A compressed logistic equation on bacteria growth: inferring time-dependent growth rate.

IF 2 4区生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY

Physical biology Pub Date : 2022-09-15 DOI:10.1088/1478-3975/ac8c15

Carlito Pinto, Koichi Shimakawa

引用次数: 3

Abstract

We propose a compressed logistic model for bacterial growth by invoking a time-dependent rate instead of the intrinsic growth rate (constant), which was adopted in traditional logistic models. The new model may have a better physiological basis than the traditional ones, and it replicates experimental observations, such as the case example for E. coli, Salmonella, and Staphylococcus aureus. Stochastic colonial growth at a different rate may have a fractal-like nature, which should be an origin of the time-dependent reaction rate. The present model, from a stochastic viewpoint, is approximated as a Gaussian time evolution of bacteria (error function).

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细菌生长的压缩逻辑方程:推断随时间变化的生长速率。

我们提出了一个细菌生长的压缩逻辑模型，通过调用时间依赖的速率来代替传统逻辑模型中采用的固有增长率(常数)。新模型可能比传统模型具有更好的生理基础，并且它重复了实验观察，例如案例forE。大肠杆菌、沙门氏菌和金黄色葡萄球菌。不同速率的随机群体生长可能具有分形性质，这应该是随时间变化的反应速率的来源。从随机的角度来看，该模型近似为细菌的高斯时间演化(误差函数)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physical biology 生物-生物物理

CiteScore

4.20

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity. Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as: molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division systems biology, e.g. signaling, gene regulation and metabolic networks cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis cell-cell interactions, cell aggregates, organoids, tissues and organs developmental dynamics, including pattern formation and morphogenesis physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation neuronal systems, including information processing by networks, memory and learning population dynamics, ecology, and evolution collective action and emergence of collective phenomena.