Reconfiguring Gene Regulatory Neural Network Computing for Regulating Biofilm Formation

IF 7.9 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-31 DOI:10.1109/TNSE.2025.3555962

Adrian Ratwatte;Samitha Somathilaka;Sasitharan Balasubramaniam;Megan Taggart;Keerthi M. Nair;Alan O'Riordan;James Dooley

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引用次数: 0

Abstract

The Gene Regulatory Network (GRN) in biological cells orchestrates essential functions for adaptation and survival in diverse environments, drawing on structural similarities with the Artificial Neural Network (ANN), which can be transformed into a Gene Regulatory Neural Network (GRNN). This transformation enables exploration of their natural computing capabilities regarding network reconfigurability and controllability, facilitating dynamic adjustments of gene-gene interaction weights to regulate biological processes. In this paper, we present a control-theoretic model for the GRNN that determines optimal chemical input concentrations, steering the GRNN towards desired weight configurations using the Linear Quadratic Regulator (LQR) approach. This method enhances network robustness by balancing stability and reconfigurability, ensuring responsive weight adjustments in dynamic environments. We develop mathematical models to identify critical genes using a Continuous-Time Markov Chain (CTMC) and derive temporal weight configurations, providing insights into the system's reconfiguration dynamics, while also quantifying stability and reconfigurability. Our findings demonstrate the effectiveness of the control model in mitigating Clostridioides difficile biofilm formation, outperforming sub-optimal and stochastic perturbation inputs, and highlighting the importance of determining optimal inputs for robust network behavior across diverse complexities.

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基因调控神经网络计算在生物膜形成调控中的应用

生物细胞中的基因调控网络（GRN）利用与人工神经网络（ANN）的结构相似性，协调在不同环境中适应和生存的基本功能，而人工神经网络可以转化为基因调控神经网络（GRNN）。这种转变可以探索它们在网络可重构性和可控性方面的自然计算能力，促进基因-基因相互作用权重的动态调整，以调节生物过程。在本文中，我们提出了GRNN的控制理论模型，该模型确定了最佳的化学输入浓度，并使用线性二次调节器（LQR）方法将GRNN转向所需的权重配置。该方法通过平衡稳定性和可重构性增强了网络的鲁棒性，保证了动态环境下权值调整的响应性。我们开发了数学模型，使用连续时间马尔可夫链（CTMC）来识别关键基因，并推导出时间权重配置，从而深入了解系统的重构动态，同时量化稳定性和可重构性。我们的研究结果证明了控制模型在减轻艰难梭菌生物膜形成，优于次优和随机扰动输入方面的有效性，并强调了在不同复杂性下确定鲁棒网络行为的最佳输入的重要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.