Pattern recognition in living cells through the lens of machine learning

At a coarse level, pattern recognition within cells involves sensing of environmental signals by surface receptors, and activating downstream signalling pathways that ultimately drive a transcriptome response, enabling biological functions such as differentiation, migration, proliferation, apoptosis...

Full description

Saved in:
Bibliographic Details
Published in:Open biology Vol. 15; no. 7; pp. 240377 - 10
Main Authors: Britto Bisso, Frank, Aguilar, Rodrigo, Shree, Durga, Zhu, Yinan, Espinoza, Mijael, Diaz, Benjamin, Cuba Samaniego, Christian
Format: Journal Article
Language:English
Published: England The Royal Society Publishing 01.07.2025
The Royal Society
Subjects:
Apoptosis
biocomputation
Cell differentiation
Cell migration
Classification
Computational Biology - methods
Decision making
Design
Enzymes
Gene Regulatory Networks
Genes
Humans
Learning algorithms
Ligands
Machine Learning
Mental task performance
Neural networks
Neural Networks, Computer
Open Questions
Pattern recognition
Pattern Recognition, Automated - methods
Phosphorylation
Proteins
Signal Transduction
signalling pathways
Structure-function relationships
Synthetic biology
Transcriptomes
synthetic biology
biocomputation
signalling pathways
neural networks
pattern recognition
ISSN:2046-2441, 2046-2441
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:At a coarse level, pattern recognition within cells involves sensing of environmental signals by surface receptors, and activating downstream signalling pathways that ultimately drive a transcriptome response, enabling biological functions such as differentiation, migration, proliferation, apoptosis or cell-type specification. This kind of decision-making process resembles a classification task that, inspired by machine learning concepts, can be understood in terms of a decision boundary: the combination of inputs relative to the classification region defined by this boundary defines context-specific responses. In this report, we contextualize machine learning concepts within a biological framework to explore the structural and functional similarities (and differences) between artificial neural networks, signalling pathways and gene regulatory networks. We take a preliminary look at neural network architectures that may better suit biological classification tasks, explore how learning fits into this paradigm, and address the role of competitive binding in cellular computation. Altogether, we envision a new research direction at the intersection of systems and synthetic biology, advancing our understanding of the inherent computational capacities of signalling pathways and gene regulatory networks.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
Joint first authors.
ISSN:2046-2441
2046-2441
DOI:10.1098/rsob.240377