Synthetic and Systems Biology

The course addresses mathematical techniques to analyze the operating principles of cellular systems. It is about the understanding of the dynamic interactions between several components of a biological system (Systems Biology). This knowledge can be applied in Synthetic Biology with the aim to (re)design cells or parts thereof. The course also teaches how unknown parameters in a mathematical model can be determined, based on experimental data.

Date: Apr 6-Jun 19, 2015

Target audience: Bioinformaticians; Biochemists; Biologists; Computer scientists; Mathematicians; Medical doctors; Metabolic researchers; Molecular biologists; System biologists

Program: Learning objectives: To become acquainted with systems biology and synthetic biology. Modelling of cellular networks using reaction kinetics. Learn how to describe and analyse continuous systems in time and frequency domain. Understanding the dynamic behaviour of biological systems by analysing models. Understand how interaction between processes (e.g. feedback) influences system behaviour, and can result in biological phenomena such as homeostasis. Understand how this knowledge can be applied in the design of new biochemical circuits (synthetic biology). Determining unknown values of model parameters using experimental data obtained from the system. This is called system identification and provides a general approach to determine quantities (parameters) without direct measurement. Understand and describe how noise and experimental inaccuracies affect the result. Be able to quantify the confidence of the parameter estimates and design experiments to increase this confidence.
Introduction of the Laplace transform for modelling and analysis of dynamic systems (system analysis): transfer function, pole-zero analysis, state-space models and frequency response / Bode plots. Feedback systems (root-locus method). Maximum Likelihood Estimation, white noise, optimization algorithms, experimental design.

Keywords: dynamic system modeling; system analysis; feedback; parameter estimation; Maximum Likelihood Estimation; uncertainty analysis; hypothesis testing; experimental design

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