arXiv subjects

Robert S. Eisenberg

Publications and source records attributed to Robert S. Eisenberg.

14 recordsLinked to original sources

arXiv2024

Maxwell's Current in Mitochondria and Nerve

Maxwell defined a true or total current in a way not widely used today. He said "... true electric current ... is not the same thing as the current of conduction but that the time-variation of the electric displacement must be taken into account in estimating the total movement of electricity". We show that true or total current is a universal property of electrodynamics independent of properties of matter. We use mathematics without a dielectric constant. The resulting Maxwell Current Law is a generalization of the Kirchhoff Law of Current used in circuit analysis, that also includes displacement current. The generalization is not a long-time low frequency approximation in contrast to traditional presentation of Kirchhoff's Law. The Maxwell Current Law does not require currents to be in circuits. It has been applied to three dimensional systems like the signaling system of nerve and muscle fibers. The Maxwell Current Law clarifies the flow of electrons, protons, and ions in mitochondria that generate ATP, the molecule that stores chemical energy throughout life. The currents are globally coupled because mitochondria are short. Focusing on Maxwell current reinterprets the classical chemiosmotic hypothesis of ATP production. The conduction current of protons in mitochondria is driven by the protonmotive force including its component electrical potential, just as in the classical chemiosmotic hypothesis. The electrical potential is now the electrical potential as defined in physical sciences by Maxwell partial differential equations. The conduction current is now just a part of the true current analyzed by Maxwell. Details of accumulation of charges do not have to be considered in analysis of true current because true current does not accumulate. It is true total current that provides the energy that generates the ATP, not just the protonmotive force.

physics.bio-ph↗

arXiv2022

Kirchhoff's Law Can Be Exact

Kirchhoff's current law is thought to describe the translational movement of charged particles through resistors. But Kirchhoff's law is widely used to describe movements of current through resistors in high speed devices. Current at high frequencies/short times involves much more than the translation of particles. Transients abound. Augmentation of the resistors with ad hoc 'stray' capacitances is often used to introduce transients into models like those in real resistors. But augmentation hides the underlying problem, rather than solves it: the location, value and dielectric properties of the stray capacitances are not well determined. Here, we suggest a more general approach, that is well determined. If current is redefined as in Maxwell's equations, independent of the properties of dielectrics, Kirchhoff's law is exact and transients arise automatically without ambiguity. The transients in a particular real circuit-a high density integrated circuit for example-can then be described by measured constitutive equations together with Maxwell's equations without the introduction of arbitrary circuit elements.

physics.class-ph↗

arXiv2021

Membranes in Optic Nerve Models

Membranes are present in all cells and tissues. Mathematical models of cells and tissues need a compact mathematical description of membranes with a resolution of about 1 nm. Membranes isolate cells because ions have difficulty penetrating the dielectric barrier they create. Here we introduce a dielectric mathematical membrane condition to replace a condition that did not include dielectric properties. Our mathematical membrane condition includes a dielectric lipid bilayer punctured by channels that conduct ions selectively.

math.DS↗

arXiv2021

A Tridomain Model for Potassium Clearance in Optic Nerve of Necturus

The accumulation of potassium in the narrow space outside nerve cells is a classical subject of biophysics that has received much attention recently. It may be involved in potassium accumulation \textcolor{black}{including} spreading depression, perhaps migraine and some kinds of epilepsy, even (speculatively) learning. Quantitative analysis is likely to help evaluate the role of potassium clearance from the extracellular space after a train of action potentials. Clearance involves three structures that extend down the length of the nerve: glia, extracellular space, and axon and so need to be described as systems distributed in space in the tradition used for electrical potential in the `cable equations' of nerve since the work of Hodgkin in 1937. A three-compartment model is proposed here for the optic nerve and is used to study the accumulation of potassium and its clearance. The model allows the convection, diffusion, and electrical migration of water and ions. We depend on the data of Orkand et al to ensure the relevance of our model and align its parameters with the anatomy and properties of membranes, channels, and transporters: our model fits their experimental data quite well. The aligned model shows that glia has an important role in buffering potassium, as expected. The model shows that potassium is cleared mostly by convective flow through the syncytia of glia driven by osmotic pressure differences. A simplified model might be possible, but it must involve flow down the length of the optic nerve. It is easy for compartment models to neglect this flow. Our model can be used for structures quite different from the optic nerve that might have different distributions of channels and transporters in its three compartments. It can be generalized to include a fourth (distributed) compartment representing blood vessels to deal with the glymphatic flow into the circulatory system.

physics.bio-ph↗

arXiv2021

Optic Nerve Microcirculation: Fluid Flow and Electrodiffusion

Complex fluids flow in complex ways in complex structures. Transport of water and various organic and inorganic molecules in the central nervous system are important in a wide range of biological and medical processes [C. Nicholson, and S. Hrabětová, Biophysical Journal, 113(10), 2133(2017)]. However, the exact driving mechanisms are often not known. In this paper, we investigate flows induced by action potentials in an optic nerve as a prototype of the central nervous system (CNS). Different from traditional fluid dynamics problems, flows in biological tissues such as the CNS are coupled with ion transport. It is driven by osmosis created by concentration gradient of ionic solutions, which in term influence the transport of ions. Our mathematical model is based on the known structural and biophysical properties of the experimental system used by the Harvard group Orkand et al [R.K. Orkand, J.G. Nicholls, S.W. Kuffler, Journal of Neurophysiology, 29(4), 788(1966)]. Asymptotic analysis and numerical computation show the significant role of water in convective ion transport. The full model (including water) and the electrodiffusion model (excluding water) are compared in detail to reveal an interesting interplay between water and ion transport. In the full model, convection due to water flow dominates inside the glial domain. This water flow in the glia contributes significantly to the spatial buffering of potassium in the extracellular space. Convection in the extracellular domain does not contribute significantly to spatial buffering. Electrodiffusion is the dominant mechanism for flows confined to the extracellular domain.

physics.bio-ph↗

arXiv2020

Maxwell Equations without a Polarization Field using a paradigm from biophysics

Electrodynamics is usually written with a polarization vector field to describe the response of matter to electric fields, or more specifically, to describe changes in distribution of charge as an electric field is changed. This approach does not allow unique specification of a polarization field from measurements of electric and magnetic fields. Many polarization fields produce the same electric and magnetic fields, because only the divergence of the polarization enters Maxwell's first equation, relating charge and electric field. The curl of any function can be added to a polarization field without changing the electric field at all. The divergence of the curl is always zero. Models of structures that produce polarization cannot be uniquely determined from electrical measurements for the same reason. Models must describe charge distribution not just distribution of polarization to be unique. I propose a different paradigm to describe field dependent charge, i.e., to describe the phenomena of polarization. I propose an operational definition of polarization that has worked well in biophysics where a field dependent, time dependent polarization provides the gating current that makes neuronal sodium and potassium channels respond to voltage. The operational definition has been applied successfully to experiments for nearly fifty years. Estimates of polarization have been computed from simulations, models, and theories using this definition and they fit experimental data quite well. I propose that the same operational definition be used to define polarization charge in experiments, models, computations, theories, and simulations of other systems. Charge movement needs to be computed from a combination of electrodynamics and mechanics because 'everything interacts with everything else'. The classical polarization field need not enter into that treatment at all.

physics.gen-ph↗

arXiv2020

Updating Maxwell with Electrons, Charge, and More Realistic Polarization

Maxwell's equations describe the relation of charge and electric force almost perfectly even though electrons and permanent charge were not in his equations, as he wrote them. For Maxwell, all charge depended on electric field. Charge was induced and polarization was described by a single dielectric constant. Electrons, permanent charge, and polarization are important when matter is involved. Polarization of matter cannot be described by a single dielectric constant ${\varepsilon }_{\mathrm{r}}$ with reasonable realism today. Only vacuum is well described by a single dielectric constant ${\varepsilon }_{\mathrm{0}}$. Here, Maxwell's equations are rewritten to include permanent charge and any type of polarization. Rewriting is in one sense petty, and in another sense profound, in either case presumptuous. Rewriting confirms the legitimacy of electrodynamics. One cannot be sure ahead of time that a theory of electrodynamics without electrons or permanent charge (like Maxwell's equations as he wrote them) would be legitimate or not. After all, a theory cannot calculate the fields produced by charges (for example, electrons) that are not in the theory at all! After updating: (1) Maxwell's equations seem universal and exact. (2) Polarization must be described explicitly to use Maxwell`s equations in applications. (3) Conservation of total current (including displacement current ${\varepsilon }_{\mathrm{0\\ }}\frac{\mathrm{\partial }\boldsymbol{\mathrm{E}}}{\mathrm{\partial }t}$) becomes exact, independent of matter, allowing precise definition of electromotive force EMF in circuits.(4) Kirchhoff's current law becomes as exact as Maxwell`s equations themselves. (5) Conservation of total current needs to be satisfied in a wide variety of systems where it has not traditionally received much attention. (6) Classical chemical kinetics is seen to need revision to conserve current.

physics.class-ph↗

arXiv2020

Energetic Controls are Essential: New and Notable Note for Biophysical Journal

Mikhail Shapiro's lab investigated a promising new method for non-invasive control of calcium currents in individual cells in the nervous system by the selective heating of nanoparticles and show that simple physical laws, properly applied, explain what is happening, and so can be a foundation for constructing improved methods and techniques. They use custom built instrumentation and detailed quantitative measurement to show that special surface properties are not needed to explain their results. Their work is taken as an example of the need for quantitative measurement in biophysics and biology in general. Classical examples are cited and the argument is made that the success of structural and molecular biology has hidden the need for quantitative measurements and controls. Semiconductor and computational electronics is cited as a science even more successful than structural and molecular biology that depends on quantitative measurement, controls, and analysis.

q-bio.BM↗

arXiv2020

On the Polarization of Ligands by Proteins

Although ligand-binding sites in many proteins contain a high number density of charged side chains that can polarize small organic molecules and influence binding, the magnitude of this effect has not been studied in many systems. Here, we use a quantum mechanics/molecular mechanics (QM/MM) approach in which the ligand is the QM region to compute the ligand polarization energy of 286 protein-ligand complexes from the PDBBind Core Set (release 2016). We observe that the ligand polarization energy is linearly correlated with the magnitude of the electric field acting on the ligand, the magnitude of the induced dipole moment, and the classical polarization energy. The influence of protein and cation charges on the ligand polarization diminishes with the distance and is below 2 kcal/mol at 9 $\unicode{x212B}$ and 1 kcal/mol at 12 $\unicode{x212B}$. Considering both polarization and solvation appears essential to computing negative binding energies in some crystallographic complexes. Solvation, but not polarization, is essential for achieving moderate correlation with experimental binding free energies.

physics.chem-ph↗

arXiv2019

A Bidomain Model for Lens Microcirculation

There exists a large body of research on the lens of mammalian eye over the past several decades. The objective of the current work is to provide a link between the most recent computational models to some of the pioneering work in the 1970s and 80s. We introduce a general non-electro-neutral model to study the microcirculation in lens of eyes. It describes the steady state relationships among ion fluxes, water flow and electric field inside cells, and in the narrow extracellular spaces between cells in the lens. Using asymptotic analysis, we derive a simplified model based on physiological data and compare our results with those in the literature. We show that our simplified model can be reduced further to the first generation models while our full model is consistent with the most recent computational models. In addition, our simplified model captures the main features of the full model. Our results serve as a useful link intermediate between the computational models and the first generation analytical models.

q-bio.TO↗

arXiv2018

Quasi-incompressible Multi-species Ionic Fluid Models

In traditional hydrodynamic theories for ionic fluids, conservation of the mass and linear momentum is not properly taken care of. In this paper, we develop hydrodynamic theories for a viscous, ionic fluid of $N$ ionic species enforcing mass and momentum conservation as well as considering the size effect of the ionic particles. The theories developed are quasi-incompressible in that the mass-average velocity is no longer divergence-free whenever there exists variability in densities of the fluid components, and the models are dissipative. We present several ways to derive the transport equations for the ions, which lead to different rates of energy dissipation. The theories can be formulated in either number densities, volume fractions or mass densities of the ionic fluid components. We show that the theory with the Cahn-Hilliard transport equation for ionic species reduces to the classical Poisson-Nernst-Planck (PNP) model with the size effect for ionic fluids when the densities of the fluid components are equal and the entropy of the solvent is neglected. It further reduces to the PNP model when the size effect is neglected. A linear stability analysis of the model together with two of its limits, which is the extended PNP model (EPNP defined in the text) and the classical PNP model (CPNP) with the finite size effect, on a constant state and a comparison among the three models in 1D space are presented to highlight the similarity and the departure of this model from the EPNP and the CPNP model.

physics.flu-dyn↗

arXiv2017

Continuum gating current models computed with consistent interactions

The action potential signal of nerve and muscle is produced by voltage sensitive channels that include a specialized device to sense voltage. Gating currents of the voltage sensor are now known to depend on the back-and-forth movements of positively charged arginines through the hydrophobic plug of a voltage sensor domain. Transient movements of these permanently charged arginines, caused by the change of transmembrane potential, further drag the S4 segment and induce opening/closing of ion conduction pore by moving the S4-S5 linker. The ion conduction pore is a separate device from the voltage sensor, linked (in an unknown way) by the mechanical motion and electric field changes of the S4-S5 linker. This moving permanent charge induces capacitive current flow everywhere. Everything interacts with everything else in the voltage sensor so everything must interact with everything else in its mathematical model, as everything does in the whole protein. A PNP-steric model of arginines and a mechanical model for the S4 segment are combined using energy variational methods in which all movements of charge and mass satisfy conservation laws of current and mass. The resulting 1D continuum model is used to compute gating currents under a wide range of conditions, corresponding to experimental situations. Chemical-reaction-type models based on ordinary differential equations cannot capture such interactions with one set of parameters. Indeed, they may inadvertently violate conservation of current. Conservation of current is particularly important since small violations (<0.01%) quickly (<< 10-6 seconds) produce forces that destroy molecules. Our model reproduces signature properties of gating current: (1) equality of on and off charge in gating current (2) saturating voltage dependence in QV curve and (3) many (but not all) details of the shape of gating current as a function of voltage.

physics.bio-ph↗

arXiv2011

A Model of Electrodiffusion and Osmotic Water Flow and its Energetic Structure

We introduce a model for ionic electrodiffusion and osmotic water flow through cells and tissues. The model consists of a system of partial differential equations for ionic concentration and fluid flow with interface conditions at deforming membrane boundaries. The model satisfies a natural energy equality, in which the sum of the entropic, elastic and electrostatic free energies are dissipated through viscous, electrodiffusive and osmotic flows. We discuss limiting models when certain dimensionless parameters are small. Finally, we develop a numerical scheme for the one-dimensional case and present some simple applications of our model to cell volume control.

q-bio.CB↗

arXiv2009

An Efficient Algorithm for Classical Density Functional Theory in Three Dimensions: Ionic Solutions

Classical density functional theory (DFT) of fluids is a valuable tool to analyze inhomogeneous fluids. However, few numerical solution algorithms for three-dimensional systems exist. Here we present an efficient numerical scheme for fluids of charged, hard spheres that uses $\mathcal{O}(N\log N)$ operations and $\mathcal{O}(N)$ memory, where $N$ is the number of grid points. This system-size scaling is significant because of the very large $N$ required for three-dimensional systems. The algorithm uses fast Fourier transforms (FFT) to evaluate the convolutions of the DFT Euler-Lagrange equations and Picard (iterative substitution) iteration with line search to solve the equations. The pros and cons of this FFT/Picard technique are compared to those of alternative solution methods that use real-space integration of the convolutions instead of FFTs and Newton iteration instead of Picard. For the hard-sphere DFT we use Fundamental Measure Theory. For the electrostatic DFT we present two algorithms. One is for the \textquotedblleft bulk-fluid\textquotedblright functional of Rosenfeld [Y. Rosenfeld. \textit{J. Chem. Phys.} 98, 8126 (1993)] that uses $\mathcal{O}(N\log N)$ operations. The other is for the \textquotedblleft reference fluid density\textquotedblright (RFD) functional [D. Gillespie et al., J. Phys.: Condens. Matter 14, 12129 (2002)]. This functional is significantly more accurate than the bulk-fluid functional, but the RFD algorithm requires $\mathcal{O}(N^{2})$ operations.

physics.chem-ph↗