The Laboratory of Computational Systems Biotechnology group (LCSB) works at the interface of biology, process systems engineering, and reaction engineering. We are developing expertise in the mechanistic modelling of chemical and biophysical cellular processes and in the application of systems engineering methods for the analysis of these models. Our objectives are to develop approaches that will provide guidance in the study of problems in basic and applied biology and medicine, and they will accelerate discovery and development in pharmaceutical and industrial biotechnology.
Problems in basic biology, medical research, and bioengineering involve complex systems with multiple, interacting components. Successful approaches will require approaches that account for this complexity and provide innovative solutions.
In our work we integrate biological information about complex biological systems from different levels, and we perform model-based analysis of this information to advance basic research and guide the efforts for accelerating research and development in biotechnology.
We build expertise in integrating information from biochemistry, biophysics, and molecular biology, and we translate conceptual models into mechanistic mathematical models. We then develop and implement systems engineering and computational methods for the analysis of these mathematical models. The results of these analyses are translated into knowledge that drives progress and innovation.
Projects and Services
Discovery of Novel Biotransformations
Living organisms utilize enzyme-catalyzed reactions to synthesize a large array of complex molecules. Enzyme catalyzed processes are characterized by mild conditions, fast reaction rates, highly stereospecific interactions, and minimal toxic byproduct formation. The ability of harness this potential is important for the biosynthesis of industrial chemicals or novel pharmaceuticals. However, biochemical routes for the synthesis of these compounds is unknown and it remains to be discovered in nature or to be design through protein and metabolic engineering. We have been developing methods that can be used in the design of synthetic biochemical pathways and guide the mining of natural organisms. Our methods have been applied in many problems in academic and industrial research and development.
Modeling and Analysis of Large Biopolymerization Networks
Transcription and translation are two central cellular processes and are very important to understand for biochemical engineering and medical applications. The large number of components involved in these processes and the sequence of elementary steps that comprise these processes introduce a high degree of complexity. Moreover, if we consider these processes in the context of integrated cellular function, their complexity further increases as a large number of components, such as genes and mRNAs, simultaneously compete for the catalytic components such as RNA polymerases and ribosomes, respectively. We are developing genome wide deterministic models for the translation and transcription machinery. The study of these models will help us understand the key parameters that affect the regulation and the dynamic properties of these networks.
Metabolic and cellular engineering under Uncertainty
Metabolic engineering is the purposeful manipulation of cellular and metabolic processes. Successful application of metabolic engineering in biotechnological applications and medical research requires the identification of the most promising targets to modify in order to achieve a desired performance, e.g. production of pharmaceuticals and industrial chemicals, and the identification of drug targets. Advancements of recombinant DNA technology and analytical biochemistry techniques (Omics technologies) provide useful information for metabolic engineering. However, this data is subject to variation among individual organism and precise measurement is experimentally daunting and unrealistic. We are developing systems engineering methods that can use this wealth of information and account for the uncertainty. These methods allow us to design metabolic and cellular engineering strategies and quantify the probability of success of alternative strategies based on uncertainty quantification and analysis.
Websites for Further Information
Laboratory of Computational Systems Biotechnology group (LCSB): http://lcsb.epfl.ch