Hysteresis and noise floor in gene expression optimised for persistence against lethal events

Pavol Bokes, Abhyudai Singh
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Abstract

Bacterial cell persistence, crucial for survival under adverse conditions like antibiotic exposure, is intrinsically linked to stochastic fluctuations in gene expression. Certain genes, while inhibiting growth under normal circumstances, confer tolerance to antibiotics at elevated expression levels. The occurrence of antibiotic events lead to instantaneous cellular responses with varied survival probabilities correlated with gene expression levels. Notably, cells with lower protein concentrations face higher mortality rates. This study aims to elucidate an optimal strategy for protein expression conducive to cellular survival. Through comprehensive mathematical analysis, we determine the optimal burst size and frequency that maximise cell proliferation. Furthermore, we explore how the optimal expression distribution changes as the cost of protein expression to growth escalates. Our model reveals a hysteresis phenomenon, characterised by discontinuous transitions between deterministic and stochastic optima. Intriguingly, stochastic optima possess a noise floor, representing the minimal level of fluctuations essential for optimal cellular resilience.
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优化基因表达的滞后性和噪声底限,以持续抵御致死事件
细菌细胞的持久性对于在抗生素暴露等不利条件下的生存至关重要,它与基因表达的随机波动有着内在联系。某些基因虽然在正常情况下会抑制生长,但在表达水平升高时会产生对抗生素的耐受性。抗生素事件的发生会导致细胞瞬时反应,其存活概率与基因表达水平相关。值得注意的是,蛋白质浓度较低的细胞死亡率较高。本研究旨在阐明有利于细胞存活的最佳蛋白质表达策略。通过全面的数学分析,我们确定了能使细胞增殖最大化的最佳爆发大小和频率。此外,我们还探讨了最佳表达分布如何随着蛋白质表达对生长的成本增加而发生变化。我们的模型揭示了一种滞后现象,其特点是确定性最优和随机最优之间的不连续转换。耐人寻味的是,随机最佳值具有噪音底限,代表了细胞最佳复原力所必需的最低波动水平。
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