We develop and apply methods from Bayesian inference and information theory to construct mechanistic models for biological systems.
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We are applying mathematical models to understand how cells make decisions. Ultimately we want to control these decisions for biotechnological and therapeutic purposes.
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We are interested in combining approaches from text-mining, bioinformatics, comparative genomics, statistical inference, machine learning, and mathematical modelling in order to arrive at models for whole cells.
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The processes inside cells are controlled by dynamical systems that can be — with some caveats — described by molecular interaction networks.
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Synthetic and engineering biology seek to generate synthetic biological solutions for applications in biotechnology, modern agriculture, biofuel production, bioremediation, and medical applications, such as tissue engineering and antimicrobial therapies.
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