Frank J. Bruggeman

Metabolic control analysis to identify optimal drug targets.

This chapter describes the basic principles of Metabolic Control Analysis (MCA) which is a quantitative methodology to evaluate the importance and relative contribution of individual metabolic steps in the overall functioning of a particular system. The control on the flux through a metabolic pathway or subsystem can be quantified by the control coefficients of the individual enzymes or components which reflects the extent to which the component is rate-limiting. The perturbation of an individual step is measured by its elasticity coefficient. The effect of perturbation of a single step on the entire pathway or subsystem is, in turn, measured by the response coefficient. Differential control analysis can be used to compare flux through a single metabolic pathway in a pathogen with the same pathway in its host to identify uniquely vulnerable steps with the greatest potential for specifically inhibiting flux through the pathogen metabolic pathway. The utility of this methodology is illustrated with the glycolysis in Trypanosomes and with oncogenic signaling.

BioComplexity: a pluralist research strategy is necessary for a mechanistic explanation of the "live" state.

The biological sciences study (bio)complex living systems. Research directed at the mechanistic explanation of the live state truly requires a pluralist research program, i.e. BioComplexity research. The program should apply multiple intra-level and inter-level theories and methodologies. We substantiate this thesis with analysis of BioComplexity: metabolic and modular control analysis of metabolic pathways, emergence of oscillations, and the analysis of the functioning of glycolysis.

Introduction to systems biology

The developments in the molecular biosciences have made possible a shift to combined molecular and system-level approaches to biological research under the name of Systems Biology. It integrates many types of molecular knowledge, which can best be achieved by the synergistic use of models and experimental data. Many different types of modeling approaches are useful depending on the amount and quality of the molecular data available and the purpose of the model. Analysis of such models and the structure of molecular networks have led to the discovery of principles of cell functioning overarching single species. Two main approaches of systems biology can be distinguished. Top-down systems biology is a method to characterize cells using system-wide data originating from the Omics in combination with modeling. Those models are often phenomenological but serve to discover new insights into the molecular network under study. Bottom-up systems biology does not start with data but with a detailed model of a molecular network on the basis of its molecular properties. In this approach, molecular networks can be quantitatively studied leading to predictive models that can be applied in drug design and optimization of product formation in bioengineering. In this chapter we introduce analysis of molecular network by use of models, the two approaches to systems biology, and we shall discuss a number of examples of recent successes in systems biology.

Approaches to Biosimulation of Cellular Processes

Modelling and simulation are at the heart of the rapidly developing field of systems biology. This paper reviews various types of models, simulation methods, and theoretical approaches that are presently being used in the quantitative description of cellular processes. We first describe how molecular interaction networks can be represented by means of stoichiometric, topological and kinetic models. We briefly discuss the formulation of kinetic models using mesoscopic (stochastic) or macroscopic (continuous) approaches, and we go on to describe how detailed models of molecular interaction networks (silicon cells) can be constructed on the basis of experimentally determined kinetic parameters for cellular processes. We show how theory can help in analyzing models by applying control analysis to a recently published silicon cell model. Finally, we review some of the theoretical approaches available to analyse kinetic models and experimental data, respectively.