Retroactivity Affects the Adaptive Robustness of Transcriptional Regulatory Networks | IEEE Conference Publication | IEEE Xplore

Retroactivity Affects the Adaptive Robustness of Transcriptional Regulatory Networks


Abstract:

Adaptation refers to the system's ability to respond transiently to an input signal and subsequently recover to the initial states. Adaptive robustness, the ability of a ...Show More

Abstract:

Adaptation refers to the system's ability to respond transiently to an input signal and subsequently recover to the initial states. Adaptive robustness, the ability of a network to achieve adaptation, is subject to the loading effects arising from modular interconnections, known as retroactivity. Studying the effects of retroactivity on adaptive robustness facilitates the employment of retroactivity to improve circuit performance in synthetic biology. In this paper, we developed a framework for quantifying adaptive robustness via statistical model checking and used this framework to investigate the effects of retroactivity on adaptive robustness. We found that increasing retroactivity tends to raise adaptive robustness in networks where the output protein does not perform regulatory functions, such as incoherent feedforward loops.
Date of Conference: 10-12 July 2019
Date Added to IEEE Xplore: 29 August 2019
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Conference Location: Philadelphia, PA, USA

I. Introduction

Transcriptional regulatory networks (TRN) are collections of gene regulations that are mediated by transcriptional factors (TF). Variation in TRN contributes to diverse biological functions inside the cells. Adaptation is a biological function concerning the temporal dynamics of gene expression. It consists of a response phase, where the expression level of a gene responds transiently to an external stimulus, and a recovery phase, where the expression level adapts gradually to the initial value (Fig. 1) [1], [2]. Examples of adaptation include signal transduction [3]–[6], bacteria chemotaxis [7]–[9], and homeostasis [10]. It is well known that TRN with certain topologies such as incoherent feedforward loops (IFFL) and negative feedback loops (NFBL) can mediate adaptations robustly. In other words, even though chemical kinetic rates vary significantly across a cell population, networks like IFFL are more likely to execute adaptations than most other networks. Many studies used the term adaptive robustness to describe the ability of a network to achieve adaptations regardless of the parameters [2], [11], [12]. In synthetic biology, investigating the adaptive robustness of various networks is especially important, as it is impossible to achieve precise control of chemical kinetic rates in synthetic TRN due to complex cellular environments. However, a standard quantitative definition of adaptive robustness has not been formalized.

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