Control Theory and Systems BiologyOpen OpportunitiesMathematical modeling has increasingly become a pivotal tool in understanding the complex biomolecular mechanisms that underpin systems biology, as well as in the innovation of novel designs in synthetic biology. Unlike traditional disciplines in science and engineering, the field of biology presents formidable challenges that significantly complicate these modeling efforts. These challenges include but are not limited to, the inherent nonlinear nature of biological systems, their high dimensionality, the scarcity of available data, and the stochastic behaviors they may exhibit. This Master's Thesis is dedicated to the development of an advanced deep learning methodology tailored for the construction of reliable biological models. These models will be designed to accurately represent a wide spectrum of biological phenomena, ranging from dose-response relationships (steady-state input/output data) to dynamic system behaviors. The successful development of such a tool is expected to have a profound impact on multiple domains, including systems and synthetic biology, metabolic engineering, and biotechnology, among others. - Modeling and Simulation, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Optimisation, Systems Biology and Networks, Systems Theory and Control
- ETH Zurich (ETHZ), Master Thesis
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