Project: Modelling therapeutic interventions in mitochondrial bioenergetics
DESCRIPTION
NWO and ZonMW have announced that they will fund with €13 M three Dutch Centres for Systems Biology,
including the Centre for Systems Biology and Bioenergetics (CSBB) at the RUNMC.
The CSBB will receive € 4.5 M to study mitochondrial energy metabolism.
In the Centre for Systems Biology and Bioenergetics more than thirty-five research groups of the UMC St Radboud
and the Radboud University will work together to develop an innovative computer model that can accurately predict energy metabolism.
The diverse knowledge and know-how of physicists, chemists, computer scientists, physiologists, biologists and medical practitioners,
will be combined in a unique integrative systems biology approach to develop a computer model that will allow realistic calculations,
analysis and predictions of energy metabolism, as well as the effects of medicine and nutraceuticals upon these metabolic pathways.
The CSBB has received € 4.5 M million for the initial development of in silico models that will integrate the available knowledge about
the energy metabolism of the human muscle. These models will be used to study hereditary muscle diseases, defects in energy homeostasis
as well as the effects of medicine and nutrition interventions on energy processes. CSBB researchers will focus on understanding a vital
complex in mitochondrion functionality, Complex I, involved in many rare metabolic diseases where patients would benefit from more predictable
therapy outcomes.
This project proposal, “Modelling therapeutic interventions in mitochondrial bioenergetics” involves the integration of nine CSBB Principal
Investigator (PI) groups, each having strong backgrounds in muscle bioenergetics and (bio)informatics.
The machine learning group (Elena Marchiori) participates to this project (one PhD position).
The PhD student will work in close collaboration with the comparative genomics group at the NCMLS (Martijn Huynen and Richard Notebaart)
to develop novel multi-objective optimization methods for deriving a muscle-specific model for mitochondria. The main goal is to integrate experimental
data on gene expression, proteins, and metabolite concentrations with ad-hoc multiple objectives of mitochondria for constraining the model.