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Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

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Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies. / Intosalmi, Jukka; Scott, Adrian C.; Hays, Michelle; Flann, Nicholas; Yli-Harja, Olli; Lähdesmäki, Harri; Dudley, Aimée M.; Skupin, Alexander.

In: BMC Molecular and Cell Biology, Vol. 20, No. 1, 59, 19.12.2019.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Intosalmi, J, Scott, AC, Hays, M, Flann, N, Yli-Harja, O, Lähdesmäki, H, Dudley, AM & Skupin, A 2019, 'Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies', BMC Molecular and Cell Biology, vol. 20, no. 1, 59. https://doi.org/10.1186/s12860-019-0234-z

APA

Intosalmi, J., Scott, A. C., Hays, M., Flann, N., Yli-Harja, O., Lähdesmäki, H., ... Skupin, A. (2019). Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies. BMC Molecular and Cell Biology, 20(1), [59]. https://doi.org/10.1186/s12860-019-0234-z

Vancouver

Author

Intosalmi, Jukka ; Scott, Adrian C. ; Hays, Michelle ; Flann, Nicholas ; Yli-Harja, Olli ; Lähdesmäki, Harri ; Dudley, Aimée M. ; Skupin, Alexander. / Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies. In: BMC Molecular and Cell Biology. 2019 ; Vol. 20, No. 1.

Bibtex - Download

@article{bbad79756a454934a594ea41ca3b8ed7,
title = "Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies",
abstract = "Background: Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited. Results: Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. Conclusions: We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.",
keywords = "Bayesian optimization, Diauxic shift, Markov chain Monte Carlo, Metabolic coupling, Multicellular systems, Multiscale modeling, Yeast colony",
author = "Jukka Intosalmi and Scott, {Adrian C.} and Michelle Hays and Nicholas Flann and Olli Yli-Harja and Harri L{\"a}hdesm{\"a}ki and Dudley, {Aim{\'e}e M.} and Alexander Skupin",
note = "EXT={"}Intosalmi, Jukka{"} EXT={"}L{\"a}hdesm{\"a}ki, Harri{"}",
year = "2019",
month = "12",
day = "19",
doi = "10.1186/s12860-019-0234-z",
language = "English",
volume = "20",
journal = "BMC Molecular and Cell Biology",
issn = "2661-8850",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

AU - Intosalmi, Jukka

AU - Scott, Adrian C.

AU - Hays, Michelle

AU - Flann, Nicholas

AU - Yli-Harja, Olli

AU - Lähdesmäki, Harri

AU - Dudley, Aimée M.

AU - Skupin, Alexander

N1 - EXT="Intosalmi, Jukka" EXT="Lähdesmäki, Harri"

PY - 2019/12/19

Y1 - 2019/12/19

N2 - Background: Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited. Results: Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. Conclusions: We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

AB - Background: Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited. Results: Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. Conclusions: We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

KW - Bayesian optimization

KW - Diauxic shift

KW - Markov chain Monte Carlo

KW - Metabolic coupling

KW - Multicellular systems

KW - Multiscale modeling

KW - Yeast colony

U2 - 10.1186/s12860-019-0234-z

DO - 10.1186/s12860-019-0234-z

M3 - Article

VL - 20

JO - BMC Molecular and Cell Biology

JF - BMC Molecular and Cell Biology

SN - 2661-8850

IS - 1

M1 - 59

ER -