How to fit in: The learning principles of cell differentiation.

Bibliographic Details
Title:	How to fit in: The learning principles of cell differentiation.
Authors:	Brun-Usan, Miguel¹ (AUTHOR), Thies, Christoph¹ (AUTHOR), Watson, Richard A.¹ (AUTHOR) R.A.Watson@soton.ac.uk
Source:	PLoS Computational Biology. 4/13/2020, Vol. 16 Issue 4, p1-29. 29p. 1 Diagram, 6 Graphs.
Subject Terms:	CELL differentiation, MIRROR neurons, GENE regulatory networks, CELLULAR evolution, NATURAL selection, PHENOTYPIC plasticity
Abstract:	Cell differentiation in multicellular organisms requires cells to respond to complex combinations of extracellular cues, such as morphogen concentrations. Some models of phenotypic plasticity conceptualise the response as a relatively simple function of a single environmental cues (e.g. a linear function of one cue), which facilitates rigorous analysis. Conversely, more mechanistic models such those implementing GRNs allows for a more general class of response functions but makes analysis more difficult. Therefore, a general theory describing how cells integrate multi-dimensional signals is lacking. In this work, we propose a theoretical framework for understanding the relationships between environmental cues (inputs) and phenotypic responses (outputs) underlying cell plasticity. We describe the relationship between environment and cell phenotype using logical functions, making the evolution of cell plasticity equivalent to a simple categorisation learning task. This abstraction allows us to apply principles derived from learning theory to understand the evolution of multi-dimensional plasticity. Our results show that natural selection is capable of discovering adaptive forms of cell plasticity associated with complex logical functions. However, developmental dynamics cause simpler functions to evolve more readily than complex ones. By using conceptual tools derived from learning theory we show that this developmental bias can be interpreted as a learning bias in the acquisition of plasticity functions. Because of that bias, the evolution of plasticity enables cells, under some circumstances, to display appropriate plastic responses to environmental conditions that they have not experienced in their evolutionary past. This is possible when the selective environment mirrors the bias of the developmental dynamics favouring the acquisition of simple plasticity functions–an example of the necessary conditions for generalisation in learning systems. These results illustrate the functional parallelisms between learning in neural networks and the action of natural selection on environmentally sensitive gene regulatory networks. This offers a theoretical framework for the evolution of plastic responses that integrate information from multiple cues, a phenomenon that underpins the evolution of multicellularity and developmental robustness. Author summary: In organisms composed of many cell types, the differentiation of cells relies on their ability to respond to complex extracellular cues, such as morphogen concentrations, a phenomenon known as cell plasticity. Although cell plasticity plays a crucial role in development and evolution, it is not clear how, and if, cell plasticity can enhance adaptation to a novel environment and/or facilitate robust developmental processes. In some models, the relationships between the environmental cues (inputs) and the phenotypic responses (outputs) are conceptualised as one-to-one (i.e. simple 'reaction norms'); whereas the phenotype of plastic cells commonly depends on several simultaneous inputs (i.e. many-to-one, multi-dimensional reaction norms). One alternative is the use of a gene-regulatory network (GRN) models that allow for much more general responses; but this can make analysis difficult. In this work we use a theoretical framework based on logical functions and learning theory to characterize such multi-dimensional reaction norms produced by GRNs. This allows us to reveal a strong and previously unnoticed bias towards the acquisition of simple forms of cell plasticity, which increases their ability to adapt to novel environments. Recognising this bias helps us to understand when the evolution of cell plasticity will increase the ability of plastic cells to adapt to novel environments, to respond appropriately to complex extracellular cues and to enhance developmental robustness. Since this set of properties are required for the evolution of multicellularity, our approach can also contribute to our understanding of this evolutionary transition. [ABSTRACT FROM AUTHOR]
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Items	– Name: Title Label: Title Group: Ti Data: How to fit in: The learning principles of cell differentiation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Brun-Usan%2C+Miguel%22">Brun-Usan, Miguel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Thies%2C+Christoph%22">Thies, Christoph</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Watson%2C+Richard+A%2E%22">Watson, Richard A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> R.A.Watson@soton.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22PLoS+Computational+Biology%22">PLoS Computational Biology</searchLink>. 4/13/2020, Vol. 16 Issue 4, p1-29. 29p. 1 Diagram, 6 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22CELL+differentiation%22">CELL differentiation</searchLink><br /><searchLink fieldCode="DE" term="%22MIRROR+neurons%22">MIRROR neurons</searchLink><br /><searchLink fieldCode="DE" term="%22GENE+regulatory+networks%22">GENE regulatory networks</searchLink><br /><searchLink fieldCode="DE" term="%22CELLULAR+evolution%22">CELLULAR evolution</searchLink><br /><searchLink fieldCode="DE" term="%22NATURAL+selection%22">NATURAL selection</searchLink><br /><searchLink fieldCode="DE" term="%22PHENOTYPIC+plasticity%22">PHENOTYPIC plasticity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cell differentiation in multicellular organisms requires cells to respond to complex combinations of extracellular cues, such as morphogen concentrations. Some models of phenotypic plasticity conceptualise the response as a relatively simple function of a single environmental cues (e.g. a linear function of one cue), which facilitates rigorous analysis. Conversely, more mechanistic models such those implementing GRNs allows for a more general class of response functions but makes analysis more difficult. Therefore, a general theory describing how cells integrate multi-dimensional signals is lacking. In this work, we propose a theoretical framework for understanding the relationships between environmental cues (inputs) and phenotypic responses (outputs) underlying cell plasticity. We describe the relationship between environment and cell phenotype using logical functions, making the evolution of cell plasticity equivalent to a simple categorisation learning task. This abstraction allows us to apply principles derived from learning theory to understand the evolution of multi-dimensional plasticity. Our results show that natural selection is capable of discovering adaptive forms of cell plasticity associated with complex logical functions. However, developmental dynamics cause simpler functions to evolve more readily than complex ones. By using conceptual tools derived from learning theory we show that this developmental bias can be interpreted as a learning bias in the acquisition of plasticity functions. Because of that bias, the evolution of plasticity enables cells, under some circumstances, to display appropriate plastic responses to environmental conditions that they have not experienced in their evolutionary past. This is possible when the selective environment mirrors the bias of the developmental dynamics favouring the acquisition of simple plasticity functions–an example of the necessary conditions for generalisation in learning systems. These results illustrate the functional parallelisms between learning in neural networks and the action of natural selection on environmentally sensitive gene regulatory networks. This offers a theoretical framework for the evolution of plastic responses that integrate information from multiple cues, a phenomenon that underpins the evolution of multicellularity and developmental robustness. Author summary: In organisms composed of many cell types, the differentiation of cells relies on their ability to respond to complex extracellular cues, such as morphogen concentrations, a phenomenon known as cell plasticity. Although cell plasticity plays a crucial role in development and evolution, it is not clear how, and if, cell plasticity can enhance adaptation to a novel environment and/or facilitate robust developmental processes. In some models, the relationships between the environmental cues (inputs) and the phenotypic responses (outputs) are conceptualised as one-to-one (i.e. simple 'reaction norms'); whereas the phenotype of plastic cells commonly depends on several simultaneous inputs (i.e. many-to-one, multi-dimensional reaction norms). One alternative is the use of a gene-regulatory network (GRN) models that allow for much more general responses; but this can make analysis difficult. In this work we use a theoretical framework based on logical functions and learning theory to characterize such multi-dimensional reaction norms produced by GRNs. This allows us to reveal a strong and previously unnoticed bias towards the acquisition of simple forms of cell plasticity, which increases their ability to adapt to novel environments. Recognising this bias helps us to understand when the evolution of cell plasticity will increase the ability of plastic cells to adapt to novel environments, to respond appropriately to complex extracellular cues and to enhance developmental robustness. Since this set of properties are required for the evolution of multicellularity, our approach can also contribute to our understanding of this evolutionary transition. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of PLoS Computational Biology is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo	BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1371/journal.pcbi.1006811 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1 Subjects: – SubjectFull: CELL differentiation Type: general – SubjectFull: MIRROR neurons Type: general – SubjectFull: GENE regulatory networks Type: general – SubjectFull: CELLULAR evolution Type: general – SubjectFull: NATURAL selection Type: general – SubjectFull: PHENOTYPIC plasticity Type: general Titles: – TitleFull: How to fit in: The learning principles of cell differentiation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Brun-Usan, Miguel – PersonEntity: Name: NameFull: Thies, Christoph – PersonEntity: Name: NameFull: Watson, Richard A. IsPartOfRelationships: – BibEntity: Dates: – D: 13 M: 04 Text: 4/13/2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 1553734X Numbering: – Type: volume Value: 16 – Type: issue Value: 4 Titles: – TitleFull: PLoS Computational Biology Type: main
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