NETCONTROLOGY · Controllability of biological networks

Controllability of biological networks

What is the problem/issue being addressed? A dynamic system is controllable if, given suitable inputs, it can be driven from any initial state to any desired final state in finite time. Computational approaches that can be used to characterize the dynamics of complex, biological systems are still lacking. This project aims at determining how biological networks can be controlled with focus on two cutting-edge case studies from medicine. Why is it important for society? Current disease definitions lack mechanistic understanding allowing only symptom-based chronic therapies, but rarely a cure. Network medicine defines diseases as dysregulated signaling networks allowing precise diagnosis and intervention. This has been succeeded in ischemic stroke, a subtype of hypertension and heart failure and is now expanded to cancer. These models are no tested in clinical trials and if successful this interdisciplinary approach will entirely change our approach to disease, how we define it, how we diagnose it and how we treat it – or even better – how we prevent it. What are the overall objectives? In this project, nonlinear, quantitative, and dynamic network models will be developed for biological networks with multiple regulatory mechanisms. In these networks, it is vital to identify the subset of key components and regulatory interactions whose perturbation leads to the desirable functional changes. However, it is typically neither feasible, nor necessary to control the whole network. Instead, for many practical applications, it would suffice to control a preselected subsystem of target nodes. Besides full controllability, target controllability of the networks will also be addressed. Different measures will be developed to compare networks based on their controllability. The established network control principles will be exploited to (a) reprogram cancer networks through their druggable vulnerabilities to improve anticancer therapeutics (case study 1), and (b) target the enzymatic sources of relevant oxidative stress to support neuroprotection in stroke (case study 2).

Data: CORDIS, © European Union