+publisher:"Rutgers University" +contributor:("Sengupta, Anirvan M")

Rutgers University

1. Ramezanali, Mohammad. Extracting sparse signals from high-dimensional data: a statistical mechanics approach.

Degree: PhD, Physics and Astronomy, 2015, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/48628/

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Sparse reconstruction algorithms aim to retrieve high-dimensional sparse signals from a limited amount of measurements under suitable conditions. As the number of variables go to… (more)

Subjects/Keywords: Statistical mechanics

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APA (6^th Edition):

Ramezanali, M. (2015). Extracting sparse signals from high-dimensional data: a statistical mechanics approach. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/48628/

Chicago Manual of Style (16^th Edition):

Ramezanali, Mohammad. “Extracting sparse signals from high-dimensional data: a statistical mechanics approach.” 2015. Doctoral Dissertation, Rutgers University. Accessed January 15, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/48628/.

MLA Handbook (7^th Edition):

Ramezanali, Mohammad. “Extracting sparse signals from high-dimensional data: a statistical mechanics approach.” 2015. Web. 15 Jan 2021.

Vancouver:

Ramezanali M. Extracting sparse signals from high-dimensional data: a statistical mechanics approach. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2021 Jan 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48628/.

Council of Science Editors:

Ramezanali M. Extracting sparse signals from high-dimensional data: a statistical mechanics approach. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48628/

Rutgers University

2. Manhart, Michael. Biophysics and stochastic processes in molecular evolution.

Degree: PhD, Physics and Astronomy, 2014, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/45338/

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Evolution is the defining feature of living matter. It occurs most fundamentally on the scale of biomolecules such as DNA and proteins, which carry out… (more)

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Evolution is the defining feature of living matter. It occurs most fundamentally on the scale of biomolecules such as DNA and proteins, which carry out all the processes of cells. How do the physical properties of these molecules shape the course of evolution? We address this question using a synthesis of biophysical models, theoretical tools from stochastic processes, and high-throughput data. We first review some basic features of population and evolutionary dynamics, focusing especially on fitness landscapes and how they determine accessible pathways of evolution. We then derive a universal scaling law describing time reversibility and steady state of monomorphic populations on arbitrary fitness landscapes. We use this result to study the evolution of transcription factor (TF) binding sites using high-throughput data on TF-DNA interactions and genome-wide site locations. We find that binding sites for a given TF appear to be subjected to universal selection pressures, independent of the properties of their corresponding genes, and their binding energy-dependent fitness is consistent with a simple functional form inspired by a thermodynamic model. We next consider the properties of evolutionary pathways. We develop a general approach for calculating statistical properties of the path ensemble in a stochastic process. We first demonstrate this approach on a series of simple examples, including evolution on a neutral network and two reaction rate problems. We then apply these techniques to a model of how proteins evolve new binding interactions while maintaining folding stability. In particular we show how the structural coupling of protein folding and binding results in protein traits emerging as evolutionary "spandrels'': proteins can evolve strong binding interactions that confer no intrinsic fitness advantage but merely serve to stabilize the protein if misfolding is deleterious. These observations may explain the abundance of apparently nonfunctional interactions among proteins observed in high-throughput assays. When there are distinct selection pressures on both folding and binding, evolutionary paths of proteins can be tightly constrained so that folding stability is first gained and then partially lost as the new binding function is developed. This suggests the evolution of many natural proteins is highly predictable at the level of biophysical traits.

Advisors/Committee Members: Morozov, Alexandre V (chair), Sengupta, Anirvan M (internal member), Bhanot, Gyan (internal member), Andrei, Natan (internal member), Chen, Kevin (outside member).

Subjects/Keywords: Molecular evolution; Evolution (Biology); Protein folding

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APA (6^th Edition):

Manhart, M. (2014). Biophysics and stochastic processes in molecular evolution. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/45338/

Chicago Manual of Style (16^th Edition):

Manhart, Michael. “Biophysics and stochastic processes in molecular evolution.” 2014. Doctoral Dissertation, Rutgers University. Accessed January 15, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/45338/.

MLA Handbook (7^th Edition):

Manhart, Michael. “Biophysics and stochastic processes in molecular evolution.” 2014. Web. 15 Jan 2021.

Vancouver:

Manhart M. Biophysics and stochastic processes in molecular evolution. [Internet] [Doctoral dissertation]. Rutgers University; 2014. [cited 2021 Jan 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45338/.

Council of Science Editors:

Manhart M. Biophysics and stochastic processes in molecular evolution. [Doctoral Dissertation]. Rutgers University; 2014. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45338/

Rutgers University

3. Dai, Wei. First passage times and relaxation times of unfolded proteins and the funnel model of protein folding.

Degree: PhD, Physics and Astronomy, 2016, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/49952/

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Protein folding has been a challenging puzzle for decades but it is still not fully understood. One important way to gain insights of the mechanism… (more)

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Protein folding has been a challenging puzzle for decades but it is still not fully understood. One important way to gain insights of the mechanism is to study how kinetics in the unfolded state affects protein folding. The answer to this fundamental issue hinges on the time scale to equilibrate the unfolded state and the energy landscape of the unfolded state. We construct Markov state models (MSMs) of several mini-proteins to study the kinetics of their unfolded state ensemble and find that the folding kinetics are two-state even though there are multiple folding pathways with nonuniform barriers, which are the direct consequences of rapid mixing within the unfolded state. Also, we introduce a time integral of a proper correlation function, namely relaxation time, to characterize the time scale of equilibration within the unfolded state. However, the mean first passage times (MFPTs) between different regions of the unfolded state are observed to be orders of magnitude longer than the folding time. This seeming paradox is solved by the derivation of a simple relation that shows the mean first passage time to any state is equal to the relaxation time of that state divided by its equilibrium population. This simple relation explains why MFPTs among unfolded states can be very long but the energy landscape can still be smooth (minimally frustrated). As a matter of fact, when the folding kinetics is two-state, all of the unfolded state relaxation times are faster than the folding time. This result supports the well-established funnel-like energy landscape picture and resolves an apparent contradiction between this model and the recently proposed kinetic hub model of protein folding. Markov state model is a powerful tool but we seek for alternative ways of studying kinetics when MSM does not work very well. For example, diffusion maps of dimensionality reduction and discrete transition-based reweighting analysis method, are very useful in determining a geometrical measure that preserves intrinsic dynamics and in fully utilizing enhanced sampling simulation data.

Advisors/Committee Members: Levy, Ronald M. (chair), Sengupta, Anirvan M. (internal member), Bhanot, Gyan (internal member), Croft, Mark C. (internal member), Tan, Zhiqiang (outside member).

Subjects/Keywords: Protein folding; Markov processes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dai, W. (2016). First passage times and relaxation times of unfolded proteins and the funnel model of protein folding. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/49952/

Chicago Manual of Style (16^th Edition):

Dai, Wei. “First passage times and relaxation times of unfolded proteins and the funnel model of protein folding.” 2016. Doctoral Dissertation, Rutgers University. Accessed January 15, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/49952/.

MLA Handbook (7^th Edition):

Dai, Wei. “First passage times and relaxation times of unfolded proteins and the funnel model of protein folding.” 2016. Web. 15 Jan 2021.

Vancouver:

Dai W. First passage times and relaxation times of unfolded proteins and the funnel model of protein folding. [Internet] [Doctoral dissertation]. Rutgers University; 2016. [cited 2021 Jan 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49952/.

Council of Science Editors:

Dai W. First passage times and relaxation times of unfolded proteins and the funnel model of protein folding. [Doctoral Dissertation]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49952/

Rutgers University

4. Yung, Long Yan Aaron, 1992-. Semi-analytic forecasts for galaxy formation and cosmic reionization in the ultrahigh-redshift universe.

Degree: PhD, Cosmic reionization, 2020, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/64847/

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Concerning the early evolution history of our universe and distant galaxies that are expected to be uncovered with future telescopes, we present a collection of… (more)

Subjects/Keywords: Physics and Astronomy

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APA (6^th Edition):

Yung, Long Yan Aaron, 1. (2020). Semi-analytic forecasts for galaxy formation and cosmic reionization in the ultrahigh-redshift universe. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/64847/

Chicago Manual of Style (16^th Edition):

Yung, Long Yan Aaron, 1992-. “Semi-analytic forecasts for galaxy formation and cosmic reionization in the ultrahigh-redshift universe.” 2020. Doctoral Dissertation, Rutgers University. Accessed January 15, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/64847/.

MLA Handbook (7^th Edition):

Yung, Long Yan Aaron, 1992-. “Semi-analytic forecasts for galaxy formation and cosmic reionization in the ultrahigh-redshift universe.” 2020. Web. 15 Jan 2021.

Vancouver:

Yung, Long Yan Aaron 1. Semi-analytic forecasts for galaxy formation and cosmic reionization in the ultrahigh-redshift universe. [Internet] [Doctoral dissertation]. Rutgers University; 2020. [cited 2021 Jan 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64847/.

Council of Science Editors:

Yung, Long Yan Aaron 1. Semi-analytic forecasts for galaxy formation and cosmic reionization in the ultrahigh-redshift universe. [Doctoral Dissertation]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64847/

5. Apratim, Manjul, 1984-. On the physical and chemical dynamics of chromatin.

Degree: Physics and Astronomy, 2012, Rutgers University

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066601

Subjects/Keywords: Chromatin; Eukaryotic cells – Research

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APA (6^th Edition):

Apratim, Manjul, 1. (2012). On the physical and chemical dynamics of chromatin. (Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066601

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Apratim, Manjul, 1984-. “On the physical and chemical dynamics of chromatin.” 2012. Thesis, Rutgers University. Accessed January 15, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066601.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Apratim, Manjul, 1984-. “On the physical and chemical dynamics of chromatin.” 2012. Web. 15 Jan 2021.

Vancouver:

Apratim, Manjul 1. On the physical and chemical dynamics of chromatin. [Internet] [Thesis]. Rutgers University; 2012. [cited 2021 Jan 15]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066601.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Apratim, Manjul 1. On the physical and chemical dynamics of chromatin. [Thesis]. Rutgers University; 2012. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066601

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rutgers University

6. Bhatia, Aatish, 1985-. Detecting signatures of natural selection in genetic data.

Degree: Physics and Astronomy, 2013, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/41725/

Subjects/Keywords: Natural selection; Variation (Biology); Genetics; Maasai (African people) – Food

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Bhatia, Aatish, 1. (2013). Detecting signatures of natural selection in genetic data. (Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/41725/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Bhatia, Aatish, 1985-. “Detecting signatures of natural selection in genetic data.” 2013. Thesis, Rutgers University. Accessed January 15, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/41725/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Bhatia, Aatish, 1985-. “Detecting signatures of natural selection in genetic data.” 2013. Web. 15 Jan 2021.

Vancouver:

Bhatia, Aatish 1. Detecting signatures of natural selection in genetic data. [Internet] [Thesis]. Rutgers University; 2013. [cited 2021 Jan 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/41725/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bhatia, Aatish 1. Detecting signatures of natural selection in genetic data. [Thesis]. Rutgers University; 2013. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/41725/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rutgers University

7. Chereji, Răzvan V., 1983-. Statistical mechanics of nucleosomes.

Degree: Physics and Astronomy, 2013, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/41749/

Subjects/Keywords: Eukaryotic cells – Genetics; Genetic regulation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chereji, Răzvan V., 1. (2013). Statistical mechanics of nucleosomes. (Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/41749/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chereji, Răzvan V., 1983-. “Statistical mechanics of nucleosomes.” 2013. Thesis, Rutgers University. Accessed January 15, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/41749/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chereji, Răzvan V., 1983-. “Statistical mechanics of nucleosomes.” 2013. Web. 15 Jan 2021.

Vancouver:

Chereji, Răzvan V. 1. Statistical mechanics of nucleosomes. [Internet] [Thesis]. Rutgers University; 2013. [cited 2021 Jan 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/41749/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chereji, Răzvan V. 1. Statistical mechanics of nucleosomes. [Thesis]. Rutgers University; 2013. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/41749/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rutgers University

8. Wagh, Kshitij. Studies in viral population genetics and bioinformatics.

Degree: Physics and Astronomy, 2013, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/41942/

Subjects/Keywords: Viruses; Viral genetics; Viral genomes; Avian influenza A virus – Research

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wagh, K. (2013). Studies in viral population genetics and bioinformatics. (Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/41942/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wagh, Kshitij. “Studies in viral population genetics and bioinformatics.” 2013. Thesis, Rutgers University. Accessed January 15, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/41942/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wagh, Kshitij. “Studies in viral population genetics and bioinformatics.” 2013. Web. 15 Jan 2021.

Vancouver:

Wagh K. Studies in viral population genetics and bioinformatics. [Internet] [Thesis]. Rutgers University; 2013. [cited 2021 Jan 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/41942/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wagh K. Studies in viral population genetics and bioinformatics. [Thesis]. Rutgers University; 2013. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/41942/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rutgers University

9. Traldi, Eliane Zerbetto, 1975-. Modeling regulation of transcription initiation.

Degree: Computational Biology and Molecular Biophysics, 2011, Rutgers University

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063670

Subjects/Keywords: Genetic transcription—Regulation; RNA—Synthesis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Traldi, Eliane Zerbetto, 1. (2011). Modeling regulation of transcription initiation. (Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063670

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Traldi, Eliane Zerbetto, 1975-. “Modeling regulation of transcription initiation.” 2011. Thesis, Rutgers University. Accessed January 15, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063670.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Traldi, Eliane Zerbetto, 1975-. “Modeling regulation of transcription initiation.” 2011. Web. 15 Jan 2021.

Vancouver:

Traldi, Eliane Zerbetto 1. Modeling regulation of transcription initiation. [Internet] [Thesis]. Rutgers University; 2011. [cited 2021 Jan 15]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063670.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Traldi, Eliane Zerbetto 1. Modeling regulation of transcription initiation. [Thesis]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063670

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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