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Suman K Banik

Publications and source records attributed to Suman K Banik.

At least 19 recordsLinked to original sources

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

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 account of the broader pattern is lacking. To address this gap, we adopt an information-theoretic approach. An FFL transmits information through two paths that share an input and converge on an output, suggesting that their interaction may contribute to differences among motifs. To investigate this, we decompose input-output mutual information (MI) into pathway MI and interference MI (IMI). Here, the pathway MI collects the contributions of individual pathways, whereas IMI identifies the contribution that arises from their interference. We find that within a representative parameter regime, IMI values of FFLs produce a hierarchy that closely resembles the abundance ordering in \textit{E. coli}, whereas total and pathway MIs do not. Motivated by this observation, we perform constrained optimization that identifies parameter regimes compatible with abundance-like targets for both \textit{S. cerevisiae} and \textit{E. coli}. We relate the optimized IMI hierarchy to pathway interference strength and local pathway sensitivities to understand the underlying biophysical origin. Our framework thus probes the correspondence between pathway interference, information transmission, and motif-abundance hierarchies.

physics.bio-ph

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

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

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

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

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

The coherent feed-forward loop acts as an efficient information transmitting motif

We present a theoretical formalism to study steady state information transmission in type 1 coherent feed-forward loop motif with an additive signal integration mechanism. Our construct allows a two-step cascade to be slowly transformed into a bifurcation network via a feed-forward loop which is a prominent network motif. Utilizing a Gaussian framework, we show that the feed-forward loop motif harnesses the maximum amount of Shannon mutual information fractions constructed between the input signaling species and the final gene product and also between the two downstream gene products. Our findings may explain why a feed-forward loop is naturally selected by the force of evolution as a motif in bacterial transcription control network while the two-step cascade and bifurcation network are not.

q-bio.MN

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

Interplay of synergy and redundancy in diamond motif

The formalism of partial information decomposition provides independent or non-overlapping components constituting total information content provided by a set of source variables about the target variable. These components are recognised as unique information, synergistic information and, redundant information. The metric of net synergy, conceived as the difference between synergistic and redundant information, is capable of detecting synergy, redundancy and, information independence among stochastic variables. And it can be quantified, as it is done here, using appropriate combinations of different Shannon mutual information terms. Utilisation of such a metric in network motifs with the nodes representing different biochemical species, involved in information sharing, uncovers rich store for interesting results. In the current study, we make use of this formalism to obtain a comprehensive understanding of the relative information processing mechanism in a diamond motif and two of its sub-motifs namely bifurcation and integration motif embedded within the diamond motif. The emerging patterns of synergy and redundancy and their effective contribution towards ensuring high fidelity information transmission are duly compared in the sub-motifs and independent motifs (bifurcation and integration). In this context, the crucial roles played by various time scales and activation coefficients in the network topologies are especially emphasised. We show that the origin of synergy and redundancy in information transmission can be physically justified by decomposing diamond motif into bifurcation and integration motif.

q-bio.MN

Stochastic study of information transmission and population stability in a generic bacterial two-component system

Studies on the role of fluctuations in signal propagation and on gene regulation in monoclonal bacterial population have been extensively pursued based on the machinery of two-component system. The bacterial two-component system shows noise utilisation through its inherent plasticity. The fluctuations propagation takes place using the phosphotransfer module and the feedback mechanism during gene regulation. To delicately observe the noisy kinetics the generic cascade needs stochastic investigation at the mRNA and protein levels. To this end, we propose a theoretical framework to investigate the noisy signal transduction in a generic two-component system. The model shows reliability in information transmission through quantification of several statistical measures. We further extend our analysis to observe the protein distribution in a population of cells. Through numerical simulation, we identify the regime of the kinetic parameter set that generates a stability switch in the steady state distribution of proteins. The results of our theoretical analysis show key features of the network. The noise permeation and information propagation in the autoregulation module is feeble. However, the phosphotransfer module compensates such weakness and plays a significant role in information transmission. The bimodality due to fluctuations pampers the emergence of persistence in an isogenic bacterial pool.

q-bio.MN

Redundancy in the information transmission in a two-step cascade

We present a stochastic framework to study signal transmission in a generic two-step cascade $\text{S} \rightarrow \text{X} \rightarrow \text{Y}$. Starting from a set of Langevin equations obeying Gaussian noise processes we calculate the variance and covariance while considering both linear and nonlinear production terms for different biochemical species of the cascade. These quantities are then used to calculate the net synergy within the purview of partial information decomposition. We show that redundancy in information transmission is essentially an important consequence of Markovian property of the two-step cascade motif. We also show that redundancy increases fidelity of the signalling pathway.

q-bio.MN

Controlling mobility via rapidly oscillating time-periodic stimulus

To address the dynamics of a Brownian particle on a periodic symmetric substrate under high-frequency periodic forcing with a vanishing time average, we construct an effective Langevin dynamics by invoking Kapitza-Landau time window. Our result is then exploited to simulate the mobility both for original and effective dynamics which are in good agreement with theoretical predictions. This close agreement and the enhancement of mobility are very robust against the tailoring of amplitude-to-frequency ratio which substantiates the correctness of our calculation. Present results may be illuminating for understanding the dynamics of cold atoms in electromagnetic fields.

cond-mat.soft

Breathing dynamics based parameter sensitivity analysis of hetero-polymeric DNA

We study the parameter sensitivity of hetero-polymeric DNA within the purview of DNA breathing dynamics. The degree of correlation between the mean bubble size and the model parameters are estimated for this purpose for three different DNA sequences. The analysis leads us to a better understanding of the sequence dependent nature of the breathing dynamics of hetero-polymeric DNA. Out of the fourteen model parameters for DNA stability in the statistical Poland-Scheraga approach, the hydrogen bond interaction $ε_{hb}(\mathtt{AT})$ for an $\mathtt{AT}$ base pair and the ring factor $ξ$ turn out to be the most sensitive parameters. In addition, the stacking interaction $ε_{st}(\mathtt{TA}-\mathtt{TA})$ for an $\mathtt{TA}-\mathtt{TA}$ nearest neighbor pair of base-pairs is found to be the most sensitive one among all stacking interactions. Moreover, we also establish that the nature of stacking interaction has a deciding effect on the DNA breathing dynamics, not the number of times a particular stacking interaction appears in a sequence. We show that the sensitivity analysis can be used as an effective measure to guide a stochastic optimization technique to find the kinetic rate constants related to the dynamics as opposed to the case where the rate constants are measured using the conventional unbiased way of optimization.

q-bio.BM

Role of functionality in two-component signal transduction: A stochastic study

We present a stochastic formalism for signal transduction processes in bacterial two-component system. Using elementary mass action kinetics, the proposed model takes care of signal transduction in terms of phosphotransfer mechanism between the cognate partners of a two-component system, viz, the sensor kinase and the response regulator. Based on the difference in functionality of the sensor kinase, the noisy phosphotransfer mechanism has been studied for monofunctional and bifunctional two component system using the formalism of linear noise approximation. Steady state analysis of both models quantifies different physically realizable quantities, e.g., variance, coefficient of variation, mutual information. The resultant data reveals that both systems reliably transfer information of extra-cellular environment under low external stimulus and at high kinase and phosphatase regime. We extend our analysis further by studying the role of two-component system in downstream gene regulation.

q-bio.SC

Analysis of DevR regulated genes in Mycobacterium tuberculosis

The DevRS two component system of Mycobacterium tuberculosis is responsible for its dormancy in host and becomes operative under hypoxic condition. It is experimentally known that phosphorylated DevR controls the expression of several downstream genes in a complex manner. In the present work we propose a theoretical model to show role of binding sites in DevR mediated gene expression. Individual and collective role of binding sites in regulating DevR mediated gene expression has been shown via modeling. Objective of the present work is two fold. First, to describe qualitatively the temporal dynamics of wild type genes and their known mutants. Based on these results we propose that DevR controlled gene expression follows a specific pattern which is efficient in describing other DevR mediated gene expression. Second, to analyze behavior of the system from information theoretical point of view. Using the tools of information theory we have calculated molecular efficiency of the system and have shown that it is close to the maximum limit of isothermal efficiency.

q-bio.MN

Stochastic Optimization Based Study of Dimerization Kinetics

We investigate the potential of numerical algorithms to decipher the kinetic parameters involved in multi-step chemical reactions. To this end we study a dimerization kinetics of protein as a model system. We follow the dimerization kinetics using a stochastic simulation algorithm and combine it with three different optimization techniques (Genetic Algorithm, Simulated Annealing and Parallel Tempering) to obtain the rate constants involved in each reaction step. We find good convergence of the numerical scheme to the rate constants of the process. We also perform a sensitivity test on the reaction kinetic parameters to see the relative effects of the parameters for the associated profile of the monomer/dimer distribution.

physics.bio-ph

Quantification of noise in the bifunctionality-induced post-translational modification

We present a generic analytical scheme for the quantification of fluctuations due to bifunctionality-induced signal transduction within the members of bacterial two-component system. The proposed model takes into account post-translational modifications in terms of elementary phosphotransfer kinetics. Sources of fluctuations due to autophosphorylation, kinase and phosphatase activity of the sensor kinase have been considered in the model via Langevin equations, which are then solved within the framework of linear noise approximation. The resultant analytical expression of phosphorylated response regulators are then used to quantify the noise profile of biologically motivated single and branched pathways. Enhancement and reduction of noise in terms of extra phosphate outflux and influx, respectively, have been analyzed for the branched system. Furthermore, role of fluctuations of the network output in the regulation of a promoter with random activation/deactivation dynamics has been analyzed.

q-bio.SC

Determining the DNA stability parameters for the breathing dynamics of heterogeneous DNA by stochastic optimization

We suggest that the thermodynamic stability parameters (nearest neighbor stacking and hydrogen bonding free energies) of double-stranded DNA molecules can be inferred reliably from time series of the size fluctuations (breathing) of local denaturation zones (bubbles). On the basis of the reconstructed bubble size distribution, this is achieved through stochastic optimization of the free energies in terms of Simulated Annealing. In particular, it is shown that even noisy time series allow the identification of the stability parameters at remarkable accuracy. This method will be useful to obtain the DNA stacking and hydrogen bonding free energies from single bubble breathing assays rather than equilibrium data.

q-bio.BM