My work focuses on understanding the process of photocurrent generation in organic photovoltaic materials. In particular, our group makes use of a variety of multi-scale computational approaches to investigate the morphology and optoelectronic properties of high-performing P3HT blends commonly employed in organic photocells.
My research program is focused on developing druggable binding site identification and drug design method for protein-protein interactions based on structure-based molecular docking and sequence-based co-evolutionary analysis, as well as applying them on the cancer targets, i.e., NEET proteins, HSP90β, BRAC1, PD-1, etc. Specifically, my current projects focus on identifying allosteric binding sites of GPCR, understanding the allosteric regulation mechanism, as well as the relationship between the allosteric sites and active site and designing potent selective allosteric modulators.
I am interested in computational problems that challenge design principles in Nano and micro scale in chemistry, biology, and engineering toward expanding an interdisciplinary common ground. My current research interests are in modeling molecular mechanisms that lead to biological pattern formation in the early mammalian embryo. At the moment I am combining Monte Carlo simulations with PDE analysis and discussing the computational results with our experimental collaborators at Rice.
My research at the CTBP involves quantifying the essential features of large biological datasets. I have primarily worked on inferring fitness landscapes for bacterial signaling proteins to better understand the origin of interaction specificity, “cross-talk,” and mutational phenotypes. Recently, I am also working in a collaborative effort to elucidate the biochemical origins of the 3D spatial organization of human chromosomes.
My research involves the studies of biological macromolecules through computational simulations using simple models. The current investigation includes protein folding, protein engineering, and genome architecture.
I use theoretical and computational techniques to study biological processes such as protein folding and assembly and protein-facilitated membrane remodeling. I specialize on development and application of multi-scale modeling techniques, where information obtained from various sources and at different resolutions (i.e. from theory, all-atom simulations, coarse-grained and mesoscopic modeling, and experiments) is combined to gain a more complex understanding of the system of interest. At CTBP I am currently studying the collective dynamics of motor proteins and the function of p53 protein, which plays a central role in natural mechanisms of cancer suppression.
MICHELE DI PIERRO
My research includes statistical mechanics, machine learning and optimization techniques to the study of biological systems. Recently, the focus of this research has been the study of genome architecture and its epigenetic regulation.
I am currently working towards understanding how genomes fold in three dimensions. We probe the 3D architecture of whole genomes by coupling proximity-based ligation with massively parallel sequencing – the Hi-C method. I am particularly interested in large-scale genome remodeling events associated with the process known as X chromosome inactivation: a mechanism that ensures that males who possess one X chromosome and females with two X chromosomes exhibit similar gene expression levels.
CYNTHIA PEREZ ESTRADA
Molecular cell biologist, I work applying 3D genome folding concepts to various areas of biology, I get help from super-resolution microscopy, chromatin conformation capture (Hi-C) and mathematical modeling to understand how the genome folds in 3D. My final goal is to decipher the biological roles of genome architecture in our cells.
I am mainly interested in the mechanical aspect of Cell and Extracellular Matrix interactions. In particular, cells exert traction forces on the surrounding Extracellular Matrix (ECM) and cause remodeling. The remodeled ECM in turn regulates both cell mechanical behavior and gene expression. My goal is to understand such mechanical interactions and their influence on cell motility.
I am studying the pleiotropic hormone leptin through an array of strategic in silico molecular dynamics simulations and in vitro experiments combined with in cell biological activity-assays to investigate the relationship between folding, structure, stability, conformational dynamics and activity. Importantly, leptin is the founding member of the Pierced Lasso (PL) topology, a new class of “knotted” proteins that discovered. Here, two cysteines form a covalent-loop where part of the protein backbone is threaded through, creating a so-called PL topology. Interestingly, the PL topology is not unique to leptin, as we discovered over 1000 proteins with a PL topology searching through the Protein Data Bank (PDB), and they are found in all kingdoms of life.
I am investigating the gene regulatory mechanism for cancer metastasis, including Epithelial-to-Mesenchymal (EMT) transition, and Amoeboid-to-Mesenchymal (AMT) transition by quantitatively modeling the underlying gene-gene interaction network. I am also developing a new computational method to predict gene expression patterns with the only topology information of a gene regulatory network.
The aim of my current research is to shed light on the enigma of three-dimensional chromatin structure, a complex of DNA and proteins. As a first step, I am working on better understanding of the cohesion complex, which is considered as the motor, translocating segments of chromatin to form structural loops.
MOHIT JOLLY KUMAR
My current research focuses on elucidating the principles of cellular plasticity during cancer metastasis. I work in close collaboration with multiple experimental and clinical groups to develop mathematical models of intracellular and intercellular signaling pathways to understand how cellular plasticity drives metastasis, drug resistance and tumor relapse – the deadliest yet poorly understood aspects of cancer biology. Specifically, I am currently focusing on characterizing a hybrid epithelial/mesenchymal phenotype and mechanisms underlying the formation of clusters of Circulating Tumor Cells – the primary harbingers of cancer metastasis.
My research involves simulation studies of biomolecules. Quantifying the configurational entropy of protein side-chains in the native state. Modeling the functional transition of the hexameric AAA peptidase FtsH. RNA folding, and the effect of chelation on the ion atmosphere of RNA.
My current research focuses on the cancer microenvironment, i.e., interactions between different types of cells in a solid tumor. Particularly, I am interested in interactions between cytotoxic T cells, which can kill cancer cells, and other types of cells in tumors. Through computational studies, I am focusing on mechanisms that restrict the infiltration of cytotoxic T cells into tumor cell clusters and de-activate cytotoxic T cells even if they managed to infiltrate a tumor. The results may help to improve the efficacy of current immunotherapies that work through activating cytotoxic T cells.
My work focuses on quantitatively studying the post-infection decision of bacteriophage lambda between lysis and lysogeny at the single-cell, and single-molecule level.
My research is concerned with the extraction of essential information from biophysical simulation data and the bridging of different levels of resolution in biophysical modeling. My first objective is the improvement of the variational approach to conformational dynamics (VAC), a method to find a low-dimensional representation of the slow kinetic processes in molecular dynamics simulations, based on the transfer operator description of stochastic dynamics. The second objective is to combine the VAC with suitable learning algorithms to derive effective equations on the slow coordinates from data.
How bacteria cooperate among themselves and discriminate against others to direct collective behaviors and to evade competition is the major theme of my research. In one project, I am trying to understand how social motility, which allows movement only in cell groups but not as individual cells, is regulated via various cellular processes such single cell motility, cell reversals, exopolysaccharide production etc. In another project, I am working to find the mechanism behind kin discrimination in swarming bacteria that is evident in the form of non-merger of approaching colonies of distinct strains in contrast to the merging of identical colonies.
My research at the CTBP focuses on understanding the regulatory control mechanism of non-coding RNA element in expressing the downstream genes. I am interested in understanding the interplay between ligand binding, folding and switching of a nascent RNA between alternative conformations, using both experimental and computational approaches, in close collaboration. I am also characterizing the effect of ion sensitivity of all these control processes using a recently developed model of RNA electrostatics implemented within our coarse-grained structured based molecular dynamics simulation program.
I use optimized coarse-grained protein folding simulation models to address questions in molecular protein biophysics. Topics of interest include structure prediction, protein-protein association, misfolding, aggregation, design, pressure and cold denaturation, and membrane proteins.
I have most principally been interested in a number of unexplained phenomena in gene expression and have consequently developed two frameworks for understanding basic properties of transcription. The first is a stochastic formulation of the constraints mechanical constraints put on properties of transcription. The second is a mechanical framework that explicitly captures the interplay between transcriptional dynamics and DNA mechanics.
My current work is focused on the optoelectronic behavior, or more specifically, on the relationship between conformation and optical properties of materials, considered to be structurally amorphous or soft. I use a variety of computational tools in order to gain a conceptual understanding, and in some cases predict the behavior of “smart” materials, which are able to respond in a meaningful way to changes in their environment, such as bio-sensors (e.g., genetically encoded fluorescent voltage probes), as well as, of passive optoelectronic components, such as the active layer in organic solar cells. The ultimate goal of this work is to guide the rational design of amorphous optoelectronic active and passive components.
I use computational methods to study protein-protein and protein-DNA interactions, which can help understand molecular mechanism of protein aggregation and gene transcriptional regulation in neurodegenerative diseases. My research will be impactful in that it will provide useful knowledge in guiding drug design, for example, the use of small molecule inhibitors to target protein-protein/protein-DNA interactions.
My research mainly focuses on understanding the mechanism of single cytoskeleton motor protein and collective dynamics of multiple motor proteins. I am also investigating the dendritic spine mechano-biology in order to understand the memory formation.
My research focuses on deciphering the mechanism of transcriptional regulation of key segmentation genes in Drosophila melanogaster embryos, using high-resolution fluorescence imaging, quantitative image analysis, and stochastic modeling.