Workshop 2: Multiple Faces of Biomolecular Electrostatics

(October 12,2015 - October 16,2015 )

Organizers


Emil Alexov
Computational Biophysics and Bioinformatics, Clemson University
Bo Li
Department of Mathematics, University of California, San Diego
Ray Luo
Molecular Biology and Biochemistry, University of California,
Guowei Wei
Department of Mathematics, Michigan State University

Electrostatic interactions are fundamental in nature and ubiquitous in all biomolecules, including proteins, nucleic acids, lipid bilayers, sugars, etc. Electrostatic interactions are inherently of long range, which leads to computational challenges. Since 65-90 percent of cellular mass is water under physiological condition, biomolecules live in a heterogeneous environment, where they interact with a wide range of aqueous ions, counterions, and other molecules. As a result, electrostatic interactions often manifest themselves in a vast variety of different forms, due to polarization, hyperpolarization, vibrational and rotational averages, screening effect, etc, to mention just a few. The importance of electrostatics in biomolecular systems cannot be overemphasized because they underpin the molecular mechanism for almost all important biological processes, including signal transduction, DNA recognition, transcription, post-translational modification, translation, protein folding and protein ligand binding. In general, electrostatics is often the fundamental mechanism for macromolecular structure, function, dynamics and transport. Modeling and understanding the role of electrostatics in biomolecular systems are challenging tasks, since these systems are very complicated, made of macromolecules composed of hundreds of thousands or millions of atoms, and at the same time, surrounded by millions of water molecules, which in turn constantly change their positions and orientations. The number of degrees of freedom in explicit modeling of biomolecular systems is so large that it is frequently computationally prohibited for large systems or cases involving extremely large dimensions. Implicit models and multiscale approaches offer an alternative approach that dramatically reduces the computational cost, while being accurate enough to predict experimentally measurable quantities. Despite enormous efforts in the past two decades, important challenges remain in electrostatic modeling and computation. These include the definition of solvent-solute interfaces, nonlocal dielectric effects, finite size effects, nonlinear solvent response to solute perturbation, the representation of solvent microstructures, the solution of the corresponding nonlinear partial differential equations (PDEs) for irregularly shaped molecular boundaries, the treatment of solvent polarization and multi-valent ions, the formulation and solution of nonlinear integral equations (IEs), liquid density functional theory, and variational multiscale modeling of the dynamics and transport of biomolecular systems. The advantages and limitations of various methodologies are to be explored. Successful approaches to these challenges require combined efforts of physicists, mathematicians, computer scientists and biologists. This workshop will enable interactions between scientists from a diverse set of relevant disciplines. In particular, it will be of interest to mathematicians working in the areas of multiscale modeling, differential geometry of surfaces, PDE analysis, numerical PDE, and fast algorithm, to name a few. It will significantly strengthen the leading role that the US researchers can play in mathematical molecular biosciences by pursuing cutting-edge research and collaboratively training a new generation of mathematicians in this emerging interdisciplinary field.

Accepted Speakers

Nathan Baker
Computational and Statistical Analytics Division, Pacific Northwest National Laboratory
Charles Brooks
Biophysics Program, University of Michigan
Wei Cai
Department of Mathematics & Statistics, University of North Carolina, Charlotte
Chia-en (Angelina) Chang
Zhan Chen
Mathematics, Michigan State University
Qiang Cui
Chemistry & Theoretical Chemistry Institute, University of Wisconsin
Weihua Geng
Mathematics, University of Michigan
Sharon Hammes-Schiffer
Department of Chemistry, University of Illinois at Urbana-Champaign
Teresa Head-Gordon
Robert Krasny
Department of Mathematics, University of Michigan
Chun Liu
Mathematics, Penn State University
Tyler Luchko
Dmitry Matyushov
Julie Mitchell
Mathematics and Biochemistry, University of Wisconsin
Irina Moreira
Yoichiro Mori
Alexey Onufriev
B. Montgomery Pettitt
Pengyu Ren
Ridgway Scott
Computer Science and Mathematics, University of Chicago
Jana Shen
Xueyu Song
Zhen-Gang Wang
Stephen White
Dept. of Physiology & Biophysics, University of California, Irvine
John Zhang
Yingkai Zhang
Huan-Xiang Zhou
Monday, October 12, 2015
Time Session
Tuesday, October 13, 2015
Time Session
Wednesday, October 14, 2015
Time Session
Thursday, October 15, 2015
Time Session
Friday, October 16, 2015
Time Session
Name Email Affiliation
Alexov, Emil ealexov@clemson.edu Computational Biophysics and Bioinformatics, Clemson University
Baker, Nathan nathan.baker@pnl.gov Computational and Statistical Analytics Division, Pacific Northwest National Laboratory
Barba, Lorena labarba@bu.edu
Brooks, Charles brookscl@umich.edu Biophysics Program, University of Michigan
Cai, Wei wcai@uncc.edu Department of Mathematics & Statistics, University of North Carolina, Charlotte
Chang, Chia-en chiaenc@ucr.edu
Chen, Zhan zchen@georgiasouthern.edu Mathematics, Michigan State University
Chen, Long chenlong@math.uci.edu Department of Mathematics, University of California at Irvine
Cisneros, Gerardo Andres andres@chem.wayne.edu
Cui, Qiang cui@chem.wisc.edu Chemistry & Theoretical Chemistry Institute, University of Wisconsin
Fenley, Marcia mfenley@fsu.edu IMB, FSU
Geng, Weihua wgeng@mail.smu.edu Mathematics, University of Michigan
Hammes-Schiffer, Sharon shs3@illinois.edu Department of Chemistry, University of Illinois at Urbana-Champaign
Head-Gordon, Teresa thg@berkeley.edu
Hohn, Maryann maryann.hohn@uconn.edu
Im, Wonpil wonpil@ku.edu
Jacobs, Donald djacobs1@uncc.edu
Koehl, Patrice koehl@cs.ucdavis.edu
Krasny, Robert krasny@umich.edu Department of Mathematics, University of Michigan
Li, Bo bli@math.ucsd.edu Department of Mathematics, University of California, San Diego
Li, Lin lli5@clemson.edu
Liu, Chun liu@math.psu.edu Mathematics, Penn State University
Luchko, Tyler tyler.luchko@csun.edu
Luo, Ray ray.luo@uci.edu Molecular Biology and Biochemistry, University of California,
Makhatadze, George makhag@rpi.edu
Matyushov, Dmitry dmitrym@asu.edu
Mitchell, Julie jcmitchell@wisc.edu Mathematics and Biochemistry, University of Wisconsin
Moreira, Irina irina.moreira@fc.up.pt
Mori, Yoichiro ymori@math.umn.edu
Onufriev, Alexey alexey@cs.vt.edu
Pettitt, B. Montgomery pettitt@uh.edu
Ren, Pengyu pren@mail.utexas.edu
Scott, Ridgway ridg@uchicago.edu Computer Science and Mathematics, University of Chicago
Shen, Jana jshen@rx.umaryland.edu
Song, Xueyu xsong@iastate.edu
Sun, Weitao sunwt@mail.tsinghua.edu.cn Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University
Sushko, Maria Maria.Sushko@pnnl.gov
Wang, Zhen-Gang zgw@cheme.caltech.edu
Wei, Guowei wei@math.msu.edu Department of Mathematics, Michigan State University
White, Stephen stephen.white@uci.edu Dept. of Physiology & Biophysics, University of California, Irvine
White, Michael mrwhite@umn.edu
Wilson, Leighton lwwilson1@crimson.ua.edu
Wong, Chung wongch@msx.umsl.edu
Yan, Jue jyan@iastate.edu Mathematics, Iowa State University
Zhang, Yingkai yingkai.zhang@nyu.edu
Zhang, John john.zhang@nyu.edu
Zhou, Huan-Xiang hzhou4@fsu.edu
Modeling Electrodiffusion and Osmosis in Physiological Systems

Electrolyte and cell volume regulation is essential in physiological systems. After a brief introduction to cell volume control and electrophysiology, I will discuss the classical pump-leak model of electrolyte and cell volume control. I will then generalize this to a PDE model that allows for the modeling of tissue-level electrodiffusive, convective and osmotic phenomena. This model will then be applied to the study of cortical spreading depression, a wave of ionic homeostasis breakdown, that is the basis for migraine aura and other brain pathologies.

Modeling of enhanced catalysis in multienzyme nanostructures: effect of molecular scaffolds, spatial organization, and concentration

No abstract has been provided.

Partial Molar Volume Corrected Solvation Energies, Entropies and Free Energies from 3D-RISM

No abstract has been provided.

What Data-Driven Models of Biophysics Tell Us About Protein Electrostatics

No abstract has been provided.

Electrostatics beyond Poisson-Boltzmann: Effects of Self Energy

No abstract has been provided.