynamics

Mathematical models of mitochondrial bioenergetics provide powerful analytical tools to help interpret experimental data and facilitate experimental design for elucidating the supporting biochemical and physical processes. As a next step towards constructing a complete physiologically faithful mitochondrial bioenergetics model, a mathematical model was developed targeting the cardiac mitochondrial bioenergetic based upon previous efforts, and corroborated using both transient and steady state data. The model consists of several modified rate functions of mitochondrial bioenergetics, integrated calcium dynamics and a detailed description of the K+-cycle and its effect on mitochondrial bioenergetics and matrix volume regulation. Model simulations were used to fit 42 adjustable parameters to four independent experimental data sets consisting of 32 data curves. During the model development, a certain network topology had to be in place and some assumptions about uncertain or unobserved experimental factors and conditions were explicitly constrained in order to faithfully reproduce all the data sets. These realizations are discussed, and their necessity helps contribute to the collective understanding of the mitochondrial bioenergetics

CELLmicrocosmos - Integrative cell modeling at the molecular, mesoscopic and functional level

Sommer B. CELLmicrocosmos - Integrative cell modeling at the  molecular, mesoscopic and functional level. Bielefeld: Bielefeld University; 2012.The modeling of cells is an important application area of Systems Biology. In the context of this work, three cytological levels are defined: the mesoscopic, the molecular and the functional level. A number of related approaches which are quite diverse will be introduced during this work which can be categorized into these disciplines. But none of these approaches covers all areas. In this work, the combination of all three aforementioned cytological levels is presented, realized by the CELLmicrocosmos project, combining and extending different Bioinformatics-related methods. The mesoscopic level is covered by CellEditor which is a simple tool to generate eukaryotic or prokaryotic cell models. These are based on cell components represented by three-dimensional shapes. Different methods to generate these shapes are discussed by using partly external tools such as Amira, 3ds Max and/or Blender; abstract, interpretative, 3D-microscopy-based and molecular-structure-based cell component modeling. To communicate with these tools, CellEditor provides import as well as export capabilities based on the VRML97 format. In addition, different cytological coloring methods are discussed which can be applied to the cell models. MembraneEditor operates at the molecular level. This tool solves heterogeneous Membrane Packing Problems by distributing lipids on rectangular areas using collision detection. It provides fast and intuitive methods supporting a wide range of different application areas based on the PDB format. Moreover, a plugin interface enables the use of custom algorithms. In the context of this work, a high-density-generating lipid packing algorithm is evaluated; The Wanderer. The semi-automatic integration of proteins into the membrane is enabled by using data from the OPM and PDBTM database. Contrasting with the aforementioned structural levels, the third level covers the functional aspects of the cell. Here, protein-related networks or data sets can be imported and mapped into the previously generated cell models using the PathwayIntegration. For this purpose, data integration methods are applied, represented by the data warehouse DAWIS-M.D. which includes a number of established databases. This information is enriched by the text-mining data acquired from the ANDCell database. The localization of proteins is supported by different tools like the interactive Localization Table and the Localization Charts. The correlation of partly multi-layered cell components with protein-related networks is covered by the Network Mapping Problem. A special implementation of the ISOM layout is used for this purpose. Finally, a first approach to combine all these interrelated levels is represented; CellExplorer which integrates CellEditor as well as PathwayIntegration and imports structures generated with MembraneEditor. For this purpose, the shape-based cell components can be correlated with networks as well as molecular membrane structures using Membrane Mapping. It is shown that the tools discussed here can be applied to scientific as well as educational tasks: educational cell visualization, initial membrane modeling for molecular simulations, analysis of interrelated protein sets, cytological disease mapping. These are supported by the user-friendly combination of Java, Java 3D and Web Start technology. In the last part of this thesis the future of Integrative Cell Modeling is discussed. While the approaches discussed here represent basically three-dimensional snapshots of the cell, prospective approaches have to be extended into the fourth dimension; time

Automates cellulaires pour la modélisation multi-échelle des systèmes biologiques

This PhD thesis project is part of a research program in the fields of biology, physics and computer science aiming to propose a simulation approach for performing experiments in silico. For this, we propose to develop a software platform dedicated to multi-scale modeling of biological systems that can be combined with particle physics simulation tools. We also propose a general individual-based model of biological cell in which data obtained from in vitro experiments can be used. We present the development of this platform and the validation process of its functionalities through the implementation of cellular automata from the literature. We then present the design of the biological cell model by giving the hypothesis we made, how we model and how we parameterize the model. Starting from a simple biological system, bacteria, observed in liquid culture, our model uses a multi-scale middle-out approach. We focus on the cell and we model internal processes, assuming that all their properties come from genetic information carried out by the cell’s genome. This model allows to consider the cell behavior, and then to obtain the behavior of a cell population. Data from fluxomic experiments have been used in this model to parameterize the biochemical processes. The results we obtain allow us to consider the model as validated as simulation results match the experimental data.Ce projet de thèse, dans le cadre d’une collaboration entre le LIMOS et le LPC, s’inscrit dans une démarche de recherche permettant la mise en synergie des domaines de la biologie, de la physique et de l’informatique par la proposition d’une démarche de simulation permettant la réalisation d’expériences in silico. Pour cela, nous nous proposons de développer une plateforme logicielle dédiée à la modélisation multiéchelle des systèmes biologiques qui pourra par la suite être interfacée avec les outils de simulation de physique des particules. Nous proposons également un modèle individu-centré de cellule biologique paramétrable à l’aide de données obtenues d’expériences in vitro. Nous présentons l’élaboration de cette plateforme et une démarche de validation de ses fonctionnalités à travers l’implémentation de modèles d’automates cellulaires de la littérature. Nous présentons ensuite la construction du modèle de cellule biologique en prenant le temps d’expliquer comment est pris en compte le système biologique, comment nous le modélisons puis comment nous paramétrons le modèle. Nous modélisons les processus internes de la cellule, dont les caractéristiques sont liées à l’information génétique qu’elle porte. Ce modèle de cellule permet de reproduire le comportement d’une cellule isolée, et à partir de là, d’un ensemble de cellules via l'automate. Le modèle est ensuite utilisé pour retrouver les courbes de croissance d'une population de bactéries Escherichia coli. Des valeurs de données de fluxomique ont été exploitées et ont permis la reproduction in silico des expériences in vitro dont elles étaient issues