SFB 1310:  Predictability in Evolution

• Subject Area: Biology ; Medicine
• Term: since 2018
• Website: Homepage
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 325931972

Evolutionary biology has traditionally been concerned with reconstructing past processes and ancestral relationships over long time scales. But can we predict pathways and outcomes of future evolutionary processes, at least over short periods? This is the central question of CRC 1310. We address this question in fast-evolving systems, including microbial populations in the laboratory, viruses and immune repertoires, and cancer cell populations. Predictive analysis in these systems includes the evolution of drug resistance and antigenicity, the evolution of antibodies in immune systems, and the evolution of cancer cells in their organismic environment.To predict evolution, we must link genetic, phenotypic, and environmental changes to causal and reproducible effects on organismic functions and fitness. To map such effects, we analyze massively parallel and time-resolved evolutionary processes in experiment and theory. Quantifying power and limitations of predictability sheds new light on long-standing questions of chance and necessity in evolution. At the same time, we ask a new question: can we harvest predictions for control of evolution? Answering this question is key to the bio-medical applications of our work. These include the design of antibiotics, of vaccines for influenza and SARS-CoV-2, and of therapies for cancer.Our research programme builds on high-throughput analysis of genomic sequences, molecular interactions, cell metabolism and growth. We are advancing these tools to a coherent technology for evolution. The CRC unites a strong and interdisciplinary spectrum of competence in molecular genetics, biophysics, medicine, and theoretical modelling. Together, we endeavor to increase the predictability of evolution.

• DFG Programme: Collaborative Research Centres
• International Connection: Netherlands

Current projects

• A01 - Cellular mechanisms of drug resistance evolution (Project Head Bollenbach, Tobias )
• A02 - Predicting evolutionary pathways to β-lactam resistance (Project Heads Krug, Joachim ;  de Visser, Arjan G.M. )
• A03 - Predicting evolutionary shifts of cell metabolism (Project Head Lässig, Michael )
• A04 - Metabolic fitness landscapes for evolutionary predictions (Project Heads Lercher, Martin ;  Pang, Tin Yau )
• A05 - Fitness effects of cross-species gene transfer in bacteria (Project Head Maier, Berenike )
• B01 - Predicting viral escape as a consequence of antibody-mediated selection pressure (Project Head Klein, Florian )
• B02 - Predicting viral-immune co-evolution (Project Head Lässig, Michael )
• B04 - Co-evolution of gut microbiota and immune cells during ageing in killifish (Project Heads Dönertas, Handan Melike ;  Valenzano, Ph.D., Dario Riccardo )
• C01 - How cell death shapes tumor evolution during carcinogenesis (Project Heads Berg, Johannes ;  von Karstedt, Silvia )
• C02 - The influence of stroma-metastasis interaction on tumor evolution (Project Heads Beyer, Andreas ;  Hillmer, Axel )
• C03 - Predicting therapy response and relapse of aggressive B cell lymphomas (Project Heads Bozek, Katarzyna ;  Büttner, Reinhard )
• C04 - Predicting molecular mechanisms of adaptation to radio-chemotherapy in cancer (Project Heads Büttner, Reinhard ;  Hillmer, Axel )
• Z01 - Central activities (Project Head Lässig, Michael )
• Z02 - Technology for massively parallel selection and evolution experiments (Project Heads Bollenbach, Tobias ;  Kreer, Christoph ;  Maier, Berenike )
• Z03 - Evolutionary bioinformatics (Project Heads Beyer, Andreas ;  Lässig, Michael )

Completed projects

• B03 - Adaptive immune control of evolving pathogens (Project Head Nour Mohammad, Armita )
• B06 - Evolutionary models for spatially structured tumors (Project Head Berg, Johannes )
• B07 - Predicting the constrained evolution of tumors (Project Head Beyer, Andreas )
• B08 - Predicting patterns of adaptation to radio-chemotherapy in cancer (Project Head Büttner, Reinhard )

• Applicant Institution: Universität zu Köln
• Participating University: Heinrich-Heine-Universität Düsseldorf; Wageningen University; École Normale Supérieure - PSL
• Participating Institution: Instituto Gulbenkian Ciência (IGC); Leibniz-Institut für Alternsforschung - Fritz-Lipmann-Institut e.V. (FLI); Max-Planck-Institut für Biologie des Alterns
• Spokesperson: Professor Dr. Michael Lässig