Abstract

A hallmark of systems biology is the interdisciplinary approach to the complexity of biological systems, in which mathematical modeling constitutes an important part. Here, we use the example of sugar metabolism in the simple bacterium Escherichia coli and its associated control to illustrate the process of model development. Even for this well-characterized biological system, a close interaction between experimentation and theoretical analysis revealed novel, unexpected features. Additionally, the example shows how concepts from engineering sciences can facilitate the formal investigation of biological networks. More generally, we argue that analogies between complex biological and technical systems such as modular structures and common design principles provide crystallization points for fruitful research in both domains. 1 Systems biology: an interdisciplinary approach For the past 30 years, it has been characteristic for biology to be qualitative and descriptive, directed towards the understanding of the molecular detail. However, for the understanding of complex system properties like optimal control, adaptation and memory, both the systems’ components and their interactions have to be considered. Primarily the new ’omic technologies now make the complete determination of biological systems a realistic goal (Selinger et al. 2003). As a result, biology moves from the focus on few components to the study of networks of molecular interactions that give rise to complex physiological functions (Alm and Arkin 2003). Systems biology adopts this holistic view on biological function. However, several characteristics distinguish it from, and extend bioinformatics approaches to network analysis. A hallmark of many cellular networks, such as the intricate networks in cellular regulation, is that they respond dynamically towards extraand intracellular conditions and signals. Only by means of a quantitative description of the systems’ constituents and interactions, the resulting behavior can be understood in terms of the quantitative dynamics. Furthermore, achieving this goal requires a theory-based approach to the complexity not understandable by intuition alone. Mathematical modeling of complex biological systems plays a central role in systems biology because it allows for a formalized treatment of biological networks in the computer, using tools from mathematics and systems sciences (Kitano 2002b; Tyson et al. 2001). Ideally, mathematical modeling requires and entails a precise representation of the knowledge on the Topics in Current Genetics, Vol. 13 L. Alberghina, H.V. Westerhoff (Eds.): Systems Biology DOI 10.1007/4735a88 / Published online: 6 July 2005 “ Springer-Verlag Berlin Heidelberg 2005 216 Metabolic networks: biology meets engineering sciences system, and of hypotheses for unknown mechanisms. It allows one to apply formal methods of analysis. Mainly these two characteristics are expected to lead to a deepened understanding of the biological systems under consideration (Endy and Brent 2001; Gilman and Arkin 2002). Consequently, the efforts directed towards a quantitative, system-level understanding in biology rely on an interdisciplinary approach combining concepts from biology, information sciences and systems engineering. A central objective of systems biology is finally to develop virtual representations of cells and organisms. These representations should allow for computer experiments similar to experiments with real biological systems. Thereby, the way for a predictive biology can be paved, which will enhance, for instance, the understanding and the treatment of human diseases (Kitano 2002a; Stelling et al. 2001). There are already some examples of systems biological approaches that successfully couple experimental and theoretical approaches. They cover a broad spectrum of organisms and systems (www.siliconcell.net). The analysis of bacterial chemotaxis can be regarded as a paradigm of such an approach. The extensive experimental and theoretical analysis has helped substantially in the understanding of the system (Barkai and Leibler 1997). Currently, however, the knowledge on virtually any biological system does not permit to detail a complete list of parts, interactions and mechanisms, on which ‘true’mathematical representations could be built. Instead, despite considerable progress in high-throughput experimentation, the resulting networks are still incomplete and bear inaccuracies (von Mering et al. 2002). Under these circumstance, an often encountered argument is that theoretical analysis should await an – in some sense – complete biological knowledge before becoming meaningful. We and others, however, argue that only an iterative cycle of experimentation and theory will be able to fulfill the promises of systems biology. Experiments generate data and hypotheses, subsequent mathematical modeling allows to assess the compatibility of both, and to derive novel or alternative explanations that can be evaluated in new experiments (Kitano 2002b; Stelling et al. 2001). ‘Traditional’ biology integrates new findings into cartoons of pathways or regulatory networks, or uses new knowledge to revise these representations. Similarly, mathematical models are ‘work in progress’ (Lee et al. 2003). In this process, however, unbiased predictions from formal representations can reveal unexpected properties of, or critical components in biological systems as in a recent experimental and theoretical analysis of the Wnt signaling pathway (Lee et al. 2003). In another case, mathematical modeling suggested a bistable trigger as a core element of cell cycle regulation a long time before an experimental confirmation of the mechanism was obtained (Novak and Tyson 1993; Pomerening et al. 2003; Sha et al. 2003). Here, we use the control of sugar uptake in the simple bacterium Escherichia coli to show that an iterative cycle of experimentation and model development can yield deeper insight into apparently well-understood systems. In particular, our background in engineering sciences provides concepts and methods for this study. We will focus the discussion of recent developments and future challenges in systems biology on potential (further) contributions that engineering could make to understand complex biological systems. A. Kremling, J. Stelling, K. Bettenbrock, S. Fischer, and E.D. Gilles 217

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call