遗传逻辑电路的能量感知技术映射

IF 3.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
ACS Synthetic Biology Pub Date : 2024-10-18 Epub Date: 2024-10-08 DOI:10.1021/acssynbio.4c00395
Erik Kubaczka, Maximilian Gehri, Jérémie J M Marlhens, Tobias Schwarz, Maik Molderings, Nicolai Engelmann, Hernan G Garcia, Christian Hochberger, Heinz Koeppl
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引用次数: 0

摘要

能量及其耗散是包括细胞在内的所有生命系统的基本要素。能量载体的不足--如人工基因回路造成的额外负担--会将细胞的优先权转移到生存上,也会损害基因回路的功能。此外,最近的研究表明了能量消耗在信息传递中的重要性。尽管生物体是一个非平衡系统,但遗传设计自动化(GDA)软件尚未采用能够考虑能量消耗和非平衡响应曲线的非平衡模型。为此,我们引入了能量感知技术映射,即根据能效和功能自动设计基因逻辑电路。其基础是基因表达的能量感知非平衡稳态模型,该模型捕捉了能量耗散(我们将其与熵产生率联系起来)和转录猝发等与真核生物和原核生物相关的特征。我们的评估结果表明,遗传逻辑电路的功能性能和能效是互不相关的优化目标。就我们的基准而言,与功能优化变体相比,能效平均提高了 37.2%。我们发现,能量消耗和整体蛋白质表达量随电路大小呈线性增长,而能量感知技术映射允许以比电路小一到两个门的能量成本来设计基因逻辑电路。结构变体进一步改善了这一情况,同时结果显示了单一布尔函数结构之间的帕累托优势。通过将能源需求纳入设计,能源感知技术映射实现了设计能效。这扩展了当前的 GDA 工具,并补充了应对体内负担的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy Aware Technology Mapping of Genetic Logic Circuits.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

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来源期刊
CiteScore
8.00
自引率
10.60%
发文量
380
审稿时长
6-12 weeks
期刊介绍: The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.
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