How microbial model systems help decipher cancer evolution Wie mikrobielle Modellsysteme helfen, Tumorevolution zu entschlüsseln

Eiche M, Kayser J (2022)

Publication Type: Journal article, Review article

Publication year: 2022

Journal

BioSpektrum Spektrum Akademischer Verlag GmbH

Book Volume: 28

Pages Range: 250-252

Journal Issue: 3

DOI: 10.1007/s12268-022-1745-2

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 biofilms 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.

Involved external institutions

Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light DE Germany (DE)

How to cite

APA:

Eiche, M., & Kayser, J. (2022). How microbial model systems help decipher cancer evolution Wie mikrobielle Modellsysteme helfen, Tumorevolution zu entschlüsseln. BioSpektrum, 28(3), 250-252. https://doi.org/10.1007/s12268-022-1745-2

MLA:

Eiche, Maximilian, and Jona Kayser. "How microbial model systems help decipher cancer evolution Wie mikrobielle Modellsysteme helfen, Tumorevolution zu entschlüsseln." BioSpektrum 28.3 (2022): 250-252.

BibTeX: Download