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From mathematical models and machine learning to clinical reality

From mathematical models and machine learning to clinical reality
From mathematical models and machine learning to clinical reality
Mathematical models are routinely used in the physical and engineering sciences to help understand complex systems and optimize industrial processes. There are numerous examples of the fruitful application of mathematical principles to problems in cell and molecular biology, and recent years have seen increasing interest in applying quantitative techniques to problems in biotechnology. This chapter reviews some ways, in which mathematical models and machine learning may be used to advance our understanding of the complex biological processes involved in cellular differentiation and tissue growth and development, for applications in tissue engineering and regenerative medicine. We will discuss how mathematical models can advance our understanding of stem cell differentiation; growth and development of stem and progenitor cell colonies; and the mechanisms that underpin spatial organization of structure in three-dimensional developing tissues. We will also discuss how recent developments in machine leaning are able to extract biological knowledge from the most complex experimental datasets. We will conclude by considering how computational techniques can be further applied to the design and optimization of effective tissue engineering strategies for clinical application. Although this field is in its infancy, the appropriate use of mathematical methods has considerable potential to transform tissue engineering from experimental concept to clinical reality.
37-51
Academic Press
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Stumpf, Patrick Simon
3aa7f861-0629-4a6c-a7be-3afd99e11314
Oreffo, Richard
ff9fff72-6855-4d0f-bfb2-311d0e8f3778
Lanza, Robert
Langer, Robert
Vacanti, Joseph
Atala, Anthony
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Stumpf, Patrick Simon
3aa7f861-0629-4a6c-a7be-3afd99e11314
Oreffo, Richard
ff9fff72-6855-4d0f-bfb2-311d0e8f3778
Lanza, Robert
Langer, Robert
Vacanti, Joseph
Atala, Anthony

Macarthur, Benjamin, Stumpf, Patrick Simon and Oreffo, Richard (2020) From mathematical models and machine learning to clinical reality. In, Lanza, Robert, Langer, Robert, Vacanti, Joseph and Atala, Anthony (eds.) Principles of Tissue Engineering. 5th ed. Academic Press, pp. 37-51. (doi:10.1016/B978-0-12-818422-6.00001-0).

Record type: Book Section

Abstract

Mathematical models are routinely used in the physical and engineering sciences to help understand complex systems and optimize industrial processes. There are numerous examples of the fruitful application of mathematical principles to problems in cell and molecular biology, and recent years have seen increasing interest in applying quantitative techniques to problems in biotechnology. This chapter reviews some ways, in which mathematical models and machine learning may be used to advance our understanding of the complex biological processes involved in cellular differentiation and tissue growth and development, for applications in tissue engineering and regenerative medicine. We will discuss how mathematical models can advance our understanding of stem cell differentiation; growth and development of stem and progenitor cell colonies; and the mechanisms that underpin spatial organization of structure in three-dimensional developing tissues. We will also discuss how recent developments in machine leaning are able to extract biological knowledge from the most complex experimental datasets. We will conclude by considering how computational techniques can be further applied to the design and optimization of effective tissue engineering strategies for clinical application. Although this field is in its infancy, the appropriate use of mathematical methods has considerable potential to transform tissue engineering from experimental concept to clinical reality.

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More information

e-pub ahead of print date: 3 April 2020
Published date: 2020

Identifiers

Local EPrints ID: 444568
URI: http://eprints.soton.ac.uk/id/eprint/444568
PURE UUID: 2ae3bdbf-fb74-46c4-b770-b610b3d1fb58
ORCID for Richard Oreffo: ORCID iD orcid.org/0000-0001-5995-6726

Catalogue record

Date deposited: 26 Oct 2020 17:31
Last modified: 18 Feb 2021 16:54

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