I am interested in building mathematical models aimed at understanding how protein-mediated physical manipulation of the chromosomal polymer at hundreds-of-nanometers length scale may lead to large scale structural reorganization and topological disentanglement, also interesting are the associated time scales. Such models not only have the potential to make our notion of the inner workings of the cellular machinery more nuanced via interpreting existing observations, but can also predict new experiments.
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.
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 use systems biology approaches to uncover the principles underlying the phenotypic plasticity and cell-fate decision making in cancer. Specifically, I integrate mathematical modeling and data analysis to elucidate the emergent dynamics of cellular networks governing metastasis and cancer metabolism. My work contributes to a systems-level understanding of tumorigenesis and metastasis and eventually facilitates the personalized therapeutic strategies for patients.
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.
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 project involves expanding the mathematical framework developed for the modeling of amorphous glassy materials to investigate the structure and statistical properties of the active matter systems such as cytoskeleton.
JOEL D. MALLORY
My research centers on exploring the thermodynamic efficiency of driven, nonequilibrium cycles that are fundamentally important for cellular information processing, namely those of DNA replication by the T7DNA polymerase enzyme and protein translation by the E. Coli ribosome. The thermodynamic efficiency of these molecular machines is assessed by their ability to keep unnecessary stochastic fluctuations and errors under control while at the same time to minimize the rate of heat dissipated to the environment. The trade-off between these two competing objectives is captured by a quantity called the uncertainty measure, which should be small and suppressed for an efficient molecular machine operating in a nonequilibrium steady state.
I am interested in the nonequilibrium statistical mechanics of active matter systems, including self-propelled particles and living cells.
My main focus is active gels, and specifically understanding their departure from equilibrium and the breakage of time-reversal-symmetry.
I further study the consequences of this breakage of time-reversal-symmetry on biological systems and in the formulation of general continuum theories.
My research is concerned with the extraction of essential information from biophysical simulations and the bridging of different levels of resolution in biophysical modeling. I am currently working on data-driven methods to extract relevant dynamical processes from simulation data, and to use this information to define accurate coarse grained models.
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 am interested in uncovering the ways in which transcription changes - and is changed by - the mechanical state of DNA. To help better understand this interplay I use a combination of statistical and computational tools to make strong qualitative predictions which can be compared against experiments. I am specifically interested in finding key phenomena which will lead to updates to the traditional ways of viewing gene regulation. In doing so we may uncover mechanisms of disease as well as a guide for the future of bioengineering.
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.