In order to mimic biological cells in real world to separate chemical mixtures, we need to understand how they behave. My research proposes a new theory to understand the mechanism of them when very efficient, fast, and a robust separation happens in membranes with nanometer size. Also, my thesis focuses on using carbon nanotube as a nanopore for separating DNA, and optimizing carbon nanotube optical property at single molecular level for future sensing applications.
Many bacterial species exhibit self-organization behaviors where individual cells move collectively and organize themselves into a variety of multi-cellular structures. Majority of previous studies focused on biochemical signaling among cells to understand the mechanisms behind the bacterial self-organization. However, mechanical interactions among cells can also play an important role in this self-organization process. My research work focuses on understanding the role of mechanical interactions in various self-organization behaviors observed in Myxococcus xanthus bacteria using biophysical cell models and agent-based computer simulations. I investigate the mechanisms of individual to collective cell motility, collective alignment of cells into groups, aggregation of cells into circular/spiral patterns in M. xanthus.
Metal–organic frameworks (MOFs) are a rapidly emerging class of nanoporous materials with largely tunable chemistry and diverse applications in gas storage, gas purification, catalysis, etc. Intensive efforts are being made to develop new MOFs with desirable properties both experimentally and computationally in the past decades. To guide experimental synthesis with limited throughput, we develop a computational methodology to explore MOFs with high methane deliverable capacity. This de novo design procedure applies known chemical reactions, considers synthesizability and geometric requirements of organic linkers, and evolves a population of MOFs with desirable property efficiently. We identify about 500 MOFs with higher deliverable capacity than MOF-5 in 10 networks. We also investigate the relationship between deliverable capacity and internal surface area of MOFs. This methodology can be extended to MOFs with multiple types of linkers and multiple SBUs.
My current projects include investigations into the following three topics; chemotaxis, intercellular signaling and pattern formation. On the chemotaxis front, I am trying to build a theoretical model to explain early stages of chemotactic aggregation in the slime mold dictyostelium discoideum. This involves modeling at multiple space and time scales. I am also studying how cell-cell contact signaling can lead to tissue behavior and patterning in the context of the Notch signaling pathway and the planar cell polarity (PCP) pathway. The overall goal is to investigate design principles of these cellular mechanisms (physical or biochemical) that lead to specific cellular behaviors (cell fate, pattern formation) using numerical methods and simulations.
My current research interests focus on the formulation of mathematical approaches to model biologically-relevant systems where the interactions between microscopic components (i. e. cells, proteins) play a crucial role in establishing the emerging features of a system. Bacterial biofilms are a fascinating example where cell-cell interaction and communication mechanisms determine cell phenotypic expression at a single cell level and emerging characteristics at the colony level. Another application of current interest is the modeling of the epithelial-to-mesenchimal transition (EMT) that characterizes cancer cells that enter the bloodstream and adhere to tissues in secondary sites, giving rise to metastases.
My research interests focus on how statistical mechanics principles can be used to describe macromolecular dynamics in an effective way. Specifically, I have worked on developing methods for designing suitable collective coordinates out of complex data. Currently, I am investigating new data-based strategies to formulate a rigorous theoretical framework for the systematic coarse graining of complex macromolecules.
Currently, I am working on a comprehensive literature review comprising fundamental, applied, and prospective research on the CRISPR (clustered regularly interspaced short palindromic repeats) genetic adaptive immune system of prokaryotes. I have also been conducting background research that unifies principles from physics and neuroimaging to develop a theoretical framework for understanding multimodal integration in the human brain.
The goal of my work is to improve the models used to predict the stability of proteins. I use several methods including direct coupling analysis, the AWSEM forcefield and normal mode analysis. The insights obtained by these analyses can also be used to analyze the movement of the protein during allosteric regulation.
I develop tools (software, hardware, and wetware) to introduce precise time-varying perturbations into bacterial gene networks using optogenetics. I am applying these methods to study the network responsible for spore differentiation in B. subtilis.
Coarse-grained (CG) models are an attractive way to reduce computation time as protein sizes grow larger. My work focuses on the definition of a theoretical framework that incorporates experimental data (e.g. FRET efficiencies, free energy differences, NMR measurements) into the definition of an effective CG force-field that can correctly reproduce the observed results.
My current research majorly focuses on protein aggregation and its role in the formation of long-term memory and neurodegenerative diseases using energy landscape theory. For aggregation in the formation of long-term memory, we proposed the idea of the mechanical prion (CPEB) that couples aggregation and cytoskeleton dynamics in the synapse. We are still working on the molecular details of the coupling, as well as understanding the formation of LTP from a systems biology level. For aggregation in neurodegenerative diseases, we detailed the aggregation free energy landscapes of polyglutamine repeats and Amyloid-beta proteins.
My work focuses on modeling the cell-fate decision in bacteriophage lambda. My goal is to quantitatively describe the gene expression kinetics of the lambda decision and to identify and characterize the main factors that drive it.
I am interested in the mechanistic processes by which viruses infect their host and replicate, and more generally in lipid dynamics and fusion. The spread of length and timescales involved in various aspects of the viral life cycle necessitates a blend of modeling approaches, ranging from very coarse-grained simulations to detailed atomistic models. I primarily use molecular dynamics simulations to study conformational transitions in proteins, with the eventual goal of coupling these functional motions in viral systems to the lipid dynamics themselves for a complete structural picture of viral infection. Understanding this problem provides not only general insights about bilayer fusion, important for many cellular processes, but also has a very practical upside in potentially aiding therapeutic development.
My project seeks to understand the mechanism of encoding calmodulin states via Ca 2+ signals, and its downstream interaction with the multiple states of calmodulin-dependent kinase II. Our expected result will provide a tool for predicting protein-protein interaction that extends beyond the CaM/CaMKII examples.
Our goal is to understand protein folding and protein dynamics in vivo, using a combined approach of theory and computer simulations. Inside a cell, protein folding occurs in a highly crowded environment, where volume exclusion from surrounding macromolecules affects the dynamics and conformation space. Density fluctuation of these macromolecules creates a void where the protein resides that statistically favors a compact conformation over an extended one. Furthermore, new folded states appear in the presence of macromolecular crowding. We have modified our coarse-grained molecular dynamics simulations to account for pressure, and provide molecular insight to experiments.
My general research interest involves mathematical modeling of mechanisms of drug resistance in cancer. Currently, I study the immune-cancer dynamics in the setting of T cell immunotherapy, as well as inferential statistical models of partial epithelial-mesenchymal transition (EMT) signatures.
The purpose of my work is to study models of multiple particle systems. We are trying to analyze theoretical models and Monte Carlo simulations as a way of describing many particles behavior in a one-dimension lattice.
ALEXANDRU DAN GRIGORE
My project revolves around an aggressive subtype of prostate cancer known as neuroendocrine differentiation. I am currently assessing whether these cancer cells display a true neuronal phenotype, which might better explain the resistance to treatment and poor prognosis. Next, I will assess whether these cancer cells communicate via action potential-like impulses and/or synapse-like connections. If such true neuronal signaling does exist, I will then try to block it at various levels by using pharmacological agents that are currently being used for neurological and/or psychiatric diseases. This might ultimately lead to new therapeutic approaches for aggressive prostate cancer.
I study the durotaxis and chemotaxis. For durotaxis, I got and solved Fokker–Planck equation. I also utilized Monte Carlo Simulation to calculate the variance of persistent random walk. I also simulated the trajectory of single cell in uniform substrate and gradient substrate. For chemotaxis, I studied the advantage of exosomes. I studied the stability and instability of coupled partial differential equation by simulation. I studied the influence of exosome lifetime to the attraction. This theory maybe used to explain why some immune neurotransmitters are transported in exosomes.
My research focuses on the analysis of macromolecular dynamics and the development of new strategies for enhanced sampling. Current methods for adaptive sampling are mostly based on dimensionality reduction tools, but the speed-up achieved for complex systems is still limited. Analyzing the shortcomings of the current methods allows us to improve these methods and develop new approaches, in order to better simulate and characterize protein dynamics over long timescales.
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.
My research work focuses on uncovering the operating principles underlying Epithelial-to-Mesenchymal Transition (EMT) and cancer metabolism. Using the network modeling approach, we identified a hybrid Epithelial/Mesenchymal (E/M) phenotype during EMT and several 'phenotypic stability factors', such as OVOL and GRHL2, which can stabilize the hybrid E/M phenotype. Both predictions have been verified experimentally. Currently I am working on modeling the interplay between glycolysis and oxidative phosphorylation in cancer. The long-term goal is to understand the connection between EMT and metabolism during cancer progression.
My research investigates how the essential physics regulating macromolecular dynamics and function can be captured in coarse-grained models. I am exploring new strategies to design coarse-grain models by considering new functional forms and by incorporating experimental measurements in the simulation.
Protein search for their binding sites on DNA is an essential biological phenomenon crucial for the most biological processes. Proteins have to find and recognize their specific targets fast and precise in spite of the huge length of DNA and small size of target as well as a complicated nature of protein-DNA interactions in vivo. Although, significant experimental and theoretical efforts were made in recent years, the mechanism of this process remains not well understood and many aspects are still uncovered. Our goal is to create mathematical framework, capturing the most relevant physical-chemical processes that are consistent with the experiments in all range of parameters and gives correct prediction in the limits.
I am currently working on understanding long-term morphological plasticity of dendritic spines by computationally integrating known short-term biochemical signals and actin cytoskeleton reorganization. This study, when successful, will considerably advance our understanding of cellular mechanism of learning and memory.
I am interested in investigating the physical mechanism underlying the functions of various biological molecules. Specifically, I have been using different tools from physics and chemistry to understand the dynamical picture of how influenza viruses invade their host cells. I am also working on the development of coarse-grained models for protein structure predictions.
My current research includes studies of ERK and kinesin motor proteins. I use both theoretical methods and computational tools (such as molecular dynamics, docking, etc.) to understand their properties and their role in the living cells.
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 research utilizes the associative memory, water mediated, structure and energy model (AWSEM) to study different aspects of protein folding. One particular focus of mine has involved using simulations to better understand the pressure and cold denaturation that occurs in proteins. Another project I’ve been working on compares the AWSEM force field with statistical potentials obtained from coevolutionary data. We compare both the structure prediction quality as well as the frustration patterns obtained from the different potentials.
I am working on the talin–vinculin force sensor during focal adhesion formation and proposed a force-induced vinculin activation pathway that proceeds by altering the order of breaking three interdomain interfaces to reduce the kinetic bottleneck for activation.
My main focus is the quantification of the stochastic kinetics of transcription at a single gene locus in E. coli.
My research concerns multiscale modeling of gene regulatory networks, using ideas and knowledges from dynamical systems, statistical mechanics and kinetic theory of physical chemistry. I am also interested in analyzing genomics data using machine learning techniques.
I am interested in the collective behaviors of cells. Specifically, I have been investigating the mechanisms for the formation of finger-like instabilities arising from the tissue border in collective migrating cell sheets.
I am a graduate student in the department of Biochemistry and Molecular Biology at Baylor College of Medicine, working in the lab of Dr. Ido Golding. I am working on the system comprising the bacterium E. coli and phage lambda. My goal is to discover the undetected variables that contribute to the heterogeneity of lambda gene expression and the resulting decision between lysis and lysogeny.
My research mainly focuses on understanding human cognitive process and psychiatric diseases (e.g. major depression). Data is acquired from resting-state fMRI and both computational and theoretical approaches are adopted.
My research consists in implementing and developing coarse-grained models with increasing level of detail, in order to understand unusual features observed in protein systems that cannot be explained with existing simple models. For example, in the case of the proteins of the spectrin family, the mutation of just a few residues results in a 2-to-3 order of magnitude change in their folding rates. In order to reproduce the drastic effect caused by a small perturbation, like the mutation of a few residues, requires the incorporation of new physical ingredients into the coarse-grained model, such as explicit electrostatic interactions.
I study protein folding under conditions that are present in the interior of a cell. My current interest is to understand how hydrodynamics interaction and the macromolecular crowding effect affects the folding of proteins.
My main focus is the quantification of transcription kinetics at the level of individual gene copies in live E. coli cells.
Cell migration is critical to many important biological processes, such as cancer metastasis, wound healing, and so on. Understanding what’s going on in individual moving cells remains an attractive problem in the world of biological physics. Previous experimental and theoretical researches have clarified quite a few details in different aspects of moving cells, such as intracellular hydrodynamics, cytoskeleton dynamics, and etc. However, a model considering all such factors and cell morphology is not often used. Thanks to a phase field method, we are now able to implement such a comprehensive model. And in fact, such models have captured a lot of behavior of moving cells. My interest is to extend this phase field method to allow it to include membrane cellular dynamics and model the migration behavior of irregular-shaped individual amoeboid cells.
My current research is about the motility of Myxococcus xanthus bacteria. This Myxo bacteria are a flexible rod-shaped cells that move along long axis with periodic reversals. My research is focusing on simulating Myxo cells’ movements and how they form aggregates.