Biology subjects

Iyer-Biswas, S.

Publications and source records attributed to Iyer-Biswas, S..

10 recordsLinked to original sources

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

Architectural underpinnings of stochastic intergenerational homeostasis

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

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

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↗

bioRxiv2023

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↗

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↗

bioRxiv2022

Bacterial Strategies for Damage Management

Organisms are able to partition resources adaptively between growth and repair. The precise nature of optimal partitioning and how this emerges from cellular dynamics including insurmountable trade-offs remains an open question. We construct a mathematical framework to estimate optimal partitioning and the corresponding maximal growth rate constrained by empirical scaling laws. We model a biosynthesis tradeoff governing the partitioning of the ribosome economy between replicating functional proteins and replicating the ribosome pool, and also an energy tradeoff arising from the finite energy budget of the cell. Through exact analytic calculations we predict limits on the range of values partitioning ratios take while sustaining growth. We calculate how partitioning and cellular composition scale with protein and ribosome degradation rates and organism size. These results reveal different classes of optimizing strategies corresponding to phenotypically distinct bacterial lifestyles. We summarize these findings in a quadrant-based taxonomy including: a "greedy" strategy maximally prioritizing growth, a "prudent" strategy maximally prioritizing the management of damaged pools, and "strategically limited" intermediates.

biophysics↗

bioRxiv2022

Heterotypic Endosomal Interactions Drive Emergent Early Endosomal Maturations

Endosomal maturation is critical for robust and timely cargo transport to specific cellular compartments. The most prominent model of early endosomal maturation involves phosphoinositide-driven gain or loss of specific proteins on individual endosomes, emphasising an autonomous and stochastic description. However, limitations in fast, volumetric imaging long hindered direct whole-cell measurements of absolute numbers of maturation events. Here, we use lattice light-sheet imaging and bespoke automated analysis to track individual very early (APPL1-positive) and early (EEA1-positive) endosomes over the entire population, demonstrating that direct interendosomal contact drives maturation. Using fluorescence lifetime, we show that this interaction is underpinned by asymmetric EEA1 binding to very early and early endosomes through its N- and C-termini, respectively. In combination with agent-based simulation that confirms a trigger-and-convert model, our findings indicate that APPL1-to EEA1-positive maturation is driven not by autonomous events but by heterotypic EEA1-mediated interactions, providing a mechanism for temporal and population-level control of maturation.

biophysics↗

bioRxiv2022

Single cells tell their own story: An updated framework for understanding stochastic variations in cell cycle progression in bacteria

Our current understanding of the bacterial cell cycle is framed largely by population-based experiments that focus on the behavior of idealized average cells. Most famously, the contributions of Cooper and Helmstetter help to contextualize the phenomenon of overlapping replication cycles observed in rapidly growing bacteria. Despite the undeniable value of these approaches, their necessary reliance on the behavior of idealized average cells washes out the stochasticity inherent in single cell growth and physiology limiting their mechanistic value. To bridge this gap, we propose an updated and agnostic framework, informed by extant single-cell data, that quantitatively accounts for stochastic variations in single-cell dynamics and the impact of medium composition on cell growth and cell cycle progression. In this framework, stochastic timers sensitive to medium composition impact the relationship between cell cycle events, accounting for observed differences in the relationship between cell cycle events in slow and fast growing cells. We conclude with a roadmap for potential application of this framework to longstanding open questions in the bacterial cell cycle field.

biophysics↗

bioRxiv2022

Emergent periodicity in the collective synchronous flashing of fireflies

In isolation from their peers, Photinus carolinus fireflies flash with no intrinsic period between successive bursts. Yet, when congregating into large mating swarms, these fireflies transition into predictability, synchronizing with their neighbors with a rhythmic periodicity. Here we propose a mechanism for emergence of synchrony and periodicity, and formulate the principle in a mathematical framework. Remarkably, with no fitting parameters, analytic predictions from this simple principle and framework agree strikingly well with data. Next, we add further sophistication to the framework using a computational approach featuring groups of random oscillators via integrate-and-fire interactions controlled by a tunable parameter. This agent-based framework of P. carolinus fireflies interacting in swarms of increasing density also shows quantitatively similar phenomenology and reduces to the analytic framework in the appropriate limit of the tunable coupling strength. We discuss our findings and note that the resulting dynamics follow the style of a decentralized follow-the-leader synchronization, where any of the randomly flashing individuals may take the role of the leader of any subsequent synchronized flash burst.

animal behavior and cognition↗