Course content

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.

Learning outcome

At the end of the course the student will be able to develop models of bio-systems, both in molecular (genetic) level and organism level. They will be able to apply machine learning and neural network models to update the dynamic models in real-time (learning models). The students will understand the effect genetic circuits, and design their own regulating gene circuits. 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 cellular and genetic processes.

Learning methods and activities

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.

Recommended previous knowledge

Biology and biochemistry. Some knowledge on molecular biology will be helpful (but not mandatory). Basic university level mathematic.

Required previous knowledge

The candidate is expected to have basic university mathematics knowledge of Calculus and Linear Algebra.

Knowledge in Microbiology / general biology is advantegous but not compulsory.

Course materials

1. Lecture notes.

2. Introduction to Systems biology (Uri Alon).

3. System Modeling in Cellular Biology- FROM CONCEPTS TO NUTS AND BOLTS