arXiv subjects

Mintu Nandi

Publications and source records attributed to Mintu Nandi.

9 recordsLinked to original sources

arXiv2026

An information-theoretic perspective on feed-forward loop abundances in transcriptional networks

Biological networks contain recurring motifs, yet their unequal abundance remains poorly understood. In the transcriptional networks of \textit{Escherichia coli} and \textit{Saccharomyces cerevisiae}, the eight feed-forward loop (FFL) motifs occur at markedly different frequencies. Although previous studies have linked the abundant C1- and I1-FFLs to specific dynamical functions, a common quantitative account of the broader pattern is lacking. An FFL transmits upstream information through direct and indirect paths that share the same input and converge on the same output. Their information contributions therefore need not combine independently. To investigate this, we decompose input-output mutual information (MI) into pathway and interference components, defining the latter as interference mutual information (IMI). IMI can be positive or negative, indicating that pathway coupling can enhance or reduce information transmission. Within physiologically relevant regimes, the IMI hierarchy follows the observed abundance patterns in both FFL classes, whereas total MI and pathway MI do not consistently do so. We further relate this hierarchy to pathway-interference strength and local pathway sensitivities. These results identify pathway interference as an architectural feature of information transmission and provide a quantitative basis for understanding the unequal abundance of FFL motifs.

physics.bio-ph↗

arXiv2026

Feedback-mediated circulation and persistence of stochastic fluctuations in gene regulatory circuits

Feedback plays a significant role in biochemical networks that govern a multitude of cellular functions, including development, adaptation, and homeostasis. Yet, how feedback topology controls stochastic fluctuations remains incompletely understood. Here, we develop a theoretical framework for two-node feedback motifs composed of activating and repressive regulatory interactions between two transcription factors. Under the linear noise approximation, we identify a feedback-driven contribution to node-wise fluctuations, termed cyclic noise, that arises specifically from loop closure. Cyclic noise is the component of fluctuations that circulates through the regulatory circuit. Its sign and magnitude distinguish whether feedback amplifies or attenuates node-wise fluctuations. We further show that feedback-mediated noise circulation leaves a temporal signature in the decay of steady-state autocorrelation, revealing how loop closure modifies the persistence of fluctuations. We thus provide a minimal framework for understanding how feedback architecture regulates both the magnitude and the temporal persistence of noise in gene regulatory circuits.

physics.bio-ph↗

arXiv2026

Decoding cell signaling via optimal transport and information theory

Cellular signal processing performs reliably despite molecular noise. Mutual information (MI) is widely used to quantify signaling fidelity, capturing how well outputs discriminate input states. However, it fails to capture whether the output preserves the statistical structure of the input, a property crucial in morphogen patterning and dose-dependent signaling. To address this gap, we introduce the 2-Wasserstein (2-WD) distance, which provides a geometric basis for comparing input and output distributions. We define MI as informational fidelity (INF) and the inverse of the 2-WD as geometric fidelity (GMF). Applying this dual-fidelity framework to canonical regulatory motifs under Gaussian channel approximation reveals topology-dependent trade-offs: coherent feed-forward loops can perform well in both dimensions, whereas feedback architectures reduce INF to enhance GMF. Experimental analysis of tumor necrosis factor signaling reveals dual-fidelity behavior qualitatively consistent with feedback regulation. RAS-MAPK data analysis further shows that jointly considering INF and GMF better characterizes intracellular signal relay than INF alone. Our results thus indicate that these signaling behaviors are not fully characterized by MI alone; instead, distributional correspondence provides a complementary dimension of signaling fidelity. Our study provides a practical framework for analyzing natural networks and guiding the design of task-specific synthetic circuits.

physics.bio-ph↗

arXiv2025

Identifying the sources of noise synergy and redundancy in the gene expression of feed-forward loop motif

The propagation of noise through parallel regulatory pathways is a characteristic feature of feed-forward loops in genetic networks. Although the contributions of the direct and indirect regulatory pathways of feed-forward loops to output variability have been well characterized, the impact of their joint action arising from their shared input and output remains poorly understood. Here, we identify an additional component of noise that emerges specifically from this convergent nature of the pathways. Using inter-gene correlations, we reveal the regulatory basis of the cross-interaction noise and interpret it as synergy or redundancy in noise propagation, depending on whether the combined pathways amplify or suppress fluctuations. Synergy typically arises in coherent feed-forward loops, whereas redundancy is common in incoherent ones. This framework not only accounts for previously observed differences in noise behavior across coherent and incoherent structures but also provides a generalizable strategy to connect network structure with stochastic gene regulation. Furthermore, by relating these synergy and redundancy to dynamical properties such as sign-sensitive delay or response acceleration, the framework offers a statistical lens to interpret the functional roles in cellular decision-making.

q-bio.MN↗

arXiv2023

Channel assisted noise propagation in a two-step cascade

Signal propagation in biochemical networks is characterized by the inherent randomness in gene expression and fluctuations of the environmental components, commonly known as intrinsic and extrinsic noise, respectively. We present a theoretical framework for noise propagation in a generic two-step cascade (S$\rightarrow$X$\rightarrow$Y) regarding intrinsic and extrinsic noise. We identify different channels of noise transmission that regulate the individual and the overall noise properties of each component. Our analysis shows that the intrinsic noise of S alleviates the general noise and information transmission capacity along the cascade. On the other hand, the intrinsic noise of X and Y acts as a bottleneck of information transmission. We also show a hierarchical relationship among the intrinsic noise levels of S, X, and Y, with S exhibiting the highest level of intrinsic noise, followed by X and then Y. This hierarchy is preserved within the two-step cascade, facilitating the highest information transmission from S to Y via X.

q-bio.MN↗

arXiv2023

Interplay of degeneracy and non-degeneracy in fluctuations propagation in coherent feed-forward loop motif

We present a stochastic framework to decipher fluctuations propagation in classes of coherent feed-forward loops. The systematic contribution of the direct (one-step) and indirect (two-step) pathways is considered to quantify fluctuations of the output node. We also consider both additive and multiplicative integration mechanisms of the two parallel pathways (one-step and two-step). Analytical expression of the output node's coefficient of variation shows contributions of intrinsic, one-step, two-step, and cross-interaction in closed form. We observe a diverse range of degeneracy and non-degeneracy in each of the decomposed fluctuations term and their contribution to the overall output fluctuations of each coherent feed-forward loop motif. Analysis of output fluctuations reveals a maximal level of fluctuations of the coherent feed-forward loop motif of type 1.

q-bio.MN↗

arXiv2021

Information transmission in a two-step cascade: Interplay of activation and repression

We present an information-theoretic formalism to study signal transduction in four architectural variants of a model two-step cascade with increasing input population. Our results categorize these four types into two classes depending upon the effect played out by activation and repression on mutual information, net synergy, and signal-to-noise ratio. Within the Gaussian framework and using the linear noise approximation, we derive the analytic expressions for these metrics to establish their underlying relationships in terms of the biochemical parameters. We also verify our approximations through stochastic simulations.

q-bio.MN↗

arXiv2018

Information processing in a simple one-step cascade

Using the formalism of information theory, we analyze the mechanism of information transduction in a simple one-step signaling cascade S$\rightarrow$X representing the gene regulatory network. Approximating the signaling channel to be Gaussian, we describe the dynamics using Langevin equations. Upon discretization, we calculate the associated second moments for linear and nonlinear regulation of the output by the input, which follows the birth-death process. While mutual information between the input and the output characterizes the channel capacity, the Fano factor of the output gives a clear idea of how internal and external fluctuations assemble at the output level. To quantify the contribution of the present state of the input to predict the future output, transfer entropy is computed. We find that higher amount of transfer entropy is accompanied by the greater magnitude of external fluctuations (quantified by the Fano factor of the output) propagation from the input to the output. We notice that low input population characterized by the number of signaling molecules S, which fluctuates in a relatively slower fashion compared to its downstream (target) species X, is maximally able to predict (as quantified by transfer entropy) the future state of the output. Our computations also reveal that with increased linear nature of the input-output interaction, all three metrics of mutual information, Fano factor and, transfer entropy achieve relatively larger magnitudes.

q-bio.MN↗

arXiv2016

Emergence of Dynamic Cooperativity in the Stochastic Kinetics of Fluctuating Enzymes

Dynamic cooperativity in monomeric enzymes is characterized in terms of a non-Michaelis-Menten kinetic behaviour. The latter is believed to be associated with mechanisms that include multiple reaction pathways due to enzymatic conformational fluctuations. Recent advances in single-molecule fluorescence spectroscopy have provided new fundamental insights on the possible mechanisms underlying reactions catalyzed by fluctuating enzymes. Here, we present a bottom-up approach to understand enzyme turnover kinetics at physiologically relevant mesoscopic concentrations informed by mechanisms extracted from single-molecule stochastic trajectories. The stochastic approach, presented here, shows the emergence of dynamic cooperativity in terms of a slowing down of the Michaelis-Menten (MM) kinetics resulting in negative cooperativity. For fewer enzymes, dynamic cooperativity emerges due to the combined effects of enzymatic conformational fluctuations and molecular discreteness. The increase in the number of enzymes, however, suppresses the effect of enzymatic conformational fluctuations such that dynamic cooperativity emerges solely due to the discrete changes in the number of reacting species. These results confirm that the turnover kinetics of fluctuating enzyme based on the parallel-pathway MM mechanism switches over to the single-pathway MM mechanism with the increase in the number of enzymes. For large enzyme numbers, convergence to the exact MM equation occurs in the limit of very high substrate concentration as the stochastic kinetics approaches the deterministic behaviour.

physics.chem-ph↗