Exploring optimality principles for understanding cellular behaviors
Prof. Dr. Alexander Bockmayr
Optimality principles have become an important concept to understand cellular metabolism. The basic idea is to use mathematical optimization to predict cellular behaviors that are optimal in order to achieve certain biological objectives. A classical example is flux balance analysis (FBA) that applies linear optimization to predict steady-state flux distributions in genome-scale metabolic network reconstructions that maximize cellular growth. More recent extensions of FBA consider dynamics (dynamic FBA / dFBA) and cellular resource allocation (resource balance analysis / RBA, macromolecular expression / ME).
Over the last years, we have developed a dynamic optimization approach (dynamic enzyme-cost FBA / deFBA) to predict time-dependent enzyme profiles that are optimal for given biological objectives. Recently, this framework has been extended to include regulatory information (regulatory dynamic enzyme-cost FBA / r-deFBA).
In this context, there is a variety of possible PhD projects that can be adapted to the specific background and interests of the candidate:
- Building and solving r-deFBA models for different organisms.
- Developing more efficient r-deFBA solvers.
- Comparing cellular behaviors predicted by r-deFBA to experimental observations.
- Inferring regulatory networks for deFBA models
Liu L, Bockmayr A (2020), Regulatory dynamic enzyme-cost flux balance analysis: A unifying framework for constraint-based modeling. J Theor Biol. 501:110317
Thuillier K, Baroukh C, Bockmayr A, Cottret L, Paulevé L, Siegel A: Learning Boolean Controls in Regulated Metabolic Networks: A Case-Study. Computational Methods in Systems Biology, CMSB 2021, Bordeaux. Springer, LNCS 12881, 159-180, Sep 2021 (Preprint)
For more information visit the website of the Mathematics in Life Sciences group