ITN E-MUSE ESR11: Biology driven Complexity Reduction of Individual based Models

H2020-MSCA-ITN E-MUSE: Complex microbial Ecosystems MUltiScale modElling: mechanistic and data driven approaches integration

https://www.itn-emuse.com/

The E-MUSE training programme aims at developing young researchers’ skills at the interface between artificial intelligence and life sciences. The challenge is to acquire a shared language bridging life science questions and original modelling approaches. The research programme of the E-MUSE network is to develop innovative modelling methodologies to understand a complex microbial ecosystem and identify levers to control and/or predict its evolution. To deal with biological complexity, biologists, mathematicians, and computer scientists have to work together to develop innovative methodologies. An important complexity of this domain originates from scales and dynamics issues, ranging from local kinetics at the level of the cell to emerging macroscopic properties of the biological system. The development of high throughput techniques provides more and more large datasets, but knowledge is not easily inferred from this huge amount of data and multiscale dynamics are still incompletely characterised and predicted. E-MUSE’s transdisciplinary network gathers academic and industrial partners to equip (15) Early Stage Researchers (ESRs) with scientific, research and transferable skills to become leaders in academic research or industry. They will be at the cutting edge of the modelling methodologies that we apply to model structural and dynamic features of microbial communities, to identify key processes and biomarkers for specific applications.

ESR11 to be recruited by KU Leuven/BioTeC+ will focus on the power and limitations of two predictive microbial modelling paradigms: on the one hand Individual based Models (IbM) and on the other hand Partial Differential Equations (PDEs). In the context of multi-organism populations, Individual based Model (IbM) approaches can cope with the spatial distribution that will be useful to describe cell to cell interaction. A typical challenge is to provide a bridge between the (computationally very intensive) IbMs and population models which typically take the form of PDEs, and therefore to provide a feedback to genome scale modelling with the help of accurate spatial/temporal characterizations.

Expected results include:

• IbM model design considering complex mechanical and dynamical properties of cheese ecosystems and integrating metabolic network information at individual cell level;
• IbM model reduction. IbMs will be analysed and, applying the laws of large numbers, a tentative model reduction will be achieved in order to derive equivalent meso-scale PDE systems wh... For more information see https://www.kuleuven.be/personeel/jobsite/jobs/60065144