Groupe d’études et de recherche en analyse des décisions

Opportunities for Systems and Control Engineering in Biology: Using Mathematical Models for Better Decision Making in Life Sciences

Mathieu Cloutier

Systems Biology (SB) appeared about a decade ago as a new paradigm in biological sciences. A central concept in SB is the emergence of complex, dynamic biological behavior from simple feedback interactions. Even though the idea of biological systems theory can be traced back to the origins of cybernetics in the 1950s, its application remained marginal because of limited experimental and computational tools. These limitations were lifted in the 1990s and 2000s with the advent of high throughput experimental techniques and increases in computing power. As a result, we are now in a situation where it is possible to measure most of the cells’ components (genes, proteins, metabolites) with increasing precision and time resolution. Moreover, it is also possible to build detailed simulations of biological systems on a standard desktop computer.

In that context, it is thus desirable to have engineers and mathematicians contributing to the biological and life sciences. In this talk, I will present a few research challenges in biology where the use of engineering tools was critical in providing original solutions. I will emphasize two major themes, with mathematical modelling underlying both: 1-Understanding complex behavior in biological pathways with links to control theory; 2-Development of tools for better decision-making when dealing with complex biological systems.

For the first theme, I will highlight methods to rationalize complex, emergent properties in biological systems such as homeostasis, oscillations and robust adaptation. Interestingly, these properties can all be explained within the framework of control theory and this links to cellular decision-making in the sense that cells are continuously adapting their behavior based on external inputs and internal cues. I will show specific examples using Ordinary Differential Equations (ODE) models of energy metabolism and cell signaling pathways.

From this better understanding of 'internal' decision making in biological systems, we can move on to the second theme of the talk: how to use ODE models to optimize our management of complex biological systems? In this part, I will provide specific examples in biotechnology and medicine where the SB approach allowed producing critical predictions and testable research hypotheses. More specifically, the case of Parkinson’s disease will be briefly presented through the prism of systems theory and I will show how this different perspective can significantly improve our understanding of this complex and dynamic disease for which no unique cause is known and no satisfactory treatment exists.