Abstract
Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.
Highlights
Most cellular processes involve many different molecules
Our main interest is in transcription regulation networks and we will refer to them as "gene networks", but many principles are valid for a wide range of networks
We proposed to categorize gene networks models in four classes according to increasing level of detail in the models [1]
Summary
Most cellular processes involve many different molecules. The metabolism of a cell consists of many interlinked reactions. Tein-protein interactions in yeast have been tested; most large-scale experiments show high noise levels; and whereas the genome sequence is independent of particular growth conditions and (sometimes) is even conserved in fossils, data like protein-protein interactions and transcription factor localisations are condition dependent In this context it is important to note that some experimental methods are performed under conditions considerably different from the natural conditions in the cell. Moter constructs Davidson and co-workers constructed a model expressed as an algorithm combining Boolean and linear functions This algorithm takes as an input the occupancy information from 12 binding sites and outputs a value, that 'can be thought of as the factor by which, at any point of time, the endogenous transcription activity (...) is multiplied as a result of the interactions mediated by the cis-regulatory control system' [44]. Time is continuous in FSLM and the state of the network determines directly the concentration change rates, while the state is in turn affected by the concentrations themselves
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