Kingshuk Ghosh

Professor

Department of Physics and Astronomy, Molecular and Cellular Biophyics, University of Denver, CO

We are interested in applying theoretical and computational methods to understand complexity in biology. Some of the current research projects are listed below.

Statistical mechanics of intrinsically disordered proteins: We are advancing novel polymer physics based analytical models for heteropolymers to learn about heterogeneity of conformationl ensemble in intrinsically disordered proteins (IDP). These applications will help us understand principles of IDP conformation-function relationship. We are exploring multiple design questions harnessing the analytical nature of the theory. We are also advancing theory to model phase-separation in heteropolymers, a topic of great interest in cell biology due to formation of membraneless organelles. Analytical nature of the theory will help us disentangle different factors such as chain entropy, sequence patterning, salt partitioning in phase separation. The general theoretical framework developed will also help us model synthetic polymers used in material science applications.

Reference:

1. An analytical theory to describe sequence-specific inter-residue distance profiles for Polyamphoyltes and  Intrinsically Disordered Proteins. Jonathan Huihui and K. Ghosh, J. Chem. Phys. Communication 152:161102 (2020). Featured article Manuscript Link

2. A unified analytical theory of heteropolymers for sequence-specific phase behaviors of polyelectrolytes and polyampholytes. Yi-Hsuan Lin, J. P. Brady, H.S. Chan and K. Ghosh, J. Chem. Phys.  152:045102 (2020).

3. Modulating charge patterning and ionic strength as a strategy to induce conformational changes in Intrinsically Disordered Proteins. J. Huihui, T. Firman, and K. Ghosh, J. Chem. Phys.  149: 085101 (2018).

4. Sequence charge decoration dictates coil-globule transition in Intrinsically Disordered Proteins. T. Firman and K. Ghosh, J. Chem. Phys. 148: 123305 (2018) Invited contribution.

5. A theoretical method to compute sequence dependent configurational properties in charged polymers and proteins. L. Sawle and K. Ghosh, J. Chem. Phys.  143: 085101 (2015).

Formulating non-equilibrium statistical mechanics: Biology has a small number problem. Furthermore, life is out of equilibrium. This has prompted us to better understand fluctuations and noise in non-equilibrium biological problems. We are developing a new formalism called Maximum Caliber (MaxCal), a dynamical analog of Maximum Entropy method in equilibrium statistical mechanics. In collaboration with Prof Ken Dill at the Stony Brook University, Prof Rob Phillips at Caltech, and Prof Steve Presse at IUPUI we have tested this principle in colloidal, single molecule systems.

Reference: 

1.Perspective: Maximum Caliber is a general variational principle for dynamical systems. P.D. Dixit, J.A. Wagoner, C. Weistuch, S. Presse, K. Ghosh and K.A. Dill J. Chem. Phys. 148: 010901 (2018).

2. Principles of maximum entropy and maximum caliber. S. Presse, K. Ghosh, J. Lee and K.A Dill Rev. Mod. Phys. 85: 1115-1141 (2013)

Modeling fluctuations in genetic networks and cell-cell variability: We are extending application of MaxCal in modeling several biological problems as well. One of the areas of prime interest for us is modeling fluctuations in natural and synthetic genetic networks. Simple examples are biological switches, clocks, timers that dictate biology. MaxCal formalism allows us to quantify noise in such complex systems that lead to bistability, oscillation and cooperativity. The movie below shows stochastic expression of an antibiotic resistance gene using MaxCal model. The system behaves likes a stochastic genetic switch where noise helps flip the switch. This work is ongoing in collaboration with Prof Balazsi at Stony Brook University. The primary advantage of MaxCal is to be able to use the stochasticity in data to infer underlying model.

We are also interested in applying similar ideas to interpret single cell data and learn from cell-cell variability. One primary application of this is in cyanobacteria as part of an ongoing collaboration with Prof. Jeff Cameron and Sean Shaheen at CU Boulder. Using MaxCal we are using full stochastic cellular growth data to understand underlying evolutionary models.

References: 

1. Maximum Caliber Can Build and Infer Models of Oscillation in a Three-Gene Feedback Network. T. Firman, A. Amgalan, and K. Ghosh J. Phys. Chem. B  123:343-355 (2019).

2. Maximum Caliber Can Characterize Genetic Switches with Multiple Hidden Species. T. Firman, S. Wedekind, T.J. McMorrow and K. Ghosh J. Phys. Chem. B 122:5666-5677 (2018). 

3. Building predictive models of genetic circuits using the principle of Maximum Caliber. T. Firman, G. Balazsi and K. Ghosh Biophysical Journal  113(9): 2121-2130 (2017).

4. Modeling Stochastic Dynamics in Biochemical Systems with Feedback using Maximum Caliber: S. Presse, K. Ghosh, K.A. Dill J Phys Chem B 115, 6202-6212  (2011).

Proteome modeling: We are interested in understanding many statistical properties to carry out large scale modeling of the entire proteome, the collection of all the proteins inside an organism. This is in contrary to the usual approach in protein science where majority of studies focus on one single, specific or a class of proteins. The ultimate goal is to build transferrable proteome level models to better understand cellular level behavior and evolutionary principles. 

References:

1. Proteome folding kinetics is limited by protein halflife. T. Zou, N. Williams, S.B. Ozkan and K. Ghosh PLOS ONE: DOI: 10.1371/journal.pone.0112701 (2014).

2. Physical limits of cells and proteomes. K. A. Dill, K. Ghosh, J. Schmit Proc. Natl. Acad. Sci. 108: 17876 (2011).

3. How do thermophilic proteins and proteomes withstand high temperature ? L. Sawle, K. Ghosh Biophys J 101, 217 (2011).

4. Cellular proteomes have broad distributions of protein stability: K. Ghosh and K.A. Dill Biophys J 99, 3996-4002 (2010).

Protein stability:  We are interested in understanding enhanced thermal tolerance in thermophilic proteins. These are a class of proteins typically extracted from thermophilic organisms that denature at significantly higher temperatures compared to mesophilic proteins (extracted from organisms that live at room temperature). We are combining different theoretical approaches to probe multiple scales to better understand how evolution played its tricks to achieve such unusal thermal tolerance at a large scale. 

References:

1. All-atom simulations reveal protein charge decoration in the folded and unfolded ensemble is key in thermophilic adaptation. L. Sawle, J. HuiHui and K. Ghosh J. Chem. Theor. Comp. DOI 10.1021/acs.jctc.7b00545 (2017).

2. A theoretical method to compute sequence dependent configurational properties in charged polymers and proteins. L. Sawle and K. Ghosh J. Chem. Phys. 143, 085101 (2015).

3. How do thermophilic proteins and proteomes withstand high temperature ? L. Sawle, K. Ghosh Biophys J 101, 217 (2011).

4. Computing protein stabilities from their chain lengths: K. Ghosh and K. A. Dill. Proc. Natl. Acad. Sci. 106(26), 10649-10654 (2009).

The video below obtained using molecular dynamics simulation demonstrates that thermophilic protein (in red) is more resilient to unfolding than mesophilic protein (in blue) at high temperature.

Biophysics of disease causing proteins: In collaboration with our colleagues in Biology (Prof David Patterson) we are interested in understanding biophysical studies of disease causing proteins. One such protein is the enzyme ADSL, responsible for a disease called ADSL deficiency syndrome sharing similar phenotypic features as autism. We are probing stability, structure, function relation of different disease causing mutatnts of  the enzyme ADSL.

New opportunities in the biosynthesis of renewable energy carriers: We are also exploring the possibility of using thermophilic bacteria as a means to new opportunities in the biosynthesis of renewable energy carriers such as bio-hydrogen and lipid biodiesel at elevated temperatures. We are collaborating with Prof Sean Shaheen and Prof. Jeff Cameron at the University of Colorado, Boulder on this project.

Cottrell Scholar Award 2012

NSF CAREER AWARD 2012

Scialog Fellow 2015 (Molecules come to life)