Quantifying information of intracellular signaling: progress with machine learning

Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.

Automated summary

In this review, we explore the use of information-theoretic approaches to quantify information transmission by signaling pathways. We also discuss how recent advances in machine learning have helped address the challenges posed by complex temporal trajectory datasets, leading to a better understanding of how cells employ temporal coding to adapt to environmental perturbations.