Stochastic Models for Studying the Role of Cellular Noise and Heterogeneity

Sayuri K. Hortsch; Andreas Kremling

doi:10.1016/B978-0-12-801238-3.11466-7

Stochastic Models for Studying the Role of Cellular Noise and Heterogeneity

Sayuri K. Hortsch, Andreas Kremling

Associate Professorship of Systems biotechnology

Technical University of Munich

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

When comparing the morphology and behavior of cells that are genetically nonidentical or subject to different environmental conditions, some phenotypic diversity can often be observed. However, even mammalian cells with the same functionality, isolated from the same tissue, or microbes in a clonal population may show a remarkable degree of cellular heterogeneity, which has long been underrated. Its true extent has only been revealed in the past decades through experimental advances in single-cell technologies like flow cytometry, microfluidics combined with time-lapse microscopy, and single-cell RNA sequencing.

Original language	English
Title of host publication	Systems Medicine
Subtitle of host publication	Integrative, Qualitative and Computational Approaches: Volume 1-3
Publisher	Elsevier
Pages	34-44
Number of pages	11
Volume	1-3
ISBN (Electronic)	9780128160770
ISBN (Print)	9780128160787
DOIs	https://doi.org/10.1016/B978-0-12-801238-3.11466-7
State	Published - 1 Jan 2020

Keywords

Cellular noise
Chemical master equation
Gene expression
Phenotypic heterogeneity
Single-cell models
Stochastic models
Virus infection

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10.1016/B978-0-12-801238-3.11466-7

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AB - When comparing the morphology and behavior of cells that are genetically nonidentical or subject to different environmental conditions, some phenotypic diversity can often be observed. However, even mammalian cells with the same functionality, isolated from the same tissue, or microbes in a clonal population may show a remarkable degree of cellular heterogeneity, which has long been underrated. Its true extent has only been revealed in the past decades through experimental advances in single-cell technologies like flow cytometry, microfluidics combined with time-lapse microscopy, and single-cell RNA sequencing.

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KW - Chemical master equation

KW - Gene expression

KW - Phenotypic heterogeneity

KW - Single-cell models

KW - Stochastic models

KW - Virus infection

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PB - Elsevier

ER -

Stochastic Models for Studying the Role of Cellular Noise and Heterogeneity

Abstract

Keywords

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