arXiv subjects

Tuhin Chakrabortty

Publications and source records attributed to Tuhin Chakrabortty.

9 recordsLinked to original sources

arXiv2025

Noisy active matter

Noise threads every scale of the natural world. Once dismissed as mere background hiss, it is now recognized as both a currency of information and a source of order in systems driven far from equilibrium. From nanometer-scale motor proteins to meter-scale bird flocks, active collectives harness noise to break symmetry, explore decision landscapes, and poise themselves at the cusp where sensitivity and robustness coexist. We review the physics that underpins this paradox: how energy-consuming feedback rectifies stochastic fluctuations, how multiplicative noise seeds patterns and state transitions, and how living ensembles average the residual errors. Bridging single-molecule calorimetry, critical flocking, and robophysical swarms, we propose a unified view in which noise is not background blur but a tunable resource for adaptation and emergent order in biology and engineered active matter.

cond-mat.soft↗

arXiv2025

Temporal network restructuring improves control of indecisive collectives

Controlling multi-agent systems is a persistent challenge in organismal, robotic and social collectives, especially when agents exhibit stochastic indecisiveness -- frequently switching between conflicting behavioral rules. Here, we investigate the control of such noisy indecisive collectives through the lens of century-old sheepdog trials, where small groups of sheep exhibit unpredictable switching between fleeing and following behaviors. Unlike cohesive large flocks, these small indecisive groups are difficult to control, yet skilled dog-handler teams excel at both herding and precisely splitting them (shedding) on demand. Using a stochastic model, we introduce two central parameters -- pressure (stimulus intensity) and lightness (response isotropy) -- to simulate and quantify herding and shedding dynamics. Surprisingly, we find that stochastic indecisiveness, typically perceived as a challenge, can be leveraged as a critical tool for efficient control, enabling controlled herding and splitting of noisy groups. Building on these insights, we develop the Indecisive Swarm Algorithm (ISA) for artificial agents and benchmark its performance against standard algorithms, including the Averaging-Based Swarm Algorithm (ASA) and the Leader-Follower Swarm Algorithm (LFSA). ISA minimizes control energy in trajectory-following tasks, outperforming alternatives under noisy conditions. By framing these results within a stochastic temporal network framework, we show that even with a probabilistic description of the future dynamics, network restructuring (temporality) enhances control efficiency in a specific class of control problems. These insights establish a scalable framework for controlling noisy, behavior-switching collectives, with applications in swarm robotics, cellular engineering, opinion dynamics, and temporal networks.

physics.soc-ph↗

arXiv2022

Ultrafast reversible self-assembly of living tangled matter

Tangled active filaments are ubiquitous in nature, from chromosomal DNA and cilia carpets to root networks and worm blobs. How activity and elasticity facilitate collective topological transformations in living tangled matter is not well understood. Here, we report an experimental and theoretical study of California blackworms (Lumbriculus variegatus), which slowly form tangles over minutes but can untangle in milliseconds. Combining ultrasound imaging, theoretical analysis and simulations, we develop and validate a mechanistic model that explains how the kinematics of individual active filaments determines their emergent collective topological dynamics. The model reveals that resonantly alternating helical waves enable both tangle formation and ultrafast untangling. By identifying generic dynamical principles of topological self-transformations, our results can provide guidance for designing new classes of topologically tunable active materials.

cond-mat.soft↗

arXiv2021

Fundamental limits of accuracy and precision in single molecule biosensors

Physical limit of molecular sensing has been extensively studied in biological systems. Biosensors are engineered equivalents of molecular sensors in living systems and play critical role in disease diagnosis and management. Investigation into the physical limits of biosensors could have major beneficial impact on early disease diagnosis. Here, we present an extension of the classical works on molecular sensing limits of living systems to the realm of biosensors. Two approaches are proposed to estimate concentration with noisy biosensors. We find a trade-off between precision and accuracy.

physics.bio-ph↗

arXiv2020

Equilibrium Probability Distribution for Number of Bound Receptor-Ligand Complexes

The phenomenon of molecular binding, where two molecules, referred to as a receptor and a ligand, bind together to form a ligand-receptor complex, is ubiquitous in biology and essential for the accurate functioning of all life-sustaining processes. The probability of a single receptor forming a complex with any one of L surrounding ligand molecules at thermal equilibrium can be derived from a partition function obtained from the Gibbs-Boltzmann distribution. We extend this approach to a system consisting of R receptors and L ligands to derive the probability density function p(r;R,L) to find r bound receptor-ligand complexes at thermal equilibrium. This extension allows us to illustrate two aspects of this problem which are not apparent in the single receptor problem, namely, a) a symmetry to be expected in the equilibrium distribution of the number of bound complexes under exchange of R and L and b) the number of bound complexes obtained from chemical kinetic equations has an exact correspondence to the maximum probable value of r from the expression for p(r;R,L). We derive the number fluctuations of r and present a practically relevant molecular sensing application that benefits from the knowledge of p(r;R,L).

physics.bio-ph↗

arXiv2019

Fundamental Limitations in Biomarker Based Early Disease Diagnosis

Clinical biosensors with low detection limit hold significant promise in the early diagnosis of debilitating diseases. Recent progress in sensor development has led to the demonstration of detection capable of detecting target molecules even down to single-molecule level. One crucial performance parameter which is not adequately discussed is the issue of measurement fidelity in such sensors. We define measurement fidelity based on the false positive rate of the system as we expect systems with higher sensitivity to concomitantly respond more to interfering molecules thus increasing the false-positive rates. We present a model which allows us to estimate the limit of detection of a biosensor system constrained by a specified false-positive rate. Two major results emerging from our model is that a) there is a lower bound to the detection limit for a target molecule determined by the variation in the concentration of background molecules interfering with the molecular recognition process and b) systems which use a secondary label, such as a fluorophore, can achieve lower detection limits for a given false positive rate. We also present data collected from literature to support our model. The insights from our model will be useful in the systematic design of future clinical biosensors to achieve relevant detection limits with assured fidelity.

physics.med-ph↗

arXiv2019

Theoretical Limit Of Concentration Sensing of Single Receptor Artificial Biosensors

Artificially engineered biosensors are highly inefficient in accurately measuring the concentration of biomarkers, particularly, during early diagnosis of diseases. On the other hand, single cellular systems such as chemotactic bacteria can sense their environment with extraordinary precision. Therefore, one would expect that implementing the optimal cellular sensing strategies in state-of-the-art artificial sensors can produce optimally precise biosensors. However because of the presence of measurement noise, strategies that are optimal in biological systems may not be optimal in artificial systems. Therefore, mimicking biological strategies may not be the optimal path in case of artificial sensing systems because of the presence of inherent measurement noise.

physics.bio-ph↗

arXiv2019

Error reduction in biosensors using secondary labeling

In this article, we derive the limit of detection for a two-step molecular recognition process and show that in-spite of all the recognition reactions being in equilibrium the overall error rates can be reduced exactly as much as possible in non-equilibrium methods such as kinetic proofreading.

physics.med-ph↗

arXiv2018

A Null-model Exhibiting Synchronized Dynamics in Uncoupled Oscillators

The phenomenon of phase synchronization of oscillatory systems arising out of feedback coupling is ubiquitous across physics and biology. In noisy, complex systems, one generally observes transient epochs of synchronization followed by non-synchronous dynamics. How does one guarantee that the observed transient epochs of synchronization are arising from an underlying feedback mechanism and not from some peculiar statistical properties of the system? This question is particularly important for complex biological systems where the search for a non-existent feedback mechanism may turn out be an enormous waste of resources. In this article, we propose a null model for synchronization motivated by expectations on the dynamical behaviour of biological systems to provide a quantitative measure of the confidence with which one can infer the existence of a feedback mechanism based on observation of transient synchronized behaviour. We demonstrate the application of our null model to the phenomenon of gait synchronization in free-swimming nematodes, C. elegans.

physics.bio-ph↗