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Controllability of biological networks

Project description

New ways to control biological networks

Biological elements interact with others to form networks. The ability to control their dynamics is vital for scientific research, particularly for the pharmaceutical and medical industry. Despite advances in network science, the requisite computational approaches are still lacking. The NETCONTROLOGY project will explore how biological networks can be controlled with focus on two cutting-edge medical studies: to reprogram cancer networks (finding druggable vulnerabilities to improve anticancer therapeutics) and target the enzymatic sources of relevant oxidative stress to support neuroprotection in stroke. To do so, the project will develop nonlinear, quantitative and dynamic network models for biological networks with multiple regulatory mechanisms. It will also develop different measures to compare networks based on their controllability.

Objective

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. Despite the advances in network science, 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. Existing controllability approaches work essentially on graphs and do not consider other constraints typically arising in biological systems (e.g. steady-state). This strengthens the need for development of such methods. 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).

Coordinator

UNIVERSITEIT MAASTRICHT
Net EU contribution
€ 253 052,16
Address
MINDERBROEDERSBERG 4
6200 MD Maastricht
Netherlands

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Region
Zuid-Nederland Limburg (NL) Zuid-Limburg
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 253 052,16

Partners (1)