实验基础

In order to ensure their function(s) in the cell, proteins are organized in machineries, underlaid by a complex network of interactions. Identifying protein interactions is thus crucial to our understanding of cell functioning. Technical advances in molecular biology and genomic tec ...

The BioCyc database collection at BioCyc.org integrates genome and cellular network information for more than 1,100 organisms. This method chapter describes Web-based tools for browsing metabolic and regulatory networks within BioCyc. These tools allow visualization of comp ...

RegulonDB contains the largest and currently best-known data set on transcriptional regulation in a single free-living organism, that of Escherichia coli K-12 (Gama-Castro et al. Nucleic Acids Res 36:D120–D124, 2008). This organized knowledge has been the gold standard for the impleme ...

This introductory review synthesizes the contents of the volume Bacterial Molecular Networks of the series Methods in Molecular Biology. This volume gathers 9 reviews and 16 method chapters describing computational protocols for the analysis of metabolic pathways, protein inte ...

Interactions between macromolecules are deciphered to gain information about biological processes and protein function. This information is hidden in large interaction networks, yet very complicated to dissect. In this context, the PRODISTIN Web site is dedicated to the cluster ...

In this chapter, we present and interpret some operations on biological networks that can easily performed with NeAT, a set of Web tools aimed at studying biological networks (or graphs) and classifications. These approaches are of particular interest for biologists and scientists who ne ...

R is a powerful language and widely used software tool for the analysis and visualization of data. Its core capabilities can be extended through many different add-on packages. Among the many packages are some which offer a broad range of facilities for analyzing statistical properties of gra ...

The detection and analysis of structural invariants in cellular reaction networks is of central importance to achieve a more comprehensive understanding of metabolism. In this work, we review different kinds of structural invariants in reaction networks and their Petri net-based r ...

Using the example of phosphate regulation in enteric bacteria, we demonstrate the particular suitability of stochastic Petri nets to model biochemical phenomena and their simulative exploration by various features of the software tool Snoopy.

Genetic Network Analyzer (GNA) is a tool for the qualitative modeling and simulation of gene regulatory networks, based on so-called piecewise-linear differential equation models. We describe the use of this tool in the context of the modeling of bacterial regulatory networks, notably t ...

Mutualistic microbial symbioses are one of the key innovations in the evolution of biological diversity, enabling the expansion of species’ niches and the production of sophisticated structures such as the eukaryotic cell. For some of the best-studied cases, we are beginning to have netw ...

Bacterial virulence is a multifactorial process. In this chapter, we review some known mechanisms used by bacteria to trigger their production of virulence factors. We develop the idea that although the onset of virulence shows up an abrupt transition, the modelling of this dynamics can be cla ...

This chapter describes how to use Smoldyn, which is a computer program for modeling cellular systems with spatial and stochastic detail. Smoldyn represents each molecule of interest as an individual point-like particle. These simulated molecules diffuse, interact with surfaces (e. ...

Building a dynamic model of a complete biological cell is one of the great challenges of the 21st century. While this objective could appear unrealistic until recently, considerable improvements in high-throughput data collection techniques, computational performance, data in ...

Genetic regulatory circuits are often regarded as precise machines that accurately determine the level of expression of each protein. Most experimental technologies used to measure gene expression levels are incapable of testing and challenging this notion, as they often measure l ...

This chapter presents a discussion of metabolic modeling from graph theory and logical modeling perspectives. These perspectives are closely related and focus on the coarse structure of metabolism, rather than the finer details of system behavior. The models have been used as backgrou ...

Systematic analysis of Saccharomyces cerevisiae metabolic functions and pathways has been the subject of extensive studies and established in many aspects. With the reconstruction of the yeast genome-scale metabolic (GSM) network and in silico simulation of the GSM model, the nature ...

Metabolomics involves the investigation of the intracellular (endometabolome) and extracellular (exometabolome) pools of metabolites in biological systems. Methods to sample the exometabolome and to quench metabolism and extract intracellular metabolites for the mo ...

Phenotypic variations of an organism may arise from alterations of cellular networks, ranging from the complete loss of a gene product to the specific perturbation of a single molecular interaction. In interactome networks that are modeled as nodes (macromolecules) connected by edges ...

Early achievements in proteomics were qualitative, typified by the identification of very small quantities of proteins. However, as the subject has developed, there has been a pressure to develop approaches to define the amounts of each protein – whether in a relative or an absolute sense. A fur ...