People Education Research Industrial Agenda  


Systems theory in systems biology

Bart De Moor

Come forth into the light of things

Let nature be your teacher


Mathematics on the one hand and biology on the other, are, at first blush, an unlikely pairing: Abstract, symbolic-numeric computation, versus `wet', evolving and living organisms. However, increasingly, we find that there is a great abundance of mathematical structure in biological objects, from fractals found in the branches of an oak tree to the symmetries of DNA's double helix. Throughout history, mathematicians have been fascinated by biology: The classic studies of inheritance by Gregor Mendel, were an exercise not in biology, but in statistical inference. Claude Shannon's PhD thesis described `An algebra for Theoretical Genetics'. Alan Turing studied the morphogenesis of embryos invoking reaction-diffusion equations. And Erwin Schrödinger in `What's life?' envisioned life as an aperiodic crystal. Likewise, engineers have been inspired by biology. Think of neural networks, genetic algorithms and DNA computing methodologies.

In recent years, biology and mathematics have undergone an increasingly intense interdisciplinary merger, leading to new scientific fields such as bioinformatics and systems biology. Several major breakthroughs in biology (e.g. the Human Genome Project) and technology (e.g. microarrays or DNA chips) have catalyzed this development. We will discuss datasets that are being generated by microarray technology, which makes it possible to measure in parallel the activity or expression of thousands of genes simultaneously. We discuss the basics of the technology, how to preprocess the data, and how classical and newly developed algorithms can be used to generate insight in the biological processes that have generated the data. Algorithms we discuss are Principal Component Analysis, clustering techniques such as hierarchical clustering and Adaptive Quality Based Clustering and statistical sampling methods, such as Monte Carlo Markov Chains and Gibbs sampling. We illustrate these algorithms with several real-life cases from diagnostics and class discovery in leukemia, functional genomics research on the mitotic cell cycle of yeast, and motif detection in Arabidopsis thaliana using DNA background models. To conclude we present some future perspectives on the development of bioinformatics, including some visionary discussions on technology, algorithms, systems biology and computational biomedicine.

Some introductory references

Junhyong Kim. Computers are from Mars, organisms are from Venus. Computer, IEEE, July 2002, pp.25-32.

De Moor B., Marchal K., Mathys J., Moreau Y., ``Bioinformatics : Organisms from Venus, Technology from Jupiter, Algorithms from Mars'', European Journal of Control, vol. 9, no. 2-3, 2003, pp. 237-278.

Moreau Y., De Smet F., Thijs G., Marchal K., De Moor B., ``Functional bioinformatics of microarray data : from expression to regulation'', Proceedings of the IEEE, vol. 90, no. 11, Nov. 2002, pp. 1722-1743.


Last modified: 9 June 2004, 14:50 UTC
Search   Site map