Steffen Klamt Discusses Signal Transduction Networks
Fast Moving Front Commentary, July 2010
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Article: A methodology for the structural and functional analysis of signaling and regulatory networks
Authors: Klamt, S;Saez-Rodriguez,
J;Lindquist, JA;Simeoni, L;Gilles, ED |
Steffen Klamt talks with ScienceWatch.com and answers a few questions about this month's Fast Moving Fronts paper in the field of Computer Science.
Why do you think your paper is highly
cited?
The integrated computational and experimental analysis of signal transduction networks is one important area of research in Systems Biology. In our paper we described a new methodology for mathematical modeling of large cellular signaling networks where detailed mechanistic modeling becomes impractical.
Due to the rapidly increasing size of reconstructed signaling networks, there is a demand for modeling approaches that can cope with the typically qualitative knowledge base.
Several groups have now started to use our modeling formalism which is facilitated by our software suite CellNetAnalyzer. Moreover, it has served as a basis for methodological extensions.
Does it describe a new discovery, methodology, or
synthesis of knowledge?
This paper presents a new methodology for qualitative modeling of cellular signaling networks. We proposed a formalism that captures the structure of signaling networks from qualitative knowledge. We then introduce algorithms and methods operating on this model representation which are useful to predict the qualitative response to stimulations and perturbations or to compute intervention strategies that induce a desired behavior.
"I think modern biology needs the help of mathematical tools. I’m interested in the development of methods and algorithms for the reconstruction, analysis and targeted intervention of biological networks..."
Would you summarize the significance of your paper
in layman’s terms?
Although the number of discovered proteins and protein interactions involved in cellular signal transduction increases steadily, mechanistic and kinetic details of signaling processes often remain unknown, and available experimental data are in many cases only semi-quantitative. It is therefore difficult to construct predictive quantitative models of these molecular networks.
We introduced new theoretical methods that allow a mathematical analysis of large signaling networks without quantitative knowledge. Using this modeling approach, signaling events are described by logical formula similar as in electronic circuits, e.g., "protein X can be activated if protein A is absent and if protein B is in an activated form."
Based on this representation we developed several new algorithms that give qualitative and testable predictions on the behavior of the network when stimulated or perturbed. The toolbox also includes algorithms for computing intervention strategies useful, for instance, for designing therapeutic strategies. We demonstrated the applicability of the approaches by a network model for T-cell activation.
How did you become involved in this research, and
how would you describe the particular challenges, setbacks, and
successes that you've encountered along the way?
From the very beginning of my scientific work I have been involved in Systems Biology. It is a great experience to be involved in this dynamically growing research field and developing theoretical techniques for the analysis of biological phenomena is a fascinating challenge.
It is quite normal that it takes time until a new methodology is accepted and used by other groups. Regarding our modeling approach, we had to demonstrate that less (details) can sometimes be more if we want to understand basic functional properties of large-scale biological networks—and I think several case studies have now brought this proof-of-principle.
Where do you see your research leading in the
future?
I think modern biology needs the help of mathematical tools. I’m interested in the development of methods and algorithms for the reconstruction, analysis, and targeted intervention of biological networks (including not only signaling but also metabolic and gene regulatory networks). I deem it necessary that these methods must be able to cope with biological uncertainty while at the same time delivering meaningful predictions.
Interdisciplinary collaborations with biologists are an essential part of
my research, providing (i) necessary feedback from biological reality and
(ii) great motivation when seeing that theory works in
practice.
Dr.-Ing. Steffen Klamt
Max Planck Institute for Dynamics of Complex Technical
Systems
Magdeburg, Germany
KEYWORDS: SIGNALING AND REGULATORY NETWORKS, METHODOLOGY, STRUCTURE, FUNCTION, ANALYSIS, BIOCHEMICAL REACTION NETWORKS, T-CELL ACTIVATION, MINIMAL CUT SETS, METABOLIC NETWORKS, LOGICAL ANALYSIS, ADAPTER PROTEINS, ELEMENTARY MODES, GENE EXPRESSION, LIPID RAFTS, FRAMEWORK.