Estimating the probability of early afterdepolarizations and predicting arrhythmic risk associated with long QT syndrome type 1 mutations.

Early afterdepolarizations (EADs) are action potential (AP) repolarization abnormalities that can trigger lethal arrhythmias. Simulations using biophysically detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias; however, such analyses can pose a huge computational burden. We have previously developed a highly simplified approach in which logistic regression models (LRMs) map parameters of complex cell models to the probability of ectopic beats. Here, we extend this approach to predict the probability of EADs (P(EAD)) as a mechanistic metric of arrhythmic risk. We use the LRM to investigate how changes in parameters of the slow-activating delayed rectifier current (IKs) affect P(EAD) for 17 different long QT syndrome type 1 (LQTS1) mutations. In this LQTS1 clinical arrhythmic risk prediction task, we compared P(EAD) for these 17 mutations with two other recently published model-based arrhythmia risk metrics (AP morphology metric across populations of myocyte models and transmural repolarization prolongation based on a one-dimensional [1D] tissue-level model). These model-based risk metrics yield similar prediction performance; however, each fails to stratify clinical risk for a significant number of the 17 studied LQTS1 mutations. Nevertheless, an interpretable ensemble model using multivariate linear regression built by combining all of these model-based risk metrics successfully predicts the clinical risk of 17 mutations. These results illustrate the potential of computational approaches in arrhythmia risk prediction.

Predicting Intensive Care Delirium with Machine Learning: Model Development and External Validation.

BACKGROUND: Delirium poses significant risks to patients, but countermeasures can be taken to mitigate negative outcomes. Accurately forecasting delirium in intensive care unit (ICU) patients could guide proactive intervention. Our primary objective was to predict ICU delirium by applying machine learning to clinical and physiologic data routinely collected in electronic health records. METHODS: Two prediction models were trained and tested using a multicenter database (years of data collection 2014 to 2015), and externally validated on two single-center databases (2001 to 2012 and 2008 to 2019). The primary outcome variable was delirium defined as a positive Confusion Assessment Method for the ICU screen, or an Intensive Care Delirium Screening Checklist of 4 or greater. The first model, named "24-hour model," used data from the 24 h after ICU admission to predict delirium any time afterward. The second model designated "dynamic model," predicted the onset of delirium up to 12 h in advance. Model performance was compared with a widely cited reference model. RESULTS: For the 24-h model, delirium was identified in 2,536 of 18,305 (13.9%), 768 of 5,299 (14.5%), and 5,955 of 36,194 (11.9%) of patient stays, respectively, in the development sample and two validation samples. For the 12-h lead time dynamic model, delirium was identified in 3,791 of 22,234 (17.0%), 994 of 6,166 (16.1%), and 5,955 of 28,440 (20.9%) patient stays, respectively. Mean area under the receiver operating characteristics curve (AUC) (95% CI) for the first 24-h model was 0.785 (0.769 to 0.801), significantly higher than the modified reference model with AUC of 0.730 (0.704 to 0.757). The dynamic model had a mean AUC of 0.845 (0.831 to 0.859) when predicting delirium 12 h in advance. Calibration was similar in both models (mean Brier Score [95% CI] 0.102 [0.097 to 0.108] and 0.111 [0.106 to 0.116]). Model discrimination and calibration were maintained when tested on the validation datasets. CONCLUSIONS: Machine learning models trained with routinely collected electronic health record data accurately predict ICU delirium, supporting dynamic time-sensitive forecasting.

Estimating ectopic beat probability with simplified statistical models that account for experimental uncertainty.

Ectopic beats (EBs) are cellular arrhythmias that can trigger lethal arrhythmias. Simulations using biophysically-detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias, however such analyses can pose a huge computational burden. Here, we develop a simplified approach in which logistic regression models (LRMs) are used to define a mapping between the parameters of complex cell models and the probability of EBs (P(EB)). As an example, in this study, we build an LRM for P(EB) as a function of the initial value of diastolic cytosolic Ca2+ concentration ([Ca2+]iini), the initial state of sarcoplasmic reticulum (SR) Ca2+ load ([Ca2+]SRini), and kinetic parameters of the inward rectifier K+ current (IK1) and ryanodine receptor (RyR). This approach, which we refer to as arrhythmia sensitivity analysis, allows for evaluation of the relationship between these arrhythmic event probabilities and their associated parameters. This LRM is also used to demonstrate how uncertainties in experimentally measured values determine the uncertainty in P(EB). In a study of the role of [Ca2+]SRini uncertainty, we show a special property of the uncertainty in P(EB), where with increasing [Ca2+]SRini uncertainty, P(EB) uncertainty first increases and then decreases. Lastly, we demonstrate that IK1 suppression, at the level that occurs in heart failure myocytes, increases P(EB).

SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction.

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

Na+ microdomains and sparks: Role in cardiac excitation-contraction coupling and arrhythmias in ankyrin-B deficiency.

Cardiac sodium (Na+) potassium ATPase (NaK) pumps, neuronal sodium channels (INa), and sodium calcium (Ca2+) exchangers (NCX1) may co-localize to form a Na+ microdomain. It remains controversial as to whether neuronal INa contributes to local Na+ accumulation, resulting in reversal of nearby NCX1 and influx of Ca2+ into the cell. Therefore, there has been great interest in the possible roles of a Na+ microdomain in cardiac Ca2+-induced Ca2+ release (CICR). In addition, the important role of co-localization of NaK and NCX1 in regulating localized Na+ and Ca2+ levels and CICR in ankyrin-B deficient (ankyrin-B+/-) cardiomyocytes has been examined in many recent studies. Altered Na+ dynamics may contribute to the appearance of arrhythmias, but the mechanisms underlying this relationship remain unclear. In order to investigate this, we present a mechanistic canine cardiomyocyte model which reproduces independent local dyadic junctional SR (JSR) Ca2+ release events underlying cell-wide excitation-contraction coupling, as well as a three-dimensional super-resolution model of the Ca2+ spark that describes local Na+ dynamics as governed by NaK pumps, neuronal INa, and NCX1. The model predicts the existence of Na+ sparks, which are generated by NCX1 and exhibit significantly slower dynamics as compared to Ca2+ sparks. Moreover, whole-cell simulations indicate that neuronal INa in the cardiac dyad plays a key role during the systolic phase. Rapid inward neuronal INa can elevate dyadic [Na+] to 35-40 mM, which drives reverse-mode NCX1 transport, and therefore promotes Ca2+ entry into the dyad, enhancing the trigger for JSR Ca2+ release. The specific role of decreased co-localization of NaK and NCX1 in ankyrin-B+/- cardiomyocytes was examined. Model results demonstrate that a reduction in the local NCX1- and NaK-mediated regulation of dyadic [Ca2+] and [Na+] results in an increase in Ca2+ spark activity during isoproterenol stimulation, which in turn stochastically activates NCX1 in the dyad. This alteration in NCX1/NaK co-localization interrupts the balance between NCX1 and NaK currents in a way that leads to enhanced depolarizing inward current during the action potential plateau, which ultimately leads to a higher probability of L-type Ca2+ channel reopening and arrhythmogenic early-afterdepolarizations.

Estimating the probabilities of rare arrhythmic events in multiscale computational models of cardiac cells and tissue.

Ectopic heartbeats can trigger reentrant arrhythmias, leading to ventricular fibrillation and sudden cardiac death. Such events have been attributed to perturbed Ca2+ handling in cardiac myocytes leading to spontaneous Ca2+ release and delayed afterdepolarizations (DADs). However, the ways in which perturbation of specific molecular mechanisms alters the probability of ectopic beats is not understood. We present a multiscale model of cardiac tissue incorporating a biophysically detailed three-dimensional model of the ventricular myocyte. This model reproduces realistic Ca2+ waves and DADs driven by stochastic Ca2+ release channel (RyR) gating and is used to study mechanisms of DAD variability. In agreement with previous experimental and modeling studies, key factors influencing the distribution of DAD amplitude and timing include cytosolic and sarcoplasmic reticulum Ca2+ concentrations, inwardly rectifying potassium current (IK1) density, and gap junction conductance. The cardiac tissue model is used to investigate how random RyR gating gives rise to probabilistic triggered activity in a one-dimensional myocyte tissue model. A novel spatial-average filtering method for estimating the probability of extreme (i.e. rare, high-amplitude) stochastic events from a limited set of spontaneous Ca2+ release profiles is presented. These events occur when randomly organized clusters of cells exhibit synchronized, high amplitude Ca2+ release flux. It is shown how reduced IK1 density and gap junction coupling, as observed in heart failure, increase the probability of extreme DADs by multiple orders of magnitude. This method enables prediction of arrhythmia likelihood and its modulation by alterations of other cellular mechanisms.

Mechanisms of the cyclic nucleotide cross-talk signaling network in cardiac L-type calcium channel regulation.

Regulation of L-type Calcium (Ca2+) Channel (LCC) gating is critical to shaping the cardiac action potential (AP) and triggering the initiation of excitation-contraction (EC) coupling in cardiac myocytes. The cyclic nucleotide (cN) cross-talk signaling network, which encompasses the ß-adrenergic and the Nitric Oxide (NO)/cGMP/Protein Kinase G (PKG) pathways and their interaction (cross-talk) through distinctively-regulated phosphodiesterase isoenzymes (PDEs), regulates LCC current via Protein Kinase A- (PKA) and PKG-mediated phosphorylation. Due to the tightly-coupled and intertwined biochemical reactions involved, it remains to be clarified how LCC gating is regulated by the signaling network from receptor to end target. In addition, the large number of EC coupling-related phosphorylation targets of PKA and PKG makes it difficult to quantify and isolate changes in L-type Ca2+ current (ICaL) responses regulated by the signaling network. We have developed a multi-scale, biophysically-detailed computational model of LCC regulation by the cN signaling network that is supported by experimental data. LCCs are modeled with functionally distinct PKA- and PKG-phosphorylation dependent gating modes. The model exhibits experimentally observed single channel characteristics, as well as whole-cell LCC currents upon activation of the cross-talk signaling network. Simulations show 1) redistribution of LCC gating modes explains changes in whole-cell current under various stimulation scenarios of the cN cross-talk network; 2) NO regulation occurs via potentiation of a gating mode characterized by prolonged closed times; and 3) due to compensatory actions of cross-talk and antagonizing functions of PKA- and PKG-mediated phosphorylation of LCCs, the effects of individual inhibitions of PDEs 2, 3, and 4 on ICaL are most pronounced at low levels of ß-adrenergic stimulation. Simulations also delineate the contribution of the following two mechanisms to overall LCC regulation, which have otherwise been challenging to distinguish: 1) regulation of PKA and PKG activation via cN cross-talk (Mechanism 1); and 2) LCC interaction with activated PKA and PKG (Mechanism 2). These results provide insights into how cN signals transduced via the cN cross-talk signaling network are integrated via LCC regulation in the heart.

Models and Simulations as a Service: Exploring the Use of Galaxy for Delivering Computational Models.

We describe the ways in which Galaxy, a web-based reproducible research platform, can be used for web-based sharing of complex computational models. Galaxy allows users to seamlessly customize and run simulations on cloud computing resources, a concept we refer to as Models and Simulations as a Service (MaSS). To illustrate this application of Galaxy, we have developed a tool suite for simulating a high spatial-resolution model of the cardiac Ca(2+) spark that requires supercomputing resources for execution. We also present tools for simulating models encoded in the SBML and CellML model description languages, thus demonstrating how Galaxy's reproducible research features can be leveraged by existing technologies. Finally, we demonstrate how the Galaxy workflow editor can be used to compose integrative models from constituent submodules. This work represents an important novel approach, to our knowledge, to making computational simulations more accessible to the broader scientific community.

Modeling Na+-Ca2+ exchange in the heart: Allosteric activation, spatial localization, sparks and excitation-contraction coupling.

The cardiac sodium (Na+)/calcium (Ca2+) exchanger (NCX1) is an electrogenic membrane transporter that regulates Ca2+ homeostasis in cardiomyocytes, serving mainly to extrude Ca2+ during diastole. The direction of Ca2+ transport reverses at membrane potentials near that of the action potential plateau, generating an influx of Ca2+ into the cell. Therefore, there has been great interest in the possible roles of NCX1 in cardiac Ca2+-induced Ca2+ release (CICR). Interest has been reinvigorated by a recent super-resolution optical imaging study suggesting that ~18% of NCX1 co-localize with ryanodine receptor (RyR2) clusters, and ~30% of additional NCX1 are localized to within ~120nm of the nearest RyR2. NCX1 may therefore occupy a privileged position in which to modulate CICR. To examine this question, we have developed a mechanistic biophysically-detailed model of NCX1 that describes both NCX1 transport kinetics and Ca2+-dependent allosteric regulation. This NCX1 model was incorporated into a previously developed super-resolution model of the Ca2+ spark as well as a computational model of the cardiac ventricular myocyte that includes a detailed description of CICR with stochastic gating of L-type Ca2+ channels and RyR2s, and that accounts for local Ca2+ gradients near the dyad via inclusion of a peri-dyadic (PD) compartment. Both models predict that increasing the fraction of NCX1 in the dyad and PD decreases spark frequency, fidelity, and diastolic Ca2+ levels. Spark amplitude and duration are less sensitive to NCX1 spatial redistribution. On the other hand, NCX1 plays an important role in promoting Ca2+ entry into the dyad, and hence contributing to the trigger for RyR2 release at depolarized membrane potentials and in the presence of elevated local Na+ concentration. Whole-cell simulation of NCX1 tail currents are consistent with the finding that a relatively high fraction of NCX1 (~45%) resides in the dyadic and PD spaces, with a dyad-to-PD ratio of roughly 1:2. Allosteric Ca2+ activation of NCX1 helps to "functionally localize" exchanger activity to the dyad and PD by reducing exchanger activity in the cytosol thereby protecting the cell from excessive loss of Ca2+ during diastole.

Roles of phosphodiesterases in the regulation of the cardiac cyclic nucleotide cross-talk signaling network.

The balanced signaling between the two cyclic nucleotides (cNs) cAMP and cGMP plays a critical role in regulating cardiac contractility. Their degradation is controlled by distinctly regulated phosphodiesterase isoenzymes (PDEs), which in turn are also regulated by these cNs. As a result, PDEs facilitate communication between the ß-adrenergic and Nitric Oxide (NO)/cGMP/Protein Kinase G (PKG) signaling pathways, which regulate the synthesis of cAMP and cGMP respectively. The phenomena in which the cAMP and cGMP pathways influence the dynamics of each other are collectively referred to as cN cross-talk. However, the cross-talk response and the individual roles of each PDE isoenzyme in shaping this response remain to be fully characterized. We have developed a computational model of the cN cross-talk network that mechanistically integrates the ß-adrenergic and NO/cGMP/PKG pathways via regulation of PDEs by both cNs. The individual model components and the integrated network model replicate experimentally observed activation-response relationships and temporal dynamics. The model predicts that, due to compensatory interactions between PDEs, NO stimulation in the presence of sub-maximal ß-adrenergic stimulation results in an increase in cytosolic cAMP accumulation and corresponding increases in PKA-I and PKA-II activation; however, the potentiation is small in magnitude compared to that of NO activation of the NO/cGMP/PKG pathway. In a reciprocal manner, ß-adrenergic stimulation in the presence of sub-maximal NO stimulation results in modest cGMP elevation and corresponding increase in PKG activation. In addition, we demonstrate that PDE2 hydrolyzes increasing amounts of cAMP with increasing levels of ß-adrenergic stimulation, and hydrolyzes increasing amounts of cGMP with decreasing levels of NO stimulation. Finally, we show that PDE2 compensates for inhibition of PDE5 both in terms of cGMP and cAMP dynamics, leading to cGMP elevation and increased PKG activation, while maintaining whole-cell ß-adrenergic responses similar to that prior to PDE5 inhibition. By defining and quantifying reactions comprising cN cross-talk, the model characterizes the cross-talk response and reveals the underlying mechanisms of PDEs in this non-linear, tightly-coupled reaction system.

On the Adjacency Matrix of RyR2 Cluster Structures.

In the heart, electrical stimulation of cardiac myocytes increases the open probability of sarcolemmal voltage-sensitive Ca2+ channels and flux of Ca2+ into the cells. This increases Ca2+ binding to ligand-gated channels known as ryanodine receptors (RyR2). Their openings cause cell-wide release of Ca2+, which in turn causes muscle contraction and the generation of the mechanical force required to pump blood. In resting myocytes, RyR2s can also open spontaneously giving rise to spatially-confined Ca2+ release events known as "sparks." RyR2s are organized in a lattice to form clusters in the junctional sarcoplasmic reticulum membrane. Our recent work has shown that the spatial arrangement of RyR2s within clusters strongly influences the frequency of Ca2+ sparks. We showed that the probability of a Ca2+ spark occurring when a single RyR2 in the cluster opens spontaneously can be predicted from the precise spatial arrangements of the RyR2s. Thus, "function" follows from "structure." This probability is related to the maximum eigenvalue (λ1) of the adjacency matrix of the RyR2 cluster lattice. In this work, we develop a theoretical framework for understanding this relationship. We present a stochastic contact network model of the Ca2+ spark initiation process. We show that λ1 determines a stability threshold for the formation of Ca2+ sparks in terms of the RyR2 gating transition rates. We recapitulate these results by applying the model to realistic RyR2 cluster structures informed by super-resolution stimulated emission depletion (STED) microscopy. Eigendecomposition of the linearized mean-field contact network model reveals functional subdomains within RyR2 clusters with distinct sensitivities to Ca2+. This work provides novel perspectives on the cardiac Ca2+ release process and a general method for inferring the functional properties of transmembrane receptor clusters from their structure.

WaveformECG: A Platform for Visualizing, Annotating, and Analyzing ECG Data.

The electrocardiogram (ECG) is the most commonly collected data in cardiovascular research because of the ease with which it can be measured and because changes in ECG waveforms reflect underlying aspects of heart disease. Accessed through a browser, WaveformECG is an open source platform supporting interactive analysis, visualization, and annotation of ECGs.

Mechanistic Investigation of the Arrhythmogenic Role of Oxidized CaMKII in the Heart.

Oxidative stress and calcium (Ca(2+))/calmodulin (CaM)-dependent protein kinase II (CaMKII) both play important roles in the pathogenesis of cardiac disease. Although the pathophysiological relevance of reactive oxygen species (ROS) and CaMKII has been appreciated for some time, recent work has shown that ROS can directly oxidize CaMKII, leading to its persistent activity and an increase of the likelihood of cellular arrhythmias such as early afterdepolarizations (EADs). Because CaMKII modulates the function of many proteins involved in excitation-contraction coupling, elucidation of its role in cardiac function, in both healthy and oxidative stress conditions, is challenging. To investigate this role, we have developed a model of CaMKII activation that includes both the phosphorylation-dependent and the newly identified oxidation-dependent activation pathways. This model is incorporated into our previous local-control model of the cardiac myocyte that describes excitation-contraction coupling via stochastic simulation of individual Ca(2+) release units and CaMKII-mediated phosphorylation of L-type Ca(2+) channels (LCCs), ryanodine receptors and sodium (Na(+)) channels. The model predicts the experimentally measured slow-rate dependence of H2O2-induced EADs. Upon increased H2O2, simulations suggest that selective activation of late Na(+) current (INaL), although it prolongs action potential duration, is not by itself sufficient to produce EADs. Similar results are obtained if CaMKII effects on LCCs and ryanodine receptors are considered separately. However, EADs emerge upon simultaneous activation of both LCCs and Na(+) channels. Further modeling results implicate activation of the Na(+)-Ca(2+) exchanger (NCX) as an important player in the generation of EADs. During bradycardia, the emergence of H2O2-induced EADs was correlated with a shift in the timing of NCX current reversal toward the plateau phase earlier in the action potential. Using the timing of NCX current reversal as an indicator event for EADs, the model identified counterintuitive ionic changes-difficult to experimentally dissect-that have the greatest influence on ROS-related arrhythmia propensity.

Interaction between phosphodiesterases in the regulation of the cardiac ß-adrenergic pathway.

In cardiac myocytes, the second messenger cAMP is synthesized within the ß-adrenergic signaling pathway upon sympathetic activation. It activates Protein Kinase A (PKA) mediated phosphorylation of multiple target proteins that are functionally critical to cardiac contractility. The dynamics of cAMP are also controlled indirectly by cGMP-mediated regulation of phosphodiesterase isoenzymes (PDEs). The nature of the interactions between cGMP and the PDEs, as well as between PDE isoforms, and how these ultimately transduce the cGMP signal to regulate cAMP remains unclear. To better understand this, we have developed mechanistically detailed models of PDEs 1-4, the primary cAMP-hydrolyzing PDEs in cardiac myocytes, and integrated them into a model of the ß-adrenergic signaling pathway. The PDE models are based on experimental studies performed on purified PDEs which have demonstrated that cAMP and cGMP bind competitively to the cyclic nucleotide (cN)-binding domains of PDEs 1, 2, and 3, while PDE4 regulation occurs via PKA-mediated phosphorylation. Individual PDE models reproduce experimentally measured cAMP hydrolysis rates with dose-dependent cGMP regulation. The fully integrated model replicates experimentally observed whole-cell cAMP activation-response relationships and temporal dynamics upon varying degrees of ß-adrenergic stimulation in cardiac myocytes. Simulations reveal that as a result of network interactions, reduction in the level of one PDE is partially compensated for by increased activation of others. PDE2 and PDE4 exert the strongest compensatory roles among all PDEs. In addition, PDE2 competes with other PDEs to bind and hydrolyze cAMP and is a strong regulator of PDE interactions. Finally, an increasing level of cGMP gradually out-competes cAMP for the catalytic sites of PDEs 1, 2, and 3, suppresses their cAMP hydrolysis rates, and results in amplified cAMP signaling. These results provide insights into how PDEs transduce cGMP signals to regulate cAMP and how PDE interactions affect cardiac ß-adrenergic response.

Superresolution modeling of calcium release in the heart.

Stable calcium-induced calcium release (CICR) is critical for maintaining normal cellular contraction during cardiac excitation-contraction coupling. The fundamental element of CICR in the heart is the calcium (Ca(2+)) spark, which arises from a cluster of ryanodine receptors (RyR). Opening of these RyR clusters is triggered to produce a local, regenerative release of Ca(2+) from the sarcoplasmic reticulum (SR). The Ca(2+) leak out of the SR is an important process for cellular Ca(2+) management, and it is critically influenced by spark fidelity, i.e., the probability that a spontaneous RyR opening triggers a Ca(2+) spark. Here, we present a detailed, three-dimensional model of a cardiac Ca(2+) release unit that incorporates diffusion, intracellular buffering systems, and stochastically gated ion channels. The model exhibits realistic Ca(2+) sparks and robust Ca(2+) spark termination across a wide range of geometries and conditions. Furthermore, the model captures the details of Ca(2+) spark and nonspark-based SR Ca(2+) leak, and it produces normal excitation-contraction coupling gain. We show that SR luminal Ca(2+)-dependent regulation of the RyR is not critical for spark termination, but it can explain the exponential rise in the SR Ca(2+) leak-load relationship demonstrated in previous experimental work. Perturbations to subspace dimensions, which have been observed in experimental models of disease, strongly alter Ca(2+) spark dynamics. In addition, we find that the structure of RyR clusters also influences Ca(2+) release properties due to variations in inter-RyR coupling via local subspace Ca(2+) concentration ([Ca(2+)]ss). These results are illustrated for RyR clusters based on super-resolution stimulated emission depletion microscopy. Finally, we present a believed-novel approach by which the spark fidelity of a RyR cluster can be predicted from structural information of the cluster using the maximum eigenvalue of its adjacency matrix. These results provide critical insights into CICR dynamics in heart, under normal and pathological conditions.

Grand Challenges at the Interface of Engineering and Medicine.

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating "avatars" (herein defined as an extension of "digital twins") of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

An integrated mitochondrial ROS production and scavenging model: implications for heart failure.

It has been observed experimentally that cells from failing hearts exhibit elevated levels of reactive oxygen species (ROS) upon increases in energetic workload. One proposed mechanism for this behavior is mitochondrial Ca(2+) mismanagement that leads to depletion of ROS scavengers. Here, we present a computational model to test this hypothesis. Previously published models of ROS production and scavenging were combined and reparameterized to describe ROS regulation in the cellular environment. Extramitochondrial Ca(2+) pulses were applied to simulate frequency-dependent changes in cytosolic Ca(2+). Model results show that decreased mitochondrial Ca(2+)uptake due to mitochondrial Ca(2+) uniporter inhibition (simulating Ru360) or elevated cytosolic Na(+), as in heart failure, leads to a decreased supply of NADH and NADPH upon increasing cellular workload. Oxidation of NADPH leads to oxidation of glutathione (GSH) and increased mitochondrial ROS levels, validating the Ca(2+) mismanagement hypothesis. The model goes on to predict that the ratio of steady-state [H2O2]m during 3Hz pacing to [H2O2]m at rest is highly sensitive to the size of the GSH pool. The largest relative increase in [H2O2]m in response to pacing is shown to occur when the total GSH and GSSG is close to 1 mM, whereas pool sizes below 0.9 mM result in high resting H2O2 levels, a quantitative prediction only possible with a computational model.

Integrating mitochondrial energetics, redox and ROS metabolic networks: a two-compartment model.

To understand the mechanisms involved in the control and regulation of mitochondrial reactive oxygen species (ROS) levels, a two-compartment computational mitochondrial energetic-redox (ME-R) model accounting for energetic, redox, and ROS metabolisms is presented. The ME-R model incorporates four main redox couples (NADH/NAD(+), NADPH/NADP(+), GSH/GSSG, Trx(SH)(2)/TrxSS). Scavenging systems-glutathione, thioredoxin, superoxide dismutase, catalase-are distributed in mitochondrial matrix and extra-matrix compartments, and transport between compartments of ROS species (superoxide: O(2)(⋅-), hydrogen peroxide: H(2)O(2)), and GSH is also taken into account. Model simulations are compared with experimental data obtained from isolated heart mitochondria. The ME-R model is able to simulate: i), the shape and order of magnitude of H(2)O(2) emission and dose-response kinetics observed after treatment with inhibitors of the GSH or Trx scavenging systems and ii), steady and transient behavior of ΔΨ(m) and NADH after single or repetitive pulses of substrate- or uncoupler-elicited energetic-redox transitions. The dynamics of the redox environment in both compartments is analyzed with the model following substrate addition. The ME-R model represents a useful computational tool for exploring ROS dynamics, the role of compartmentation in the modulation of the redox environment, and how redox regulation participates in the control of mitochondrial function.

A computational model of reactive oxygen species and redox balance in cardiac mitochondria.

Elevated levels of reactive oxygen species (ROS) play a critical role in cardiac myocyte signaling in both healthy and diseased cells. Mitochondria represent the predominant cellular source of ROS, specifically the activity of complexes I and III. The model presented here explores the modulation of electron transport chain ROS production for state 3 and state 4 respiration and the role of substrates and respiratory inhibitors. Model simulations show that ROS production from complex III increases exponentially with membrane potential (ΔΨm) when in state 4. Complex I ROS release in the model can occur in the presence of NADH and succinate (reverse electron flow), leading to a highly reduced ubiquinone pool, displaying the highest ROS production flux in state 4. In the presence of ample ROS scavenging, total ROS production is moderate in state 3 and increases substantially under state 4 conditions. The ROS production model was extended by combining it with a minimal model of ROS scavenging. When the mitochondrial redox status was oxidized by increasing the proton permeability of the inner mitochondrial membrane, simulations with the combined model show that ROS levels initially decline as production drops off with decreasing ΔΨm and then increase as scavenging capacity is exhausted. Hence, this mechanistic model of ROS production demonstrates how ROS levels are controlled by mitochondrial redox balance.