In Silico Models of Coupled Biological Systems

Supporting personalized healthcare or the development of tailor-made biomedical products with computational models requires holistic yet individualized models. They must be holistic to accommodate the multiple interacting phenomena that characterize biological systems. Human variability requires that models have to be also individualized. This does become feasible only by integrating system-specific data. Our overarching goal is to develop detailed in silico models of complex biological systems that couple different scales and heterogeneous data. We concentrate our research on the neuromuscular system and on proliferative and degenerative diseases. Our focus will be both on some of the most pressing and largely unresolved research questions within these fields and on setting up a dedicated experimental platform.

RQ 1 System models: How can we link multiple, currently separated organ, tissue, or (sub-) cellular models on different length and time scales to more realistic system models?

RQ 2 Knowledge-based and data-driven coupling: How can we describe the emerging dynamical system behavior of cell populations by exploiting large amounts of single cell data and highly detailed biophysical models?

RQ 3 Individualization: How can we exploit data on the distribution of microcomponents to replace generic phenomenological descriptions with individualized models of living matter?

RQ 4 Data and model standards: How can we establish metadata descriptions for coupling different biological models and for sharing system models and their data with peers?

RQ 5 Resource limited simulations: How can we simulate, design, and control system models with limited computational resources?