ABSTRACT
Fundamental breakthroughs in the field of biotechnology over the last decade, such as cDNA microarrays and oligonucleotide chips, [1, 2], have made highthroughput and quantitative experimental measurements of biological systems much easier and cheaper to make. The availability of such an overwhelming amount of data, however, poses a new challenge for modellers: how to reverse engineer biological systems at the molecular level using their measured responses to external perturbations (e.g. drugs, signalling molecules, pathogens) and changes in environmental conditions (e.g. change in the concentration of nutrients or in the temperature level). In this chapter, we provide an overview of some promising approaches, based on techniques from systems and control theory, for reverse engineering the topology of biomolecular interaction networks from this kind of experimental data. The approaches provide a useful complement to the many powerful statistical techniques for network inference that have appeared in the literature in recent years, [3].
