Sandip Kar

This study develops a mathematical model of PPARγ dynamics to show stress-induced adipogenic commitment is a bistable, irreversible process regulated by circadian rhythms.

The paper constructs a network model to identify crosstalk between p53 and cell-cycle regulators, revealing mechanisms enabling cells to escape cell-cycle arrest and undergo mitotic catastrophe.

The paper models how transcriptional memory fluctuations and mitotic resetting influence cell cycle duration heterogeneities across lineage pairs.

This paper proposes a model of interlinked bistable switches governing Oct4 and Nanog dynamics in embryonic stem cells, explaining their expression patterns during differentiation.

The paper develops a computational method to study how mechanical stress affects enzyme-catalyzed reactions with complex kinetics.

The paper shows that transcriptional fluctuations control cell cycle duration variability in mammalian cells and identifies ways to reduce this variability through network perturbations.

This study identifies the synergistic role of pJNK and pAKT in modulating Caspase3 activation to control TNFα-induced apoptosis and proposes a predictive model for therapeutic strategies.

The paper shows that four minimal network motifs, combining positive and incoherent feedback, can generate Mushroom and Isola bifurcations in biological networks.

The paper develops a mathematical model to explain how glucose regulates Nanog expression and drives differential proliferation in cancer stem cells through STAT3 and p53 dynamics.

This study shows how altering microRNA regulation can control Nanog expression heterogeneity in ESCs to guide cell-type specific differentiation.

This paper develops a data-driven mathematical model to understand amyloidogenesis dynamics and predict therapeutic strategies for neurodegenerative diseases.

The paper models Nanog expression heterogeneity in ESCs using a stochastic simulation to show how culture conditions affect transcriptional noise partitioning.

The paper develops a mathematical model to explain cell-type specific proliferation decisions during the G1-S transition in mammalian cells and identifies feedback interactions that govern these decisions.

The paper presents a mathematical model of the Myc/E2F/mir-17-92 network to explain how differential cellular proliferation can be influenced by microRNA dynamics in a cell-type specific manner.

The paper uses mathematical modeling to show that mixed cell populations in neural stem cell differentiation arise from bistable switches and can be manipulated to achieve desired phenotypes.

This study develops a mathematical model to explain how BMP2 differentially regulates neural stem cell differentiation in central and peripheral nervous systems.