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
Baert, J. M., Janssen, C. R., Sabbe, K. & De Laender, F, Per capita interactions and stress tolerance drive stress-induced changes in biodiversity effects on ecosystem functions, Nature Communications 7, 12486 (2016).
De Laender, F., Rohr, J., Ashauer, R., Baird, D., Berger, U., Eisenhauer, N., Grimm, V., Hommen, U., Maltby, L., Melián, C. J., Pomati, F., Roessink, I., Radchuk, V. & Van den Brink, P. J, Re-introducing environmental change drivers in biodiversity-ecosystem functioning research, Trends in Ecology and Evolution, in press (2016).
C.J. Melián, V. Krivan, F. Altermatt, P. Starý, L. Pellissier and F. De Laender, Dispersal Dynamics in Food Webs, The American Naturalist 185(2):157-68 (2015)
Asllani, D. Busiello, T. Carletti, D. Fanelli and G. Planchon, Turing patterns in multiplex networks, PRE 90 (4) (2014)
Villani, A. Filisetti, A. Graudenzi, C. Damiani, T. Carletti and R. Serra, Growth and division in a dynamic protocell model, Life 4 (4), 837-864 (2014)