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 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.