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Introduction
One of the most exciting challenges in biology today is the task of deciphering how the genome controls the development of complex organisms. This endeavour is utilising the skills and techniques of a wide range of academic disciplines. Researchers in molecular biology have access to sophisticated experimental technologies capable of gathering large amounts of data on genetic processes. The quantity of information obtained is too vast to be manipulated and processed manually, leading to an increased usage of pattern detection, machine learning and data mining techniques from computer science. In addition, theory and formalisms from mathematics are being used to build models of systems. These models can help to clarify intuitions, manage data and assist in the development of a theoretical understanding of biological organisms.
For the past 50 years, the research program in molecular biology has been directed towards understanding biological systems at the level of their most fundamental components, such as genes, proteins and cells. In the last decade, the new field of systems biology has established a program aiming to reverse this reductionist trend. One of the primary aims of systems biology is to use a computational models to integrate diverse sources of experimental data back into a systems level description of biological organisms.
Another development of the last few decades is the field of complex systems, which is interested in the description and analysis of the systems consisting of large numbers of interacting parts. Such systems exist in many domains from ecology and biology to communication networks and engineering, leading to a strong emphasis on interdisciplinary studies.
Both complex biology and systems biology have much to gain from the other: systems biology can benefit from the tools and theoretical insights generated by studies of complex systems in other domains, while complex systems in turn has much to learn from the progress made in undestanding biological systems.
This document reviews some of the motivations for modelling biological systems and provides an overview of some of the the major formalisms that have been used to model genetic regulatory networks. In each section, sources for further reading are recommended, including pointers to further theoretical results and technical details, reviews of specific areas, as well as studies that are of particular historical interest.
Next: Biological background Up: Modelling Gene Regulatory Networks: Previous: Overview Nic Geard 2004-05-06