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Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2015年第34期112卷 10810-10815页

作者： Yang, Laurence Tan, Justin O'Brien, Edward J. Monk, Jonathan M. Kim, Donghyuk Li, Howard J. Charusanti, Pep Ebrahim, Ali Lloyd, Colton J. Yurkovich, James T. Du, Bin Draeger, Andreas Thomas, Alex Sun, Yuekai Saunders, Michael A. Palsson, Bernhard O. Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Univ Tubingen Ctr Bioinformat Tuebingen D-72076 Tubingen Germany Novo Nordisk Fdn Ctr Biosustainabil DK-2970 Horsholm Denmark Stanford Univ Inst Computat & Math Engn Stanford CA 94305 USA Stanford Univ Dept Management Sci & Engn Stanford CA 94305 USA

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5'UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models;it de facto defines a paleome.

关键词： constraint-based modeling metabolism gene expression minimal genome core proteome

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Co-evolution of strain design methods based on flux balance and elementary mode analysis

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Metabolic Engineering Communications 2015年 2卷 85-92页

作者： Machado, Daniel Herrgård, Markus J. Centre of Biological Engineering University of Minho Braga 4710-057 Portugal The Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Hørsholm 2970 Denmark

More than a decade ago, the first genome-scale metabolic models for two of the most relevant microbes for biotechnology applications, Escherichia coli and Saccaromyces cerevisiae, were published. Shortly after followed the publication of OptKnock, the first strain design method using bilevel optimization to couple cellular growth with the production of a target product. This initiated the development of a family of strain design methods based on the concept of flux balance analysis. Another family of strain design methods, based on the concept of elementary mode analysis, has also been growing. Although the computation of elementary modes is hindered by computational complexity, recent breakthroughs have allowed applying elementary mode analysis at the genome scale. Here we review and compare strain design methods and look back at the last 10 years of in silico strain design with constraint-based models. We highlight some features of the different approaches and discuss the utilization of these methods in successful in vivo metabolic engineering applications. © 2015 The Authors.

关键词： Computational methods constraint-based modeling Metabolic engineering Rational strain design

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Processing Overt and Null Subject Pronouns in Italian: a Cognitive Model 37

Processing Overt and Null Subject Pronouns in Italian: a Cog...

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37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015

作者： Vogelzang, Margreet Hendriks, Petra van Rijn, Hedderik Center for Language and Cognition Groningen University of Groningen P.O. Box 716 Groningen9700 AS Netherlands Department of Experimental Psychology & Psychometrics and Statistics University of Groningen Grote Kruisstraat 2/1 9712 TS Netherlands

ISBN: (纸本)9780991196722

In this paper, we present a cognitive model that simulates the processing of subject pronouns in Italian. The model is implemented in the cognitive architecture ACT-R and uses hierarchically ranked constraints to select the most likely referent of a pronoun. When this model is combined with a measure of accessibility in discourse and a processing time limit imposed by the speed of natural language production, the model accounts for novel empirical data of the interpretation of null as well as overt subject pronouns in Italian. The model generates concrete predictions on the basis of variations in cognitive capacities, which can be tested in subsequent experiments. © Cognitive Science Society, CogSci *** rights reserved.

关键词： cognitive modeling constraint-based modeling null subjects pronoun interpretation

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An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

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NATURAL COMPUTING 2014年第3期13卷 321-331页

作者： Damiani, Chiara Pescini, Dario Colombo, Riccardo Molinari, Sara Alberghina, Lilia Vanoni, Marco Mauri, Giancarlo SYSBIO Ctr Syst Biol I-20126 Milan Italy Univ Milano Bicocca Dipartimento Informat Sistemist & Comunicaz I-20126 Milan Italy Univ Milano Bicocca Dipartimento Stat & Metodi Quantitat I-20126 Milan Italy Univ Milano Bicocca Dipartimento Biotecnol & Biosci I-20126 Milan Italy

constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O-2 consumption observed in "Crabtree-positive" and "Crabtree-negative" yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into "Crabtree-positive" and "Crabtree-negative" clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.

关键词： Flux balance analysis constraint-based modeling Genetic algorithm Metabolic network modeling Crabtree effect Randomized sampling

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An Introduction to Kinetic, constraint-based and Boolean modeling in Systems Biology

An Introduction to Kinetic, Constraint-Based and Boolean Mod...

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IEEE International Conference on Control Applications Part of 2010 IEEE Multi-Conference on Systems and Control

作者： Ederer, Michael Schlatter, Rebekka Witt, Johannes Feuer, Ronny Bona-Lovasz, Judit Henkel, Sebastian Sawodny, Oliver Univ Stuttgart Inst Syst Dynam D-70569 Stuttgart Germany

ISBN: (纸本)9781424453634

Mathematical modeling and model analysis of biochemical reaction networks are becoming important tools for biological research and for applications in medicine and bioprocess engineering. We will discuss the special role of mathematical modeling in biological research. In engineering applications, models are primarily means to accomplish a design task (e. g. controller design). In contrast, the goal of modeling in biology is often to coherently and comprehensively integrate and develop the knowledge of a biological system beyond the level of the conventional verbal or graphical descriptions. In this way, modeling supports biologists in exploring biochemical networks. This tutorial gives an overview over several available modeling techniques with respect to their area of application: Kinetic modeling provides a detailed, dynamic and quantitative model of cellular reaction networks. constraint-based modeling is applicable to large metabolic networks and Boolean modeling is applicable to large signal transducing networks. This tutorial paper addresses researchers with an engineering background that start to make themselves familiar with modeling in systems biology.

关键词： Boolean algebra Boolean modeling biochemical reaction network biological research biological system biology bioprocess engineering cellular reaction network constraint-based modeling kinetic modeling mathematical analysis mathematical modeling medicine metabolic network model analysis signal transducing network systems biology

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modeling the contribution of allosteric regulation for flux control in the central carbon metabolism of E. coli

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2015年第0期3卷 154-154页

作者： Machado, Daniel Herrgard, Markus J. Rocha, Isabel Univ Minho Ctr Biol Engn P-4710057 Braga Portugal Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil Horsholm Denmark

modeling cellular metabolism is fundamental for many biotechnological applications, including drug discovery and rational cell factory design. Central carbon metabolism (CCM) is particularly important as it provides the energy and precursors for other biological processes. However, the complex regulation of CCM pathways has still not been fully unraveled and recent studies have shown that CCM is mostly regulated at post-transcriptional levels. In order to better understand the role of allosteric regulation in controlling the metabolic phenotype, we expand the reconstruction of CCM in Escherichia coli with allosteric interactions obtained from relevant databases. This model is used to integrate multi-omics datasets and analyze the coordinated changes in enzyme, metabolite, and flux levels between multiple experimental conditions. We observe cases where allosteric interactions have a major contribution to the metabolic flux changes. Inspired by these results, we develop a constraint-based method (arFBA) for simulation of metabolic flux distributions that accounts for allosteric interactions. This method can be used for systematic prediction of potential allosteric regulation under the given experimental conditions based on experimental data. We show that arFBA allows predicting coordinated flux changes that would not be predicted without considering allosteric regulation. The results reveal the importance of key regulatory metabolites, such as fructose-1,6-bisphosphate, in controlling the metabolic flux. Accounting for allosteric interactions in metabolic reconstructions reveals a hidden topology in metabolic networks, improving our understanding of cellular metabolism and fostering the development of novel simulation methods that account for this type of regulation.

关键词： metabolism systems biology constraint-based modeling allosteric regulation Escherichia coli

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Analysis of genetic variation and potential applications in genome-scale metabolic modeling

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2015年第Feb期3卷 13-13页

作者： Cardoso, Joao G. R. Andersen, Mikael Rordam Herrgard, Markus J. Sonnenschein, Nikolaus Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil Kogle Alle 6 DK-2970 Horsholm Denmark Tech Univ Denmark Dept Syst Biol Lyngby Denmark

Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.

关键词： genetic variation SNP next-generation sequencing constraint-based modeling metabolic engineering adaptive laboratory evolution metabolism high-throughput analysis

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Double and multiple knockout simulations for genome-scale metabolic network reconstructions

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ALGORITHMS FOR MOLECULAR BIOLOGY 2015年第1期10卷 1-1页

作者： Goldstein, Yaron A. B. Bockmayr, Alexander Free Univ Berlin FB Math & Informat D-14195 Berlin Germany

Background: constraint-based modeling of genome-scale metabolic network reconstructions has become a widely used approach in computational biology. Flux coupling analysis is a constraint-based method that analyses the impact of single reaction knockouts on other reactions in the network. Results: We present an extension of flux coupling analysis for double and multiple gene or reaction knockouts, and develop corresponding algorithms for an in silico simulation. To evaluate our method, we perform a full single and double knockout analysis on a selection of genome-scale metabolic network reconstructions and compare the results. Software: A prototype implementation of double knockout simulation is available at http://***/L4FC.

关键词： constraint-based modeling Metabolic network Flux coupling analysis Reaction knockout Gene knockout

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An algorithm for the reduction of genome-scale metabolic network models to meaningful core models

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BMC SYSTEMS BIOLOGY 2015年第1期9卷 1-12页

作者： Erdrich, Philipp Steuer, Ralf Klamt, Steffen Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany Humboldt Univ Inst Theoret Biol D-10115 Berlin Germany

Background: constraint-based analysis of genome-scale metabolic models has become a key methodology to gain insights into functions, capabilities, and properties of cellular metabolism. Since their inception, the size and complexity of genome-scale metabolic reconstructions has significantly increased, with a concomitant increase in computational effort required for their analysis. Many stoichiometric methods cannot be applied to large networks comprising several thousand reactions. Furthermore, basic principles of an organism's metabolism can sometimes be easier studied in smaller models focusing on central metabolism. Therefore, an automated and unbiased reduction procedure delivering meaningful core networks from well-curated genome-scale reconstructions is highly desirable. Results: Here we present NetworkReducer, a new algorithm for an automated reduction of metabolic reconstructions to obtain smaller models capturing the central metabolism or other metabolic modules of interest. The algorithm takes as input a network model and a list of protected elements and functions (phenotypes) and applies a pruning step followed by an optional compression step. Network pruning removes elements of the network that are dispensable for the protected functions and delivers a subnetwork of the full system. Loss-free network compression further reduces the network size but not the complexity (dimension) of the solution space. As a proof of concept, we applied NetworkReducer to the iAF1260 genome-scale model of Escherichia coli (2384 reactions, 1669 internal metabolites) to obtain a reduced model that (i) allows the same maximal growth rates under aerobic and anaerobic conditions as in the full model, and (ii) preserves a protected set of reactions representing the central carbon metabolism. The reduced representation comprises 85 metabolites and 105 reactions which we compare to a manually derived E. coli core model. As one particular strength of our approach, NetworkReducer de

关键词： constraint-based modeling Model reduction Stoichiometric models Escherichia coli

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Metabolite Profiling and Integrative modeling Reveal Metabolic constraints for Carbon Partitioning under Nitrogen Starvation in the Green Algae Haematococcus pluvialis

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JOURNAL OF BIOLOGICAL CHEMISTRY 2014年第44期289卷 30387-30403页

作者： Recht, Lee Toepfer, Nadine Batushansky, Albert Sikron, Noga Gibon, Yves Fait, Aaron Nikoloski, Zoran Boussiba, Sammy Zarka, Aliza Ben Gurion Univ Negev Jacob Blaustein Inst Desert Res French Associates Inst Agr & Biotechnol IL-84990 Sede Boqer Israel Max Planck Inst Mol Plant Physiol Syst Biol & Math Modeling Grp D-14476 Potsdam Germany Univ Bordeaux INRA Bordeaux Metab Facil Bordeaux UMR Fruit Biol & Pathol 1332 F-33140 Villenave Dornon France

The green alga Hematococcus pluvialis accumulates large amounts of the antioxidant astaxanthin under inductive stress conditions, such as nitrogen starvation. The response to nitrogen starvation and high light leads to the accumulation of carbohydrates and fatty acids as well as increased activity of the tricarboxylic acid cycle. Although the behavior of individual pathways has been well investigated, little is known about the systemic effects of the stress response mechanism. Here we present time-resolved metabolite, enzyme activity, and physiological data that capture the metabolic response of H. pluvialis under nitrogen starvation and high light. The data were integrated into a putative genome-scale model of the green alga to in silico test hypotheses of underlying carbon partitioning. The model-based hypothesis testing reinforces the involvement of starch degradation to support fatty acid synthesis in the later stages of the stress response. In addition, our findings support a possible mechanism for the involvement of the increased activity of the tricarboxylic acid cycle in carbon repartitioning. Finally, the in vitro experiments and the in silico modeling presented here emphasize the predictive power of large scale integrative approaches to pinpoint metabolic adjustment to changing environments.

关键词： Algae Carbohydrate Metabolism Computational Biology Fatty Acid Metabolism Systems Biology Haematococcus pluvialis constraint-based modeling Data Integration Metabolomics Nitrogen Starvation

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