Seminar in Biology for CSE

Abstract

The seminar explores computational and modeling techniques for biology and biomedicine. These include dynamic systems, control theory, machine learning, mixed effects models, and pharmacometrics. Each student conducts a literature study on a relevant topic chosen in agreement with the supervisor, critically appraises the literature/methods and presents findings in an oral presentation.

Objective

Students will learn to evaluate and present scientific literature and critically appraise proposed methods and models. They will learn how mathematical methods and concepts are applied in the context of biology and medicine, and why the translation from scientific/biological question to mathematical analysis if far from straightforward. Each year, the seminar will focus on a single application area, which allows comparison of methods from different computational fields and provides awareness of strengths, weaknesses, but also commonalities of different methodological approaches.

Content

Mathematical modeling plays a key role in understanding biological systems such as individual cells, cell populations, and human or animal physiology in healthy and disease states. From the vast array of mathematical models and techniques used in these areas, this seminar will focus on techniques surrounding dynamical systems using systems of coupled nonlinear ordinary differential equations. Dynamic models are used to understand biological systems, e.g., how cells react to environmental stress, or how drugs get distributed in the human body. They are also used to construct new cellular behaviour in synthetic biology, to optimize dosing regiments in drug development, and to enable feedback control in drug application such as insulin injections in diabetes. Unlike many physical and engineering systems, there are very few first principles in biology and medicine. This poses substantial challenges for constructing models that accurately reproduce the biological system's behaviour and allow additional insights. In addition, biological systems are often highly nonlinear and nonlinearities can play a crucial role for the behaviour of the system, such as allowing to reach different steady states during development. They can thus not be ignored. Biological systems are also very heterogenous such that individual cells of a cell population can show different responses to the same stimulus, and patients differ in their response to a drug treatment. Correctly accounting for this heterogeneity is often an important part of modeling. It requires combining dynamic systems theory with statistical approaches such as mixed effects models, adding a new layer of complexity. Each year, we will focus on a specific application area and investigate what type of models are used, how they are developed and used, how biology is translated into mathematics, and what mathematical challenges arise and what types of algorithms are used. Application areas include, e.g., dynamic system and control approaches for insulin treatment in diabetes, pharmacometric models for drug development, and approaches to study single-cell data of stress responses in unicellular organisms.

Resources