Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience

Quasistatic approximation in neuromodulation

We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuromodulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g. Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.

Quasistatic approximation in neuromodulation

We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuro-modulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g., Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.

Influence of Chronic Forced Immobilization and Consumption of a High-Fat and High-Carbohydrate Diet Containing Cholesterol on Lipid and Cholesterol Metabolism in Male Wistar Rats

We studied the effect of separate and combined influence of chronic forced physical activity reduction and high-fat and high-carbohydrate diet containing cholesterol on some indicators of carbohydrate, lipid, and cholesterol metabolism in growing male Wistar rats. Used combination of factors simulating a sedentary lifestyle and unhealthy diet did not have a synergistic effect on the selected biomarkers. On the contrary, the effect was antagonistic: body weight and appetite decreased and insulin resistance increased. The obtained results indicate certain prospects of hypercholesterolemia model using in preclinical studies of specialized food products to optimize the diet of individuals with disorders of carbohydrate and lipid metabolism.

Standards, dissemination, and best practices in systems biology

The reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other's work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.

NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON

One of the fundamental goals in neuroscience is to determine how the brain processes information and ultimately controls the execution of complex behaviors. Over the past four decades, there has been a steady growth in our knowledge of the morphological and functional diversity of neurons, the building blocks of the brain. These cells clearly differ not only for their anatomy and ion channel distribution, but also for the type, strength, location, and temporal pattern of activity of the many synaptic inputs they receive. Compartmental modeling programs like NEURON have become widely used in the neuroscience community to address a broad range of research questions, including how neurons integrate synaptic inputs and propagate information through complex neural networks. One of the main strengths of NEURON is its ability to incorporate user-defined information about the realistic morphology and biophysical properties of different cell types. Although the graphical user interface of the program can be used to run initial exploratory simulations, introducing a stochastic representation of synaptic weights, locations and activation times typically requires users to develop their own codes, a task that can be overwhelming for some beginner users. Here we describe NRN-EZ, an interactive application that allows users to specify complex patterns of synaptic input activity that can be integrated as part of NEURON simulations. Through its graphical user interface, NRN-EZ aims to ease the learning curve to run computational models in NEURON, for users that do not necessarily have a computer science background.

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

From Theoretical Network to Bedside: Translational Application of Brain-Inspired Computing in Clinical Medicine

Advances in the brain-inspired computing space are growing at a rapid rate, and many of these emerging strategies are in the field of neuromorphic control, robotics, and sensor development, just to name a few. These innovations are disruptive in their own right and have numerous, multi-dimensional medical applications within precision medicine, telematics, device development, and informed clinical decision making. For this discussion, I will define brain-inspired computing in the scope of simulating the architecture of the brain and discuss the realization of integrating hardware and other technologies with the applications of medicine, along with the considerations for the regulatory pathway for approval and evaluating the risk/consequences of failure modes. This perspective is a call for continued discussion of the development of a pathway for translating these technologies into medical treatment and diagnostic strategies. The aim is to align with global regulatory bodies and ensure that regulation does not limit the capacity of these emerging innovations while ensuring patient safety and clinical efficacy. It is my perspective that it is and will continue to be critical that these technologies are correctly perceived and understood in the lens of multiple disciplines in order to reach their full potential for medical applications.

On the Modeling of Transcatheter Therapies for the Aortic and Mitral Valves: A Review

Transcatheter aortic valve replacement (TAVR) has become a milestone for the management of aortic stenosis in a growing number of patients who are unfavorable candidates for surgery. With the new generation of transcatheter heart valves (THV), the feasibility of transcatheter mitral valve replacement (TMVR) for degenerated mitral bioprostheses and failed annuloplasty rings has been demonstrated. In this setting, computational simulations are modernizing the preoperative planning of transcatheter heart valve interventions by predicting the outcome of the bioprosthesis interaction with the human host in a patient-specific fashion. However, computational modeling needs to carry out increasingly challenging levels including the verification and validation to obtain accurate and realistic predictions. This review aims to provide an overall assessment of the recent advances in computational modeling for TAVR and TMVR as well as gaps in the knowledge limiting model credibility and reliability.

Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength

Information processing in the nervous system critically relies on temporally precise spiking activity. In the auditory system, various degrees of phase-locking can be observed from the auditory nerve to cortical neurons. The classical metric for quantifying phase-locking is the vector strength (VS), which captures the periodicity in neuronal spiking. More recently, another metric, called the correlation index (CI), was proposed to quantify the temporally reproducible response characteristics of a neuron. The CI is defined as the peak value of a normalized shuffled autocorrelogram (SAC). Both VS and CI have been used to investigate how temporal information is processed and propagated along the auditory pathways. While previous analyses of physiological data in cats suggested covariation of these two metrics, general characterization of their connection has never been performed. In the present study, we derive a rigorous relationship between VS and CI. To model phase-locking, we assume Poissonian spike trains with a temporally changing intensity function following a von Mises distribution. We demonstrate that VS and CI are mutually related via the so-called concentration parameter that determines the degree of phase-locking. We confirm that these theoretical results are largely consistent with physiological data recorded in the auditory brainstem of various animals. In addition, we generate artificial phase-locked spike sequences, for which recording and analysis parameters can be systematically manipulated. Our analysis results suggest that mismatches between empirical data and the theoretical prediction can often be explained with deviations from the von Mises distribution, including skewed or multimodal period histograms. Furthermore, temporal relations of spike trains across trials can contribute to higher CI values than predicted mathematically based on the VS. We find that, for most applications, a SAC bin width of 50 ms seems to be a favorable choice, leading to an estimated error below 2.5% for physiologically plausible conditions. Overall, our results provide general relations between the two measures of phase-locking and will aid future analyses of different physiological datasets that are characterized with these metrics.

Preclinical modeling of mechanical thrombectomy

Mechanical thrombectomy to treat large vessel occlusions (LVO) causing a stroke is one of the most effective treatments in medicine, with a number needed to treat to improve clinical outcomes as low as 2.6. As the name implies, it is a mechanical solution to a blocked artery and modeling these mechanics preclinically for device design, regulatory clearance and high-fidelity physician training made clinical applications possible. In vitro simulation of LVO is extensively used to characterize device performance in representative vascular anatomies with physiologically accurate hemodynamics. Embolus analogues, validated against clots extracted from patients, provide a realistic simulated use experience. In vitro experimentation produces quantitative results such as particle analysis of distal emboli generated during the procedure, as well as pressure and flow throughout the experiment. Animal modeling, used mostly for regulatory review, allows estimation of device safety. Other than one recent development, nearly all animal modeling does not incorporate the desired target organ, the brain, but rather is performed in the extracranial circulation. Computational modeling of the procedure remains at the earliest stages but represents an enormous opportunity to rapidly characterize and iterate new thrombectomy concepts as well as optimize procedure workflow. No preclinical model is a perfect surrogate; however, models available can answer important questions during device development and have to date been successful in delivering efficacious and safe devices producing excellent clinical outcomes. This review reflects on the developments of preclinical modeling of mechanical thrombectomy with particular focus on clinical translation, as well as articulate existing gaps requiring additional research.

Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

One should assume that in silico experiments in systems biology are less susceptible to reproducibility issues than their wet-lab counterparts, because they are free from natural biological variations and their environment can be fully controlled. However, recent studies show that only half of the published mathematical models of biological systems can be reproduced without substantial effort. In this article we examine the potential causes for failed or cumbersome reproductions in a case study of a one-dimensional mathematical model of the atrioventricular node, which took us four months to reproduce. The model demonstrates that even otherwise rigorous studies can be hard to reproduce due to missing information, errors in equations and parameters, a lack in available data files, non-executable code, missing or incomplete experiment protocols, and missing rationales behind equations. Many of these issues seem similar to problems that have been solved in software engineering using techniques such as unit testing, regression tests, continuous integration, version control, archival services, and a thorough modular design with extensive documentation. Applying these techniques, we reimplement the examined model using the modeling language Modelica. The resulting workflow is independent of the model and can be translated to SBML, CellML, and other languages. It guarantees methods reproducibility by executing automated tests in a virtual machine on a server that is physically separated from the development environment. Additionally, it facilitates results reproducibility, because the model is more understandable and because the complete model code, experiment protocols, and simulation data are published and can be accessed in the exact version that was used in this article. We found the additional design and documentation effort well justified, even just considering the immediate benefits during development such as easier and faster debugging, increased understandability of equations, and a reduced requirement for looking up details from the literature.

Applicability analysis to evaluate credibility of an in silico thrombectomy procedure

Intra-arterial thrombectomy is a minimally invasive procedure in which an obstructing thrombus (clot) is removed using a minimally-invasive device: a stent-retriever. The stent-retriever is first deployed, and then the thrombus is removed during stent-retriever retraction. This procedure can be simulated using a detailed computational model. However, to be useful for an in silico trial in a clinical setting, model credibility should be demonstrated. The aim of this work is to apply a credibility process for the validation phases to the thrombectomy procedure in order to deem it credible for use in an in silico trial. Validation evidence is proposed for the identified context of use and then used to build credibility to the numerical model. Applicability of the proposed model is justified and assessed using a rigorous step-by-step method based on the ASME V&V40 protocol.

The first virtual patient-specific thrombectomy procedure

Treatment of acute ischemic stroke has been recently improved with the introduction of endovascular mechanical thrombectomy, a minimally invasive procedure able to remove a clot using aspiration devices and/or stent-retrievers. Despite the promising and encouraging results, improvements to the procedure and to the stent design are the focus of the recent efforts. Computational studies can pave the road to these improvements, providing their ability to describe and accurately reproduce a real procedure. A patient with ischemic stroke due to intracranial large vessel occlusion was selected and after the creation of the cerebral vasculature from computed tomography images and a histologic analysis to determine the clot composition, the entire thrombectomy procedure was virtually replicated. As in the real situation, the computational replica showed that two attempts were necessary to remove the clot, as a result of the position of the stent retriever with respect to the clot. Furthermore, the results indicated that clot fragmentation did not occur as the deformations were mainly in a compressive state without the possibility for clot cracks to propagate. The accurate representation of the procedure can be used as an important step for operative optimization planning and future improvements of stent designs.

Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective

Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.

Modern techniques of teaching and learning in medical education: a descriptive literature review

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OBJECTIVES

Education is a dynamic process that has to be refined periodically. Lack of innovative teaching techniques in academics makes medical curricula inadequate in making a significant stride towards the future. The objective of this review is to describe and assess alternative methods of teaching and learning which can be supplementive or alternative to traditional lectures for promoting active student participation and smooth flow of information.

METHODS

A review of literature is performed with PubMed and EBSCO using the keywords: "learning" OR "didactic learning" OR "alternative learning" OR "modern learning techniques" AND "medical education". Databases were searched and 500 studies were identified out of which 200 were selected for further screening based on inclusion criteria and exclusion criteria. Articles were surveyed based on their relevance and significance to our study objectives with both qualitative and quantitative studies were examined.

RESULTS

Case-based learning, evidence-based medicine, problem-based learning, simulation-based learning, e-learning, peer-assisted learning, observational learning, flipped classroom and team based learning are some of the modern learning methodologies. The various learning methods discussed attend to individual learning differences allowing students to broaden their thinking and professional knowledge by improving logical and critical thinking, clinical reasoning, and time management. Early introduction of integrative approaches develop student competency and leadership equipping students for a smooth transit into the clinical practice.

CONCLUSION

This study highlights the importance and challenges of modern learning systems. With technological advancement and wider implications of medical information, students require innovative skills through inter-professional learning. It is necessary to introduce and implement flexible medical curricula that accommodates distinct modern teaching to effectively balance and bridge the gap between traditional teaching methodologies and modern educational requirements.

In silico approaches for transcatheter aortic valve replacement inspection

Introduction: Increasing applications of transcatheter aortic valve replacement (TAVR) to treat high- or medium-risk patients with aortic diseases have been proposed in recent years. Despite its increasing use, many influential factors are still to be understood. Furthermore, innovative applications of TAVR such as in bicuspid aortic valves or in low-risk patients are emerging in clinical use. Numerical analyses are increasingly used to reproduce clinical treatments. The future trends in this area are foreseen for in silico trials and personalized medicine. Areas covered: This review paper analyzes the recent years (Jan 2018 - Aug 2020) of in silico studies simulating the behavior of transcatheter aortic valves with emphasis on the addressed clinical question and the used modeling strategies. The manuscripts are firstly classified based on their clinical hypothesis. A second classification is based on the adopted modeling approach in terms of patient domain, device modeling, and inclusion or exclusion of the fluid domain. Expert opinion: The TAVR can be virtually performed in numerous vessel geometries and with different devices. This versatility allows a rapid evaluation of the feasibility of different implantation approaches for specific patients, and patient populations, resulting in faster and safer introduction or optimization of new treatments or devices.

Reliability in Healthcare Simulation Setting: A Definitional Review

The construct of reliability in health professions education serves as a measure of the congruence of interpretations across assessment tools. When used as an assessment strategy, healthcare simulation serves to elicit specific participant behaviors sought by medical educators. In healthcare simulation, reliability often refers to the ability to consistently reproduce a simulation and that reproducing a simulation setting can consistently expose participants to the same conditions, thus achieving simulation reliability. However, some articles have expressed that simulations are vulnerable to error stemming from design conceptualization to implementation, and the impact of social factors when participants interact and engage with others during participation. The purpose of this definitional review is to examine how reliability has been conceptualized and defined in healthcare simulation, and how the attributes of simulations may present challenges for the traditional concept of reliability in health professions education. Data collection and analysis was approached through a constructivist perspective and grounded theory strategies. Articles between 2009-2019 were filtered applying keywords related to simulation development and design. Data winnowing was structured around a framework viewing simulation as a social practice where participants interact with simulation setting attributes. Healthcare simulation setting reliability is not directly defined but described as errors introduced by the interactions between simulation design attributes and tasks performed by simulated participants. Based on the ontology of simulation's design attributes believed to introduce setting errors, lexical terms related to reliability suggest how simulated participants are trained to refine or maintain their performance tasks that aim to mitigate errors. To achieve reliability in health professions education (HPE) and healthcare simulation, both domains seek to assess the consistency of a construct being measured. In HPE, reliability refers to the consistency of quality measures across a range of psychometric tests used to assess a participant's medical aptitude. In healthcare simulation setting, reliability refers to the consistency of a simulated participant (SP) performing a task that is tailored to mitigate errors introduced by simulation design attributes. Consequently, inconsistencies in SP performance subject participants to setting errors exposing them to unequal conditions that influence competency achievement. What is already known on this subject: Performance competency assessment using healthcare simulation is increasingly common. The types of design attributes incorporated into a simulation setting. The use of incorporating simulated participants into a simulation setting. Simulated participants require training prior to simulation setting implementation. What this paper adds: Identifies attributes of a simulation setting that are most commonly thought to interfere with setting reliability. Identifies the relationships among setting attributes and simulated participant performances that influence setting reliability. Identifies terms tied to the achievement of simulation setting reliability. Examines simulated participant training processes aimed to mitigate errors introduced by simulation design attributes.

Mechanistically detailed systems biology modeling of the HGF/Met pathway in hepatocellular carcinoma

Hepatocyte growth factor (HGF) signaling through its receptor Met has been implicated in hepatocellular carcinoma tumorigenesis and progression. Met interaction with integrins is shown to modulate the downstream signaling to Akt and ERK (extracellular-regulated kinase). In this study, we developed a mechanistically detailed systems biology model of HGF/Met signaling pathway that incorporated specific interactions with integrins to investigate the efficacy of integrin-binding peptide, AXT050, as monotherapy and in combination with other therapeutics targeting this pathway. Here we report that the modeled dynamics of the response to AXT050 revealed that receptor trafficking is sufficient to explain the effect of Met-integrin interactions on HGF signaling. Furthermore, the model predicted patient-specific synergy and antagonism of efficacy and potency for combination of AXT050 with sorafenib, cabozantinib, and rilotumumab. Overall, the model provides a valuable framework for studying the efficacy of drugs targeting receptor tyrosine kinase interaction with integrins, and identification of synergistic drug combinations for the patients.

The superior longitudinal fasciculus and its functional triple-network mechanisms in brooding

Brooding, which refers to a repetitive focus on one's distress, is associated with functional connectivity within Default-Mode, Salience, and Executive-Control networks (DMN; SN; ECN), comprising the so-called "triple-network" of attention. Individual differences in brain structure that might perseverate dysfunctional connectivity of brain networks associated with brooding are less clear, however. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between brooding severity, white-matter microstructure, and resting-state functional connectivity in depressed adults (N = 32-44), and then examined whether findings directly replicated in a demographically-similar, independent sample (N = 36-45). Among the fully-replicated results, three core findings emerged. First, brooding severity is associated with functional integration and segregation of the triple-network, particularly with a Precuneal subnetwork of the DMN. Second, microstructural asymmetry of the Superior Longitudinal Fasciculus (SLF) provides a robust structural connectivity basis for brooding and may account for over 20% of its severity (Discovery: adj. R² = 0.18; Replication: adj. R² = 0.22; MSE = 0.06, Predictive R² = 0.22). Finally, microstructure of the right SLF and auxiliary white-matter is associated with the functional connectivity correlates of brooding, both within and between components of the triple-network (Discovery: adj. R² = 0.21; Replication: adj. R² = 0.18; MSE = 0.03, Predictive R² = 0.21-0.22). By cross-validating multimodal discovery with replication, the present findings help to reproducibly unify disparate perspectives of brooding etiology. Based on that synthesis, our study reformulates brooding as a microstructural-functional connectivity neurophenotype.

Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models

Recent efforts to ensure the reliability of computational model-based predictions in healthcare, such as the ASME V&V40 Standard, emphasize the importance of uncertainty quantification (UQ) and sensitivity analysis (SA) when evaluating computational models. UQ involves empirically determining the uncertainty in model inputs-typically resulting from natural variability or measurement error-and then calculating the resultant uncertainty in model outputs. SA involves calculating how uncertainty in model outputs can be apportioned to input uncertainty. Rigorous comprehensive UQ/SA provides confidence that model-based decisions are robust to underlying uncertainties. However, comprehensive UQ/SA is not currently feasible for whole heart models, due to numerous factors including model complexity and difficulty in measuring variability in the many parameters. Here, we present a significant step to developing a framework to overcome these limitations. We: (i) developed a novel action potential (AP) model of moderate complexity (six currents, seven variables, 36 parameters); (ii) prescribed input variability for all parameters (not empirically derived); (iii) used a single "hyper-parameter" to study increasing levels of parameter uncertainty; (iv) performed UQ and SA for a range of model-derived quantities with physiological relevance; and (v) present quantitative and qualitative ways to analyze different behaviors that occur under parameter uncertainty, including "model failure". This is the first time uncertainty in every parameter (including conductances, steady-state parameters, and time constant parameters) of every ionic current in a cardiac model has been studied. This approach allowed us to demonstrate that, for this model, the simulated AP is fully robust to low levels of parameter uncertainty - to our knowledge the first time this has been shown of any cardiac model. A range of dynamics was observed at larger parameter uncertainty (e.g., oscillatory dynamics); analysis revealed that five parameters were highly influential in these dynamics. Overall, we demonstrate feasibility of performing comprehensive UQ/SA for cardiac cell models and demonstrate how to assess robustness and overcome model failure when performing cardiac UQ analyses. The approach presented here represents an important and significant step toward the development of model-based clinical tools which are demonstrably robust to all underlying uncertainties and therefore more reliable in safety-critical decision-making.

Propagation of Pericentral Necrosis During Acetaminophen-Induced Liver Injury: Evidence for Early Interhepatocyte Communication and Information Exchange

Acetaminophen (APAP)-induced liver injury is clinically significant, and APAP overdose in mice often serves as a model for drug-induced liver injury in humans. By specifying that APAP metabolism, reactive metabolite formation, glutathione depletion, and mitigation of mitochondrial damage within individual hepatocytes are functions of intralobular location, an earlier virtual model mechanism provided the first concrete multiattribute explanation for how and why early necrosis occurs close to the central vein (CV). However, two characteristic features could not be simulated consistently: necrosis occurring first adjacent to the CV, and subsequent necrosis occurring primarily adjacent to hepatocytes that have already initiated necrosis. We sought parsimonious model mechanism enhancements that would manage spatiotemporal heterogeneity sufficiently to enable meeting two new target attributes and conducted virtual experiments to explore different ideas for model mechanism improvement at intrahepatocyte and multihepatocyte levels. For the latter, evidence supports intercellular communication via exosomes, gap junctions, and connexin hemichannels playing essential roles in the toxic effects of chemicals, including facilitating or counteracting cell death processes. Logic requiring hepatocytes to obtain current information about whether downstream and lateral neighbors have triggered necrosis enabled virtual hepatocytes to achieve both new target attributes. A virtual hepatocyte that is glutathione-depleted uses that information to determine if it will initiate necrosis. When a less-stressed hepatocyte is flanked by at least two neighbors that have triggered necrosis, it too will initiate necrosis. We hypothesize that the resulting intercellular communication-enabled model mechanism is analogous to the actual explanation for APAP-induced hepatotoxicity at comparable levels of granularity.

NetPyNE, a tool for data-driven multiscale modeling of brain circuits

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

Using NEURON for Reaction-Diffusion Modeling of Extracellular Dynamics

Development of credible clinically-relevant brain simulations has been slowed due to a focus on electrophysiology in computational neuroscience, neglecting the multiscale whole-tissue modeling approach used for simulation in most other organ systems. We have now begun to extend the NEURON simulation platform in this direction by adding extracellular modeling. The extracellular medium of neural tissue is an active medium of neuromodulators, ions, inflammatory cells, oxygen, NO and other gases, with additional physiological, pharmacological and pathological agents. These extracellular agents influence, and are influenced by, cellular electrophysiology, and cellular chemophysiology-the complex internal cellular milieu of second-messenger signaling and cascades. NEURON's extracellular reaction-diffusion is supported by an intuitive Python-based where/who/what command sequence, derived from that used for intracellular reaction diffusion, to support coarse-grained macroscopic extracellular models. This simulation specification separates the expression of the conceptual model and parameters from the underlying numerical methods. In the volume-averaging approach used, the macroscopic model of tissue is characterized by free volume fraction-the proportion of space in which species are able to diffuse, and tortuosity-the average increase in path length due to obstacles. These tissue characteristics can be defined within particular spatial regions, enabling the modeler to account for regional differences, due either to intrinsic organization, particularly gray vs. white matter, or to pathology such as edema. We illustrate simulation development using spreading depression, a pathological phenomenon thought to play roles in migraine, epilepsy and stroke. Simulation results were verified against analytic results and against the extracellular portion of the simulation run under FiPy. The creation of this NEURON interface provides a pathway for interoperability that can be used to automatically export this class of models into complex intracellular/extracellular simulations and future cross-simulator standardization.