Arne Traulsen

January 12, 2022

For further information please refer to https://www.evolbio.mpg.de/16397/group_evolutionarytheory
and contact the co-supervisors
Philipp Altrock for project 1: altrock@evolbio.mpg.de
Michael Sieber for project 1 and project 2: sieber@evolbio.mpg.de

Project 1. Theoretical and Data-driven Modeling of Cancer Evolution under Immunotherapy

Supervisors: Philipp Altrock and Arne Traulsen

Introduction: Many cancer types initially respond well to therapy, but later relapse. Combination therapy has become standard in many settings. These therapies expose the cancer cell population and its environment within the body to multiple stresses simultaneously or in fast sequence. The precise action and efficacy of many such combinations depends on the amount of cellular proliferation, the immune-status, and other complex mechanisms of evasion such a growth signaling. Based on features of the tumor at diagnosis, the beginning of treatment, and at selected (clinically accessible) timepoints, we wish to quantitatively model the dynamics of response and evasion.

Rationale: It has become increasingly evident that tumor dynamics and cellular interactions resemble features of an evolving ecosystem. It is now possible to measure properties of this ecosystem, especially regarding tumor cell dynamics and interactions that relate to the cancer-immune axis and immunotherapy. In addition, minimal residual disease (MRD) estimates have allowed to quantify response to treatment. To integrate these technology-driven advances, novel mathematical, computational and statistical approaches are needed.

Approach: Nonlinear deterministic dynamics that involve immune and tumor cells can be used to estimate interaction networks, multiplicative effects on growth rates, and to rank parameter importance. However, important effects at very low tumor burden may necessitate the integration of stochastic approaches. Together, these nonlinear and stochastic systems approaches will be used to model cellular and drug kinetics. In this project, novel mathematical approaches will be developed and applied to clinical scenarios, with the goal to understand the effects of tumor growth and tumor-immune interactions on therapy outcomes.

Qualifications: We are looking for a candidate with a solid background in a quantitative field with a clear motivation to work on human biological questions, or a biologist who is sufficiently self-motivated to develop the necessary skillset in theoretical biology.

Literature

The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy

GJ Kimmel, FL Locke, PM Altrock

Proceedings of the Royal Society B: Biological Sciences 288 (1947), 20210229, 2021.

The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

M Raatz, S Shah, G Chitadze, M Brüggemann, A Traulsen

PLoS Computational Biology 17 (2), e1008702, 2021.

The mathematics of cancer: integrating quantitative models

PM Altrock, LL Liu LL, F Michor

Nature Reviews Cancer 15 (12), 730, 2015.

Project 2. Microbiomes as metacommunities: Feedbacks between host-associated and environmental microbial communities

Supervisors: Michael Sieber and Arne Traulsen

All animals and plants are inhabited by diverse communities of microbial organisms – the microbiome - which can have fundamental roles in host functioning. The host organism and its microbiome form an intertwined ecological system shaped by ecological interactions, both within a single host and between different hosts and the environment. To fully capture the features of this system of interconnected, biotic habitats (the hosts) and the microbes living in those habitats and the environment requires several extensions of metacommunity theory [1]. This project aims to develop such extensions of metacommunity models. One concrete addition is that the reproduction and dispersal of hosts along with their microbiomes have the potential to change environmental microbial communities. This will in turn affect which microbes other hosts can pick up from the environment, thereby creating a feedback between host-associated and environmental communities. This feedback has the potential to change the evolutionary fate of both the host and the microbes [2].

References

[1] Miller, E. T., Svanbäck, R., and Bohannan, B. J. (2018). Microbiomes as metacommunities: understanding host-associated microbes through metacommunity ecology. Trends in Ecology & Evolution, 33, 926-935.

[2] Sieber, M., Traulsen, A., Schulenburg, H., & Douglas, A. E. (2021). On the evolutionary origins of host–microbe associations. Proceedings of the National Academy of Sciences, 118:e2016487118.

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