arXiv subjects

Srividya Iyer-Biswas

Publications and source records attributed to Srividya Iyer-Biswas.

13 recordsLinked to original sources

arXiv2025

The transition from homeostasis to stochasticity induced catastrophe

What are the signatures of the onset of catastrophe? Here we present the rich system physics characterizing the transition from homeostasis to stochasticity driven breakdown in an experimentally motivated minimal model. Recent high-precision experiments on individual bacterial cells, growing and dividing repeatedly in a variety of environments, have revealed a previously unknown intergenerational scaling law which not only uniquely determines the stochastic map governing homeostasis, but also, as we show here, offers quantitative insights into the transition from the "conspiracy principle" regime (homeostasis) to the "catastrophe principle" regime and then to system breakdown. In fact, upon closer examination, the stochastic map turns out to be a one-dimensional Kesten process; these transitions occur as a single parameter, the strength of the multiplicative noise term, is continuously tuned. Emergence of asymptotically scale invariant distributions with quantifiable power law tails, outlier driven extremal behavior and reverse monotonicity of the conditional exceedance distribution characterize this transition to catastrophe. In turn, prevention of rapid increase in the extremal event-driven rate of failure, in the interest of system preservation, causes the catastrophe regime to be strategically unfavorable.

cond-mat.stat-mech↗

arXiv2024

Emergent Simplicities in the Living Histories of Individual Cells

Organisms maintain the status quo, holding key physiological variables constant to within an acceptable tolerance, and yet adapt with precision and plasticity to dynamic changes in externalities. What organizational principles ensure such exquisite yet robust control of systems-level "state variables" in complex systems with an extraordinary number of moving parts and fluctuating variables? Here we focus on these issues in the specific context of intra- and intergenerational life histories of individual bacterial cells, whose biographies are precisely charted via high-precision dynamic experiments using the SChemostat technology. We highlight intra- and intergenerational scaling laws and other "emergent simplicities" revealed by these high-precision data. In turn, these facilitate a principled route to dimensional reduction of the problem, and serve as essential building blocks for phenomenological and mechanistic theory. Parameter-free data-theory matches for multiple organisms validate theory frameworks, and explicate the systems physics of stochastic homeostasis and adaptation.

cond-mat.stat-mech↗

arXiv2023

Emergent Spatiotemporal Organization in Stochastic Intracellular Transport Dynamics

The interior of a living cell is an active, fluctuating, and crowded environment. Yet, it maintains a high level of coherent organization, which is readily apparent in the intracellular transport network. Membrane-bound compartments called endosomes play a key role in carrying cargo, in conjunction with myriad components including cargo adaptor proteins, membrane sculptors, motor proteins, and the cytoskeleton. These components coordinate to effectively navigate the crowded cell interior and transport cargo to specific intracellular locations, even though the underlying protein interactions and enzymatic reactions exhibit stochastic behavior. A major challenge is to measure, analyze, and understand how, despite the inherent stochasticity of the constituent processes, the collective outcomes show an emergent spatiotemporal order that is precise and robust. This review focuses on this intriguing dichotomy, providing insights into the known mechanisms of noise suppression and noise utilization in intracellular transport processes, and also identifies opportunities for future inquiry.

physics.bio-ph↗

arXiv2023

Gravitational Response of Topological Quantum States of Matter

Identifying novel topological properties of topological quantum states of matter, such as exemplified by the quantized Hall conductance, is a valuable step towards realizing materials with attractive topological attributes that guarantee their imperviousness to realistic imperfections, disorder and environmental disturbances. Is the gravitational coupling coefficient of topological quantum states of matter a promising candidate? Substantially building on well established results for quantum Hall states, using disclinations as tools for controlled creation of pristine spatial curvature free of undesirable artifacts such as would interfere with the electronic motion of interest, herein we report that a large class of lattice topological states of matter exhibit gravitational response, i.e., charge response to intrinsic spatial curvature. This phenomenon is characterized by a topologically quantized coupling constant. Remarkably, the charge-gravity relationship remains linear in the curvature, up to the maximum curvature achievable on the lattice, demonstrating absence of higher order nonlinear response. Our findings facilitate articulating the physical principles underlying the topological quantization of the gravitational coupling constant, in analogy with the insights offered by the Chern number description of the quantized Hall conductance.

cond-mat.mes-hall↗

arXiv2018

Bridging the time scales of single-cell and population dynamics

How are granular details of stochastic growth and division of individual cells reflected in smooth deterministic growth of population numbers? We provide an integrated, multiscale perspective of microbial growth dynamics by formulating a data-validated theoretical framework that accounts for observables at both single-cell and population scales. We derive exact analytical complete time-dependent solutions to cell-age distributions and population growth rates as functionals of the underlying interdivision time distributions, for symmetric and asymmetric cell division. These results provide insights into the surprising implications of stochastic single-cell dynamics for population growth. Using our results for asymmetric division, we deduce the time to transition from the reproductively quiescent (swarmer) to replication-competent (stalked) stage of the {\em Caulobacter crescentus} lifecycle. Remarkably, population numbers can spontaneously oscillate with time. We elucidate the physics leading to these population oscillations. For {\em C. crescentus} cells, we show that a simple measurement of the population growth rate, for a given growth condition, is sufficient to characterize the condition-specific cellular unit of time, and thus yields the mean (single-cell) growth and division timescales, fluctuations in cell division times, the cell age distribution, and the quiescence timescale.

q-bio.QM↗

arXiv2017

Phenomenology of stochastic exponential growth

Stochastic exponential growth is observed in a variety of contexts, including molecular autocatalysis, nuclear fission, population growth, inflation of the universe, viral social media posts, and financial markets. Yet literature on modeling the phenomenology of these stochastic dynamics has predominantly focused on one model, Geometric Brownian Motion (GBM), which can be described as the solution of a Langevin equation with linear drift and linear multiplicative noise. Using recent experimental results on stochastic exponential growth of individual bacterial cell sizes, we motivate the need for a more general class of phenomenological models of stochastic exponential growth, which are consistent with the observation that the mean-rescaled distributions are approximately stationary at long times. We show that this behavior is not consistent with GBM, instead it is consistent with power law multiplicative noise with positive fractional powers. Therefore, we consider this general class of phenomenological models for stochastic exponential growth, provide analytical solutions, and identify the important dimensionless combination of model parameters which determines the shape of the mean-rescaled distribution. We also provide a prescription for robustly inferring model parameters from experimentally observed stochastic growth trajectories.

cond-mat.stat-mech↗

arXiv2017

Biological timekeeping in the presence of stochasticity

Causal ordering of key events in the cell cycle is essential for proper functioning of an organism. Yet, it remains a mystery how a specific temporal program of events is maintained despite ineluctable stochasticity in the biochemical dynamics which dictate timing of cellular events. We propose that if a change of cell fate is triggered by the {\em time-integral} of the underlying stochastic biochemical signal, rather than the original signal, then a dramatic improvement in temporal specificity results. Exact analytical results for stochastic models of hourglass-timers and pendulum-clocks, two important paradigms for biological timekeeping, elucidate how temporal specificity is achieved through time-integration. En route, we introduce a natural representation for time-integrals of stochastic processes, provide an analytical prescription for evaluating corresponding first-passage-time distributions, and uncover a mechanism by which a population of identical cells can spontaneously bifurcate into subpopulations of early and late responders, depending on hierarchy of timescales in the dynamics. Moreover, our approach reveals how time-integration of stochastic signals may be realized biochemically, through a simple chemical reaction scheme.

q-bio.MN↗

arXiv2015

Intergenerational continuity of cell shape dynamics in Caulobacter crescentus

We investigate the intergenerational shape dynamics of single Caulobacter crescentus cells using a novel combination of imaging techniques and theoretical modeling. We determine the dynamics of cell pole-to-pole lengths, cross-sectional widths, and medial curvatures from high accuracy measurements of cell contours. Moreover, these shape parameters are determined for over 250 cells across approximately 10000 total generations, which affords high statistical precision. Our data and model show that constriction is initiated early in the cell cycle and that its dynamics are controlled by the time scale of exponential longitudinal growth. Based on our extensive and detailed growth and contour data, we develop a minimal mechanical model that quantitatively accounts for the cell shape dynamics and suggests that the asymmetric location of the division plane reflects the distinct mechanical properties of the stalked and swarmer poles. Furthermore, we find that the asymmetry in the division plane location is inherited from the previous generation. We interpret these results in terms of the current molecular understanding of shape, growth, and division of C. crescentus

q-bio.CB↗

arXiv2015

First Passage processes in cellular biology

Often sharp changes in cellular behavior are triggered by thresholded events, i.e., by the attainment of a threshold value of a relevant cellular or molecular dynamical variable. Since the governing variable itself typically undergoes noisy or stochastic dynamics, there is a corresponding variability in the times when the same change occurs in each cell of a population. This time is called the "first passage" time and the corresponding process is a "first passage" (FP) process, referring to the event when a random variable first passes the threshold value. In this review we first present and elucidate fundamentals of the FP formalism within a unified conceptual framework, which naturally integrates the existing techniques. We then discuss applications thereof, with emphasis on the practical use of FP techniques in biophysical systems. Our focus here is on covering a diverse set of analytical techniques; the number of reviewed biological applications is thus limited, out of necessity. We focus on three specific areas: channel transport; receptor binding and adhesion; and single-cell growth and division.

cond-mat.stat-mech↗

arXiv2014

Mixed Poisson distributions in exact solutions of stochastic auto-regulation models

In this paper we study the interplay between stochastic gene expression and system design using simple stochastic models of auto-activation and auto-inhibition. Using the Poisson Representation, a technique whose particular usefulness in the context of non-linear gene regulation models we elucidate, we find exact results for these feedback models in the steady state. Further, we exploit this representation to analyze the parameter spaces of each model, determine which dimensionless combinations of rates are the shape determinants for each distribution, and thus demarcate where in the parameter-space qualitatively different behaviors arise. These behaviors include power-law tailed distributions, bimodal distributions and sub-Poisson distributions. We also show how these distribution shapes change when the strength of the feedback is tuned. Using our results, we reexamine how well the auto-inhibition and auto-activation models serve their conventionally assumed roles as paradigms for noise suppression and noise exploitation, respectively.

q-bio.QM↗

arXiv2014

Scaling laws governing stochastic growth and division of single bacterial cells

Uncovering the quantitative laws that govern the growth and division of single cells remains a major challenge. Using a unique combination of technologies that yields unprecedented statistical precision, we find that the sizes of individual Caulobacter crescentus cells increase exponentially in time. We also establish that they divide upon reaching a critical multiple ($\approx$1.8) of their initial sizes, rather than an absolute size. We show that when the temperature is varied, the growth and division timescales scale proportionally with each other over the physiological temperature range. Strikingly, the cell-size and division-time distributions can both be rescaled by their mean values such that the condition-specific distributions collapse to universal curves. We account for these observations with a minimal stochastic model that is based on an autocatalytic cycle. It predicts the scalings, as well as specific functional forms for the universal curves. Our experimental and theoretical analysis reveals a simple physical principle governing these complex biological processes: a single temperature-dependent scale of cellular time governs the stochastic dynamics of growth and division in balanced growth conditions.

physics.bio-ph↗

arXiv2014

Universality in stochastic exponential growth

Recent imaging data for single bacterial cells reveal that their mean sizes grow exponentially in time and that their size distributions collapse to a single curve when rescaled by their means. An analogous result holds for the division-time distributions. A model is needed to delineate the minimal requirements for these scaling behaviors. We formulate a microscopic theory of stochastic exponential growth as a Master Equation that accounts for these observations, in contrast to existing quantitative models of stochastic exponential growth (e.g., the Black-Scholes equation or geometric Brownian motion). Our model, the stochastic Hinshelwood cycle (SHC), is an autocatalytic reaction cycle in which each molecular species catalyzes the production of the next. By finding exact analytical solutions to the SHC and the corresponding first passage time problem, we uncover universal signatures of fluctuations in exponential growth and division. The model makes minimal assumptions, and we describe how more complex reaction networks can reduce to such a cycle. We thus expect similar scalings to be discovered in stochastic processes resulting in exponential growth that appear in diverse contexts such as cosmology, finance, technology, and population growth.

cond-mat.stat-mech↗

arXiv2007

Transcriptional pulsing and consequent stochasticity in gene expression

Transcriptional pulsing has been observed in both prokaryotes and eukaryotes and plays a crucial role in cell to cell variability of protein and mRNA numbers. The issue is how the time constants associated with episodes of transcriptional bursting impact cellular mRNA and protein distributions and reciprocally, to what extent experimentally observed distributions can be attributed to transcriptional pulsing. We address these questions by investigating the exact time-dependent solution of the Master equation for a transcriptional pulsing model of mRNA distributions. We find a plethora of results: we show that, among others, bimodal and long-tailed (power law) distributions occur in the steady state as the rate constants are varied over biologically significant time scales. Since steady state distributions may not be reached experimentally we present results for the time evolution of the distributions. Because cellular behavior is essentially determined by proteins, we investigate the effect of the different mRNA distributions on the corresponding protein distributions. We delineate the regimes of rate constants for which the protein distribution mimics the mRNA distribution and those for which the protein distribution deviates significantly from the mRNA distribution.

q-bio.QM↗