Biochemical network motifs can transduce and process oscillatory information
Biological networks that are formed through amalgamation of signaling pathways include recurrent configurations called network motifs. These statistically over-represented subgraphs are often formed through interconnected enzyme-substrate relationships that are known to result in highly dynamic downstream behavior, including oscillatory output. Such signals are abundant in biology: heartbeats, circadian rhythms, and cell cycles all exhibit characteristic frequencies. Though there has been great emphasis on how oscillations can be generated through network dynamics, little is known about oscillatory information processing or transduction capacity of network motifs. We employ ordinary differential equations-based dynamical modeling to understand how different network topologies impact oscillatory signal propagation through a multi-enzyme network. We model enzyme-substrate interactions of 20 commonly observed motifs using Michaelis-Menten kinetics. We then perform deterministic Monte Carlo simulations using a range of biologically relevant enzymatic parameters and input frequencies. From these simulations, we quantify signal propagation characteristics using cluster analysis, categorize different motif responses based on output characteristics, and identify potential mechanisms for oscillatory signal processing using parameter sensitivity analysis. We see that the input-output responses depend on network topology and enzyme kinetic parameters. Enzymatic motifs show median oscillatory suppression of 30-135 decibels, with three-node coherent feedforward loops showing the lowest propensity for oscillatory signal suppression. Motifs that contained negative feedback or four-node coherent feedforward loops had the biggest potential to act as AC-to-DC converters, translating oscillatory input signals into transient impulses or sustained continuous outputs, respectively. We conclude that enzyme networks can process and decode information within oscillatory inputs in a frequency- and network-dependent manner.