Assessment of the Metabolic Capabilities of Haemophilus influenzae Rd through a Genome-scale Pathway Analysis

Schilling, Christophe H.; Palsson, Bernhard Ø.

doi:10.1006/jtbi.2000.1088

Cited by 201 publications

(132 citation statements)

References 34 publications

Supporting

Mentioning

129

Contrasting

Unclassified

Order By: Relevance

“…Metabolic networks are a case in point. Here, many individual mutations that eliminate enzyme-coding genes affect cell growth very little [84][85][86][87] . Nonetheless, pairs of mutations often have strong effects, indicating epistasis.…”

Section: Pervasive Epistasismentioning

confidence: 99%

Neutralism and selectionism: a network-based reconciliation

2008

View full text Add to dashboard Cite

Neutralism and selectionism: a network-based reconciliation . Neutralism and selectionism: a network-based reconciliation. Nature Reviews. Genetics, 9(12):965-974. Postprint available at: http://www.zora.uzh.ch Posted at the Zurich Open Repository and Archive, University of Zurich. http://www.zora.uzh.ch Originally published at: Nature Reviews. Genetics 2008, 9(12):965-974. Neutralism and selectionism: a network-based reconciliation Abstract Neutralism and selectionism are extremes of an explanatory spectrum for understanding patterns of molecular evolution and the emergence of evolutionary innovation. Although recent genome-scale data from protein-coding genes argue against neutralism, molecular engineering and protein evolution data argue that neutral mutations and mutational robustness are important for evolutionary innovation. Here I propose a reconciliation in which neutral mutations prepare the ground for later evolutionary adaptation. Key to this perspective is an explicit understanding of molecular phenotypes that has only become accessible in recent years. Summary: Neutralism and selectionism are extremes of an explanatory spectrum for patterns of molecular evolution. They also have broad implications for our understanding of evolutionary innovation. Neutralism maintains that most mutations with appreciable frequency in a population convey no benefit to their carrier. Selectionism maintains that a large fraction of such mutations do provide a benefit. While recent genome-scale data from protein-coding genes argue against neutralism, molecular engineering and protein evolution data argue that neutral mutations and mutational robustness are important for evolutionary innovation. In the reconciliation proposed here, neutral mutations prepare the ground for later evolutionary adaptation. Key to this perspective is an explicit understanding of molecular phenotypes that has only become accessible in recent years. 1 Neutralism and selectionism: a network-based reconciliation 2 Neutralism and SelectionismThe tension between neutralism and selectionism is at least as old as the field of molecular evolution 1 . Most of the historical neutralism-selectionism debate centered on explanations for genetic variation in populations. In this context, neutralists and selectionists agreed that deleterious mutations occur frequently in evolving molecules, but they profoundly disagreed on the relative importance of effectively neutral and beneficial mutations. To neutralism, beneficial mutations are rare. In the words of Motoo Kimura, one of neutralism's principal proponents "…random fixation of selectively neutral or slightly deleterious mutations occur far more frequently in evolution than positive Darwinian selection of definitely advantageous mutants" 2 . In contrast, according to selectionism, beneficial mutations are abundant. In consequence, most mutations that go to fixation in a population would be beneficial, or at least linked to abundantly occurring beneficial mutations. Selectionists such as Ernst Mayr dismis...

show abstract

Section: Pervasive Epistasismentioning

confidence: 99%

Neutralism and selectionism: a network-based reconciliation

2008

View full text Add to dashboard Cite

show abstract

“…To predict the metabolic fluxes, metabolic network analysis was used (Schilling and Palsson, 2000;Price et al, 2004;Klamt et al, 2007). This method facilitates a first approximation of fluxes in a metabolic network on the basis of reaction stoichiometry.…”

Section: Metabolic Flux Analysismentioning

confidence: 99%

CARMEN - Comparative Analysis and in silico Reconstruction of organism-specific MEtabolic Networks

Schneider¹,

Vorhölter²,

Trost³

et al. 2010

Genet. Mol. Res.

View full text Add to dashboard Cite

ABSTRACT. New sequencing technologies provide ultra-fast access to novel microbial genome data. For their interpretation, an efficient bioinformatics pipeline that facilitates in silico reconstruction of metabolic networks is highly desirable. The software tool CARMEN performs in silico reconstruction of metabolic networks to interpret genome data in a functional context. CARMEN supports the visualization of automatically derived metabolic networks based on pathway information from the KEGG database or from user-defined SBML templates; this software also enables comparative genomics. The reconstructed networks are stored in standardized SBML format. We demonstrated the functionality of CARMEN with a major application 1661 ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 9 (1): 1660-1672 (2010) In silico metabolic network reconstruction example focusing on the reconstruction of glycolysis and related metabolic reactions of Xanthomonas campestris pv. campestris B100. The curation of such pathways facilitates enhanced visualization of experimental results, simulations and comparative genomics. A second application of this software was performed on a set of corynebacteria to compare and to visualize their carbohydrate metabolism. In conclusion, using CARMEN, we developed highly automated data analysis software that rapidly converts sequence data into new knowledge, replacing the time-consuming manual reconstruction of metabolic networks. This tool is particularly useful for obtaining an overview of newly sequenced genomes and their metabolic blueprints and for comparative genome analysis. The generated pathways provide automated access to modeling and simulation tools that are compliant with the SBML standard. A user-friendly web interface of CARMEN is available at http://carmen.cebitec.uni-bielefeld.de.

show abstract

“…It is a common practice for biologists to discuss metabolism in terms of distinct biochemical pathways, such as sugar metabolism, lipid metabolism, and amino acid metabolism. Schilling and Palsson proposed to subdivide the network into relatively isolated clusters of reactions, according to intuitive biological criteria [31] . On the other hand, considering that the topology of network is a reflection of its function and the metabolic network has been revealed to be organized in a hierarchically modular manner, more algorithms purely based on the network topology have been proposed.…”

Section: Decomposition Of Metabolic Networkmentioning

confidence: 99%

Complex networks theory for analyzing metabolic networks

Zhao

Luo

et al. 2006

CHINESE SCI BULL

View full text Add to dashboard Cite

One of the main tasks of post-genomic informatics is to systematically investigate all molecules and their interactions within a living cell so as to understand how these molecules and the interactions between them relate to the function of the organism, while networks are appropriate abstract description of all kinds of interactions. In the past few years, great achievement has been made in developing theory of complex networks for revealing the organizing principles that govern the formation and evolution of various complex biological, technological and social networks. This paper reviews the accomplishments in constructing genome-based metabolic networks and describes how the theory of complex networks is applied to analyze metabolic networks. Keywords: bioinformatics, systems biology, metabolic network, network topology, network decomposition, network robustness.The completion of the Human Genome Project started the post-genomic era. The hot topic of biological research is now shifting from the study of single genes or proteins to whole genome analyses. All kinds of "omics" technologies such as genomics, transcriptomics, proteomics and metabonomics gradually bring molecular biology into the era of system biology, and bioinformatics into that of post-genome informatics. Since genes and proteins tend to function through interacting in networks, the interaction networks must be analyzed for the purpose of studying biological functions. It is very important to analyze the biological functions in terms of the network of interacting molecules and genes, such as genetic regulatory network, signal tranduction network, protein interaction network, and metabolic network, with the aim of understanding how a biological system is organized from its individual building blocks. It represents an integration of biological knowledge from genomic information towards the understanding of the basic principles of life for biomedical applications [1] . Metabolic process is essential for the maintenance of life. In metabolism some materials are broken down to yield energy for vital processes while other substances, necessary for life, are synthesized. The metabolic network, a characteristic complex network including all metabolites and enzyme catalyzed reactions occurring within a living cell, as well as the interactions between the reactants and enzymes, is an abstract representation of cellular metabolism. The analysis of metabolic networks can help to understand and utilize cellular metabolic process in order to * Corresponding authors:.

show abstract

Assessment of the Metabolic Capabilities of Haemophilus influenzae Rd through a Genome-scale Pathway Analysis

Cited by 201 publications

References 34 publications

Neutralism and selectionism: a network-based reconciliation

Neutralism and selectionism: a network-based reconciliation

CARMEN - Comparative Analysis and in silico Reconstruction of organism-specific MEtabolic Networks

Complex networks theory for analyzing metabolic networks

Contact Info

Product

Resources

About