Summary
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).
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/841281 |
| Start date: | 06-01-2020 |
| End date: | 05-01-2023 |
| Total budget - Public funding: | 253 052,16 Euro - 253 052,00 Euro |
Cordis data
Original description
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).Status
TERMINATEDCall topic
MSCA-IF-2018Update Date
28-04-2024
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