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.
My overall research interests include understanding the mechanical and dynamic properties of soft and biological materials. Biopolymers, as well as carbon nanotubes can be characterized as semiflexible polymers, which have been shown to exhibit elastic and relaxational properties that differ strongly from flexible polymers. By simulating networks of semiflexible polymers, we can predict their mechanical response, as well as the dynamics of their stress relaxation.
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.
My research investigates modular structure and function in a variety of biological topics. In CRISPR-Cas, I model the kinetics of crRNA:Cas9 recognition of invading DNA to understand how modularity contributes to specificity and efficiency. In the human immune system, I model vaccine efficacy against influenza based on antibody recognition of modular sites on the virus's hemagglutinin protein. In the human brain, I analyze network modularity in fMRI data of people listening to music to work towards personalizing music therapy. I also model brain network changes that lead to cognitive impairment and analyze MEG data from patients with dementia.
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.
I’m interested in dynamic properties of soft materials such as semiflexible biopolymers. Currently, I’m working on non-equilibrium force generation in actin filaments.
My research mainly focuses on the dynamics of protein and DNA in vivo, included protein aggregation, protein-DNA interaction and membrane protein. The transition of molecular structure in vivo determine their function in human life. I want to use physical tools to understand these kind of action better. At the same time, I am also interested in develop the Hamiltonian of AWSEM model to describe the protein folding, which also can help us understand the protein function better.
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.
LUCAS HILDEBRAND PIRES DA CUNHA
My overall research interest is in the mechanical behaviour of complex fluids in the microscale. In the past years, I have worked with numerical simulations of droplets flowing through microchannels and magnetic droplets under the influence of external magnetic fields and imposed flows. Recently, I got interested in the use of chains of magnetic colloidal particles as tools to understand the rheological response of biological materials.
My research concerns efforts in modeling the couplings between the mechanics of ECM (extra-cellular matrices) and biology of cells embedded inside the former.
We seek to understand the physics of living systems. Specifically, I study the theory of active matter inside a cell. I am highly interested how actomyosin networks evolve and store information as mechano-biological machines. By harnessing ideas from computer science and applied mathematics, we aim to explain the physical principles of self-assembly in biological matter. The end goal is to elucidate how long-term memory is formed and maintained.
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.
I'm interested in the mechanism of cancer metastases and pathways that can be targeted to slow/stop cancer growth. My research focuses on using mathematical models of the gene networks involved, such as the notch signaling pathway.
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 overall research interest focuses on developing stochastic models of cancer evolution. I currently study the stochastic co-evolution between cancer cells and the adaptive immune system in the setting of T-cell immunotherapy. The ultimate goal of this research is to optimize immunotherapy treatment for cancer patients.
Simulations are playing a significant role in advances of different areas of science. While every simulation framework supports different platforms to be executed on, they need constant modification in order to adapt to new architectures. As a result, supporting upcoming architectures is time-consuming and error-prone. To address this issue, new programming models are introduced that are platform agnostic and make simulation code portable on different architectures. However, they cannot reach performance of target-specific implementations. In a nutshell, portability leads to performance degradation. While we make our code more portable, we lose performance. My research tries to address this trade-off.
My research is focused on studying stochastic out of equilibrium models to improve the comprehension of Molecular motors. These motors are biological molecules that play important role in functioning of living systems. They support processes including muscle contractions, cellular transport and cell division. We understand now single motors quite well, but to understand and simulate systems of multiple motors is currently a challenge. Multiple motor proteins behavior deviates from what one may expect. The efficiency and the cooperation of these systems are intriguing. The purpose of this research is to study models of multiple motors with interactions. I analyze modifications of the asymmetric simple exclusion model to describe many motors through analytical methods. I used mean-field calculations, and modified mean-field approximation where we take explicitly into account the energy of forming and breaking cluster in the lattice. Our calculations are compared with large-scale computer simulations.
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'm interested in computational and theoretical chemistry, especially in topics about biological systems, like proteins. My recent research direction aims at improving protein structure prediction, basing on AWSEM coarse grain model.
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.
My research interests center on employing computational modeling and theoretical physics to explore the dynamics of biological systems. I currently study the role evolution plays on the energy landscapes of proteins by examining pseudogenes, using our AWSEM coarse grain model and DCA model.
My research mainly focuses on cancer system biology. Transitions between epithelial and mesenchymal phenotypes play important roles in both tissue repair and cancer metastasis. Currently, I’m investigating the influences of new feedback terms on EMT and MET. Some would have significant effects on the transitions and need to be further studied.
My research interests include protein structure prediction and application in solving crystallographic phasing problem. The phasing problem, which arises from the fact that X-ray diffraction experiments only record the intensities, but not the phases, of the diffracting electromagnetic waves. With de novo predicted structure, we can solve phasing problem by molecular replacement and get real structure from X-ray data. This method will greatly broadens the protein structure database.
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.
My research interests include computational modeling and analysis of biomolecular dynamics and applications of machine learning. I am particularly interested in coarse-grained network modeling and the interplay of flexibility and frustration in the large scale conformational changes that accompany biomolecular functions.
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.
Membrane proteins account for more than 20 percent of all human protein-coding genes and more than 50 percent of the drug targets using today. My current research focuses on the studying of force induced membrane protein unfolding dynamics using our coarse grain model (AWSEM).
My research interests include the modeling and computational simulation and analysis of biomolecular architecture. Currently, I am mainly focussed on the chromosome conformation, to be more specific, how the Minimal Chromatin Model (MichroM) would work on higher-ordered contacts within a single chromosome.
My research involves the Prediction of Actin-Actin interactions via co-evolutionary sequence analysis and molecular dynamics simulation.
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 am interested in the elastic properties of semi-flexible polymer networks. In living systems, such networks provide striking nonlinear mechanical behavior to cells and tissues, including strain stiffening and negative normal stresses. Currently, I am working to characterize the dependence of negative normal stress on network structure and applied strain.
My research focuses on understanding the structure-function relationship of organic photovoltaic materials. Currently we are focusing on electron donor-acceptor heterojunctions such as C60-SubPC and C70-DBP. We model the interface using molecular dynamics and obtain significant structures using statistical mechanics and machine learning techniques. We then compute charge transfer statistics using quantum chemistry and link this to device performance using a kinetic Monte Carlo method. This link between interfacial geometry and device performance is up-to-now unknown and will allow for the creation of improved solar technology.
I am interested in studying the dynamics of evolution in heterogeneous and changing environments, using stochastic simulations, physical principles, and mathematical models. Of particular interest are the emergence of antibiotic resistance in bacteria and the emergence of drug resistance in cancer cells. I am also interested in understanding the general theoretical principles governing biological evolution and how these can be applied in a clinical setting, for example, in cancer prognosis.
My research focuses on the effect of modularity on biological evolution. Currently I am studying how modularity coupled with horizontal gene transfer accelerates the migration of human beings and bacteria. The insights from my studies could also be used in the study of cancer metastasis.
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 focuses on quantitative modeling of complex biological systems. Such systems include gene interaction networks in cancer cells, functional connectivity in human brain, genome-scale metabolic network of E.coli, and so on. I use methods such as network clustering, dimensionality reduction, neural networks, and flux balance analysis. The overall goal of my research is to understand the relation between the structure and function of biological networks.
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.
Myxococcus xanthus is a soil bacterium that serves as a model system for biological self-organization. Cells form distinct, dynamic patterns depending on environmental conditions. My work is focused on using agent-based model (ABM) to understand how M. xanthus cells aggregate into multicellular mounds in response to starvation. We demonstrated that a chemotaxis model with adaptation can reproduce the observed experimental results leading to the formation of stable aggregates.