ABSTRACT
INTRODUCTION The science of biology has drastically changed over the last few decades in the sense that it is becoming an increasingly quantitative and progressively less descriptive science. In fact, the appearance of new instrumental techniques, such as HPLC, MS, capillary electrophoresis, DNA microarrays (Buchholz, Takors, and Wandrey 2001; Buchholz et al. 2002; Schaefer et al. 1999) and others, makes it possible to detect biologically significant compounds in small quantities with a high degree of accuracy. Assays based on these techniques have enabled the measurement of the level of expression of various genes and the concentration of a range of proteins and intermediates in intracellular metabolic and signalling pathways. In consequence, the number of publications devoted to quantitative characterization of intracellular processes has increased significantly. This avalanche-like increase in biological information has posed a new problem for biologists: how to analyze and to interpret the ever growing body of experimental data. Indeed, if the biological system under study consists of tens or hundreds of components and its behaviour is characterised by a set of hundreds or thousands of experimentally measured dependencies, then a mathematical model is needed for study and analysis of such a system. Like any other idealization,
the mathematical model of any biological system is a formalised representation of our knowledge about the components of this system and the laws governing their functions and interactions. In this chapter, we describe an approach to construction of mathematical models, their study and application for reconstructing the dynamic and regulatory behaviour of biochemical systems using multilevel experimental data.
