Energy allocation theory for bacterial growth control in and out of steady state.
Energy allocation theory for bacterial growth control in and out of steady state.
复制标题
用于稳态和稳态外细菌生长控制的能量分配理论。
DOI：
10.1101/2024.01.09.574890
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Banerjee,Shiladitya
中科院分区：
文献类型：
--
作者：
Cylke,Arianna;Serbanescu,Diana;Banerjee,Shiladitya
将能量资源有效地分配给关键的生理功能，使生物体能够在不同的环境中生长和茁壮成长，并适应各种各样的扰动。为了定量地了解单细胞生物如何利用它们的能量资源来响应生长环境的变化，我们引入了一种动态能量分配理论，该理论通过将代谢能划分为关键的生理功能来描述细胞生长动态：生长，分裂，细胞形状调节，能量储存和耗散损失。通过优化生长的能量通量，我们开发了不同环境中细胞形态和生长速率的时间演化方程。由此产生的模型准确地捕捉实验观察到的细菌细胞大小对生长速率的依赖性，代谢率与细胞大小的超线性缩放，并预测营养依赖的生长，分裂和形状维持所消耗的能量之间的权衡。通过校准模型参数与实验数据的模式organismEscherichia coli，我们的模型描述了在动态条件下的细菌生长控制，特别是在营养变化和渗透冲击。结合细胞的机械特性和潜在的生化调节，我们的模型预测了广泛的观察到的形态和生长现象背后的驱动因素，增加的复杂性最小。
Efficient allocation of energy resources to key physiological functions allows living organisms to grow and thrive in diverse environments and adapt to a wide range of perturbations. To quantitatively understand how unicellular organisms utilize their energy resources in response to changes in growth environment, we introduce a theory of dynamic energy allocation that describes cellular growth dynamics by partitioning metabolizable energy into key physiological functions: growth, division, cell shape regulation, energy storage and loss through dissipation. By optimizing the energy flux for growth, we develop the equations governing the time evolution of cell morphology and growth rate in diverse environments. The resulting model accurately captures experimentally observed dependencies of bacterial cell size on growth rate, superlinear scaling of metabolic rate with cell size and predicts nutrient-dependent trade-offs between energy expended for growth, division and shape maintenance. By calibrating model parameters with experimental data for the model organismEscherichia coli, our model describes bacterial growth control in dynamic conditions, particularly during nutrient shifts and osmotic shocks. Integrating both the mechanical properties of the cell and underlying biochemical regulation, our model predicts the driving factors behind a wide range of observed morphological and growth phenomena with minimal added complexity.