Biology subjects

Ghosh, K.

Publications and source records attributed to Ghosh, K..

3 recordsLinked to original sources

bioRxiv2026

Who rests with whom? Sex composition and group demography shape resting associations in free-ranging dogs

Free-ranging dogs frequently rest near conspecifics, but the demographic factors structuring their resting associations remain poorly understood. We quantified dyadic resting associations in 26 free-ranging dog groups in West Bengal, India, observed between 2019 and 2023. Association strength was estimated from scan based resting co-occurrences using the Half-Weight Index. We tested whether dyadic association strength varied with dyad sex composition, dyad life stage composition, group size, and group sex ratio using a generalised additive model for location, scale and shape that accounted for group identity and repeated occurrence of individuals across dyads. Male-male dyads had lower association strengths than female-female dyads, whereas mixed-sex dyads did not differ from female-female dyads. Association strength decreased with increasing group size but increased as the male-to-female ratio within the group increased, while life-stage composition had no detectable effect. Individual level network metrics, including strength, reach, clustering coefficient, affinity, and eigenvector centrality, did not vary with sex or season. Mixed-sex pairs were also frequently represented among the strongest dyadic associations within groups. These findings indicate that resting associations in free-ranging dogs vary with dyad sex composition and group demography. Further opportunity-controlled analyses are required to determine whether the prominence of mixed-sex dyads reflects preferential association rather than group composition alone.

animal behavior and cognition↗

bioRxiv2018

Maximum Caliber can build and infer models of oscillation in a three-gene feedback network

Single-cell protein expression time trajectories provide rich temporal data quantifying cellular variability and its role in dictating fitness. However, theoretical models to analyze and fully extract information from these measurements remain limited for three reasons: i) gene expression profiles are noisy, rendering models of averages inapplicable, ii) experiments typically measure only a few protein species while leaving other molecular actors - necessary to build traditional bottom-up models - unnoticed, and iii) measured data is in fluorescence, not particle number. We have recently addressed these challenges in an alternate top-down approach using the principle of Maximum Caliber (MaxCal) to model genetic switches with one and two protein species. In the present work we address scalability and broader applicability of MaxCal by extending to a three-gene (A, B, C) feedback network that exhibits oscillation, commonly known as the repressilator. We test MaxCals inferential power by using synthetic data of noisy protein number time traces - serving as a proxy for experimental data - generated from a known underlying model. We notice that the minimal MaxCal model - accounting for production, degradation, and only one type of symmetric coupling between all three species - reasonably infers several underlying features of the circuit such as the effective production rate, degradation rate, frequency of oscillation, and protein number distribution. Next, we build models of higher complexity including different levels of coupling between A, B, and C and rigorously assess their relative performance. While the minimal model (with four parameters) performs remarkably well, we note that the most complex model (with six parameters) allowing all possible forms of crosstalk between A, B, and C slightly improves prediction of rates, but avoids ad-hoc assumption of all the other models. It is also the model of choice based on Bayesian Information Criteria. We further analyzed time trajectories in arbitrary fluorescence (using synthetic trajectories) to mimic realistic data. We conclude that even with a three-protein system including both fluorescence noise and intrinsic gene expression fluctuations, MaxCal can faithfully infer underlying details of the network, opening up future directions to model other network motifs with many species.

synthetic biology↗

bioRxiv2018

Ancient Thioredoxins Evolved to Modern Day Stability-Function Requirement by Altering Native State Ensemble

Thioredoxins (Thrxs) - small globular proteins that reduce other proteins - are ubiquitous in all forms of life, from archaea to mammals. Although ancestral Thioredoxins share sequential and structural similarity with the modern day (extant) homologs, they exhibit significantly different functional activity and stability. We investigate this puzzle by comparative studies of their (ancient and modern day Thrxs) native state ensemble, as quantified by the Dynamic Flexibility Index (DFI), a metric for the relative resilience of an amino acid to perturbations in the rest of the protein. Clustering proteins using DFI profiles strongly resembles an alternate classification scheme based on their activity and stability. The DFI profiles of the extant proteins are substantially different around the 3, 4 helices and catalytic regions. Likewise, allosteric coupling of the active site with the rest of the protein is different between ancient and extant Thrxs, possibly explaining the decreased catalytic activity at low pH with evolution. At a global level, we note that the population of low flexibility (called hinges) and high flexibility sites increases with evolution. The heterogeneity (quantified by the variance) in DFI distribution increases with the decrease in the melting temperature typically associated with the evolution of ancient proteins to their modern-day counterparts.

biophysics↗