Biomolecular mechanisms for signal differentiation

Emmanouil Alexis,Carolin C.M Schulte,Luca Cardelli,Antonis Papachristodoulou

doi:10.1016/j.isci.2021.103462

Emmanouil Alexis, Carolin C.M Schulte + Show 2 more

Open Access

https://doi.org/10.1016/j.isci.2021.103462

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Journal: iScience	Publication Date: Nov 17, 2021
Citations: 10	License type: cc-by

Affiliation: University of Oxford

Abstract

SummaryCells can sense temporal changes of molecular signals, allowing them to predict environmental variations and modulate their behavior. This paper elucidates biomolecular mechanisms of time derivative computation, facilitating the design of reliable synthetic differentiator devices for a variety of applications, ultimately expanding our understanding of cell behavior. In particular, we describe and analyze three alternative biomolecular topologies that are able to work as signal differentiators to input signals around their nominal operation. We propose strategies to preserve their performance even in the presence of high-frequency input signal components which are detrimental to the performance of most differentiators. We find that the core of the proposed topologies appears in natural regulatory networks and we further discuss their biological relevance. The simple structure of our designs makes them promising tools for realizing derivative control action in synthetic biology.

Highlights

Measuring the speed at which a physical process evolves over time is of central importance to science and engineering
We find that the core of the proposed topologies appears in natural regulatory networks and we further discuss their biological relevance
In the context of cellular energy metabolism, in silico studies have revealed the role of creatine phosphate as a buffering species that allows for adaptation to a changing demand of adenosine triphosphate (ATP), exploiting the anticipatory action enabled by derivative control (Cloutier and Wellstead, 2010)

Summary

Introduction

Measuring the speed at which a physical process evolves over time is of central importance to science and engineering. In the context of cellular energy metabolism, in silico studies have revealed the role of creatine phosphate as a buffering species that allows for adaptation to a changing demand of adenosine triphosphate (ATP), exploiting the anticipatory action enabled by derivative control (Cloutier and Wellstead, 2010). This observation is a specific example of a broader class of biomolecular processes where the presence of rapid buffering proves to be equivalent to negative derivative feedback (Hancock et al, 2017)

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