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Publications From IITB

In 2024

25.  Interlinked bi-stable switches govern the cell fate commitment of embryonic stem cells

       A. Giri and S. Kar,  FEBS Letters 2024       

      https://doi.org/10.1002/1873-3468.14832 

In 2023

24.  Deciphering the impact of pulsatile input in the population-level synchrony of the Hes1 oscillators

       A. Giri and S. Kar,  J. Chem. Sci. 2023  (Special Issue on Interplay of Structure and Dynamics in Reaction                   Pathways, Chemical Reactivity and Biological Systems)     

       https://link.springer.com/article/10.1007/s12039-023-02177-y

In 2022

23.  Transcriptional fluctuations govern the serum dependent cell cycle duration heterogeneities in Mammalian cells

       G. Vinodhini, S. Sarma, S. Karulkar, R. Purwar and S. Kar  ACS Synthetic Biology 2022

        https://pubs.acs.org/doi/10.1021/acssynbio.2c00347 

22.  Elucidating the Implications of Diverse Dynamical Responses in p53 Protein

         K. Charan*, A Giri* and S. Kar ChemPhysChem 2022

          (* Equal contribution 1st author)

           https://doi.org/10.1002/cphc.202200537

21.  Modulation of signaling cross-talk between pJNK and pAKT generates optimal apoptotic response

       S. Biswas*, B. Tikader*, S. Kar and  G. A. Viswanathan PLOS COMPUTATIONAL BIOLOGY 2022

       (* Equal contribution 1st author)

          https://doi.org/10.1371/journal.pcbi.1010626

20.  Intrinsic Elasticity of a Three-Dimensional Macroporous Scaffold Governs the Kinetics of In Situ Biomimetic                 Reactions

        L. Hegde*, B. Tikader*, A. Srivatsav, S. Kar and K. Sharma 

       (* Equal contribution 1st author) Chem. Mater. 2022, 

          https://doi.org/10.1021/acs.chemmater.2c01792

In 2021

19. Unraveling the origin of glucose mediated disparate proliferation dynamics of cancer stem cells

    T. Samanta and S. Kar, J. Theo. Biol., 526, 110774, (2021).

     https://doi.org/10.1016/j.jtbi.2021.110774

18. Role of microRNAs in oncogenesis: Insights from computational and systems-level modeling approaches

    G. Vinodhini and S. KarComput Syst Oncol., 1:e1028. (2021;).

    https://doi.org/10.1002/cso2.1028

17. Deciphering the Role of Fluctuation Dependent Intercellular Communication in Neural Stem Cell Development

    A. Giri, D. Sengupta and S. Kar, ACS Chemical Neuroscience, 12, 2360-2372, (2021).

    https://doi.org/10.1021/acschemneuro.1c00116

16. Incoherent modulation of bi-stable dynamics orchestrates the Mushroom and Isola bifurcations

    A. Giri and S. Kar, J. Theo. Biol., 530, 110882, (2021).

    https://doi.org/10.1016/j.jtbi.2021.110882

15. A generic approach to decipher the mechanistic pathway of heterogeneous protein aggregation kinetics

    B. Tikader, S. K. Maji and S. Kar, Chem. Sci., 12, 13530, (2021).

    https://doi.org/10.1039/D1SC03190B

In 2020

14. Fine-tuning Nanog expression heterogeneity in embryonic stem cells by regulating a Nanog transcript-       specific microRNA

   T. Samanta and S. Kar, FEBS Letters, 594, 4292-4306, (2020). '

     https://doi.org/10.1002/1873-3468.13936

13. Alteration in cross diffusivities governs the nature and dynamics of spatiotemporal pattern formation

    A. Giri, S. Jain, and S. Kar,  Chem. Phys. Chem., 21, 1608-1616, (2020).

      https://doi.org/10.1002/cphc.202000142

In 2019

 

12. Investigating the effect of circularly polarized electric field on spatially extended Gray-Scott model

    A. Giri and S. Kar,  J. Indian Chem. Soc., 96, 809-816, (2019).

    (Special issue of JICS on "Theoretical and Computational Chemistry", Edited by Prof. P. K. Chattaraj)

        https://doi.org/10.5281/zenodo.5644586

11. Dynamical reorganization of transcriptional events governs robust Nanog heterogeneity

    T. Samanta and S. Kar, J. Phys. Chem. B, 123, 5246-5255, (2019).

    https://doi.org/10.1021/acs.jpcb.9b03411                                                      

10. Unraveling the diverse nature of electric field induced spatial pattern formation in Gray-Scott model

    A. Giri and S. Kar, Journal of Chemical Physics, 150, 094904-11, (2019).

   https://doi.org/10.1063/1.5080553

In 2018

9. Disproportionate feedback interaction govern cell-type specific proliferation in mammalian cells

    D. Sengupta*, V. P. S. Kompella* and S. Kar,  FEBS Letters, 592, 3248-3263, (2018).

       https://doi.org/10.1002/1873-3468.13241

8. Alteration in microRNA-17-92 dynamics accounts for differential nature of cellular proliferation

    D. Sengupta*, G. Vinodhini* and S. KarFEBS Letters, 592, 446-458, (2018).

    https://doi.org/10.1002/1873-3468.12974    (* Equal contribution 1st author)                                                  

7. Deciphering the dynamical origin of mixed population during neural stem cell development

    D. Sengupta and S. Kar, Biophysical Journal, 114, 992-1004, (2018).

    DOI: https://doi.org/10.1016/j.bpj.2017.12.035

In 2017

6. Alteration in microRNA expression governs the nature and timing of cellular fate commitment

    D. Sengupta and S. Kar, ACS Chemical Neuroscience, 9(4), 725-737, (2018).

    DOI: https://doi.org/10.1021/acschemneuro.7b00423

5.  Decoding the regulatory mechanism of Glucose and Insulin induced Phosphatidyinositol 3,4,5-     Trisphosphate

    dynamics in β-cells

    T. Samanta*, P. Sharma*, D. Kukri and S. Kar, Molecular BioSystems, 13, 1512-1523, (2017).

    DOI: https://doi.org/10.1039/C7MB00227K   (* Equal contribution 1st author)

4.  Protein abundance of AKT and ERK pathway components governs cell-type-specific regulation of       proliferation

    L. Adlung*, S. Kar*, M. C. Wagner*, B. She*, S. Chakraborty, J. Bao, S. Lattermann, M. Boerries, H.  Busch, J.  

    Timmer, M. Schilling, T. Hoefer and U. Klingmueller. Molecular Systems Biology, 13: 904 (2017).

    DOI: https://doi.org/10.15252/msb.20167258  (* Equal contribution 1st author)

In 2016

3. Unraveling the differential dynamics of developmental fate in central and peripheral nervous system

    D. Sengupta and S. Kar, Scientific Reports, 6: 36397, (2016).  

    DOI: https://doi.org/10.1038/srep36397

2. Unraveling Cell-Cycle Dynamics in Cancer

    S. Kar, Cell Systems, 2: 8, (2016).

    http://dx.doi.org/10.1016/j.cels.2016.01.007 (Preview article)

In 2015

1. Are Quasi-Steady-State Approximated Models Suitable for Quantifying Intrinsic Noise Accurately?

      D. Sengupta and S. Kar, Plos One, 10(9): e0136668, (2015).

     DOI: https://doi.org/10.1371/journal.pone.0136668

Representative Publications before joining IITB

1. Heterogeneous kinetics of AKT signaling in individual cells are accounted for by variable protein concentration     

    R. Meyer, L. A. D`Alessandro, S. Kar, B. Kramer, S. Bin, D. Kaschek, B. Hahn, D. Wrangborg, J. Karlsson,

    M. Kvarnstrom, M. Jirstrand, W. D. Lehmann, J. Timmer, T. Höfer and U. Klingmüller, Frontiers in Physiology,

    doi: 10.3389/fphys.2012.00451  (2012). ( Equal contribution 1st author)https://doi.org/10.3389/fphys.2012.0045

 

2. Exploring the Roles of Noise in the Eukaryotic Cell Cycle S. Kar, W. Baumann, M. R. Paul and J. J. Tyson, Proc.       Natl. Acad. Sci., 106, 6471, (2009).https://doi.org/10.1073/pnas.0810034106

 

3. Sustained simultaneous Glycolytic and Insulin oscillations in β-cells

    S. Kar and D.S. Ray, J. Theo. Biol., 237, 58, (2005). 10.1016/j.jtbi.2005.03.031

 

4. Collapse and Revival of Glycolytic oscillation

    S. Kar and D.S. Ray, Physical Review Letters, 90, 238102, (2003).https://doi.org/10.1103/PhysRevLett.90.238102

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