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Title: Signaling-based neural networks for cellular computation
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 multilayer 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.  more » « less
Award ID(s):
2020039
PAR ID:
10427301
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2021 American Control Conference (ACC)
Page Range / eLocation ID:
1883 to 1890
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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