Integration from proteins to organs: the IUPS Physiome Project

Automated model calibration with parallel MCMC: Applications for a cardiovascular system model

Computational physiological models continue to increase in complexity, however, the task of efficiently calibrating the model to available clinical data remains a significant challenge. One part of this challenge is associated with long calibration times, which present a barrier for the routine application of model-based prediction in clinical practice. Another aspect of this challenge is the limited available data for the unique calibration of complex models. Therefore, to calibrate a patient-specific model, it may be beneficial to verify that task-specific model predictions have acceptable uncertainty, rather than requiring all parameters to be uniquely identified. We have developed a pipeline that reduces the set of fitting parameters to make them structurally identifiable and to improve the efficiency of a subsequent Markov Chain Monte Carlo (MCMC) analysis. MCMC was used to find the optimal parameter values and to determine the confidence interval of a task-specific prediction. This approach was demonstrated on numerical experiments where a lumped parameter model of the cardiovascular system was calibrated to brachial artery cuff pressure, echocardiogram volume measurements, and synthetic cerebral blood flow data that approximates what can be obtained from 4D-flow MRI data. This pipeline provides a cerebral arterial pressure prediction that may be useful for determining the risk of hemorrhagic stroke. For a set of three patients, this pipeline successfully reduced the parameter set of a cardiovascular system model from 12 parameters to 8–10 structurally identifiable parameters. This enabled a significant (>4×) efficiency improvement in determining confidence intervals on predictions of pressure compared to performing a naive MCMC analysis with the full parameter set. This demonstrates the potential that the proposed pipeline has in helping address one of the key challenges preventing clinical application of such models. Additionally, for each patient, the MCMC approach yielded a 95% confidence interval on systolic blood pressure prediction in the middle cerebral artery smaller than ±10 mmHg (±1.3 kPa). The proposed pipeline exploits available high-performance computing parallelism to allow straightforward automation for general models and arbitrary data sets, enabling automated calibration of a parameter set that is specific to the available clinical data with minimal user interaction.

Development of Chemical Kinetics Models from Atomistic Reactive Molecular Dynamics Simulations: Application to Iso-octane Combustion and Rubber Ablative Degradation

Modeling the complex chemical phenomena resulting from multiple active species and long-chain polymers is limited by uncertainties in the reaction rate parameters, which increase rapidly with the number of active species and/or reaction pathways. Reactive molecular dynamics simulations have the potential to help obtain in-depth information on the chemical reactions that occur between the polymer (e.g., ablative material) and the multiple active species in an aggressive environment. In this work, we demonstrate that molecular dynamics (MD) simulations using the ReaxFF interatomic potential can be used to obtain the reaction kinetics of complex reaction pathways at high temperatures. We report two recently developed tools, namely, MolfrACT and KinACT, designed to extract chemical kinetic pathways by postprocessing reactive MD simulation data. The pathway extraction is based on a new algorithm, Consistent Reaction Stoichiometry via an Iterative Scheme (CReSIS), for the automated extraction of reactions and kinetics from MD trajectories. As a validation of the methodology, we first report the kinetic analysis and mechanisms for the high-temperature combustion of iso-octane. The observed reaction pathways are consistent with experimental models. In addition, we compare the activation energies of select iso-octane combustion pathways with experimental data and show that nanosecond timescale molecular dynamics simulations are sufficient to obtain realistic estimates of activation energies for different fuel consumption reaction pathways at high temperatures. The framework developed here can potentially be combined with time-series forecasting and machine learning methods to further reduce the computational complexity of transient molecular dynamics simulations, yet yielding realistic chemical kinetics information. Subsequently, the CReSIS scheme applied to ethylene-propylene-diene-monomer (EPDM) rubber ablative reveals that H₂O, C₂H₄, and HCHO are the major products during the initial stages of the polymer degradation in high-temperature oxidative environments. While prior work involving ReaxFF simulations is restricted to overall rates of formation of any species, we extract kinetic information for individual reaction pathways. In this paper, we present several reaction pathways observed during the EPDM rubber degradation into the dominant products and report the pathway-specific reaction rates. Arrhenius analysis reveals that the dominant reaction pathway activation energies for the formation of water, ethylene, and formaldehyde are 34.42, 27.26, and 6.37 kcal/mol, respectively. In contrast, the activation energies for the overall formation (across all reaction pathways) of these products are in the 40-50 kcal/mol range.

Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning

Microstructural features and mechanical properties are closely related in all soft biological tissues. Both yet exhibit considerable inter-individual differences and are affected by factors such as aging and disease and its progression. Histological analysis, modern in situ imaging, and biomechanical testing have deepened our understanding of these complex interrelations, yet two key questions remain: (1) Given the specific microstructure, can one predict the macroscopic mechanical properties without mechanical testing? (2) Can one quantify individual contributions of the different microstructural features to the macroscopic mechanical properties in an automated, systematic and largely unbiased way? Here we propose a bidirectional deep learning architecture to address these two questions. Our architecture uses data from standard histological analyses, two-photon microscopy and biaxial biomechanical testing. Its capabilities are demonstrated by predicting with high accuracy (R²=0.92) the evolving mechanical properties of the murine aorta during maturation and aging. Moreover, our architecture reveals that the extracellular matrix composition and organization are the most prominent factors governing the macroscopic mechanical properties of the tissues studied herein. STATEMENT OF SIGNIFICANCE:  .

The Impact of 3-D Models versus Animations on Perceptions of Osteoporosis and Treatment Motivation: A Randomised Trial

BACKGROUND

Osteoporosis is a degenerative bone disorder that disproportionately affects older women worldwide. Raising awareness regarding osteoporosis within this demographic is significant for health promotion. Initial evidence suggests that visualisations of illness and treatment can improve illness perceptions, increase treatment motivations and even promote health behaviours. We are yet to understand whether different visualisation mediums vary in their impact on perceptions and motivations.

PURPOSE

We investigated whether physical models or virtual animations had a greater impact on changing perceptions of osteoporosis and treatment motivation in an at-risk population of older women.

METHODS

A total of 128 women aged 50 and over were randomly assigned to view a brief presentation about osteoporosis using either 3-D printed bone models or electronic tablet animations. Illness perceptions, medication beliefs and motivations were measured at baseline and post-presentation. Mixed ANOVAs were used to identify significant changes over time between groups.

RESULTS

There were no significant interaction effects, revealing that neither medium had a greater impact on beliefs over time. Significant main effects of time revealed that from baseline to post-presentation, both mediums increased consequence beliefs, personal and treatment control, understanding of osteoporosis, motivations to take treatment if needed and medication necessity beliefs. Timeline beliefs and medication concerns decreased over time for both groups.

CONCLUSIONS

Both 3-D models and animations of osteoporosis are equally effective in changing beliefs and treatment motivation in an at-risk population. Visualisation devices are brief, cost-effective, have high acceptability and have considerable clinical applicability to promote awareness and prevention.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

A composite visualization method for electrophysiology-morphous merging of human heart

BACKGROUND

Electrophysiological behavior is of great importance for analyzing the cardiac functional mechanism under cardiac physiological and pathological condition. Due to the complexity of cardiac structure and biophysiological function, visualization of a cardiac electrophysiological model compositively is still a challenge. The lack of either modality of the whole organ structure or cardiac electrophysiological behaviors makes analysis of the intricate mechanisms of cardiac dynamic function a difficult task. This study aims at exploring 3D conduction of stimulus and electrical excitation reactivity on the level of organ with the authentic fine cardiac anatomy structure.

METHODS

In this paper, a cardiac electrical excitation propagation model is established based on the human cardiac cross-sectional data to explore detailed cardiac electrical activities. A novel biophysical merging visualization method is then presented for biophysical integration of cardiac anatomy and electrophysiological properties in the form of the merging optical model, which provides the corresponding position, spatial relationship and the whole process in 3D space with the context of anatomical structure for representing the biophysical detailed electrophysiological activity.

RESULTS

The visualization result present the action potential propagation of the left ventricle within the excitation cycle with the authentic fine cardiac organ anatomy. In the visualized images, all vital organs are identified and distinguished without ambiguity. The three dimensional spatial position, relation and the process of cardiac excitation conduction and re-entry propagation in the anatomical structure during the phase of depolarization and repolarization is also shown in the result images, which exhibits the performance of a more detailed biophysical understanding of the electrophysiological kinetics of human heart in vivo.

CONCLUSIONS

Results suggest that the proposed merging optical model can merge cardiac electrophysiological activity with the anatomy structure. By specifying the respective opacity for the cardiac anatomy structure and the electrophysiological model in the merging attenuation function, the visualized images can provide an in-depth insight into the biophysical detailed cardiac functioning phenomena and the corresponding electrophysiological behavior mechanism, which is helpful for further speculating cardiac physiological and pathological responses and is fundamental to the cardiac research and clinical diagnoses.

Multiscale musculoskeletal modelling, data-model fusion and electromyography-informed modelling

This paper proposes methods and technologies that advance the state of the art for modelling the musculoskeletal system across the spatial and temporal scales; and storing these using efficient ontologies and tools. We present population-based modelling as an efficient method to rapidly generate individual morphology from only a few measurements and to learn from the ever-increasing supply of imaging data available. We present multiscale methods for continuum muscle and bone models; and efficient mechanostatistical methods, both continuum and particle-based, to bridge the scales. Finally, we examine both the importance that muscles play in bone remodelling stimuli and the latest muscle force prediction methods that use electromyography-assisted modelling techniques to compute musculoskeletal forces that best reflect the underlying neuromuscular activity. Our proposal is that, in order to have a clinically relevant virtual physiological human, (i) bone and muscle mechanics must be considered together; (ii) models should be trained on population data to permit rapid generation and use underlying principal modes that describe both muscle patterns and morphology; and (iii) these tools need to be available in an open-source repository so that the scientific community may use, personalize and contribute to the database of models.

A mechanostatistical approach to cortical bone remodelling: an equine model

In this study, the development of a mechanostatistical model of three-dimensional cortical bone remodelling informed with in vivo equine data is presented. The equine model was chosen as it is highly translational to the human condition due to similar Haversian systems, availability of in vivo bone strain and biomarker data, and furthermore, equine models are recommended by the US Federal Drugs Administration for comparative joint research. The model was derived from micro-computed tomography imaged specimens taken from the equine third metacarpal bone, and the Frost-based 'mechanostat' was informed from both in vivo strain gauges and biomarkers to estimate bone growth rates. The model also described the well-known 'cutting cone' phenomena where Haversian canals tunnel and replace bone. In order to make this model useful in practice, a partial least squares regression (PLSR) surrogate model was derived based on training data from finite element simulations with different loads. The PLSR model was able to predict microstructure and homogenised Young's modulus with errors less than 2.2% and 0.6%, respectively.

Applications of computational models to better understand microvascular remodelling: a focus on biomechanical integration across scales

Microvascular network remodelling is a common denominator for multiple pathologies and involves both angiogenesis, defined as the sprouting of new capillaries, and network patterning associated with the organization and connectivity of existing vessels. Much of what we know about microvascular remodelling at the network, cellular and molecular scales has been derived from reductionist biological experiments, yet what happens when the experiments provide incomplete (or only qualitative) information? This review will emphasize the value of applying computational approaches to advance our understanding of the underlying mechanisms and effects of microvascular remodelling. Examples of individual computational models applied to each of the scales will highlight the potential of answering specific questions that cannot be answered using typical biological experimentation alone. Looking into the future, we will also identify the needs and challenges associated with integrating computational models across scales.

Bone remodelling in the natural acetabulum is influenced by muscle force-induced bone stress

A modelling framework using the international Physiome Project is presented for evaluating the role of muscles on acetabular stress patterns in the natural hip. The novel developments include the following: (i) an efficient method for model generation with validation; (ii) the inclusion of electromyography-estimated muscle forces from gait; and (iii) the role that muscles play in the hip stress pattern. The 3D finite element hip model includes anatomically based muscle area attachments, material properties derived from Hounsfield units and validation against an Instron compression test. The primary outcome from this study is that hip loading applied as anatomically accurate muscle forces redistributes the stress pattern and reduces peak stress throughout the pelvis and within the acetabulum compared with applying the same net hip force without muscles through the femur. Muscle forces also increased stress where large muscles have small insertion sites. This has implications for the hip where bone stress and strain are key excitation variables used to initiate bone remodelling based on the strain-based bone remodelling theory. Inclusion of muscle forces reduces the predicted sites and degree of remodelling. The secondary outcome is that the key muscles that influenced remodelling in the acetabulum were the rectus femoris, adductor magnus and iliacus.

Investigation on cardiovascular risk prediction using physiological parameters

Cardiovascular disease (CVD) is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI), and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.

A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

Integrating degenerative mechanisms in bone and cartilage: a multiscale approach

At the whole organ level, degenerative mechanisms in bone and cartilage are primarily attributed to modifications in loading pattern. Either a change in magnitude or location can initiate a degenerative path. At the micro scale we often see changes in structure such as porosity increase in bone and fibrillation in cartilage. These changes contribute to a reduced structural integrity that weakens the bulk strength of tissue. Finally, at the cell level we have modeling and remodeling pathways that may be disrupted through disease, drugs and altered stimulus from the micro and macro scales. In order to understand this entire process and the roles each level plays a multiscale modeling framework is necessary. This framework can take whole body loadings and pass information through finer spatial scales in order to understand how everyday dynamic movements influence micro and cellular response. In a similar manner, cellular and microstructural processes regulate whole bulk properties and modify whole organ strength. In this study we highlight the multiscale links developed as part of the open-source ontologies for the Physiome Project using the lower limb as an example. We consider the influence of remodeling in (i) anabolic treatments in cortical bone; and (ii) subchondral bone and cartilage degeneration.

Asthma phenotyping, therapy, and prevention: what can we learn from systems biology?

Asthma has a high prevalence worldwide, and contributes significantly to the socioeconomic burden. According to a classical paradigm, asthma symptoms are attributable to an allergic, Th2-driven airway inflammation that causes airway hyperresponsiveness and results in reversible airway obstruction. Diagnosis and therapy are based mainly on these pathophysiologic concepts. However, these have increasingly been challenged by findings of recent studies, and the frequently observed failure in controlling asthma symptoms. Important recent findings are the protective "farm effect" in children, the possible prenatal mechanisms of this protection, the recognition of many different asthma phenotypes in children and adults, and the partly disappointing clinical effects of new targeted therapeutic approaches. Systems biology approaches may lead to a more comprehensive view of asthma pathophysiology and a higher success rate of new therapies. Systems biology integrates clinical and experimental data by means of bioinformatics and mathematical modeling. In general, the "-omics" approach, and the "mathematical modeling" approach can be described. Recently, several consortia have been attempting to bring together clinical and molecular data from large asthma cohorts, using novel experimental setups, biostatistics, bioinformatics, and mathematical modeling. This "systems medicine" approach to asthma will help address the different asthma phenotypes with adequate therapy and possibly preventive strategies.

Mechanics of the foot Part 1: a continuum framework for evaluating soft tissue stiffening in the pathologic foot

Soft tissue stiffening is a common mechanical observation reported in foot pathologies including diabetes mellitus and gout. These material changes influence the spatial distribution of stress and affect blood flow, which is essential to nutrient entry and waste removal. An anatomically-based subject-specific foot model was developed to explore the influence of tissue stiffening on plantar pressure and internal von Mises stress at heel-strike, midstance and toe-off. This work draws on the model database developed for the Physiome project consisting of muscles, bones, soft tissue and other structures such as sensory nerves. The anisotropic structure of soft tissue was embedded in a single continuum as an efficient model for finite soft tissue deformation, and customisation methods were used to capture the unique foot profile. The model was informed by kinetics from an instrumented treadmill and kinematics from motion capture, synchronised together. Foot sole pressure predictions were evaluated against a commercial pressure platform. Key outcomes showed that internal stress can be up to 1.6 times the surface pressure with implications for internal soft tissue damage not observed at the surface. The main nerve branch stimulated during gait was the lateral plantar nerve. This subject-specific modelling framework can play an integral part in therapeutic treatments by informing assistive strategies such as mechanical noise stimulation and orthotics.

Towards omni-tomography--grand fusion of multiple modalities for simultaneous interior tomography

We recently elevated interior tomography from its origin in computed tomography (CT) to a general tomographic principle, and proved its validity for other tomographic modalities including SPECT, MRI, and others. Here we propose “omni-tomography”, a novel concept for the grand fusion of multiple tomographic modalities for simultaneous data acquisition in a region of interest (ROI). Omni-tomography can be instrumental when physiological processes under investigation are multi-dimensional, multi-scale, multi-temporal and multi-parametric. Both preclinical and clinical studies now depend on in vivo tomography, often requiring separate evaluations by different imaging modalities. Over the past decade, two approaches have been used for multimodality fusion: Software based image registration and hybrid scanners such as PET-CT, PET-MRI, and SPECT-CT among others. While there are intrinsic limitations with both approaches, the main obstacle to the seamless fusion of multiple imaging modalities has been the bulkiness of each individual imager and the conflict of their physical (especially spatial) requirements. To address this challenge, omni-tomography is now unveiled as an emerging direction for biomedical imaging and systems biomedicine.

Comparative computational modeling of airflows and vapor dosimetry in the respiratory tracts of rat, monkey, and human

Computational fluid dynamics (CFD) models are useful for predicting site-specific dosimetry of airborne materials in the respiratory tract and elucidating the importance of species differences in anatomy, physiology, and breathing patterns. We improved the imaging and model development methods to the point where CFD models for the rat, monkey, and human now encompass airways from the nose or mouth to the lung. A total of 1272, 2172, and 135 pulmonary airways representing 17±7, 19±9, or 9±2 airway generations were included in the rat, monkey and human models, respectively. A CFD/physiologically based pharmacokinetic model previously developed for acrolein was adapted for these anatomically correct extended airway models. Model parameters were obtained from the literature or measured directly. Airflow and acrolein uptake patterns were determined under steady-state inhalation conditions to provide direct comparisons with prior data and nasal-only simulations. Results confirmed that regional uptake was sensitive to airway geometry, airflow rates, acrolein concentrations, air:tissue partition coefficients, tissue thickness, and the maximum rate of metabolism. Nasal extraction efficiencies were predicted to be greatest in the rat, followed by the monkey, and then the human. For both nasal and oral breathing modes in humans, higher uptake rates were predicted for lower tracheobronchial tissues than either the rat or monkey. These extended airway models provide a unique foundation for comparing material transport and site-specific tissue uptake across a significantly greater range of conducting airways in the rat, monkey, and human than prior CFD models.

Computational modeling of airway and pulmonary vascular structure and function: development of a "lung physiome"

Computational models of lung structure and function necessarily span multiple spatial and temporal scales, i.e., dynamic molecular interactions give rise to whole organ function, and the link between these scales cannot be fully understood if only molecular or organ-level function is considered. Here, we review progress in constructing multiscale finite element models of lung structure and function that are aimed at providing a computational framework for bridging the spatial scales from molecular to whole organ. These include structural models of the intact lung, embedded models of the pulmonary airways that couple to model lung tissue, and models of the pulmonary vasculature that account for distinct structural differences at the extra- and intra-acinar levels. Biophysically based functional models for tissue deformation, pulmonary blood flow, and airway bronchoconstriction are also described. The development of these advanced multiscale models has led to a better understanding of complex physiological mechanisms that govern regional lung perfusion and emergent heterogeneity during bronchoconstriction.

Computational models of the pulmonary circulation: Insights and the move towards clinically directed studies

Biophysically-based computational models provide a tool for integrating and explaining experimental data, observations, and hypotheses. Computational models of the pulmonary circulation have evolved from minimal and efficient constructs that have been used to study individual mechanisms that contribute to lung perfusion, to sophisticated multi-scale and -physics structure-based models that predict integrated structure-function relationships within a heterogeneous organ. This review considers the utility of computational models in providing new insights into the function of the pulmonary circulation, and their application in clinically motivated studies. We review mathematical and computational models of the pulmonary circulation based on their application; we begin with models that seek to answer questions in basic science and physiology and progress to models that aim to have clinical application. In looking forward, we discuss the relative merits and clinical relevance of computational models: what important features are still lacking; and how these models may ultimately be applied to further increasing our understanding of the mechanisms occurring in disease of the pulmonary circulation.

A multiscale framework based on the physiome markup languages for exploring the initiation of osteoarthritis at the bone-cartilage interface

The initiation of osteoarthritis (OA) has been linked to the onset and progression of pathologic mechanisms at the cartilage-bone interface. Most importantly, this degenerative disease involves cross-talk between the cartilage and subchondral bone environments, so an informative model should contain the complete complex. In order to evaluate this process, we have developed a multiscale model using the open-source ontologies developed for the Physiome Project with cartilage and bone descriptions at the cellular, micro, and macro levels. In this way, we can effectively model the influence of whole body loadings at the macro level and the influence of bone organization and architecture at the micro level, and have cell level processes that determine bone and cartilage remodeling. Cell information is then passed up the spatial scales to modify micro architecture and provide a macro spatial characterization of cartilage inflammation. We evaluate the framework by linking a common knee injury (anterior cruciate ligament deficiency) to proinflammatory mediators as a possible pathway to initiate OA. This framework provides a "virtual bone-cartilage" tool for evaluating hypotheses, treatment effects, and disease onset to inform and strengthen clinical studies.

Simulating human cardiac electrophysiology on clinical time-scales

In this study, the feasibility of conducting in silico experiments in near-realtime with anatomically realistic, biophysically detailed models of human cardiac electrophysiology is demonstrated using a current national high-performance computing facility. The required performance is achieved by integrating and optimizing load balancing and parallel I/O, which lead to strongly scalable simulations up to 16,384 compute cores. This degree of parallelization enables computer simulations of human cardiac electrophysiology at 240 times slower than real time and activation times can be simulated in approximately 1 min. This unprecedented speed suffices requirements for introducing in silico experimentation into a clinical workflow.

CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models

A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models’ source codes to create a large monolithic model (computer program) that runs on a single computer. Such a larger model is difficult, if not impossible, to maintain given ongoing updates to the source codes of the smaller models. This paper describes a new system called CytoSolve that dynamically integrates computations of smaller models that can run in parallel across different machines without the need to merge the source codes of the individual models. This approach is demonstrated on the classic Epidermal Growth Factor Receptor (EGFR) model of Kholodenko. The EGFR model is split into four smaller models and each smaller model is distributed on a different machine. Results from four smaller models are dynamically integrated to generate identical results to the monolithic EGFR model running on a single machine. The overhead for parallel and dynamic computation is approximately twice that of a monolithic model running on a single machine. The CytoSolve approach provides a scalable method since smaller models may reside on any computer worldwide, where the source code of each model can be independently maintained and updated.

Strategies for health data exchange for secondary, cross-institutional clinical research

Secondary use of health data has a vital role in improving and advancing medical knowledge. While digital health records offer scope for facilitating the flow of data to secondary uses, it remains essential that steps are taken to respect wishes of the patient regarding secondary usage, and to ensure the privacy of the patient during secondary use scenarios. Consent, together with depersonalisation and its related concepts of anonymisation, pseudonymisation, and data minimisation are key methods used to provide this protection. This paper gives an overview of technical, practical, legal, and ethical aspects of secondary data use and discusses their implementation in the multi-institutional @neurIST research project.

Biofabrication: a 21st century manufacturing paradigm

Biofabrication can be defined as the production of complex living and non-living biological products from raw materials such as living cells, molecules, extracellular matrices, and biomaterials. Cell and developmental biology, biomaterials science, and mechanical engineering are the main disciplines contributing to the emergence of biofabrication technology. The industrial potential of biofabrication technology is far beyond the traditional medically oriented tissue engineering and organ printing and, in the short term, it is essential for developing potentially highly predictive human cell- and tissue-based technologies for drug discovery, drug toxicity, environmental toxicology assays, and complex in vitro models of human development and diseases. In the long term, biofabrication can also contribute to the development of novel biotechnologies for sustainable energy production in the future biofuel industry and dramatically transform traditional animal-based agriculture by inventing 'animal-free' food, leather, and fur products. Thus, the broad spectrum of potential applications and rapidly growing arsenal of biofabrication methods strongly suggests that biofabrication can become a dominant technological platform and new paradigm for 21st century manufacturing. The main objectives of this review are defining biofabrication, outlining the most essential disciplines critical for emergence of this field, analysis of the evolving arsenal of biofabrication technologies and their potential practical applications, as well as a discussion of the common challenges being faced by biofabrication technologies, and the necessary conditions for the development of a global biofabrication research community and commercially successful biofabrication industry.

Computational modeling of cardiac disease: potential for personalized medicine

Cardiovascular diseases are leading causes of death, reduce life quality and consume almost half a trillion dollars in healthcare expenses in the USA alone. Cardiac modeling and simulation technologies hold promise as important tools to improve cardiac care and are already in use to elucidate the fundamental mechanisms of cardiac physiology and pathophysiology. However, the emphasis has been on simulating average or exemplar cases. This report describes two classes of cardiac modeling efforts. First, electrophysiological models of channelopathies simulate the altered electrical activity that is thought to promote arrhythmias. Second, electromechanical models attempt to capture both the electrophysiological and mechanical aspects of heart function. One goal of the community is to develop models with sufficient patient customization to assist in personalized treatment planning. Some model aspects can be customized with existing data collection techniques to more closely represent individual patients while other model aspects will likely remain based on generic data. Despite important challenges, cardiac models hold promise to be important enablers of personalized medicine.

The EuroPhysiome, STEP and a roadmap for the virtual physiological human

Biomedical science and its allied disciplines are entering a new era in which computational methods and technologies are poised to play a prevalent role in supporting collaborative investigation of the human body. Within Europe, this has its focus in the virtual physiological human (VPH), which is an evolving entity that has emerged from the EuroPhysiome initiative and the strategy for the EuroPhysiome (STEP) consortium. The VPH is intended to be a solution to common infrastructure needs for physiome projects across the globe, providing a unifying architecture that facilitates integration and prediction, ultimately creating a framework capable of describing Homo sapiens in silico. The routine reliance of the biomedical industry, biomedical research and clinical practice on information technology (IT) highlights the importance of a tailor-made and robust IT infrastructure, but numerous challenges need to be addressed if the VPH is to become a mature technological reality. Appropriate investment will reap considerable rewards, since it is anticipated that the VPH will influence all sectors of society, with implications predominantly for improved healthcare, improved competitiveness in industry and greater understanding of (patho)physiological processes. This paper considers issues pertinent to the development of the VPH, highlighted by the work of the STEP consortium.

An overview of cardiac systems biology

The cardiac system has been a major target for intensive studies in the multi-scale modeling field for many years. Reproduction of the action potential and the ionic currents of single cardiomyocytes, as well as the construction of a whole organ model is well established. Still, there are major hurdles to overcome in creating a realistic and predictive functional cardiac model due to the lack of a profound understanding of the complex molecular interactions and their outcomes controlling both normal and pathological cardiophysiology. The recent advent of systems biology offers the conceptual and practical frameworks to tackle such biological complexities. This review provides an overview of major themes in the developing field of cardiac systems biology, summarizing some of the high-throughput experiments and strategies used to integrate the datasets, and various types of computational approaches used for developing useful quantitative models capable of predicting complex biological behavior.

Computational biology of cardiac myocytes: proposed standards for the physiome

Predicting information about human physiology and pathophysiology from genomic data is a compelling, but unfulfilled goal of post-genomic biology. This is the aim of the so-called Physiome Project and is, undeniably, an ambitious goal. Yet if we can exploit even a small proportion of the rich and varied experimental data currently available, significant insights into clinically important aspects of human physiology will follow. To achieve this requires the integration of data from disparate sources into a common framework. Extrapolation of available data across species, laboratory techniques and conditions requires a quantitative approach. Mathematical models allow us to integrate molecular information into cellular, tissue and organ-level, and ultimately clinically relevant scales. In this paper we argue that biophysically detailed computational modelling provides the essential tool for this process and, furthermore, that an appropriate framework for annotating, databasing and critiquing these models will be essential for the development of integrative computational biology.

Toward a control theory analysis of aging

Aging is due to the accumulation of damage over time that affects the function and survival of the organism; however, it has proven difficult to infer the relative importance of the many processes that contribute to aging. To address this, here we outline an approach that may prove useful in analyzing aging. In this approach, the function of the organism is described as a set of interacting physiological systems. Degradation of their outputs leads to functional decline and death as a result of aging. In turn, degradation of the system outputs is attributable to changes at the next hierarchical level down, the cell, through changes in cell number or function, which are in turn a consequence of the metabolic history of the cell. Within this framework, we then adapt the methods of metabolic control analysis (MCA) to determine which modifications are important for aging. This combination of a hierarchical framework and the methodologies of MCA may prove useful both for thinking about aging and for analyzing it experimentally.

A mathematical model of the slow force response to stretch in rat ventricular myocytes

We developed a model of the rat ventricular myocyte at room temperature to predict the relative effects of different mechanisms on the cause of the slow increase in force in response to a step change in muscle length. We performed simulations in the presence of stretch-dependent increases in flux through the Na(+)-H(+) exchanger (NHE) and Cl(-)-HCO(3)(-) exchanger (AE), stretch-activated channels (SAC), and the stretch-dependent nitric oxide (NO) induced increased open probability of the ryanodine receptors to estimate the capacity of each mechanism to produce the slow force response (SFR). Inclusion of stretch-dependent NHE & AE, SACs, and stretch-dependent NO effects caused an increase in tension following 15 min of stretch of 0.87%, 32%, and 0%, respectively. Comparing [Ca(2+)](i) dynamics before and after stretch in the presence of combinations of the three stretch-dependent elements, which produced significant SFR values (>20%), showed that the inclusion of stretch-dependent NO effects produced [Ca(2+)](i) transients, which were not consistent with experimental results. Further simulations showed that in the presence of SACs and the absence of stretch-dependent NHE & AE inhibition of NHE attenuated the SFR, such that reduced SFR in the presence of NHE blockers does not indicate a stretch dependence of NHE. Rather, a functioning NHE is responsible for a portion of the SFR. Based on our simulations we estimate that in rat cardiac myocytes at room temperature SACs play a significant role in producing the SFR, potentially in the presence of stretch-dependent NHE & AE and that NO effects, if any, must involve more mechanisms than just increasing the open probability of ryanodine receptors.

Computed tomography studies of lung mechanics

The study of lung mechanics has progressed from global descriptions of lung pressure and volume relationships to the high-resolution, three-dimensional, quantitative measurement of dynamic regional mechanical properties and displacements. X-ray computed tomography (CT) imaging is ideally suited to the study of regional lung mechanics in intact subjects because of its high spatial and temporal resolution, correlation of functional data with anatomic detail, increasing volumetric data acquisition, and the unique relationship between CT density and lung air content. This review presents an overview of CT measurement principles and limitations for the study of regional mechanics, reviews some of the early work that set the stage for modern imaging approaches and impacted the understanding and management of patients with acute lung injury, and presents evolving novel approaches for the analysis and application of dynamic volumetric lung image data.

A quantitative analysis of cardiac myocyte relaxation: a simulation study

The determinants of relaxation in cardiac muscle are poorly understood, yet compromised relaxation accompanies various pathologies and impaired pump function. In this study, we develop a model of active contraction to elucidate the relative importance of the [Ca2+]i transient magnitude, the unbinding of Ca2+ from troponin C (TnC), and the length-dependence of tension and Ca2+ sensitivity on relaxation. Using the framework proposed by one of our researchers, we extensively reviewed experimental literature, to quantitatively characterize the binding of Ca2+ to TnC, the kinetics of tropomyosin, the availability of binding sites, and the kinetics of crossbridge binding after perturbations in sarcomere length. Model parameters were determined from multiple experimental results and modalities (skinned and intact preparations) and model results were validated against data from length step, caged Ca2+, isometric twitches, and the half-time to relaxation with increasing sarcomere length experiments. A factorial analysis found that the [Ca2+]i transient and the unbinding of Ca2+ from TnC were the primary determinants of relaxation, with a fivefold greater effect than that of length-dependent maximum tension and twice the effect of tension-dependent binding of Ca2+ to TnC and length-dependent Ca2+ sensitivity. The affects of the [Ca2+]i transient and the unbinding rate of Ca2+ from TnC were tightly coupled with the effect of increasing either factor, depending on the reference [Ca2+]i transient and unbinding rate.

Systems modeling: a pathway to drug discovery

Systems modeling is emerging as a valuable tool in therapeutics. This is seen by the increasing use of clinically relevant computational models and a rise in systems biology companies working with the pharmaceutical industry. Systems models have helped understand the effects of pharmacological intervention at receptor, intracellular and intercellular communication stages of cell signaling. For instance, angiogenesis models at the ligand-receptor interaction level have suggested explanations for the failure of therapies for cardiovascular disease. Intracellular models of myeloma signaling have been used to explore alternative drug targets and treatment schedules. Finally, modeling has suggested novel approaches to treating disorders of intercellular communication, such as diabetes. Systems modeling can thus fill an important niche in therapeutics by making drug discovery a faster and more systematic process.