Bio-Systems Engineering

The course consists of theory, assignments and computer laboratory work to practice and simulate bio-systems and artificial intelligence. Introduction to modeling and dynamical systems. Modeling Michaelis-Menten and monod kinetics, microbial growth and fermentation processes, Phosphorylation processes, Hill dynamic, gene regulatory networks (GRN), mass action low, Quasi steady-state, modeling of cellular signal transduction, Dynamic modeling of Synthetic biology, genetic circuits, motifs such as toggle switch and oscillations in bacteria, feedback and feed-forward motifs. The second part of the course: Modeling dynamic equations in real-time, parameter estimation theory and sensitivity analysis. Artificial intelligence in practice: Hybrid and grey models, the integration of neural network (machine learning) models to adapt the bio-models to real-life experiments, data learning. We will also learn about bio-systems and dynamics: Stability, Bi-stability, limit cycles and oscillations in cellular biology.

At the end of the course the student will be able to develop mechanistic first principle models of bio-systems and bioprocesses, both in molecular (genetic) level, cellular and organism level. They will also be able to apply machine learning and neural network models to update the dynamic models. The students will understand the effect positive and negative feedback in biology and genetic circuits. They will learn the basic principle of engineering cybenetics and control theory. They will also understand and will be able to describe dynamical properties such as genetic switches and oscillations. They will learn to use models to develop and control industrial bioprocesses.

Lectures, assignments and laboratory work. The assignments will be both in the computer and theoretical. The course can be taught in English if needed (for international students). The course consists of much programming in Matlab and python and offers entrance to the programming world.