Identifying critical regions for spike propagation in axon segments

Morphological reconstructions of axon segments reveal the abundance of geometrical ultrastructures that can dramatically affect the propagation of Action Potentials (AP). Moreover, deformations and swellings in axons resulting from brain traumas are associated to many neural dysfunctions and disorders. Our aim is to develop a computational framework to distinguish between geometrical enlargements that lead to minor changes in propagation from those that result in critical phenomenon such as reflection or blockage of the original traveling spike. We use a few geometrical parameters to model a prototypical shaft enlargement and explore the parameter space characterizing all possible propagation regimes and dynamics in an unmylienated AP model. Contrary to earlier notions that large diameter increases mostly lead to blocking, we demonstrate transmission is stable provided the geometrical changes occur in a slow manner. Our method also identifies a narrow range of parameters leading to a reflection regime. The distinction between these three regimes can be evaluated by a simple function of the geometrical parameters inferred through numerical simulations. We suggest that evaluating this function along axon segments can detect regions most susceptible to (i) transmission failure due to perturbations, (ii) structural plasticity, (iii) critical swellings caused by brain traumas and/or (iv) neurological disorders associated with the break down of spike train propagation.     

Simulation and Theory of Antibody Binding to Crowded Antigen-Covered Surfaces

In this paper we introduce a fully flexible coarse-grained model of immunoglobulin G (IgG) antibodies parametrized directly on cryo-EM data and simulate the binding dynamics of many IgGs to antigens adsorbed on a surface at increasing densities. Moreover, we work out a theoretical model that allows to explain all the features observed in the simulations. Our combined computational and theoretical framework is in excellent agreement with surface-plasmon resonance data and allows us to establish a number of important results. (i) Internal flexibility is key to maximize bivalent binding, flexible IgGs being able to explore the surface with their second arm in search for an available hapten. This is made clear by the strongly reduced ability to bind with both arms displayed by artificial IgGs designed to rigidly keep a prescribed shape. (ii) The large size of IgGs is instrumental to keep neighboring molecules at a certain distance (surface repulsion), which essentially makes antigens within reach of the second Fab always unoccupied on average. (iii) One needs to account independently for the thermodynamic and geometric factors that regulate the binding equilibrium. The key geometrical parameters, besides excluded-volume repulsion, describe the screening of free haptens by neighboring bound antibodies. We prove that the thermodynamic parameters govern the low-antigen-concentration regime, while the surface screening and repulsion only affect the binding at high hapten densities. Importantly, we prove that screening effects are concealed in relative measures, such as the fraction of bivalently bound antibodies. Overall, our model provides a valuable, accurate theoretical paradigm beyond existing frameworks to interpret experimental profiles of antibodies binding to multi-valent surfaces of different sorts in many contexts.     

Evaluating the nucleus effect on the dynamic indentation behavior of cells

The effect of the nucleus on the cell mechanical behavior was investigated based on the dynamic indentation response of cells under a spherical tip. A “two-component” cell model (including cytoplasm and nucleus) is used, and the dynamic indentation behavior is studied by a semiempirical method, which is established based on fitting the numerical simulation results of the quasi-static indentation response of cells. We found that the “routine analysis” (based on the Hertz’s contact solution of homogeneous model) significantly overestimated the nucleus effect on the overall cell indentation response due to the effects of the Hertz contact radius and the substrate stiffening. These effects are significantly stronger in the “two-component” cell model than in the homogeneous model. The inaccuracy created by the “routine analysis” slightly increases with the modulus ratio of nucleus to cytoplasm and the volume fraction of nucleus. Finally, the error sensitivity to the geometrical parameters used in the model is discussed, which shows the indentation analysis is not very sensitive to these parameters, and the reasonable assumptions for these parameters are effective. This systematic analysis can provide a useful guideline to understanding the mechanical behavior of cells and nuclei.     

Solvent-amino acid interaction energies in three-dimensional-lattice Monte Carlo simulations of a model 27-mer protein: Folding thermodynamics and kinetics

Amino acid residue-solvent interactions are required for lattice Monte Carlo simulations of model proteins in water. In this study, we propose an interaction-energy scale that is based on the interaction scale by Miyazawa and Jernigan. It permits systematic variation of the amino acid-solvent interactions by introducing a contrast parameter for the hydrophobicity, C(s), and a mean attraction parameter for the amino acids, omega. Changes in the interaction energies strongly affect many protein properties. We present an optimized energy parameter set for best representing realistic behavior typical for many proteins (fast folding and high cooperativity for single chains). Our optimal parameters feature a much weaker hydrophobicity contrast and mean attraction than does the original interaction scale. The proposed interaction scale is designed for calculating the behavior of proteins in bulk and at interfaces as a function of solvent characteristics, as well as protein size and sequence.     

Local network parameters can affect inter-network phase lags in central pattern generators

Weakly coupled phase oscillators and strongly coupled relaxation oscillators have different mechanisms for creating stable phase lags. Many oscillations in central pattern generators combine features of each type of coupling: local networks composed of strongly coupled relaxation oscillators are weakly coupled to similar local networks. This paper analyzes the phase lags produced by this combination of mechanisms and shows how the parameters of a local network, such as the decay time of inhibition, can affect the phase lags between the local networks. The analysis is motivated by the crayfish central pattern generator used for swimming, and uses techniques from geometrical singular perturbation theory.     

A model of quick phase generation in the vestibuloocular reflex

The vestibuloocular reflex of the cat was studied during step and sinusoidal head velocity stimuli. A model is presented which simulates the observed slow phase and quick phase behavior. The model is constructed to be compatible with neurophysiological observations of the behavior of neurons in the pons. Emphasis is placed on the amplitude and timing of quick phases which are active orienting movements that drive the eyes into the direction of turning. It is proposed that quick phases, like saccades, are generated by a local feedback loop in the pons which rapidly drives the eyes to a point in the orbit specified by a vestibular signal. It is suggested that two internal signals specify the eye positions at which quick phases start and end. The statistics of the fluctuations of these signals was measured and correlation between them was discovered and incorporated in the model.     

Mathematical and numerical models for transfer of low-density lipoproteins through the arterial walls: a new methodology for the model set up with applications to the study of disturbed lumenal flow

In this work we introduce and discuss several mathematical models, based on partial differential equations, devised to study the coupled transport of macromolecules as low-density lipoproteins in the blood stream and in the arterial walls. These models are accurate provided that a suitable set of physical parameters characterizing the physical properties of the molecules and of the wall layers are available. Here we turn our attention on this aspect, and propose a new methodology to compute the physical parameters needed for the model set up, starting from available in vivo measurements. Then, we focus on the study of the accumulation of low-density lipoproteins in vascular districts featuring a highly disturbed flow. Our results demonstrate that mathematical models whose set up procedure benefits from an experimental feedback provide reliable information not only qualitatively, but also quantitatively. Their application to geometrically perturbed vascular districts (as for example a severe stenosis) shows that geometrical parameters such as curvature and variations of the lumenal section strongly influence the accumulation of low-density lipoproteins within the wall. For instance, in a stenotic segment with 75% area constriction, the LDL concentration at the lumenal side of the wall is about 10% higher than for the undisturbed segment.     

Design issues for the Michaelis-Menten model

We discuss design issues for the Michaelis-Menten model and use geometrical arguments to find optimal designs for estimating a subset of the model parameters, or a linear combination of the parameters. We propose multiple-objective optimal designs when the parameters have different levels of interest to the researcher. In addition, we compare six commonly used sequence designs in the biological sciences for estimating parameters and, propose optimal choices for the parameters for geometric designs using closed-form efficiency formulas.     

Modularity and the cost of complexity

Abstract. In this work we consider the geometrical model of R. A. Fisher, in which individuals are characterized by a number of phenotypic characters under optimizing selection. Recent work on this model by H. A. Orr has demonstrated that as the number of characters increases, there is a significant reduction in the rate of adaptation. Orr has dubbed this a "cost of complexity." Although there is little evidence as to whether such a cost applies in the natural world, we suggest that the prediction is surprising, at least naively. With this in mind, we examine the robustness of Orr's prediction by modifiying the model in various ways that might reduce or remove the cost. In particular, we explore the suggestion that modular pleiotropy, in which mutations affect only a subset of the traits, could play an important role. We conclude that although modifications of the model can mitigate the cost to a limited extent, Orr's finding is robust.     

Discretization error when using finite element models: Analysis and evaluation of an underestimated problem

Mesh convergence tests are often insufficiently performed in finite element analyses. There are many parameters which may have an effect on the mesh convergence behavior. The aim of this study was to identify the influence of different parameters on the mesh convergence behavior.For this purpose we used a simplified axis-symmetrical model of a single pedicle screw flank with surrounding bone to simulate a pull-out test. In parameter studies, the flank radii and the contact conditions at the bone–screw interface were varied. These parameter studies were carried out using an implicit and explicit solver. Thereby, the convergence criteria and the number of the substeps for the implicit nonlinear iteration process as well as the velocity and the material density for the explicit approach were considered.The mesh convergence behavior was influenced by varying the flank radii and the contact conditions. The implicit calculations led to a reaction force, which converged rapidly to a certain value with increasing mesh density, whereas the maximum von-Mises stress showed substantial convergence problems. The number of substeps and the convergence criteria of the iteration process strongly influenced the implicit solutions. In contrast, the maximum von-Mises stresses resulting from explicit calculations converged to a certain value after only a few refinement steps. Different pull-out velocities substantially affected the mesh convergence behavior, while the material density showed only a negligible influence.The results indicated the need to perform an appropriate mesh convergence test when using finite element methods. We were able to show that different parameters strongly influence the mesh convergence behavior and we demonstrated that convergence tests do not always lead to a satisfactory or acceptable solution.     

A Brownian simulation model of glutamate synaptic diffusion in the femtosecond time scale

 To gain a better understanding of the elementary unit of synaptic communication between hippocampal neurons, we simulated the release of glutamate from a single pre-synaptic vesicle and its diffusion into the synaptic cleft. Diffusion of glutamate was simulated by a Brownian model based on Langevin equations. The model was implemented for parallel computer simulation and tested under different conditions of glutamate release and different geometrical and physical characteristics of the synaptic cleft. All the tested parameters have shown to be important for the synaptic responses. The results show that the synaptic transmission efficacy is influenced by many different geometrical parameters and, as a consequence, the quality of the excitatory post-synaptic response can be very different in the same synapse. The variability in the quantal response found by several authors can also be explained by physical parameters other than by variations in the quantal content of the synaptic vesicle as proposed by these authors. Received: 6 October 1999 / Accepted: 29 February 2000     

A two-current model for the dynamics of cardiac membrane

In this paper we introduce and study a model for electrical activity of cardiac membrane which incorporates only an inward and an outward current. This model is useful for three reasons: (1) Its simplicity, comparable to the FitzHugh-Nagumo model, makes it useful in numerical simulations, especially in two or three spatial dimensions where numerical efficiency is so important. (2) It can be understood analytically without recourse to numerical simulations. This allows us to determine rather completely how the parameters in the model affect its behavior which in turn provides insight into the effects of the many parameters in more realistic models. (3) It naturally gives rise to a one-dimensional map which specifies the action potential duration as a function of the previous diastolic interval. For certain parameter values, this map exhibits a new phenomenon—subcritical alternans—that does not occur for the commonly used exponential map.     

A three-dimensional model of intercellular calcium signaling in epithelial cells

We have developed a fully three-dimensional (3D) model of calcium signaling in epithelial cells based on a set of reaction diffusion equations that are solved on a large-scale finite-element code in three dimensions. We have explicitly included the cellular compartments including the cell nucleus, cytoplasm, and gap junctions. The model allows for buffering of free Ca2+, calcium-induced calcium release, and the explicit inclusion of mobile buffers. To make quantitative comparisons to experimental results, we used fluorescence microscopy images of cells to generate an accurate mesh describing cell morphology. We found that Ca2+ wave propagation through the tissue is a function of both initial conditions used to start the wave and various geometrical parameters that affect propagation such as gap junction density and distribution, and the presence of nuclei. The exogenous dyes used in experimental imaging also affect wave propagation.     

A dynamical neural network model for motor cortical activity during movement: population coding of movement trajectories

As a dynamical model for motor cortical activity during hand movement we consider an artificial neural network that consists of extensively interconnected neuron-like units and performs the neuronal population vector operations. Local geometrical parameters of a desired curve are introduced into the network as an external input. The output of the model is a time-dependent direction and length of the neuronal population vector which is calculated as a sum of the activity of directionally tuned neurons in the ensemble. The main feature of the model is that dynamical behavior of the neuronal population vector is the result of connections between directionally tuned neurons rather than being imposed externally. The dynamics is governed by a system of coupled nonlinear differential equations. Connections between neurons are assigned in the simplest and most common way so as to fulfill basic requirements stemming from experimental findings concerning the directional tuning of individual neurons and the stabilization of the neuronal population vector, as well as from previous theoretical studies. The dynamical behavior of the model reveals a close similarity with the experimentally observed dynamics of the neuronal population vector. Specifically, in the framework of the model it is possible to describe a geometrical curve in terms of the time series of the population vector. A correlation between the dynamical behavior of the direction and the length of the population vector entails a dependence of the neural velocity on the curvature of the tracing trajectory that corresponds well to the experimentally measured covariation between tangential velocity and curvature in drawing tasks.On leave of absencefrom the Institute of Molecular Genetics, Russian Academy of Sciences, Moscow, Russia.     

A simple, yet highly accurate, QSAR model captures the complement inhibitory activity of compstatin

Compstatin is a 13-residue cyclic peptide inhibitor of complement activation that was originally identified through phage-mediated presentation of a peptide library to C3b. Recent efforts to improve its activity have led to a rich dataset of complement analogs, with the most active analog being approximately 260 times more active than the parent compstatin. In the present work, a highly transparent quantitative structure-activity relationship model (Radj2=0.89) with four parameters is presented that captures important physico-chemical and geometrical properties of the analog molecules with regard to activity. The number of aromatic bonds and hydrophobicity of the fourth residue of compstatin correlated strongly with activity. Also important were the hydrophobic patch size near the disulfide bond and the solvent-accessible surface area occupied by nitrogen atoms of basic amino acid residues.     

What are the relevant parameters for the geometrical optimization of an implantable bioartificial pancreas?

A sphere within a cylinder representing the islet encapsulated in a hollow fiber can model an implantable bioartificial pancreas. Based on a finite element model for insulin response to a glucose load in the presence of various oxygen supplies, the present study aimed at pointing out the major parameters influencing this secretion. The computational results treated with the Taguchi method clearly demonstrated that geometrical parameters (fiber length and islet density) should be precisely optimized for an enhanced insulin response. This requires the collection of more relevant experimental data concerning the islet oxygen consumption. Moreover, the relative errors on glucose consumption or insulin secretion by the islets do not seem to affect the whole optimization process, which should focus on the oxygen supply to islets.     

Three-dimensional geometrical and mechanical modelling of the lumbar spine.

The main objective of this study is to design a three-dimensional geometrical and mechanical finite element model of the lumbar spine. The model's geometry is constructed using six parameters per vertebra. These parameters are digitized from two X-rays (anterio-posterior and lateral), thus yielding an individualized model which can be arrived at from the radiographs of a tested specimen. This procedure makes the model validation easier, as geometry is generally a factor of dispersion in experimental results. The geometrical reconstruction, in the form of a finite elements mesh, was effected for the whole lumbar spine. The global coherence of the model was verified.     

A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales

Volitional behavior relies on the brain's ability to remap sensory flow to motor programs whenever demanded by a changed behavioral context. To investigate the circuit basis of such flexible behavior, we have developed a biophysically based decision-making network model of spiking neurons for arbitrary sensorimotor mapping. The model quantitatively reproduces behavioral and prefrontal single-cell data from an experiment in which monkeys learn visuomotor associations that are reversed unpredictably from time to time. We show that when synaptic modifications occur on multiple timescales, the model behavior becomes flexible only when needed: slow components of learning usually dominate the decision process. However, if behavioral contexts change frequently enough, fast components of plasticity take over, and the behavior exhibits a quick forget-and-learn pattern. This model prediction is confirmed by monkey data. Therefore, our work reveals a scenario for conditional associative learning that is distinct from instant switching between sets of well-established sensorimotor associations.     

Optimization of a stochastically simulated gene network model via simulated annealing

By rearranging naturally occurring genetic components, gene networks can be created that display novel functions. When designing these networks, the kinetic parameters describing DNA/protein binding are of great importance, as these parameters strongly influence the behavior of the resulting gene network. This article presents an optimization method based on simulated annealing to locate combinations of kinetic parameters that produce a desired behavior in a genetic network. Since gene expression is an inherently stochastic process, the simulation component of simulated annealing optimization is conducted using an accurate multiscale simulation algorithm to calculate an ensemble of network trajectories at each iteration of the simulated annealing algorithm. Using the three-gene repressilator of Elowitz and Leibler as an example, we show that gene network optimizations can be conducted using a mechanistically realistic model integrated stochastically. The repressilator is optimized to give oscillations of an arbitrary specified period. These optimized designs may then provide a starting-point for the selection of genetic components needed to realize an in vivo system.