Signaling-based neural networks for cellular computation

Cuba Samaniego, C.; Moorman, A.; Giordano, G.; Franco, E.

doi:10.1101/2020.11.10.377077

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bioRxiv · 10.1101/2020.11.10.377077

Signaling-based neural networks for cellular computation

Cuba Samaniego, C.·Moorman, A.·Giordano, G.·Franco, E.

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Abstract

Cellular signaling pathways are responsible for decision making that sustains life. Most signaling pathways include post-translational modification cycles, that process multiple inputs and are tightly interconnected. Here we consider a model for phosphorylation/dephosphorylation cycles, and we show that under some assumptions they can operate as molecular neurons or perceptrons, that generate sigmoidal-like activation functions by processing sums of inputs with positive and negative weights. We carry out a steady-state and structural stability analysis for single molecular perceptrons as well as for feedforward interconnections, concluding that interconnected phosphorylation/dephosphorylation cycles may work as multi-layer biomolecular neural networks (BNNs) with the capacity to perform a variety of computations. As an application, we design signaling networks that behave as linear and non-linear classifiers.

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Cuba Samaniego, C., Moorman, A., Giordano, G., Franco, E.. 2020-11-10. Signaling-based neural networks for cellular computation. https://doi.org/10.1101/2020.11.10.377077

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