Biology subjects

Ziegler, K. F.

Publications and source records attributed to Ziegler, K. F..

5 recordsLinked to original sources

bioRxiv2026

Contemporaneity of the past in stochastic intergenerational homeostasis

What recurring patterns of behaviors are hidden in the stochastic intergenerational dynamics of individual bacterial cells in different environments? Embracing the inherently stochastic nature of the homeostasis process, first we reconceptualize homeostasis as representing a "standing pattern of variation in trajectory space" of a naturally occurring adaptive complex system (rather than the simple "set-point" of a self-regulating apparatus), then delineate two mechanistically distinct potential routes to achieving homeostasis (either elastic, reflexive, memory-free adaptation or plastic, reflective, memoryful adaptation) and show that both schemes are simultaneously utilized during multigenerational stochastic growth and division of an individual cell. From experimental data we identify an intergenerational scaling law which directly yields the exact stochastic map governing stochastic intergenerational cell size homeostasis of individual bacterial cells. Its broad applicability across bacterial species, growth conditions, and microenvironments suggests that the organizational motif representing the nature of coupling of growth to division is effectively the same in all of these scenarios, despite apparent differences in actualization through molecular circuitry. The precise parameters characterizing the intergenerational scaling law vary from condition to condition and provide early hints of two tradeoffs: precision-speed and precision-energy.

biophysics↗

bioRxiv2025

Emergent simplicities in an individual cell's stochastic response to disruptive change

Here we seek and find recurring patterns of behaviors in the stochastic response of an individual bacterial cell to a disruptive change in growth 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 show using experimental data that a dynamic rescaling of the cellular unit of time captures the predominant effect of temporal variations in environmental conditions. Furthermore, we identify the instantaneous exponential growth rate as the scaling factor that scales the internal clocks of the cells to the laboratory time. Our results reveal the natural representation for these time-dependent dynamics. When recast in its terms the cell age distribution for suitable initial conditions evolves under time-invariant rules even as growth conditions remain dynamic! Through the experimental realization at different temperatures of otherwise identical disruptive changes, we not only substantiate the general applicability of the cellular frame of reference but also uncover more emergent simplicities. Motivated by this representation, when time and instantaneous growth rate are expressed in terms of their naturally dimensionless counterparts, remarkably consistent patterns are revealed. These include a unimodal-bimodal-unimodal transition in the shape of the instantaneous growth rate distribution as cells initially in homeostasis experience a disruptive change in nutrient quality and subsequently recover and attain a new homeostasis. The remarkable scaling of the pattern of responses across temperatures suggests that the organizational rules and processes governing the response to disruptive change in nutrient quality remain the same at different temperatures. While the progression of the response appears to proceed at different tempos at different temperatures, upon shifting from the laboratory to the cellular frame of reference these changes progress at the same pace even at different temperatures.

biophysics↗

bioRxiv2023

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not readily possible owing to experiment-specific details affecting cell physiology in confounding ways. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine, and benchmarked the differences in stochastic growth and division dynamics in the Mother Machine with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase in the interdivision times, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

biophysics↗

bioRxiv2023

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↗

bioRxiv2023

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↗