An information-theoretic measure for patterning in epithelial tissues

William Waites; Matteo Cavaliere; Elise Cachat; Vincent Danos; Jamie A. Dvies

doi:10.1109/ACCESS.2018.2853624

An information-theoretic measure for patterning in epithelial tissues

William Waites^*, Matteo Cavaliere, Elise Cachat, Vincent Danos, Jamie A. Dvies

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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Abstract

We present path entropy, an information-theoretic measure that captures the notion of patterning due to phase separation in organic tissues. Recent work has demonstrated, both in silico and in vitro, that phase separation in epithelia can arise simply from the forces at play between cells with differing mechanical properties. These qualitative results give rise to numerous questions about how the degree of patterning relates to model parameters or underlying biophysical properties. Answering these questions requires a consistent and meaningful way of quantifying degree of patterning that we observe. We define a resolution-independent measure that is better suited than image-processing techniques for comparing cellular structures. We show how this measure can be usefully applied in a selection of scenarios from biological experiment and computer simulation, and argue for the establishment of a tissue-graph library to assist with parameter estimation for synthetic morphology.

Original language	English
Article number	8405520
Pages (from-to)	40302-40312
Number of pages	11
Journal	IEEE Access
Volume	6
DOIs	https://doi.org/10.1109/ACCESS.2018.2853624
Publication status	Published - 5 Jul 2018

Funding

The work of W. Waites was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/J02175X/1, in part by the U.K. Research Councils’ Synthetic Biology for Growth Programme, in part by the Biotechnology and Biological Sciences Research Council (BBSRC), in part by EPSRC, in part by MRC, and in part by the National Academies Keck Futures Initiative of the National Academy of Sciences Award under Grant NAKFI CB12. The work of M. Cavaliere was supported in part by EPSRC under Grant EP/J02175X/1, in part by the U.K. Research Councils’ Synthetic Biology for Growth Programme, in part by BBSRC, in part by EPSRC, in part by MRC. The work of E. Cachat and J. A. Davies were supported in part by the BBSRC under Grant BB/M018040/1 and in part by the Leverhulme Trust under Grant RPG-2012-558. This work was supported by The Alan Turing Institute through EPSRC under Grant EP/N510129/1.

Keywords

biomedical image processing
distance measurement
entropy
graph theory
pattern analysis
biology
image resolution
probability distribution
image color analysis
mathematical model
biology computing
cellular biophysics

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10.1109/ACCESS.2018.2853624

Waites-etal-IEEE-Access-2018-An-information-theoretic-measure-for-patterningFinal published version, 3.16 MBLicence: Strath_1

Cite this

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title = "An information-theoretic measure for patterning in epithelial tissues",

abstract = "We present path entropy, an information-theoretic measure that captures the notion of patterning due to phase separation in organic tissues. Recent work has demonstrated, both in silico and in vitro, that phase separation in epithelia can arise simply from the forces at play between cells with differing mechanical properties. These qualitative results give rise to numerous questions about how the degree of patterning relates to model parameters or underlying biophysical properties. Answering these questions requires a consistent and meaningful way of quantifying degree of patterning that we observe. We define a resolution-independent measure that is better suited than image-processing techniques for comparing cellular structures. We show how this measure can be usefully applied in a selection of scenarios from biological experiment and computer simulation, and argue for the establishment of a tissue-graph library to assist with parameter estimation for synthetic morphology.",

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author = "William Waites and Matteo Cavaliere and Elise Cachat and Vincent Danos and Dvies, \{Jamie A.\}",

note = "2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission.",

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AU - Waites, William

AU - Cavaliere, Matteo

AU - Cachat, Elise

AU - Danos, Vincent

AU - Dvies, Jamie A.

N1 - 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission.

PY - 2018/7/5

Y1 - 2018/7/5

N2 - We present path entropy, an information-theoretic measure that captures the notion of patterning due to phase separation in organic tissues. Recent work has demonstrated, both in silico and in vitro, that phase separation in epithelia can arise simply from the forces at play between cells with differing mechanical properties. These qualitative results give rise to numerous questions about how the degree of patterning relates to model parameters or underlying biophysical properties. Answering these questions requires a consistent and meaningful way of quantifying degree of patterning that we observe. We define a resolution-independent measure that is better suited than image-processing techniques for comparing cellular structures. We show how this measure can be usefully applied in a selection of scenarios from biological experiment and computer simulation, and argue for the establishment of a tissue-graph library to assist with parameter estimation for synthetic morphology.

AB - We present path entropy, an information-theoretic measure that captures the notion of patterning due to phase separation in organic tissues. Recent work has demonstrated, both in silico and in vitro, that phase separation in epithelia can arise simply from the forces at play between cells with differing mechanical properties. These qualitative results give rise to numerous questions about how the degree of patterning relates to model parameters or underlying biophysical properties. Answering these questions requires a consistent and meaningful way of quantifying degree of patterning that we observe. We define a resolution-independent measure that is better suited than image-processing techniques for comparing cellular structures. We show how this measure can be usefully applied in a selection of scenarios from biological experiment and computer simulation, and argue for the establishment of a tissue-graph library to assist with parameter estimation for synthetic morphology.

KW - biomedical image processing

KW - distance measurement

KW - entropy

KW - graph theory

KW - pattern analysis

KW - biology

KW - image resolution

KW - probability distribution

KW - image color analysis

KW - mathematical model

KW - biology computing

KW - cellular biophysics

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DO - 10.1109/ACCESS.2018.2853624

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SP - 40302

EP - 40312

JO - IEEE Access

JF - IEEE Access

M1 - 8405520

ER -

An information-theoretic measure for patterning in epithelial tissues

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Keywords

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