Mathematical biology


The main objective of this research pole is to mathematically describe complex biological mechanisms. These mechanisms may be located at any level of biological organization (from genes to ecosystems), and cover any temporal or spatial scale (from local short-term events to the global evolutionary history of life). Adopted techniques include mining of big data and machine-learning approaches, various types of modelling (from statistical and phenomenological to mechanistic dynamic modelling), and inference of biological mechanisms through model-data comparison.

Keywords: Theoretical biology, Ecology, Evolution, Data mining, Mathematical modelling, Inference

Contact: Timoteo Carletti, Frederik de Laender and Karine Van Doninck

Relevant references:

  • I. Adam, D. Fanelli, T. Carletti, & G. Innocenti, Reactive explorers to unravel network topology, may 2019, European Physical Journal B. 92, 99
  • Q. Zhao, P. J. Van den Brink, C. Carpentier, Y. X. G. Wang, P. Rodríguez-Sánchez, C. Xu, S. Vollbrecht, F. Gillissen, M. Vollebregt, S. Wang, & F. De Laender, Horizontal and vertical diversity jointly shape food web stability against small and large perturbations, jan. 2019, Ecology Letters.
  • E. Etoundi, J. Marescaux, M. Vastrade, N. Debortoli, S. M. Hedtke, L-M. Pigneur, J. Virgo, J-F. Flot, & K. V. Doninck, Distinct biogeographic origins of androgenetic Corbicula lineages followed by genetic captures, 2019, bioRxiv. 590836.
  • M. Lucas, D. Fanelli, T. Carletti, & J. Petit, Desynchronization induced by time-varying network, may 2018, Europhysics Letters 121, 5, p. 50008p1-p7 7 p., 50008.
  • J. Baert, N. Eisenhauer, C. JANSSEN, & F. De Laender, Biodiversity effects on ecosystem functioning respond unimodally to environmental stress, Aug 2018, Ecology Letters. 21, 8, p. 1191-1199 9 p.