Biology subjects

KUNDU, S.

Publications and source records attributed to KUNDU, S..

5 recordsLinked to original sources

bioRxiv2026

A discrete-to-continuous mathematical model for ensemble distributions of a ligand-interacting macromolecular species across milieux-dependent conformational states may offer insights into the genesis and progression of cooperative binding

Small molecule modifiers whence bound, allosterically, will alter the binding of a macromolecule to one- or more-cognate substrates/partners via conformational and non-conformational changes. Although allostery is inferred directly from empirical data, the mathematical basis of these models, constraints deployed and choice of parameter(s) are not clear. Here, we present and characterize a discrete-to-continuous mathematical model for ensemble distributions of a ligand-interacting macromolecular species across milieux-dependent conformational states and examine its role in the genesis and progression of cooperative binding. The premise, of our model, is a set of occupancy matrices (sparse, binary, strictly delocalized) which can be partitioned by a probability-based hyperparameter into mutually exclusive proper subsets of occupancy matrices with identical multinomial probabilities. Since each subset is canonical with a constituent occupancy matrix, it is characterized by a unique multinomial probability. The inner product of combinatorial pairs of all mutually exclusive subsets of occupancy matrices, with an expression for the summed transitional probabilities (finite differences between unique multinomial probabilities), is the differentiable matrix of strictly positive real-valued numbers for the system of ensemble distributions. Whilst the harmonic mean is presented as a generic solution for a system of ensemble distributions, the row-wise definite integral for each column is the finite union of open intervals (contiguous, strictly monotone) which in tandem with a set of interval-specific and bounded transitional probabilities constitutes a piecewise smooth curve (path-connected-, closed- and compact-set). Our discrete-to-continuous model is phenomenological and able to recapitulate the basic tenets of cooperative binding whilst offering insights into the genesis and progression of the same.

biochemistry↗

bioRxiv2026

Strategic Segmentation: A Nash Equilibrium based Approach for Weed Segmentation in Agricultural Fields

Weeds present a major challenge to agricultural productivity, competing with crops for critical resources like water, nutrients, and sunlight, resulting in significant yield reductions. Prompt weed identification is essential for enabling effective control strategies, such as the application of herbicides or mechanical removal, to minimize their impact on crop growth. This research focuses on developing a deep learning approach based on game theory for detecting weeds. Using CWFID dataset captured at various times and days, along with multispectral data in the visible and near-infrared spectrum, the study aims to improve early detection methods for more efficient weed management in agricultural settings. A novel segmentation technique for weed regions is introduced, employing a zero-sum game theory model to reconcile conflicting classifications from different weed detectors. These regions are treated as zones of conflict between weeds and crops, with each detector representing a different strategy. By defining an appropriate utility function, the method identifies the Nash equilibrium, effectively minimizing false positive detections of weeds.

plant biology↗

bioRxiv2024

Fitness defects due to cytosolic protein misfolding in S. cerevisiae can be alleviated by decreasing mitochondrial protein import capacity

Protein misfolding affects cellular fitness. This can be caused due to the toxic aggregation of one species of protein or global protein misfolding events. Since the fitness defect arises due to the multi-modal effect of misfolding, there is no consensus mechanism to alleviate this fitness defect. Here, we used adaptive laboratory evolution of thermotolerance to identify pathways contributing to proteotoxic stress resistance in S. cerevisiae. Our results suggest a link between thermotolerance and proteotoxicity resistance, majorly routed through the loss of mitochondrial DNA. Loss of mitochondrial DNA decreased the association of mistargeted misfolded proteins on the mitochondrial surface and altered the cellular response to proteostasis to enhance protein quality control associated degradation. We show that a decrease in the abundance of import channels is sufficient to mimic the loss of mtDNA and increase cellular proteostasis. Thus, we uncover a cryptic interorganellar cooperation in combating proteotoxicity in yeast.

molecular biology↗

bioRxiv2024

Construction, analysis and assessment of relevance of an algebraic model for a class of biochemical networks

The intracellular milieu, replete with concomitantly occurring biochemical networks, constitutes a complex physicochemical environment wherein macromolecules interact and whose activities can be modified by small molecules. These metabolism-contributing, signal-transducing and response-regulating networks, and the redundant forms that result, thereof, are responsible for complex biochemical-, physiological- and clinical-outcomes. It is unclear whether a unique mathematical structure, which is concurrently resolvable into redundant, unequal and functionally relevant substructures, can be ascribed to a biochemical network. Here, we develop a reactant-dependent algebraic framework to define, construct, characterize and assess the redundant forms of a biochemical network. We deploy a constraint-based approach to populate and select plausible reaction vectors in accordance with known paradigms of biochemically relevant outcomes. There is a strict lower bound for the number of participating reactants with integral changes, across all molecules of a reactant, which are Boolean (zero, signed non-zero) and combinatorially distributed across linearly independent plausible reaction vectors. The reactants and the selected plausible reaction vectors form a set of unique stoichiometry number matrices, each of which represents a distinct biochemical network which when multiplied by a set of positive and non-unitary rational numbers will generate a library of equivalent rational number matrices. The matrices that comprise this library are unequal, disjoint, form a semigroup with respect to addition, share the same null space and represent the redundant forms of a biochemical network. The presented framework is theoretically sound, mathematically rigorous, readily implementable, easily parameterized and can be used to assess the biomedical relevance for the redundant forms of a biochemical network. We conclude our thesis with plausible explanations into the genesis of formaldehyde resistance and thence nosocomial infections by Klebsiella spp.

systems biology↗

bioRxiv2023

ReDirection: a numerically robust R-package to characterize every reaction of a user-defined biochemical network with the probable dissociation constant

Biochemical networks integrate enzyme-mediated substrate conversions with non-enzymatic complex formation and disassembly to accomplish complex biochemical and physiological function. The multitude of theoretical studies utilizing empirical/clinical data notwithstanding, the parameters used in these analyses whilst being theoretically sound are likely to be of limited biomedical relevance. There is need for a computational tool which can ascribe functionality to and generate potentially testable hypotheses for a biochemical network. "ReDirection" characterizes every reaction of a user-defined biochemical network with the probable dissociation constant and does so by combinatorially summing all non-redundant and non-trivial vectors of a null space generated subspace from the stoichiometry number matrix of the modelled biochemical network. This is followed by defining and populating a reaction-specific sequence vector with numerical values drawn from each row of this subspace, computing several descriptors and partitioning selected terms into distinct subsets in accordance with the expected outcomes (forward, reverse, equivalent) for a reaction. "ReDirection" computes the sums of all the terms that comprise each outcome-specific subset, maps these to strictly positive real numbers and bins the same to a reaction-specific outcome vector. The p1-norm of this vector is the probable dissociation constant for a reaction and is used to assign and annotate the reaction. "ReDirection" iterates these steps recursively until every reaction of the modelled biochemical network has been assigned an unambiguous outcome. "ReDirection" works on first principles, does not discriminate between enzymatic and non-enzymatic reactions, offers a mathematically rigorous and biochemically relevant environment to explore user-defined biochemical networks under naive and perturbed conditions and can be used to address empirically intractable biochemical problems. The utility and relevance of "ReDirection" is highlighted with an investigation of a constrained biochemical network of human Galactose metabolism. "ReDirection" is freely available and accessible from the comprehensive R archive network (CRAN) with the URL (https://cran.r-project.org/package=ReDirection).

systems biology↗