Prof. Dr. Martin J. Lercher

Research focus

We are interested in the molecular organization and evolution of cellular systems, in particular metabolism. Our major aim is to understand design principles that arise from the optimization of complex systems through natural selection. In our work on plants, we use multi-scale models that combine a coarse-grained anatomical description with detailed molecular models of photosynthesis, describing the flow of mass and energy through the plant based exclusively on physical and chemical laws.

Most important publications

  1. Kroll A, Rousset Y, Hu XP, Liebrand NA, Lercher MJ (2023) Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning. Nat Commun 14(1):4139. doi: 10.1038/s41467-023-39840-4.
  2. Kroll A, Ranjan S, Engqvist MKM, Lercher MJ (2023) A general model to predict small molecule substrates of enzymes based on machine and deep learning. Nat Commun 14(1):2787. doi: 10.1038/s41467-023-38347-2.
  3. Kroll A, Engqvist MKM, Heckmann D, Lercher MJ (2021) Deep learning allows genome-scale prediction of Michaelis constants from structural features. PLoS Biol 19(10):e3001402. doi: 10.1371/journal.pbio.3001402.
  4. Dourado H, Mori M, Hwa T, Lercher MJ (2021) On the optimality of the enzyme-substrate relationship in bacteria. PLoS Biol 19(10):e3001416. doi: 10.1371/journal.pbio.3001416.
  5. Hu XP, Dourado H, Schubert P, Lercher MJ (2020) The protein translation machinery is expressed for maximal efficiency in Escherichia coli. Nat Commun 11(1):5260. doi: 10.1038/s41467-020-18948-x.
  6. Dourado H, Lercher MJ (2020) An analytical theory of balanced cellular growth. Nat Commun 11(1):1226. doi: 10.1038/s41467-020-14751-w.
  7. Pang TY, Lercher MJ (2019) Each of 3,323 metabolic innovations in the evolution of E. coli arose through the horizontal transfer of a single DNA segment. Proc Natl Acad Sci U S A 116(1):187-192. doi: 10.1073/pnas.1718997115.
  8. Chen WH, Lu G, Bork P, Hu S, Lercher MJ (2016) Energy efficiency trade-offs drive nucleotide usage in transcribed regions. Nat Commun 7:11334. doi: 10.1038/ncomms11334.
  9. Heckmann D, Schulze S, Denton A, Gowik U, Westhoff P, Weber AP, Lercher MJ (2013) Predicting C4 photosynthesis evolution: modular, individually adaptive steps on a Mount Fuji fitness landscape. Cell 153(7):1579-1588. doi: 10.1016/j.cell.2013.04.058.
  10. Pal C, Papp B, Lercher MJ (2005) Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nat Genet 37(12):1372-1375. doi: 10.1038/ng1686.