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: bacterial populations including clinical pathogens, viruses and immune repertoires, and cancer cell populations. Predictive analysis in these systems includes the evolution of drug resistance, viral escape evolution, the evolution of antibodies in immune systems, and the evolution of cancer cells in their organismic environment. To predict evolution, we analyze massively parallel and time-resolved evolutionary processes in experiment and theory. We track genetic, phenotypic, and environmental changes to turn evolutionary history into computable forecasts. We use predictions to design interventions that curb the escape evolution of pathogens. Key biomedical applications include drug protocols for antibiotic treatments, vaccines for influenza and SARS-CoV-2, and targeted and immune therapies for cancer. Quantifying power and limitations of prediction and intervention sheds new light on long-standing questions of chance and necessity in evolution. Our research program builds on high-throughput analysis of genomic sequences, molecular interactions, cell metabolism, and growth. We are building a coherent technology to track and analyze fast evolution. The CRC unites an interdisciplinary spectrum of competence across molecular biology, biophysics, medicine, and theoretical modelling. Together, we endeavor to increase the predictability of evolution.

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

Current projects

• A01 - Cellular mechanisms of drug resistance evolution (Project Heads Bollenbach, Tobias ;  Macaluso, Francesca )
• A02 - Predicting evolutionary pathways to β-lactam resistance (Project Heads Ernst, Christoph ;  Krug, Joachim ;  de Visser, Arjan G.M. )
• A04 - Predicting metabolic evolution in the minimal cell (Project Heads Lercher, Martin ;  Pang, Tin Yau )
• A05 - Fitness effects of cross-species gene transfer in bacteria (Project Head Maier, Berenike )
• A07 - Predicting the evolution of multi-drug resistance in Mycobacterium tuberculosis (Project Head Rybniker, Ph.D., Jan )
• B01 - Leaving footprints: How immune imprinting shapes vaccine responses and viral evolution (Project Head Klein, Florian )
• B02 - Predicting viral-immune co-evolution (Project Head Lässig, Michael )
• B04 - Impact of aging on immune responses to pathogenic challenges (Project Heads Dönertas, Handan Melike ;  Valenzano, Ph.D., Dario Riccardo )
• C01 - Predicting and controlling resistance evolution against targeted/ferroptotic combination therapies (Project Heads Berg, Johannes ;  Brägelmann, Johannes ;  von Karstedt, Silvia )
• C02 - Mapping the fitness landscapes of metastasizing cancers (Project Heads Beyer, Andreas ;  Hillmer, Axel )
• C03 - Predicting therapy resistance in human cancer (Project Heads Bozek, Katarzyna ;  Büttner, Reinhard )
• C04 - The fitness landscape of BRCA2 loss and therapy resistance across cancers (Project Heads Büttner, Reinhard ;  Hillmer, Axel )
• C05 - Immuno-metabolic fitness models for cancer evolution (Project Heads Frezza, Christian ;  Luksza, Marta ;  Lässig, Michael )
• Z01 - Central activities (Project Head Lässig, Michael )
• Z02 - Technology for massively parallel laboratory evolution (Project Heads Bollenbach, Tobias ;  Kreer, Christoph ;  Maier, Berenike )
• Z03 - Evolutionary bioinformatics (Project Heads Beyer, Andreas ;  Kovacova, Viera ;  Lässig, Michael )

Completed projects

• A03 - Predicting evolutionary shifts of cell metabolism (Project Head Lässig, Michael )
• 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 Institution: Leibniz-Institut für Alternsforschung - Fritz-Lipmann-Institut e.V. (FLI); Memorial Sloan Kettering Cancer Center
• Participating University: Heinrich-Heine-Universität Düsseldorf; Rheinische Friedrich-Wilhelms-Universität Bonn; Wageningen University; École Normale Supérieure - PSL
• Spokesperson: Professor Dr. Michael Lässig