Wie mikrobielle Modellsysteme helfen, Tumorevolution zu entschlüsseln

Eiche, Maximilian; Kayser, Jona

doi:10.1007/s12268-022-1745-2

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Wie mikrobielle Modellsysteme helfen, Tumorevolution zu entschlüsseln

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Kayser, Jona
Kayser Research Group, Guck Division, Max-Planck-Zentrum für Physik und Medizin, Max Planck Institute for the Science of Light, Max Planck Society;
Kayser Research Group, Guck Division, Max Planck Institute for the Science of Light, Max Planck Society;

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Citation

Eiche, M., & Kayser, J. (2022). Wie mikrobielle Modellsysteme helfen, Tumorevolution zu entschlüsseln. Biospektrum, 28(3), 250-252. doi:10.1007/s12268-022-1745-2.

Cite as: https://hdl.handle.net/21.11116/0000-000F-2E8C-1

Abstract

While cellular evolution is one of the most fundamental concepts of life, its consequences are among the most pressing issues of modern health care, including cancer and the emergence of therapy resistance. We currently still lack the ability to accurately predict evolutionary trajectories, especially in spatially dense, pathogenic cellular populations such as microbial biofi lms or solid tumors. Here, we discuss the conceptual framework of evolution in dense populations and the potential of tailored microbial model systems to systematically study the underlying mechanisms.