+publisher:"Rutgers University" +contributor:("Bhanot, Gyan")

Rutgers University

1. Siu, Theodore Lordin, 1988-. Non-linear phenomena in granular materials.

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

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/51468/

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Granular materials are quite common in our everyday lives in both nature and industry. Yet much of the phenomena that they exhibit is not well… (more)

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Granular materials are quite common in our everyday lives in both nature and industry. Yet much of the phenomena that they exhibit is not well understood and in fact quite complex and non-linear. For example, it has been known since Faradayâ€˜s time that swirling sandstorms are able to generate electric charge that manifest as multi-million volt lightning discharges. Poured glass beads have been demonstrated to also charge electrically, causing random ejections of grains and creating interesting raised clump structures known as razorbacks. Lastly, granular materials are known to exhibit self- segregation based on size. An example of this phenomenon is the landscape observed on the asteroid 25143/Itokawa, which has spatially separated boulder fields and sand seas. In this work, we discuss three projects that examine non-linear behavior in granu- lar materials. In our first chapter, we discuss a computational electrostatic model of vibrated identical granular particles. We show in our model that granular materials are able to exponentially generate charge and polarization through 1/r2 electric field interactions. By adding neutralization between neighboring particles, we find complex phenomena such as charge and polarization waves that propagate through the particles. In closing, we reproduce a polarized state experimentally using vibrated glass beads which represents one of the phenomena that we observe in our model phase diagram; we also discuss our experimental efforts to image charge and polarization waves. In the second chapter, we examine the dynamical behavior of charged granular par- ticles. We find that by experimentally exposing grains to simple electric fields, a number of novel behaviors appear including dust clouds, dust columns and tendrils. In addi- tion, in our experiments and simulations we find evidence for non-linear electrostatic interactions between particles including asymmetries and particle trajectories that do not adhere to naive electric field lines. In our final chapter, we deal with granular segregation found on the asteroid known as 25143/Itokawa. On the asteroid there exists segregation between smaller sized dust and rocks on the centimeter size, from large boulders with diameters on the order of meters. While current explanations attribute the segregation to the phenomenon known as the Brazil Nut Effect, we show that "ballistic segregation" or a difference of collision types on the asteroid surface can be responsible for the separation between different sized particles. We show that the surface area growth of particles can be modeled using the non-linear Hill Equation, which shows that increasing amounts of dust accumulation promotes further dust accumulation.

Advisors/Committee Members: Shinbrot, Troy (chair), Bhanot, Gyan (internal member), Yuzbashyan, Emil (internal member), Tse, Stephen (outside member).

Subjects/Keywords: Granular materials

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APA (6^th Edition):

Siu, Theodore Lordin, 1. (2016). Non-linear phenomena in granular materials. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/51468/

Chicago Manual of Style (16^th Edition):

Siu, Theodore Lordin, 1988-. “Non-linear phenomena in granular materials.” 2016. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/51468/.

MLA Handbook (7^th Edition):

Siu, Theodore Lordin, 1988-. “Non-linear phenomena in granular materials.” 2016. Web. 16 Jan 2021.

Vancouver:

Siu, Theodore Lordin 1. Non-linear phenomena in granular materials. [Internet] [Doctoral dissertation]. Rutgers University; 2016. [cited 2021 Jan 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/51468/.

Council of Science Editors:

Siu, Theodore Lordin 1. Non-linear phenomena in granular materials. [Doctoral Dissertation]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/51468/

Rutgers University

2. Li, Junyi, 1980-. Potts model clustering for discovering patterns of epigenetic marks.

Degree: PhD, Physics and Astronomy, 2011, Rutgers University

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057612

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Study of epigenetics leads to understanding of the regulation of gene expression not caused by the changes in the underlying DNA sequences. This area of… (more)

Subjects/Keywords: Epigenesis – Research; Histones; Cluster analysis

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

APA (6^th Edition):

Li, Junyi, 1. (2011). Potts model clustering for discovering patterns of epigenetic marks. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057612

Chicago Manual of Style (16^th Edition):

Li, Junyi, 1980-. “Potts model clustering for discovering patterns of epigenetic marks.” 2011. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057612.

MLA Handbook (7^th Edition):

Li, Junyi, 1980-. “Potts model clustering for discovering patterns of epigenetic marks.” 2011. Web. 16 Jan 2021.

Vancouver:

Li, Junyi 1. Potts model clustering for discovering patterns of epigenetic marks. [Internet] [Doctoral dissertation]. Rutgers University; 2011. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057612.

Council of Science Editors:

Li, Junyi 1. Potts model clustering for discovering patterns of epigenetic marks. [Doctoral Dissertation]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057612

Rutgers University

3. 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 16, 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. 16 Jan 2021.

Vancouver:

Manhart M. Biophysics and stochastic processes in molecular evolution. [Internet] [Doctoral dissertation]. Rutgers University; 2014. [cited 2021 Jan 16]. 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

4. 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 (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 16, 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. 16 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 16]. 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/

5. Acevedo, Alison, 1991-. Analysis and integration of omics data: applications in drug pharmacogenomics and endogenous circadian behavior.

Degree: PhD, Biomedical Engineering, 2019, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/61673/

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The integration of data across omics levels is necessary to accurately reflect physiology, whose behavior is not limited to one molecular component level. Further, integration… (more)

Subjects/Keywords: Circadian; Medical informatics

10 more images…

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APA (6^th Edition):

Acevedo, Alison, 1. (2019). Analysis and integration of omics data: applications in drug pharmacogenomics and endogenous circadian behavior. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/61673/

Chicago Manual of Style (16^th Edition):

Acevedo, Alison, 1991-. “Analysis and integration of omics data: applications in drug pharmacogenomics and endogenous circadian behavior.” 2019. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/61673/.

MLA Handbook (7^th Edition):

Acevedo, Alison, 1991-. “Analysis and integration of omics data: applications in drug pharmacogenomics and endogenous circadian behavior.” 2019. Web. 16 Jan 2021.

Vancouver:

Acevedo, Alison 1. Analysis and integration of omics data: applications in drug pharmacogenomics and endogenous circadian behavior. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2021 Jan 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61673/.

Council of Science Editors:

Acevedo, Alison 1. Analysis and integration of omics data: applications in drug pharmacogenomics and endogenous circadian behavior. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61673/

Rutgers University

6. Singh, Amartya, 1990-. Tunable biclustering algorithm for analyzing large gene expression data sets.

Degree: PhD, Physics and Astronomy, 2019, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/61960/

► Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may… (more)

▼ Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We have proposed a graph-based Tunable Biclustering Algorithm (TuBA) (Chapter 2) based on a novel pairwise proximity measure that leverages the size of the data sets to identify subsets of tumor samples that co-express subsets of genes at their highest or lowest levels relative to other samples. We applied TuBA to three large gene expression datasets encompassing a total of 3,940 breast invasive carcinoma (BRCA) patients (Chapter 3). We demonstrated that there was significant agreement between the results obtained for each data set, and discovered that about 50% of the altered co-expression signatures were associated with a subtype of the disease that exhibits low levels of expression of the estrogen hormone receptor 1 (ER) and the human epidermal growth factor receptor 2 (HER2) genes. Tumors belonging to this subtype are labelled as ER-/HER2-. Since only 15% of all BRCA patients are estimated to have tumors that belong to this subtype, our algorithm was able to highlight the tremendous heterogeneity in alterations within tumors of this subtype. Quite significantly, more than 50% of these signatures were associated with alterations in the DNA that results in amplification (or deletion) of genes’ copies, which subsequently result in higher (or lower) level of gene expression. Thus, TuBA was especially effective in identifying transcriptionally active copy number variations in tumor samples. Finally, TuBA identified biclusters that were associated with the tumor microenvironment, which included biclusters associated with infiltrating immune and stromal cells. These can improve our understanding about the role played by the microenvironment in modulating tumor progression. We showed that TuBA outperforms other algorithms in identification of co-expressed genes located in transcriptionally active copy number altered sites (Chapter 4). Moreover, from a differential co-expression perspective, TuBA offers an advantage over other methods since no prior specification of subsets of samples (conditions) is necessary; the nature of our proximity measure ensures that such differential co-expression signatures are preferentially identified. In summary, our method identified a multitude of altered transcriptional profiles associated with the tremendous heterogeneity of diseased states in breast cancer. Exploring the diversity of these aberrant signatures can help identify potential biomarkers of clinical relevance that can further improve treatment outcomes, especially for ER-/HER2- breast cancers. Although transcriptomic alterations are not the ultimate determinants of progression of disease, our algorithm holds the promise to improve therapeutic selection and design by identifying… Advisors/Committee Members: Khiabanian, Hossein (chair), Bhanot, Gyan (internal member), Croft, Mark (internal member), Morozov, Alexandre (internal member), De, Subhajyoti (outside member), School of Graduate Studies.

Subjects/Keywords: Biclustering; Gene expression – Computer programs

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

APA (6^th Edition):

Singh, Amartya, 1. (2019). Tunable biclustering algorithm for analyzing large gene expression data sets. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/61960/

Chicago Manual of Style (16^th Edition):

Singh, Amartya, 1990-. “Tunable biclustering algorithm for analyzing large gene expression data sets.” 2019. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/61960/.

MLA Handbook (7^th Edition):

Singh, Amartya, 1990-. “Tunable biclustering algorithm for analyzing large gene expression data sets.” 2019. Web. 16 Jan 2021.

Vancouver:

Singh, Amartya 1. Tunable biclustering algorithm for analyzing large gene expression data sets. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2021 Jan 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61960/.

Council of Science Editors:

Singh, Amartya 1. Tunable biclustering algorithm for analyzing large gene expression data sets. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61960/

Rutgers University

7. Panda, Anshuman, 1990-. Biomarkers of response to immune checkpoint therapy.

Degree: PhD, Physics and Astronomy, 2018, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/56075/

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Immune checkpoint therapy leads to durable objective response in a subset of patients with metastatic cancer in many different cancer types, but the mechanisms and… (more)

Subjects/Keywords: Cancer – Treatment

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APA (6^th Edition):

Panda, Anshuman, 1. (2018). Biomarkers of response to immune checkpoint therapy. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/56075/

Chicago Manual of Style (16^th Edition):

Panda, Anshuman, 1990-. “Biomarkers of response to immune checkpoint therapy.” 2018. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/56075/.

MLA Handbook (7^th Edition):

Panda, Anshuman, 1990-. “Biomarkers of response to immune checkpoint therapy.” 2018. Web. 16 Jan 2021.

Vancouver:

Panda, Anshuman 1. Biomarkers of response to immune checkpoint therapy. [Internet] [Doctoral dissertation]. Rutgers University; 2018. [cited 2021 Jan 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/56075/.

Council of Science Editors:

Panda, Anshuman 1. Biomarkers of response to immune checkpoint therapy. [Doctoral Dissertation]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/56075/

Rutgers University

8. Chappelow, Jonathan, 1980-. Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment.

Degree: PhD, Biomedical Engineering, 2011, Rutgers University

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061604

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Multimodal and multiprotocol image registration refers to the process of alignment of two or more images obtained from different imaging modalities (e.g. digitized histology and… (more)

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Multimodal and multiprotocol image registration refers to the process of alignment of two or more images obtained from different imaging modalities (e.g. digitized histology and MRI) and protocols (e.g. T2-w and PD-w MRI). Registration is a critical component in medical applications including image guided surgery, image fusion for cancer diagnosis and treatment planning, and automated tissue annotation. However, registration is often complicated on account of differences in both the image intensities and the shape of the underlying anatomy. For example, non-linear differences in the overall shape of the prostate between in vivo MRI and ex vivo whole mount histology (WMH) often exist as a result of the presence of an endorectal coil during pre-operative MR imaging and deformations to the specimen during slide preparation. To overcome these challenges, we present new registration techniques termed Combined Feature Ensemble Mutual Information (COFEMI) and Collection of Image-derived Non-linear Attributes for Registration Using Splines (COLLINARUS). The goal COFEMI is to provide a similarity measure that is driven by unique low level textural features, for registration that is more robust to intensity artifacts and modality differences than measures restricted to intensities alone. COLLINARUS offers the robustness of COFEMI to artifacts and modality differences, while allowing fully automated non-linear image warping at multiple scales via a hierarchical B-spline mesh grid. In addition, since routine clinical imaging procedures often involve the acquisition of multiple imaging protocols, we present a technique termed Multi-attribute Combined Mutual Information (MACAMI) to leverage the availability of multiple image sets to improve registration. We apply our registration techniques to a unique clinical dataset comprising 150 sets of in vivo MRI and post-operative WMH images from 25 patient studies in order to retrospectively establish the spatial extent of prostate cancer (CaP) on structural (T2-w) and functional (DCE) in vivo MRI. Accurate mapping of CaP on MRI is used to facilitate the development and evaluation of a system for computer-assisted detection (CAD) of CaP on multiprotocol MRI. We also demonstrate our registration and CAD algorithms in developing radiation therapy treatment plans that provide dose escalation to CaP by elastically registering diagnostic MRI with planning CT.

Advisors/Committee Members: Chappelow, Jonathan, 1980- (author), Madabhushi, Anant (chair), Bhanot, Gyan (internal member), Boustany, Nada (internal member), Bloch, B Nicolas (outside member), Tomaszewski, John E (outside member), Rosen, Mark (outside member).

Subjects/Keywords: Imaging systems in medicine; Prostate—Cancer – Imaging; Prostate—Cancer – Treatment

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APA (6^th Edition):

Chappelow, Jonathan, 1. (2011). Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061604

Chicago Manual of Style (16^th Edition):

Chappelow, Jonathan, 1980-. “Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment.” 2011. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061604.

MLA Handbook (7^th Edition):

Chappelow, Jonathan, 1980-. “Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment.” 2011. Web. 16 Jan 2021.

Vancouver:

Chappelow, Jonathan 1. Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment. [Internet] [Doctoral dissertation]. Rutgers University; 2011. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061604.

Council of Science Editors:

Chappelow, Jonathan 1. Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment. [Doctoral Dissertation]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061604

Rutgers University

9. Kion-Crosby, Willow B., 1991-. First-passage dynamics of random walks on complex and mutational codon networks.

Degree: PhD, Physics and Astronomy, 2019, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/61780/

► The first three chapters of this dissertation describe a novel Bayesian methodology which uses random walk sampling for rapid inference of the statistical properties of… (more)

▼ The first three chapters of this dissertation describe a novel Bayesian methodology which uses random walk sampling for rapid inference of the statistical properties of undirected networks with weighted or unweighted edges. The statistics of interest include, but are not limited to, the node degree distribution, the average degree of nearest-neighbor nodes, and the node clustering coefficient. Our formalism yields high-accuracy estimates of the probability distribution of any network node-based property, and of the network size, after only a small fraction of network nodes has been explored. The Bayesian nature of our approach provides rigorous estimates of all parameter uncertainties. We demonstrate our framework on several standard examples, including random, scale-free, and small-world networks, and apply it to study epidemic spreading on a scale-free network. We also infer properties of the large-scale network formed by hyperlinks between Wikipedia pages. During our analysis of complex networks, the connection between the frequencies of codons and the first-passage dynamics on the underlying single-point mutational network, which describes the evolution of gene sequences, was investigated. Viewing codon evolution as a random walk with deleterious sequences representing absorbing states inevitably led to the development of a detailed biophysical model for the investigation of codon usage bias. Frequencies of synonymous codons are typically non-uniform, despite the fact that such codons correspond to the same amino acid in the genetic code. This phenomenon, known as codon usage bias, is believed to be due to a combination of factors including genetic drift, mutational effects, and selection for speed and accuracy of codon translation; however, its quantitative modeling has been elusive. Here we develop a biophysical population genetics model capable of explaining genome-wide codon frequencies. Our model implements codon-level treatment of mutations with transition/transversion biases, and includes two contributions to codon fitness which describe codon translation speed and accuracy. Furthermore, it allows wobble pairing - codon-anticodon base pairing mismatches at the 3' nucleotide position of the codon. We find that the observed patterns of genome-wide codon usage are consistent with a strong selective penalty for mistranslated amino acids. In contrast, the dependence of codon fitness on translation speed is weaker on average compared to the strength of selection against mistranslation. Although no constraints on codon-anticodon pairing are imposed a priori, a reasonable hierarchy of pairing rates, which conforms to the wobble hypothesis and is consistent with available structural evidence, is predicted by the model. Finally, we estimate mutation rates per nucleotide directly from the coding sequences by treating the translation process explicitly in the context of a finite ribosomal pool, and predict that mutation rates are inversely proportional to the number of genes. Overall, our approach offers a unified… Advisors/Committee Members: Morozov, Alexandre V (chair), Bhanot, Gyan (internal member), Kiryukhin, Valery (internal member), Lee, Sang-Hyuk (internal member), Shah, Premal (outside member), School of Graduate Studies.

Subjects/Keywords: Complex networks; Computer networks – Statistical methods; Random walks (Mathematics)

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APA (6^th Edition):

Kion-Crosby, Willow B., 1. (2019). First-passage dynamics of random walks on complex and mutational codon networks. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/61780/

Chicago Manual of Style (16^th Edition):

Kion-Crosby, Willow B., 1991-. “First-passage dynamics of random walks on complex and mutational codon networks.” 2019. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/61780/.

MLA Handbook (7^th Edition):

Kion-Crosby, Willow B., 1991-. “First-passage dynamics of random walks on complex and mutational codon networks.” 2019. Web. 16 Jan 2021.

Vancouver:

Kion-Crosby, Willow B. 1. First-passage dynamics of random walks on complex and mutational codon networks. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2021 Jan 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61780/.

Council of Science Editors:

Kion-Crosby, Willow B. 1. First-passage dynamics of random walks on complex and mutational codon networks. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61780/

10. Drawid, Amar Mohan. Physically interpretable machine learning methods for transcription factor binding site identification using principled energy thresholds and occupancy:.

Degree: PhD, Computational Biology and Molecular Biophysics, 2009, Rutgers University

URL: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050504

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Regulation of gene expression is pivotal to cell behavior. It is achieved predominantly by transcription factor proteins binding to specific DNA sequences (sites) in gene… (more)

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Regulation of gene expression is pivotal to cell behavior. It is achieved predominantly by transcription factor proteins binding to specific DNA sequences (sites) in gene promoters. Identification of these short, degenerate sites is therefore an important problem in biology. The major drawbacks of the probabilistic machine learning methods in vogue are the use of arbitrary thresholds and the lack of biophysical interpretations of statistical quantities. We have developed two machine learning methods and linked them to the biophysics of transcription factor binding by incorporating simple physical interactions. These methods estimate site binding energy, recognizing that it determines a site's function and evolutionary fitness. They use the occupancy probability of a transcription factor on a DNA sequence as the discriminant function because it has a straightforward physical interpretation, forms a bridge between binding energy and evolutionary fitness, and has a natural threshold for classifying sequences into sites that allows establishing the threshold in a principled manner. Our methods incorporate additional characteristics of sites to enhance their identification. The first method, based on a hidden Markov model (HMM), identifies self-overlapping sites by combining the effects of their alternative binding modes. It learns the threshold by training emission probabilities using unaligned sequences containing known sites and estimating transition probabilities to reflect site density in all promoters in a genome. While identifying sites, it adjusts parameters to model site density changing with the distance from the transcription start site. Moreover, it provides guidance for designing padding sequences in experiments involving self-overlapping sites. Our second method, the Phylogeny-based Quadratic Programming Method of Energy Matrix Estimation (PhyloQPMEME), integrates evolutionary conservation to reduce false positives while identifying sites. It learns the threshold by solving an iterative quadratic programming problem to optimize the distribution of correlated binding energies of neutrally evolving orthologous sequences while restricting the values of binding energies of known sites and their orthologs. We have used the NF-κB transcription factor family as a case study for both methods and gained new insights into its biology.

Advisors/Committee Members: Drawid, Amar Mohan (author), Sengupta, Anirvan (chair), Levy, Ronald (internal member), Bhanot, Gyan (internal member), Gelinas, Celine (outside member).

Subjects/Keywords: Machine learning; Transcription factors; Probabilities

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

APA (6^th Edition):

Drawid, A. M. (2009). Physically interpretable machine learning methods for transcription factor binding site identification using principled energy thresholds and occupancy:. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050504

Chicago Manual of Style (16^th Edition):

Drawid, Amar Mohan. “Physically interpretable machine learning methods for transcription factor binding site identification using principled energy thresholds and occupancy:.” 2009. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050504.

MLA Handbook (7^th Edition):

Drawid, Amar Mohan. “Physically interpretable machine learning methods for transcription factor binding site identification using principled energy thresholds and occupancy:.” 2009. Web. 16 Jan 2021.

Vancouver:

Drawid AM. Physically interpretable machine learning methods for transcription factor binding site identification using principled energy thresholds and occupancy:. [Internet] [Doctoral dissertation]. Rutgers University; 2009. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050504.

Council of Science Editors:

Drawid AM. Physically interpretable machine learning methods for transcription factor binding site identification using principled energy thresholds and occupancy:. [Doctoral Dissertation]. Rutgers University; 2009. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050504

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

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 16, 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. 16 Jan 2021.

Vancouver:

Apratim, Manjul 1. On the physical and chemical dynamics of chromatin. [Internet] [Thesis]. Rutgers University; 2012. [cited 2021 Jan 16]. 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

12. Nguyen, Tung Thanh, 1979-. Systems biology approaches to corticosteroid pharmacogenomics and systemic inflammation.

Degree: Computational Biology and Molecular Biophysics, 2012, Rutgers University

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064152

Subjects/Keywords: Computational biology; Bioinformatics; Systems biology

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

APA (6^th Edition):

Nguyen, Tung Thanh, 1. (2012). Systems biology approaches to corticosteroid pharmacogenomics and systemic inflammation. (Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064152

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):

Nguyen, Tung Thanh, 1979-. “Systems biology approaches to corticosteroid pharmacogenomics and systemic inflammation.” 2012. Thesis, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064152.

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

MLA Handbook (7^th Edition):

Nguyen, Tung Thanh, 1979-. “Systems biology approaches to corticosteroid pharmacogenomics and systemic inflammation.” 2012. Web. 16 Jan 2021.

Vancouver:

Nguyen, Tung Thanh 1. Systems biology approaches to corticosteroid pharmacogenomics and systemic inflammation. [Internet] [Thesis]. Rutgers University; 2012. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064152.

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

Council of Science Editors:

Nguyen, Tung Thanh 1. Systems biology approaches to corticosteroid pharmacogenomics and systemic inflammation. [Thesis]. Rutgers University; 2012. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064152

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

13. Norton, Kerri-Ann. Analysis of cancer progression and morphology using computational modeling and image processing techniques.

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

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061395

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In the US, an estimated 560,000 people died from cancer in the year 2009 (Jemal et al., 2009). The goal of this dissertation is to… (more)

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In the US, an estimated 560,000 people died from cancer in the year 2009 (Jemal et al., 2009). The goal of this dissertation is to improve the fundamental understanding of cancer morphogenesis and so introduce scientific rigor into the diagnostic and prognostic methods used in the clinical setting. To this end, we focus on defining relationships between the history of growth of a cancerous lesion – which is what reliable prognosis depends on – and the morphology observed at an instant in time – which is what clinicians can observe ‐ through a combination of computational modeling and image processing. We achieve this goal by completing three aims. The first aim of the thesis is to examine the architectural progression of ductal carcinoma in situ of the breast (DCIS) using a two‐dimensional computational model. In this work we have found that the distinct architectural subtypes can result from different cellular features or from precancerous growths with similar cellular features but observed at different time points. The second aim of this thesis is to develop a border detection algorithm for skin lesions collected by dermoscopy. This work has resulted in the production of an automated and experimentally validated computational tool for the discrimination of melanoma, benign melanocytic lesions and non‐meanocytic lesions. The third and final aim is to characterize the fully three‐dimensional (3D) morphology of DCIS. In this work, we developed a 3D reconstruction approach to build 3D representations of DCIS. From this research, we have determined for the first time that there are two distinct 3D architectures of cribriform DCIS that are indistinguishable in 2D cross‐sections. Based on this finding, we propose that 3D reconstructions hold additional clinical information that cannot be accessed from analysis of 2D histological samples.

Advisors/Committee Members: Norton, Kerri-Ann (author), Shinbrot, Troy (chair), Bhanot, Gyan (internal member), Axelrod, David (internal member), Barnard, Nicola (outside member), Ganesan, Shridar (outside member).

Subjects/Keywords: Breast – Cancer – Magnetic resonance imaging; Breast – Cancer – Epidemiology

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

APA (6^th Edition):

Norton, K. (2011). Analysis of cancer progression and morphology using computational modeling and image processing techniques. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061395

Chicago Manual of Style (16^th Edition):

Norton, Kerri-Ann. “Analysis of cancer progression and morphology using computational modeling and image processing techniques.” 2011. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061395.

MLA Handbook (7^th Edition):

Norton, Kerri-Ann. “Analysis of cancer progression and morphology using computational modeling and image processing techniques.” 2011. Web. 16 Jan 2021.

Vancouver:

Norton K. Analysis of cancer progression and morphology using computational modeling and image processing techniques. [Internet] [Doctoral dissertation]. Rutgers University; 2011. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061395.

Council of Science Editors:

Norton K. Analysis of cancer progression and morphology using computational modeling and image processing techniques. [Doctoral Dissertation]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061395

14. Seiler, Michael Walker, 1983-. Transcriptome variation in breast cancer.

Degree: Computational Biology and Molecular Biophysics, 2012, Rutgers University

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066966

Subjects/Keywords: Breast – Cancer – Treatment; Breast – Tumors

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APA (6^th Edition):

Seiler, Michael Walker, 1. (2012). Transcriptome variation in breast cancer. (Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066966

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):

Seiler, Michael Walker, 1983-. “Transcriptome variation in breast cancer.” 2012. Thesis, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066966.

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

MLA Handbook (7^th Edition):

Seiler, Michael Walker, 1983-. “Transcriptome variation in breast cancer.” 2012. Web. 16 Jan 2021.

Vancouver:

Seiler, Michael Walker 1. Transcriptome variation in breast cancer. [Internet] [Thesis]. Rutgers University; 2012. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066966.

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

Council of Science Editors:

Seiler, Michael Walker 1. Transcriptome variation in breast cancer. [Thesis]. Rutgers University; 2012. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066966

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

Rutgers University

15. Lynch, Robert Francis, 1969-. The evolution of life history traits in Iceland, 1650-1950.

Degree: Anthropology, 2014, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/44161/

Subjects/Keywords: Life expectancy – Iceland – History

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

APA (6^th Edition):

Lynch, Robert Francis, 1. (2014). The evolution of life history traits in Iceland, 1650-1950. (Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/44161/

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):

Lynch, Robert Francis, 1969-. “The evolution of life history traits in Iceland, 1650-1950.” 2014. Thesis, Rutgers University. Accessed January 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/44161/.

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

MLA Handbook (7^th Edition):

Lynch, Robert Francis, 1969-. “The evolution of life history traits in Iceland, 1650-1950.” 2014. Web. 16 Jan 2021.

Vancouver:

Lynch, Robert Francis 1. The evolution of life history traits in Iceland, 1650-1950. [Internet] [Thesis]. Rutgers University; 2014. [cited 2021 Jan 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/44161/.

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

Council of Science Editors:

Lynch, Robert Francis 1. The evolution of life history traits in Iceland, 1650-1950. [Thesis]. Rutgers University; 2014. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/44161/

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

Rutgers University

16. Riley, Todd Robert. Modeling p53 transcriptional regulation.

Degree: PhD, Computational Biology and Molecular Biophysics, 2008, Rutgers University

URL: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17551

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The p53 protein has been called the "gatekeeper" of the cell. After DNA damage, p53 transcriptionally activates downstream pathways to prevent cancer from occurring. Target… (more)

Subjects/Keywords: p53 antioncogene; Genetic transcription; Transcription factors

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

APA (6^th Edition):

Riley, T. R. (2008). Modeling p53 transcriptional regulation. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17551

Chicago Manual of Style (16^th Edition):

Riley, Todd Robert. “Modeling p53 transcriptional regulation.” 2008. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17551.

MLA Handbook (7^th Edition):

Riley, Todd Robert. “Modeling p53 transcriptional regulation.” 2008. Web. 16 Jan 2021.

Vancouver:

Riley TR. Modeling p53 transcriptional regulation. [Internet] [Doctoral dissertation]. Rutgers University; 2008. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17551.

Council of Science Editors:

Riley TR. Modeling p53 transcriptional regulation. [Doctoral Dissertation]. Rutgers University; 2008. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17551

Rutgers University

17. Bilal, Erhan, 1979-. From data to bench to bedside: therapeutic targets in breast cancer.

Degree: PhD, Computational Biology and Molecular Biophysics, 2010, Rutgers University

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056152

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Understanding individualized breast cancer treatment options can help physicians care for their patients by careful selection of personalized therapies. The first steps towards this goal… (more)

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Understanding individualized breast cancer treatment options can help physicians care for their patients by careful selection of personalized therapies. The first steps towards this goal have already been taken by clinicians, with the frequent use of molecular and genetic biomarkers to classify breast cancer into categories which direct treatment. This thesis will propose new therapeutic targets for different breast cancer subtypes, as well as a new set of biomarkers that more efficiently predict hormone resistance in estrogen positive (ER+) breast tumors. A novel methodology for therapeutic target prediction will be proposed, based on a new paradigm called “gene centrality”. In addition to being over-expressed, good therapeutic targets should have a high degree of connectivity in the tumor network. Gene centrality encompasses this concept by measuring the connectivity of genes in a network in which each edge is weighted by the level of over-expression of the target gene. Using this method, a series of high centrality SRC proto-oncogenes (LYN, YES1, HCK, FYN, and LCK) were identified in subsets of Basal-like and HER2+ breast cancers. The hypothesis that YES1 is a therapeutic target in breast cancer was experimentally tested. We found that Basal-like breast tumor cell lines showed a significant decrease in fitness upon silencing the expression of YES1. Another validated therapeutic target in breast cancer is the estrogen receptor ESR1, targeted by drugs such as Tamoxifen. However, a significant fraction (~30%) of ER+ cases doesn’t respond well to this therapy. A novel outlier analysis method was applied to gene expression data from ER+ breast cancer patients to identify genes highly associated with Tamoxifen resistance. These included cell cycle genes as well as several chromosomal amplification sites. In addition to the well known HER2 amplicon on 17q12, we discovered that amplicons in 8q24.3, 8p11.2 and 17q21.33-q25.1 correlate strongly with early distant metastasis and poor long term survival. As independent biomarkers for Tamoxifen resistance, together these chromosomal regions are predictive for ~75% of patients that suffer early disease relapse.

Advisors/Committee Members: Bilal, Erhan, 1979- (author), Bhanot, Gyan (chair), Sengupta, Anirvan (internal member), Mischaikow, Konstantin (internal member), Ganesan, Shridar (outside member).

Subjects/Keywords: Breast – Cancer – Treatment; Tamoxifen; Biochemical markers

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

APA (6^th Edition):

Bilal, Erhan, 1. (2010). From data to bench to bedside: therapeutic targets in breast cancer. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056152

Chicago Manual of Style (16^th Edition):

Bilal, Erhan, 1979-. “From data to bench to bedside: therapeutic targets in breast cancer.” 2010. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056152.

MLA Handbook (7^th Edition):

Bilal, Erhan, 1979-. “From data to bench to bedside: therapeutic targets in breast cancer.” 2010. Web. 16 Jan 2021.

Vancouver:

Bilal, Erhan 1. From data to bench to bedside: therapeutic targets in breast cancer. [Internet] [Doctoral dissertation]. Rutgers University; 2010. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056152.

Council of Science Editors:

Bilal, Erhan 1. From data to bench to bedside: therapeutic targets in breast cancer. [Doctoral Dissertation]. Rutgers University; 2010. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056152

Rutgers University

18. Reddy, Anupama Rajasekhara, 1981-. Combinatorial pattern-based survival analysis with applications in biology and medicine:.

Degree: PhD, Operations Research, 2009, Rutgers University

URL: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051891

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In the current era of targeted therapies and personalized medicine, survival analysis (predicting survival time of patients) is a very important problem. Survival analysis is… (more)

Subjects/Keywords: Survival analysis (Biometry); Medical statistics

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

APA (6^th Edition):

Reddy, Anupama Rajasekhara, 1. (2009). Combinatorial pattern-based survival analysis with applications in biology and medicine:. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051891

Chicago Manual of Style (16^th Edition):

Reddy, Anupama Rajasekhara, 1981-. “Combinatorial pattern-based survival analysis with applications in biology and medicine:.” 2009. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051891.

MLA Handbook (7^th Edition):

Reddy, Anupama Rajasekhara, 1981-. “Combinatorial pattern-based survival analysis with applications in biology and medicine:.” 2009. Web. 16 Jan 2021.

Vancouver:

Reddy, Anupama Rajasekhara 1. Combinatorial pattern-based survival analysis with applications in biology and medicine:. [Internet] [Doctoral dissertation]. Rutgers University; 2009. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051891.

Council of Science Editors:

Reddy, Anupama Rajasekhara 1. Combinatorial pattern-based survival analysis with applications in biology and medicine:. [Doctoral Dissertation]. Rutgers University; 2009. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051891

Rutgers University

19. Dayarian, Adel, 1982-. Tools from statistical physics for systems biology and for genomics.

Degree: PhD, Physics and Astronomy, 2010, Rutgers University

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056286

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My graduate studies involved three broad classes of problems, each of which are presented in diﬀerent chapters of this thesis. The ﬁrst two parts of… (more)

Subjects/Keywords: Genomics – Research; Stochastic analysis

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APA (6^th Edition):

Dayarian, Adel, 1. (2010). Tools from statistical physics for systems biology and for genomics. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056286

Chicago Manual of Style (16^th Edition):

Dayarian, Adel, 1982-. “Tools from statistical physics for systems biology and for genomics.” 2010. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056286.

MLA Handbook (7^th Edition):

Dayarian, Adel, 1982-. “Tools from statistical physics for systems biology and for genomics.” 2010. Web. 16 Jan 2021.

Vancouver:

Dayarian, Adel 1. Tools from statistical physics for systems biology and for genomics. [Internet] [Doctoral dissertation]. Rutgers University; 2010. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056286.

Council of Science Editors:

Dayarian, Adel 1. Tools from statistical physics for systems biology and for genomics. [Doctoral Dissertation]. Rutgers University; 2010. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056286

Rutgers University

20. Sasson, Ariella Syma, 1978-. From millions to one: theoretical and concrete approaches to De Novo assembly using short read DNA sequences.

Degree: PhD, Computational Biology and Molecular Biophysics, 2010, Rutgers University

URL: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056766

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One of the most significant advances in biology has been the ability to sequence the DNA of organisms. Even in the shadow of the completion… (more)

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One of the most significant advances in biology has been the ability to sequence the DNA of organisms. Even in the shadow of the completion of the human genome, intractable regions of the genome remain incomplete. Next generation high-throughput short read sequencing technologies are now available and have the ability to generate millions of short read DNA sequences per run. Although greater coverage depths are possible, de novo sequence assembly with these shorter sequences is significantly more complex than resequencing; handling them presents new computational problems and opportunities. Identifying repetitive regions, coping with sequencing errors, and manipulating the millions of short reads simultaneously, are some of the difficulties that must be overcome. As a result of these complexities and working with the short read sequences from the Waksman SOLiD sequencing platform, this work explores the problem of de novo assembly. Initially, we develop tools for filtering short read sequence data based on quality scores and find that this procedure is critical for the success of the subsequent de novo assembly. Next, we analyze the key phenomena responsible for producing contigs that are much shorter than the values provided by theoretical estimates. Finally, we explore two different routes to circumventing the difficulty imposed by short contigs. The first involves utilization of information from multiple orthologous genomes in a comparative assembly. In particular, we developed a pipeline for using the reference genome of a close by relative to improve genome assembly. The second approach uses paired read information to build scaffolds that are two orders of magnitude larger than the original contigs. For typical bacterial genomes, less than one hundred of these scaffolds are required to cover the entire genome. The combination of short reads from various platforms, assembly, and recovery pipelines brings mid-sized genomes close to completion. As a result, minimal additional work using conventional sequencing technologies are enough to close the remaining small gaps and return a finished single genome. Current advancements in sequencing technologies leave us hopeful that it would be possible to provide fairly complete assemblies for complex genomes via these technological approaches.

Advisors/Committee Members: Sasson, Ariella Syma, 1978- (author), Sengupta, Anirvan (chair), Chen, Kevin (internal member), Schliep, Alexander (internal member), Bhanot, Gyan (internal member), Sidote, David (outside member).

Subjects/Keywords: DNA – Research – Technique

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

APA (6^th Edition):

Sasson, Ariella Syma, 1. (2010). From millions to one: theoretical and concrete approaches to De Novo assembly using short read DNA sequences. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056766

Chicago Manual of Style (16^th Edition):

Sasson, Ariella Syma, 1978-. “From millions to one: theoretical and concrete approaches to De Novo assembly using short read DNA sequences.” 2010. Doctoral Dissertation, Rutgers University. Accessed January 16, 2021. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056766.

MLA Handbook (7^th Edition):

Sasson, Ariella Syma, 1978-. “From millions to one: theoretical and concrete approaches to De Novo assembly using short read DNA sequences.” 2010. Web. 16 Jan 2021.

Vancouver:

Sasson, Ariella Syma 1. From millions to one: theoretical and concrete approaches to De Novo assembly using short read DNA sequences. [Internet] [Doctoral dissertation]. Rutgers University; 2010. [cited 2021 Jan 16]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056766.

Council of Science Editors:

Sasson, Ariella Syma 1. From millions to one: theoretical and concrete approaches to De Novo assembly using short read DNA sequences. [Doctoral Dissertation]. Rutgers University; 2010. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056766

Rutgers University

21. 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 (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 16, 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. 16 Jan 2021.

Vancouver:

Bhatia, Aatish 1. Detecting signatures of natural selection in genetic data. [Internet] [Thesis]. Rutgers University; 2013. [cited 2021 Jan 16]. 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

22. 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 16, 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. 16 Jan 2021.

Vancouver:

Chereji, Răzvan V. 1. Statistical mechanics of nucleosomes. [Internet] [Thesis]. Rutgers University; 2013. [cited 2021 Jan 16]. 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

23. Tsitron, Julia, 1976-. Computational analysis of olfaction and artificial nose technologies.

Degree: Computational Biology and Molecular Biophysics, 2013, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/41937/

Subjects/Keywords: Olfactory sensors; Bayesian statistical decision theory

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

APA (6^th Edition):

Tsitron, Julia, 1. (2013). Computational analysis of olfaction and artificial nose technologies. (Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/41937/

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):

Tsitron, Julia, 1976-. “Computational analysis of olfaction and artificial nose technologies.” 2013. Thesis, Rutgers University. Accessed January 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/41937/.

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

MLA Handbook (7^th Edition):

Tsitron, Julia, 1976-. “Computational analysis of olfaction and artificial nose technologies.” 2013. Web. 16 Jan 2021.

Vancouver:

Tsitron, Julia 1. Computational analysis of olfaction and artificial nose technologies. [Internet] [Thesis]. Rutgers University; 2013. [cited 2021 Jan 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/41937/.

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

Council of Science Editors:

Tsitron, Julia 1. Computational analysis of olfaction and artificial nose technologies. [Thesis]. Rutgers University; 2013. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/41937/

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

Rutgers University

24. 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 16, 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. 16 Jan 2021.

Vancouver:

Wagh K. Studies in viral population genetics and bioinformatics. [Internet] [Thesis]. Rutgers University; 2013. [cited 2021 Jan 16]. 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

25. Haldane, Allan M., 1985-. Biophysical models of evolution.

Degree: Physics and Astronomy, 2014, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/42393/

Subjects/Keywords: Biophysics; Evolution (Biology); Population genetics

Record Details Similar Records Cite « Share

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

APA (6^th Edition):

Haldane, Allan M., 1. (2014). Biophysical models of evolution. (Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/42393/

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):

Haldane, Allan M., 1985-. “Biophysical models of evolution.” 2014. Thesis, Rutgers University. Accessed January 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/42393/.

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

MLA Handbook (7^th Edition):

Haldane, Allan M., 1985-. “Biophysical models of evolution.” 2014. Web. 16 Jan 2021.

Vancouver:

Haldane, Allan M. 1. Biophysical models of evolution. [Internet] [Thesis]. Rutgers University; 2014. [cited 2021 Jan 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/42393/.

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

Council of Science Editors:

Haldane, Allan M. 1. Biophysical models of evolution. [Thesis]. Rutgers University; 2014. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/42393/

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

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Open Access Theses and Dissertations