Dynamic Modelling, Analysis, and Control Methodologies in Biological and Biomedical Applications(DYNAMO-bio)
Coordinated project supported by grants PID2023-146275NB-C21 and PID2023-146275NB-C22 funded by MICIU/AEI/10.13039/501100011033 and ERDF/EU.
2024 - 2027. Endowment: 212.250 EUR
Project summary
Mathematical modelling is a powerful tool for understanding and manipulating complex biological systems. Dynamic models are crucial in systems biology and biomedical engineering because they enable the quantitative analysis and prediction of behaviours and interactions over time. They facilitate a deeper understanding of disease progression, drug effects, and the response of biological systems to various stimuli, leading to more effective treatments and interventions. These models have several advantages over purely data-driven and statistical models, including better predictive power, deeper understanding, greater interpretability, and less data dependency.
DYNAMObio aims to create new dynamic modelling, analysis, optimisation and control techniques tailored for biomedical engineering and systems biology, integrating approaches from systems and control engineering, applied mathematics, and computer science. In regard to dynamic model building and analysis, DYNAMO-bio will develop methods for discovering mechanistic equations from data, analysing identifiability and observability, estimating parameters, and quantifying uncertainty. An important aspect of the project is to expand the use of some of these techniques from models described by ordinary differential equations to more complex systems, including spatiotemporal dynamics and stochastic processes. In regard to control and optimization, DYNAMO-bio will develop techniques for nonlinear controllability and accessibility analysis, dynamic optimisation, observer design, and closed-loop control including impulsive (reset) techniques. These advancements will be implemented in software tools, made freely available to the scientific community.
The aforementioned techniques will be motivated by, and applied to, a number of applications in systems biology and biomedical engineering. This interdisciplinary project will combine expertise from these fields, relying on collaboration between two research subprojects (at UVIGO and MBG-CSIC) to address real-world health and biotechnology challenges:
Subproject 1, Modelling and controlling biomedical systems in ordinary differential equations and beyond, will be led by UVIGO. The research team includes four doctors with control engineering backgrounds, with extensive experience in nonlinear control theory and its application to biological and biomedical problems. They will be joined by one applied mathematician and a biologist specialized in immunology. They will collaborate with four external researchers: control engineering professors Antonis Papachristodoulou and Manolis Chatzis, and mathematics professors Ruth Baker and Werner Seiler. The first three are from the University of Oxford, and the fourth from Universität Kassel.
Subproject 2, Data-driven mechanistic modelling, uncertainty quantification and optimisation in systems biology, will be led by MBG-CSIC. This research group brings in a long and renowned experience in biological model-building (reverse engineering, network inference, nonlinear system identification) and optimization (including global and multi-criteria dynamic optimization) in the areas of systems and synthetic biology. In this project, we will reinforce this expertise with the cooperation of four external prestigious researchers: Neda Bagheri (UW Seattle), Julio Saez-Rodriguez (Heidelberg University), Sebastian Sager (OVGU Magdeburg), and Hidde de Jong (INRIA).