Copyright © 2003 Per Kraulis, Stockholm Bioinformatics Center, SBC
KTH Bioinformatics 2003, lecture 27 Feb 2003, Per Kraulis
Systems Biology can be defined as an approach to biology where organisms and biological processes should be analysed and described in terms of their components and their interactions in a framework of mathematical models.
In functional genomics one often uses statements such as 'gene or protein X performs function Y', for example 'the leptin protein regulates the amount of body fat'. But when one looks at this statement, it is clear that it is fundamentally misleading. In the given example, it is clear that the leptin protein is not a machine in itself that computes and performs the regulatory action. Rather, the leptin molecule is a component in a larger system, and it is that system that performs the regulatory function.Systems Biology begins in the insight that biological processes must be understood in terms of the components that participate in the processes, and that the complexity of biological systems make it difficult, if not impossible, to understand the workings of the system by simple qualitative arguments. Mathematically strict models must be formulated. This is required both in order to be able to capture the actual behaviour of the system with acceptable precision, but also to be able to analyse the fundamental behaviour of the system.
The mathematical models may be very simple (Boolean on/off), or very complex (including detailed descriptions of interactions at a molecular level). The important issue is that it should be possible to analyse the model, either by some mathematical approach, or to simulate it, in order to evaluate its correspondence with the observed facts.
Paradoxically, the complexity of biology is the basis for the development of Systems Biology, at the same time as it is the main reason why computational approaches to biological processes have not been particularly successful in the past. However, the appearance of bioinformatics and functional genomics, and their results (complete genomes, microarray expression analysis, etc) has had a great impact. It now appears possible to obtain data that can be used to build sensible models, and to test them. This is probably the main reason why Systems Biology has become so popular in the last few years.
So far, only very limited results have been obtained. There are only a few, well-studied systems on which any deep analysis has been done. However, there are already some insights that may prove to be generally true. For instance, it seems clear that robustness is a very important factor in biological systems. This is the property that allows a system to absorb fairly large perturbations, and still function reasonably well. The functionally important behaviour of a system has a certain degree of resilience to damage. Some studies have pointed to different ways in which evolution have favoured systems that are robust in different ways.
One important goal of Systems Biology is to understand life processes in sufficient detail to make predictions about their behaviour. If we want to make a particular system behave in a certain way, how should we change the system, or what type of perturbation should we apply? If we want to make a bacterium produce propanol instead of ethanol, then how should we change the metabolic network of the bacterium? Or, if we want to produce a pharmaceutical drug that can help with deficiencies of the insulin regulatory system that is the basis for diabetes type II (obesity-related diabetes), what components should we focus on? Which are the best drug targets?