Investigating the Effective Range of Agents by Using Integrative Modelling

Andreas Freier, Ralf Hofestadt, and ThoralfToepel Introduction

The analysis of pharmaceutical mechanisms, one of the tasks of pharmacology, is based on knowledge about physiological and biophysical processes in the cell. However, the understanding of qualitative statements requires the study of complex cellular networks. In this context, metabolic pathways are chains of enzymatically [1] and genetically [2] controlled biochemical reactions that consume nutrients, release energy, and synthesize cellular substances (amino acids, lipids, etc.). The energy is used by several endergonic processes, e.g., biosynthesis, cell division, endocytosis and exocytosis, production of ion gradients, and cellular movements. Interference with these regulatory systems often causes metabolic diseases.

Drugs are substances that influence regulatory systems. The World Health Organization (WHO) defines a drug as "any substance or product that is used or intended to be used to modify or explore physiological systems or pathological states for the benefit of the recipient." This implies that drugs positively affect cellular systems, for example, by promoting inhibited reactions or activating alternative pathways. Of course, any drug can also have negative effects, for example, if it is overdosed. Here, the toxicology of the substance has to be analysed [3].

The interactions of drugs and receptor molecules are often studied. Receptors mainly have one binding site, where only one specific substance or kind of substance can bind. Once the substance has been bound to the receptor, the conformation of the receptor changes, resulting in transfer of information. Various classes and subclasses of receptors exist, e. g., ligand-controlled ion channels, receptors coupled with guanyl nucleotide-binding proteins (G proteins), receptors showing the activity of the enzyme Tyrosine kinase, and receptors regulating DNA transcription.

The mechanism of receptor protein interaction with G proteins is, briefly, as follows : a transmembrane receptor protein made up of several peptide chains arranged as a cylinder has a binding site in the centre of the cylinder. G proteins, which are responsible for signal transduction, are located close to the receptor on the inner site of the membrane. The binding of a ligand to the receptor results in activation of a


membrane intracellular extracellular membrane receptor \J7

G-protein intracellular receptor \J7


Fig. 11.1 Domain-specific notation of a receptor with interacting G-protein.

subunit of the G protein, which transmits a signal to affected proteins nearby. The strength of the signal depends on the duration of ligand binding and the number of activated G proteins. After certain time, the system returns into its initial state. Figure 11.1 shows the mechanism of a receptor using a domain specific notation.

Biophysical processes can often, but not always, be classified by describing patterns of processes, which may involve computational modelling of biochemical networks. Classic methods for investigating biological networks use mathematical models [4]. Experimental approches combined with modelling start at the biological problem and create a hypothesis about the mechanism and the structure of the studied system. Based upon the hypothesis the experimental design as well as a mathematical model will be developed in parallel. Validating the observations gained by experiments against the computational prediction will lead to a modification of the experimental design, the model, or the hypothesis (Figure 11.2) [5]. Finally, the optimized model will give a dynamic representation of the biological system.

Classes or patterns of molecular objects and processes can often be identified in molecular databases. The use of different databases while doing computational modelling leads to the problem of dynamically integrating the data into mathematical models. This chapter presents an approach combining mathematical modelling with the integration of molecular databases. We introduce novel methods of deriving mathematical models from data, in which data structures of molecular objects are directly combined with mathematical equations. The application of these models and methods will facilitate or enable the integrative and large scale analysis of, for example, the action of drugs.

Fig. 11.2 Process of modelling biological systems [4].
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