Year: 2011
Type: book-chapter
Abstract: Vital functions of living organisms, such as immune responses or the metabolism, are controlled by complex regulatory networks. These networks comprise, amongst others, regulatory genes called transcription factors and cascades of information-processing proteins such as enzymes. The ultimate goal of the increasingly popular systems biology approach is to set-up extensive computer models that closely reflect the real-life behavior of these biological networks (Kitano, 2002; Werner, 2007). With a reasonable in silico implementation at hand, novel predictions, e.g. about the effect of gene mutations, can be generated by the computer. The two basic modes of regulation we concentrate on here, are inhibition and activation between two factors. Figure 1A visualizes the relation between the concentrations of e.g. two transcription factors, which are linked by an activation (left-hand figure) or inhibition (right-hand figure). Figure 1B shows a network of interacting activations and inhibitions as it might be found in living cells. While a single regulatory interaction can easily be understood, the complex wiring of several interactions, even for a medium-scale model as depicted here, renders themanual investigation of the system’s dynamics unfeasible. For further information on the concepts of regulation, we refer biologically interested readers to Alon (2006). Classical computational modeling approaches attempt to describe biochemical reaction networks as systems of ordinary differential equations (ODEs) (Klipp et al., 2005; Tyson et al., 2002). This requires detailed knowledge about the molecular mechanisms in order to implement precise kinetic rate laws for each biochemical reaction. However, for many biological systems, and especially gene-regulatory networks, only qualitative information about interactions, like “A inhibits B“, is available. A well-established workaround for this lack of information is the application of discrete modeling approaches. In Boolean methodologywe abstract from actual molecule quantities and assign each player in the system the state on or off (e.g. active or inactive). Despite their simplicity, Boolean models have been shown to provide valuable information about the general dynamics and capabilities of the underlying system (Albert & Othmer, 2003; Faure et al., 2006; Samaga et al., 2009). To bridge the gap between discrete and fully quantitative models, we developed Odefy, a MATLABand Octave-compatible toolbox for the automated transformation of Boolean 2
Source: InTech eBooks
DOI: 10.5772/20519
Authors: Jan Krumsiek, Dominik M., Fabian J.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W1581536631