基于 Allee 的微生物全细胞传感器分布式算法

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY NPJ Systems Biology and Applications Pub Date : 2024-04-22 DOI:10.1038/s41540-024-00363-3
Fabricio Cravo, Matthias Függer, Thomas Nowak
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引用次数: 0

摘要

可靠地检测潜在低浓度物质是许多生物医学应用中的共同问题。作为对基于酶、抗体抗原和测序的成熟方法的补充,人们提出了所谓的微生物全细胞传感器,即能够感知和报告物质的合成工程微生物细胞。在这项工作中,我们分析了微生物全细胞传感器的分布式算法,在这种算法中,细胞通过通信来协调是否检测到了分析物。该算法受生物种群中阿利效应的启发,使细胞在逻辑 0 和 1 状态之间交替,以应对与相关粒子的反应。当处于逻辑 1 状态的细胞超过阈值时,算法会将剩余细胞转换为逻辑 1 状态,从而产生易于检测的输出信号。我们通过数学分析和模拟验证了该算法,证明它即使在嘈杂的蜂窝环境中也能正常工作。
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An Allee-based distributed algorithm for microbial whole-cell sensors

Reliable detection of substances present at potentially low concentrations is a problem common to many biomedical applications. Complementary to well-established enzyme-, antibody-antigen-, and sequencing-based approaches, so-called microbial whole-cell sensors, i.e., synthetically engineered microbial cells that sense and report substances, have been proposed as alternatives. Typically these cells operate independently: a cell reports an analyte upon local detection.

In this work, we analyze a distributed algorithm for microbial whole-cell sensors, where cells communicate to coordinate if an analyte has been detected. The algorithm, inspired by the Allee effect in biological populations, causes cells to alternate between a logical 0 and 1 state in response to reacting with the particle of interest. When the cells in the logical 1 state exceed a threshold, the algorithm converts the remaining cells to the logical 1 state, representing an easily-detectable output signal. We validate the algorithm through mathematical analysis and simulations, demonstrating that it works correctly even in noisy cellular environments.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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