TY - JOUR T1 - Signaling-based neural networks for cellular computation JF - bioRxiv DO - 10.1101/2020.11.10.377077 SP - 2020.11.10.377077 AU - Christian Cuba Samaniego AU - Andrew Moorman AU - Giulia Giordano AU - Elisa Franco Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/11/10/2020.11.10.377077.abstract N2 - 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.Competing Interest StatementThe authors have declared no competing interest. ER -