Architectural underpinnings of stochastic intergenerational homeostasis DOI
Kunaal Joshi, Charles S. Wright, Rudro R. Biswas

et al.

Physical review. E, Journal Year: 2024, Volume and Issue: 110(2)

Published: Aug. 27, 2024

Living systems are naturally complex and adaptive offer unique insights into the strategies for achieving sustaining stochastic homeostasis in different conditions. Here we focus on context of growth division individual bacterial cells. We take advantage high-precision long-term dynamical data that have recently been used to extract emergent simplicities articulate empirical intra- intergenerational scaling laws governing these dynamics. From data, identify core motif mechanistic coupling between growth, which yields precise rules, thus also bridging phenomenologies. By developing utilizing techniques solving a broad class first-passage processes, derive exact analytic necessary sufficient condition cell-size within this framework. Furthermore, provide predictions precision kinematics shape interdivision time distribution, compellingly borne out by data. Taken together, results functional architecture control yield robust yet flexible homeostasis.

Language: Английский

Emergent Simplicities in Stochastic Intergenerational Homeostasis DOI Open Access
Kunaal Joshi, Charles S. Wright,

Karl F. Ziegler

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 20, 2023

How do complex systems maintain key emergent “state variables” at desired target values to within specified tolerances? This question was first posed in the context of homeostasis living over a century ago, and yet precise quantitative rules governing this phenomenon have remained fiercely debated. We herein present direct solution through synthesis high-precision experiments principles-based physics theory. After introducing general approach that incorporates inherently stochastic dynamic nature organismal homeostasis, we provide experimental evidence intergenerational is indeed maintained. Next, identify series simplicities hidden these data. Remarkably, dynamics sizes are Markovian, or history-independent. The precision data reveal an scaling law fully determines, with no fine-tuning parameters, exact map as borne out by compelling data– theory matches. These turn yield necessary sufficient condition for surprising implications architecture underlying control system. Validation across different growth conditions, cell morphologies, modalities, organisms comprehensively establishes universality results presented here.

Language: Английский

Citations

8
Coupling of cell growth modulation to asymmetric division and cell cycle regulation in Caulobacter crescentus DOI Creative Commons

Skye Glenn,

Alessio Fragasso, Wei-Hsiang Lin

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(41)

Published: Oct. 3, 2024

In proliferating bacteria, growth rate is often assumed to be similar between daughter cells. However, most of our knowledge cell derives from studies on symmetrically dividing bacteria. many α-proteobacteria, asymmetric division a normal part the life cycle, with each producing cells different sizes and fates. Here, we demonstrate that functionally distinct swarmer stalked produced by model α-proteobacterium

Language: Английский

Citations

2
Architectural underpinnings of stochastic intergenerational homeostasis DOI
Kunaal Joshi, Charles S. Wright, Rudro R. Biswas

et al.

Physical review. E, Journal Year: 2024, Volume and Issue: 110(2)

Published: Aug. 27, 2024

Living systems are naturally complex and adaptive offer unique insights into the strategies for achieving sustaining stochastic homeostasis in different conditions. Here we focus on context of growth division individual bacterial cells. We take advantage high-precision long-term dynamical data that have recently been used to extract emergent simplicities articulate empirical intra- intergenerational scaling laws governing these dynamics. From data, identify core motif mechanistic coupling between growth, which yields precise rules, thus also bridging phenomenologies. By developing utilizing techniques solving a broad class first-passage processes, derive exact analytic necessary sufficient condition cell-size within this framework. Furthermore, provide predictions precision kinematics shape interdivision time distribution, compellingly borne out by data. Taken together, results functional architecture control yield robust yet flexible homeostasis.

Language: Английский

Citations

0