Project Heads
Jochen Blath (until 03/22), Maite Wilke Berenguer
Project Members
Tobias Paul (HU)
Project Duration
01.04.2021 − 31.03.2024
Located at
HU Berlin
Dormancy is an ubiquitous trait in microbial communities. It describes the ability of an organism to switch into a metabolically inactive and protected state, for example in response to environmental stress. Dormancy has important implications for the evolutionary, ecological and pathogenic character of microbial systems. This project derives and analyses new stochastic individual based models for the dynamics of dormancy-exhibiting biological systems.
We pursue two larger goals: On one hand, we aim to understand the influence of dormany on patterns of genetic diversity through the tools of population genetics, deriving as scaling limits from individual based models dual pairs of processes of diffusions and coalescents that incorporate feaures such as varying population size.
On the other hand we study invasion, fixation and coexistence regimes withing the framework of adaptive dynamics, again, based on individual based models and their many particle limits.
Results
In our article [6] we investigate an individual-based model with rare mutations. There, we found numerous effects of introducing a competition-induced dormancy mechanism into a preexisting model. For example, dormancy may favour the emergence of evolutionary branching, it may increase (or decrease) the speed of adaptation, dormancy increases the width of niches occupied by subspecies and dormancy may enable alternative mutational pathways in the trait space.
In the preprint [7] we considered a general individual-based model with power law mutations for which we derived the canonical equation of adaptive dynamics. This equation was previously only proven for rare mutations. The canonical equation in this setting is only piecewise continuously differentiable and has a slower speed of adaptation through the trait space.
The preprint [8] is concerned with an adaptive dynamics model for the impact of cellular dormancy on the outcome of different treatment strategies under variable measures of treatment success. We consider the strategy to wake-up dormant cancer cells, to retain dormant cancer cells in their dormant state and to directly kill dormant cells. We measure success by considering the total number of cancer cells at the end of the treatment period and by conisdering the area under the curve which corresponds (up to a factor of mutation rates) to the number of resistance mutations that we may observe over the course of treatment.
Current work in progress
Current working points are
External Website
Related Publications
Related Pictures
A simulation of our model presented in [6] featuring evolutionary branching. The image shows the population size of given traits over time where the size is indicated by colour.
Simulation of the model in [6] exhibiting a “tunneling” effect as mechanism for branching
An example path of the canonical equation of adaptive dynamics with power law mutation rates.
The decay of the number of singletons (species with exactly one representative in the sample) in the SAD as the dormancy rate forwards in time increases.