Jean-Marie C. Bouteiller

Simulation of Postsynaptic Glutamate Receptors Reveals Critical Features of Glutamatergic Transmission

Activation of several subtypes of glutamate receptors contributes to changes in postsynaptic calcium concentration at hippocampal synapses, resulting in various types of changes in synaptic strength. Thus, while activation of NMDA receptors has been shown to be critical for long-term potentiation (LTP) and long term depression (LTD) of synaptic transmission, activation of metabotropic glutamate receptors (mGluRs) has been linked to either LTP or LTD. While it is generally admitted that dynamic changes in postsynaptic calcium concentration represent the critical elements to determine the direction and amplitude of the changes in synaptic strength, it has been difficult to quantitatively estimate the relative contribution of the different types of glutamate receptors to these changes under different experimental conditions. Here we present a detailed model of a postsynaptic glutamatergic synapse that incorporates ionotropic and mGluR type I receptors, and we use this model to determine the role of the different receptors to the dynamics of postsynaptic calcium with different patterns of presynaptic activation. Our modeling framework includes glutamate vesicular release and diffusion in the cleft and a glutamate transporter that modulates extracellular glutamate concentration. Our results indicate that the contribution of mGluRs to changes in postsynaptic calcium concentration is minimal under basal stimulation conditions and becomes apparent only at high frequency of stimulation. Furthermore, the location of mGluRs in the postsynaptic membrane is also a critical factor, as activation of distant receptors contributes significantly less to calcium dynamics than more centrally located ones. These results confirm the important role of glutamate transporters and of the localization of mGluRs in postsynaptic sites in their signaling properties, and further strengthen the notion that mGluR activation significantly contributes to postsynaptic calcium dynamics only following high-frequency stimulation. They also provide a new tool to analyze the interactions between metabotropic and ionotropic glutamate receptors.

libRoadRunner: a high performance SBML simulation and analysis library

MOTIVATION This article presents libRoadRunner, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language (SBML). SBML is the most widely used standard for representing dynamic networks, especially biochemical networks. libRoadRunner is fast enough to support large-scale problems such as tissue models, studies that require large numbers of repeated runs and interactive simulations. RESULTS libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings, and interactively through its Python interface. Its Python Application Programming Interface (API) is similar to the APIs of MATLAB ( WWWMATHWORKSCOM: ) and SciPy ( HTTP//WWWSCIPYORG/: ), making it fast and easy to learn. libRoadRunner uses a custom Just-In-Time (JIT) compiler built on the widely used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a variety of processors, making it appropriate for solving extremely large models or repeated runs. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and non-linear algebraic equations) including several SBML extensions (composition and distributions). It offers multiple deterministic and stochastic integrators, as well as tools for steady-state analysis, stability analysis and structural analysis of the stoichiometric matrix. AVAILABILITY AND IMPLEMENTATION libRoadRunner binary distributions are available for Mac OS X, Linux and Windows. The library is licensed under Apache License Version 2.0. libRoadRunner is also available for ARM-based computers such as the Raspberry Pi. http://www.libroadrunner.org provides online documentation, full build instructions, binaries and a git source repository. CONTACTS hsauro@u.washington.edu or somogyie@indiana.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A computational model to investigate astrocytic glutamate uptake influence on synaptic transmission and neuronal spiking

Over the past decades, our view of astrocytes has switched from passive support cells to active processing elements in the brain. The current view is that astrocytes shape neuronal communication and also play an important role in many neurodegenerative diseases. Despite the growing awareness of the importance of astrocytes, the exact mechanisms underlying neuron-astrocyte communication and the physiological consequences of astrocytic-neuronal interactions remain largely unclear. In this work, we define a modeling framework that will permit to address unanswered questions regarding the role of astrocytes. Our computational model of a detailed glutamatergic synapse facilitates the analysis of neural system responses to various stimuli and conditions that are otherwise difficult to obtain experimentally, in particular the readouts at the sub-cellular level. In this paper, we extend a detailed glutamatergic synaptic model, to include astrocytic glutamate transporters. We demonstrate how these glial transporters, responsible for the majority of glutamate uptake, modulate synaptic transmission mediated by ionotropic AMPA and NMDA receptors at glutamatergic synapses. Furthermore, we investigate how these local signaling effects at the synaptic level are translated into varying spatio-temporal patterns of neuron firing. Paired pulse stimulation results reveal that the effect of astrocytic glutamate uptake is more apparent when the input inter-spike interval is sufficiently long to allow the receptors to recover from desensitization. These results suggest an important functional role of astrocytes in spike timing dependent processes and demand further investigation of the molecular basis of certain neurological diseases specifically related to alterations in astrocytic glutamate uptake, such as epilepsy.

Computational studies of NMDA receptors: differential effects of neuronal activity on efficacy of competitive and non-competitive antagonists

N-Methyl-D-Aspartate receptors (NMDARs) play important physiological as well as pathological roles in the central nervous system (CNS). While NMDAR competitive antagonists, such as D-2-Amino-5-Phosphopentanoic acid (AP5) have been shown to impair learning and memory, the non-competitive antagonist, memantine, is paradoxically beneficial in mild to moderate Alzheimers disease (AD) patients. It has been proposed that differences in kinetic properties could account for antagonist functional differences. Here we present a new elaborated kinetic model of NMDARs that incorporates binding sites for the agonist (glutamate) and co-agonist (glycine), channel blockers, such as memantine and magnesium (Mg(2+)), as well as competitive antagonists. We first validated and optimized the parameters used in the model by comparing simulated results with a wide range of experimental data from the literature. We then evaluated the effects of stimulation frequency and membrane potential (Vm) on the characteristics of AP5 and memantine inhibition of NMDARs. Our results indicated that the inhibitory effects of AP5 were independent of Vm but decreased with increasing stimulation frequency. In contrast, memantine inhibitory effects decreased with both increasing Vm and stimulation frequency. They support the idea that memantine could provide tonic blockade of NMDARs under basal stimulation conditions without blocking their activation during learning. Moreover they underline the necessity of considering receptor kinetics and the value of the biosimulation approach to better understand mechanisms of drug action and to identify new ways of regulating receptor function.

Modeling glutamatergic synapses: insights into mechanisms regulating synaptic efficacy.

The hippocampal formation is critically involved for the long-term storage of various forms of information, and it is widely believed that the phenomenon of long-term potentiation (LTP) of synaptic transmission is a molecular/cellular mechanism participating in memory formation. Although several high level models of hippocampal function have been developed, they do not incorporate detailed molecular information of the type necessary to understand the contribution of individual molecular events to the mechanisms underlying LTP and learning and memory. We are therefore developing new technological tools based on mathematical modeling and computer simulation of the molecular processes taking place in realistic biological networks to reach such an understanding. This article briefly summarizes the approach we are using and illustrates it by presenting data regarding the effects of changing the number of AMPA receptors on various features of glutamatergic transmission, including NMDA receptor-mediated responses and paired-pulse facilitation. We conclude by discussing the significance of these results and providing some ideas for future directions with this approach.

Integrated Multiscale Modeling of the Nervous System: Predicting Changes in Hippocampal Network Activity by a Positive AMPA Receptor Modulator

One of the fundamental characteristics of the brain is its hierarchical organization. Scales in both space and time that must be considered when integrating across hierarchies of the nervous system are sufficiently great as to have impeded the development of routine multilevel modeling methodologies. Complex molecular interactions at the level of receptors and channels regulate activity at the level of neurons; interactions between multiple populations of neurons ultimately give rise to complex neural systems function and behavior. This spatial complexity takes place in the context of a composite temporal integration of multiple, different events unfolding at the millisecond, second, minute, hour, and longer time scales. In this study, we present a multiscale modeling methodology that integrates synaptic models into single neuron, and multineuron, network models. We have applied this approach to the specific problem of how changes at the level of kinetic parameters of a receptor-channel model are translated into changes in the temporal firing pattern of a single neuron, and ultimately, changes in the spatiotemporal activity of a network of neurons. These results demonstrate how this powerful methodology can be applied to understand the effects of a given local process within multiple hierarchical levels of the nervous system.

A real-scale anatomical model of the dentate gyrus based on single cell reconstructions and 3D rendering of a brain atlas

Abstract As a first step towards the creation of a cellular model of dentate gyrus (DG) anatomy, we distributed 1,000,000 digitized granule cells (gcs) in 3D in a virtual reality reconstruction of Swansons brain atlas. DG coronal sections were assembled into 3D surfaces using implicit function generation. The resulting file included hilar, granular, and molecular boundaries. 20,000 replicas of each of 50 reconstructed gcs were added to the model by packing the somata in the appropriate layer and then radially orienting the dendritic tree axes. The model can be used to evaluate stereologic parameters such as dendritic overlap probability, space occupancy, and exposure to incoming fibers.

Synaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study.

Glutamatergic synapses are the most prevalent functional elements of information processing in the brain. Changes in pre-synaptic activity and in the function of various post-synaptic elements contribute to generate a large variety of synaptic responses. Previous studies have explored postsynaptic factors responsible for regulating synaptic strength variations, but have given far less importance to synaptic geometry, and more specifically to the subcellular distribution of ionotropic receptors. We analyzed the functional effects resulting from changing the subsynaptic localization of ionotropic receptors by using a hippocampal synaptic computational framework. The present study was performed using the EONS (Elementary Objects of the Nervous System) synaptic modeling platform, which was specifically developed to explore the roles of subsynaptic elements as well as their interactions, and that of synaptic geometry. More specifically, we determined the effects of changing the localization of ionotropic receptors relative to the presynaptic glutamate release site, on synaptic efficacy and its variations following single pulse and paired-pulse stimulation protocols. The results indicate that changes in synaptic geometry do have consequences on synaptic efficacy and its dynamics.

Modeling neuron-glia interactions: From parametric model to neuromorphic hardware

Recent experimental evidence suggests that glial cells are more than just supporting cells to neurons — they play an active role in signal transmission in the brain. We herein propose to investigate the importance of these mechanisms and model neuron-glia interactions at synapses using three approaches: A parametric model that takes into account the underlying mechanisms of the physiological system, a non-parametric model that extracts its input-output properties, and an ultra-low power, fast processing, neuromorphic hardware model. We use the EONS (Elementary Objects of the Nervous System) platform, a highly elaborate synaptic modeling platform to investigate the influence of astrocytic glutamate transporters on postsynaptic responses in the detailed micro-environment of a tri-partite synapse. The simulation results obtained using EONS are then used to build a non-parametric model that captures the essential features of glutamate dynamics. The structure of the non-parametric model we use is specifically designed for efficient hardware implementation using ultra-low power subthreshold CMOS building blocks. The utilization of the approach described allows us to build large-scale models of neuron/glial interaction and consequently provide useful insights on glial modulation during normal and pathological neural function.

Modeling and simulation of organophosphate-induced neurotoxicity: Prediction and validation by experimental studies.

Exposure to organophosphorus (OP) compounds, either pesticides or chemical warfare agents, represents a major health problem. As potent irreversible inhibitors of cholinesterase, OP may induce seizures, as in status epilepticus, and occasionally brain lesions. Although these compounds are extremely toxic agents, the search for novel antidotes remains extremely limited. In silico modeling constitutes a useful tool to identify pharmacological targets and to develop efficient therapeutic strategies. In the present work, we developed a new in silico simulator in order to predict the neurotoxicity of irreversible inhibitors of acetyl- and/or butyrylcholinesterase (ChE) as well as the potential neuroprotection provided by antagonists of cholinergic muscarinic and glutamate N-methyl-d-aspartate (NMDA) receptors. The simulator reproduced firing of CA1 hippocampal neurons triggered by exposure to paraoxon (POX), as found in patch-clamp recordings in in vitro mouse hippocampal slices. In the case of POX intoxication, it predicted a preventing action of the muscarinic receptor antagonist atropine sulfate, as well as a synergistic action with the non-competitive NMDA receptor antagonist memantine. These in silico predictions relative to beneficial effects of atropine sulfate combined with memantine were recapitulated experimentally in an in vivo model of POX in adult male Swiss mice using electroencephalic (EEG) recordings. Thus, our simulator is a new powerful tool to identify protective therapeutic strategies against OP central effects, by screening various combinations of muscarinic and NMDA receptor antagonists.