TRÊS develops and analyzes mathematical models with the aim of obtaining novel insights in various biological disciplines, most notably evolutionary biology, community ecology, systems biology and the behavioural sciences. The focus is on mechanistic models of intermediate complexity. Such models are mathematically tractable, thereby providing deeper understanding at a fundamental level. At the same time, these models are framed in terms of measurable quantities, thus allowing the derivation of quantitative, testable predictions. In close cooperation with colleagues from various biological disciplines, the group strives for testing the predictions both in the lab and in the field.
Focal research lines:
Variation and diversity are key concepts in the biological sciences, but theory is often still focused on averages. In contrast, much of our work centres on the causes and consequences of variation. We are mainly interested in patterned variation that is shaped by directional processes like competition or selection. We investigate the emergence and dynamics of such 'adaptive variation' at all levels of biological organisation: developmental variation, physiological variation, behavioural variation (animal personalities), life-history variation, variation of social structure, spatial pattern formation (mussle beds, vegetation), emergence of new species, competitive exclusion and coexistence, global biodiversity patterns.
The history of life on earth may be viewed as a sequence of major transitions in which lower-level entities evolve to cooperate with each other and form ever higher levels of biological organisation. Genes cooperate to form genomes and cells, cells cooperate to form multicellular individuals, individual organisms cooperate to form social communities and species cooperate in mutualistic interactions. Yet the stability of higher-level cooperative units is constantly threatened by potential conflict between lower-level entities over their share in the genetic contribution to future generations. We study how this balance between conflict and cooperation shapes individual development, the social structure of populations and the dynamics of mutualistic interactions.
Although biologists know the complete molecular blueprint of an increasing number of organisms, the connection between the evolution of molecular mechanisms and phenotypic adaptation remains obscure. As a result, it is difficult to understand how complex molecular interaction networks function, how they have been shaped by selection, and how their architecture influences the outcome of evolution. In order to resolve these questions, we develop models and perform microbial evolution experiments that merge ideas from evolutionary theory with detailed knowledge of molecular mechanisms. Breaking with the tradition of using simple descriptive, black-box models of the genotype-phenotype map to study evolution, we rely on systems biology models to accurately reflect the processes of development. This novel approach allows us to decipher the evolutionary principles underlying molecular organization and to create insights in the adaptation and diversification of complex traits.
A major challenge in ecology is the need for a better theoretical framework for understanding how species assemblages (ecological communities) arise, why some are species-rich and others species-poor, and why some species are present or dominant whereas others are not. Current community assembly theory is largely based on static models. However, ecological dynamics (e.g. ecological drift, competition, immigration), or evolutionary dynamics (e.g. genetic drift, natural selection, speciation) generate continual changes in the constituents of communities and the sources from which they are assembled. The dynamical models that do exist do not take the community perspective or do not readily allow inferences from data. Moreover, there is often a mismatch between models and data. We attempt to solve these problems simultaneously by developing a fully stochastic, dynamical and data-friendly theory of community assembly, and testing and informing this theory with model-oriented experiments and field studies of both macro-organisms and micro-organisms.
The study of self-organisation investigates how complexity at a higher level emerges from the interactions among simple units at a lower level. We are interested in all aspects of self-organisation in social systems. In our models we try to produce complex phenomena by self-organisation as a side-effect of interactions of individuals with their environment. At present our focus is on predatory attacks of schools of fish and flocks of birds and on social systems of birds and primates.
|Last modified:||09 September 2018 2.39 p.m.|