M4 Life and Sustainability
The improvement of experimental methods, generally driven by technological development, has led to significant changes in the experimental sciences. Biology, as an experimental science, has become much more quantitative and dependent on mathematical and statistical models. The emergence of new laboratory technologies poses many challenging problems in mathematics. Indeed, this high-performance data in biology have been, is, and will be an important drive for new statistical, computational, and mathematical research. Reciprocally, mathematical models play a key role in the prediction of pandemics and constitute a very valuable tool to guide the design of public health policies, which are used to mitigate the spread of diseases.
The objective of this research program will also include the development of optimization tools for everyday activities such as transportation of people, logistics, energy supply, food production, etc. with the aim of improving the efficiency of processes and leading to a more sustainable society.
These objectives will be worked on through the following PIs.
PI Biostatistics and Biomathematics: At the population scale, we encounter challenges in ecology or population dynamics, and even in epidemiology, where the use of models of systems of differential equations has a long tradition. At the organism level, mathematical and statistical contributions to the field of medicine include both deterministic and stochastic models. In addition, knowledge of genes and their sequences allows the design of experiments to simultaneously measure their expression under various laboratory conditions. From the data, it is possible to build interaction models of the millions of molecules that make up each cell.
PI Understanding the environment and climate change: Describing and modeling environmental and ecological processes, for short and long time scales, requires the use of complex mathematical and statistical models. Numerical simulation, deterministic and stochastic modeling, and data analysis can be designed to forecast and locate extreme event risks. Risk forecasting and localization can be combined with resource management strategies in environmental emergencies with significant ecological and social impacts.
PI Towards sustainability: The exploitation of natural resources has led certain species to critical situations. The mathematical modeling of biological communities and their dynamics, both statistical and deterministic, will provide a deeper vision and knowledge about these areas, allowing the design of adequate preservation policies. On the other hand, the estimates of limited reserves of fossil fuels and, to a greater extent, the deterioration of the planet due to the emissions derived from their use, make the development of renewable energy extraction technology (solar, wind, maritime...) a priority. In this sense, mathematical modeling, numerical simulation, and optimization techniques play an important role in the development of prototypes, as well as in the creation of devices that control and manage them.