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Biswas, R. R.

Publications and source records attributed to Biswas, R. R..

4 recordsLinked to original sources

Non-Markovian memory and emergent simplicities in the stochastic and plastic adaptation of individual cells to dynamic environments

Do individual bacterial cells retain memories of the history of environmental conditions experienced in previous generations? Here we directly address this question through a synthesis of physics theory and high-precision experiments on statistically identical, non-interacting individual bacterial cells, which grow and divide with intrinsic stochasticity in precisely controlled conditions. From these data, we extract "emergent simplicities" in the seemingly complex interplay between history dependence, persistence, and transience in the stochastic memories of the dynamic environments experienced by individuals over multiple generations. First, we find that the instantaneous single-cell growth rate is the key physiologically relevant quantity where intergenerational memory is stored. In contrast, the cell size dynamics are memory free, or Markovian, over intergenerational timescales. Next, we find that the effect of experiencing dynamic environments can be captured quantitatively by recal-ibrating the cellular unit of time by the measured mean instantaneous growth rate; the dynamically rescaled cell age distributions undergo a scaling collapse. Moreover, in a given condition, an individual bacterial cell retains history-dependent, or non-Markovian, memory of its growth rate over tens of generations. We derive from first principles a physically-motivated metric to quantify the degree of non-Markovianity. Furthermore, when conditions change, the instantaneous single-cell growth distribution becomes bimodal, as the bacteriums memory of past environment encountered is reset stochastically and plastically, prior to achieving a new homeostasis.

biophysics↗

Cellular dynamics under time-varying conditions

Building on the known scaling law that a single timescale, a cellular unit of time, governs stochastic growth and division of individual bacterial cells under constant growth conditions, here we propose that a dynamic rescaling of the cellular unit of time serves to capture the dominant effect of changing conditions on the cell age distribution. This temporal scaling ansatz provides a natural representation for these time-dependent dynamics in whose terms the cell age distribution evolves under time-invariant rules! Finally, we discuss relevance of these results to recent high-precision experiments on individual bacterial cells growing and dividing in dynamic environments.

biophysics↗

Intergenerational scaling law determines the precision kinematics of stochastic individual-cell-size homeostasis

Individual bacterial cells grow and divide stochastically. Yet they maintain their characteristic sizes across generations within a tightly controlled range. What rules ensure intergenerational stochastic homeostasis of individual cell sizes? Valuable clues have emerged from high-precision longterm tracking of individual statistically-identical Caulobacter crescentus cells as reported in [1, 2]: Intergenerational cell size homeostasis is an inherently stochastic phenomenon, follows Markovian or memory-free dynamics, and cells obey an intergenerational scaling law, which governs the stochastic map characterizing generational sequences of cell sizes. These observed emergent simplicities serve as essential building blocks of the data-informed principled theoretical framework we develop here. Our exact analytic fitting-parameter-free results for the predicted intergenerational stochastic map governing the precision kinematics of cell size homeostasis are remarkably well borne out by experimental data, including extant published data on other microorganisms, Escherichia coli and Bacillus subtilis. Furthermore, our framework naturally yields the general exact and analytic condition that is necessary and sufficient to ensure that stochastic homeostasis can be achieved and maintained. Significantly, this condition is more stringent than the known heuristic result from quasi-deterministic frameworks. In turn the fully stochastic treatment we present here extends and updates extant frameworks, and highlights the inherently stochastic behaviors of individual cells in homeostasis.

biophysics↗

Emergent Simplicities in Stochastic Intergenerational Homeostasis

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

biophysics↗