Electrical stimulation of the peripheral nervous system is a promising therapeutic option for several conditions; however, its effects on tissue and the safety of the stimulation remain poorly understood. In order to devise stimulation protocols that enhance therapeutic efficacy without the risk of causing tissue damage, we constructed computational models of peripheral nerve and stimulation cuffs based on extremely high-resolution cross-sectional images of the nerves using the most recent advances in computing power and machine learning techniques. We developed nerve models using nonstimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to explore how nerve damage affects the induced current density distribution. Using our in-house computational, quasi-static, platform, and the Admittance Method (AM), we estimated the induced current distribution within the nerves and compared it for healthy and damaged nerves. We also estimated the extent of localized cell damage in both healthy and damaged nerve samples. When the nerve is damaged, as demonstrated principally by the decreased nerve fiber packing, the current penetrates deeper into the over-stimulated nerve than in the healthy sample. As safety limits for electrical stimulation of peripheral nerves still refer to the Shannon criterion to distinguish between safe and unsafe stimulation, the capability this work demonstrated is an important step toward the development of safety criteria that are specific to peripheral nerve and make use of the latest advances in computational bioelectromagnetics and machine learning, such as Python-based AM and CNN-based nerve image segmentation.

Ionotropic NMDA and AMPA glutamate receptors (iGluRs) play important roles in synaptic function under physiological and pathological conditions. iGluRs sub-synaptic localization and subunit composition are dynamically regulated by activity-dependent insertion and internalization. However, understanding the impact on synaptic transmission of changes in composition and localization of iGluRs is difficult to address experimentally. To address this question, we developed a detailed computational model of glutamatergic synapses, including spine and dendritic compartments, elementary models of subtypes of NMDA and AMPA receptors, glial glutamate transporters, intracellular calcium and a calcium-dependent signaling cascade underlying the development of long-term potentiation (LTP). These synapses were distributed on a neuron model and numerical simulations were performed to assess the impact of changes in composition and localization (synaptic vs extrasynaptic) of iGluRs on synaptic transmission and plasticity following various patterns of presynaptic stimulation. In addition, the effects of various pharmacological compounds targeting NMDARs or AMPARs were determined. Our results showed that changes in NMDAR localization have a greater impact on synaptic plasticity than changes in AMPARs. Moreover, the results suggest that modulators of AMPA and NMDA receptors have differential effects on restoring synaptic plasticity under different experimental situations mimicking various human diseases.

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: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.