Translating weighted probabilistic bits to synthetic genetic circuits.

IF 3.9 2区 生物学 Q1 GENETICS & HEREDITY Plant Genome Pub Date : 2024-10-18 DOI:10.1002/tpg2.20525
Matthew D Ciccone, Carlos D Messina
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Abstract

Synthetic genetic circuits in plants could be the next technological horizon in plant breeding, showcasing potential for precise patterned control over expression. Nevertheless, uncertainty in metabolic environments prevents robust scaling of traditional genetic circuits for agricultural use, and studies show that a deterministic system is at odds with biological randomness. We analyze the necessary requirements for assuring Boolean logic gate sequences can function in unpredictable intracellular conditions, followed by interpreted pathways by which a mathematical representation of probabilistic circuits can be translated to biological implementation. This pathway is utilized through translation of a probabilistic circuit model presented by Pervaiz that works through a series of bits; each composed of a weighted matrix that reads inputs from the environment and a random number generator that takes the matrix as bias and outputs a positive or negative signal. The weighted matrix can be biologically represented as the regulatory elements that affect transcription near promotors, allowing for an electrical bit to biological bit translation that can be refined through tuning using invertible logic prediction of the input to output relationship of a genetic response. Failsafe mechanisms should be introduced, possibly through the use of self-eliminating CRISPR-Cas9, dosage compensation, or cybernetic modeling (where CRISPR is clustered regularly interspaced short palindromic repeats and Cas9 is clustered regularly interspaced short palindromic repeat-associated protein 9). These safety measures are needed for all biological circuits, and their implementation is needed alongside work with this specific model. With applied responses to external factors, these circuits could allow fine-tuning of organism adaptation to stress while providing a framework for faster complex expression design in the field.

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将加权概率比特转化为合成基因电路。
植物合成基因回路可能是植物育种领域的下一个技术前景,它展示了以精确模式控制表达的潜力。然而,新陈代谢环境的不确定性阻碍了传统基因电路在农业应用中的稳健扩展,而且研究表明,确定性系统与生物随机性相悖。我们分析了确保布尔逻辑门序列能在不可预测的细胞内条件下发挥作用的必要条件,然后解释了将概率电路的数学表示转化为生物实施的途径。Pervaiz 提出的概率电路模型通过一系列比特来工作,每个比特由一个加权矩阵和一个随机数发生器组成,加权矩阵从环境中读取输入,随机数发生器以矩阵为偏置,输出正或负信号。加权矩阵在生物学上可以表示为影响启动子附近转录的调节元素,从而实现从电子比特到生物比特的转换,这种转换可以通过使用可逆逻辑预测遗传反应的输入输出关系来进行调整。应引入故障安全机制,可能通过使用自消除 CRISPR-Cas9、剂量补偿或控制论建模(其中 CRISPR 是簇状规则间隔短回文重复序列,Cas9 是簇状规则间隔短回文重复序列相关蛋白 9)。所有生物电路都需要这些安全措施,在使用这种特定模型的同时也需要实施这些措施。通过对外部因素的应用反应,这些回路可以对生物体对压力的适应性进行微调,同时为该领域更快地进行复杂的表达设计提供一个框架。
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来源期刊
Plant Genome
Plant Genome PLANT SCIENCES-GENETICS & HEREDITY
CiteScore
6.00
自引率
4.80%
发文量
93
审稿时长
>12 weeks
期刊介绍: The Plant Genome publishes original research investigating all aspects of plant genomics. Technical breakthroughs reporting improvements in the efficiency and speed of acquiring and interpreting plant genomics data are welcome. The editorial board gives preference to novel reports that use innovative genomic applications that advance our understanding of plant biology that may have applications to crop improvement. The journal also publishes invited review articles and perspectives that offer insight and commentary on recent advances in genomics and their potential for agronomic improvement.
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