J. Jeremy Rice

A Single Nucleotide Polymorphism in the MDM2 Gene Disrupts the Oscillation of p53 and MDM2 Levels in Cells

Oscillations of both p53 and MDM2 proteins have been observed in cells after exposure to stress. A mathematical model describing these oscillations predicted that oscillations occur only at selected levels of p53 and MDM2 proteins. This model prediction suggests that oscillations will disappear in cells containing high levels of MDM2 as observed with a single nucleotide polymorphism in the MDM2 gene (SNP309). The effect of SNP309 upon the p53-MDM2 oscillation was examined in various human cell lines and the oscillations were observed in the cells with at least one wild-type allele for SNP309 (T/T or T/G) but not in cells homozygous for SNP309 (G/G). Furthermore, estrogen preferentially stimulated the transcription of MDM2 from SNP309 G allele and increased the levels of MDM2 protein in estrogen-responsive cells homozygous for SNP309 (G/G). These results suggest the possibility that SNP309 G allele may contribute to gender-specific tumorigenesis through further elevating the MDM2 levels and disrupting the p53-MDM2 oscillation. Furthermore, using the H1299-HW24 cells expressing wild-type p53 under a tetracycline-regulated promoter, the p53-MDM2 oscillation was observed only when p53 levels were in a specific range, and DNA damage was found to be necessary for triggering the p53-MDM2 oscillation. This study shows that higher levels of MDM2 in cells homozygous for SNP309 (G/G) do not permit coordinated p53-MDM2 oscillation after stress, which might contribute to decreased efficiency of the p53 pathway and correlates with a clinical phenotype (i.e., the development of cancers at earlier age of onset in female).

Distribution of Electromechanical Delay in the Heart: Insights from a Three-Dimensional Electromechanical Model

In the intact heart, the distribution of electromechanical delay (EMD), the time interval between local depolarization and myocyte shortening onset, depends on the loading conditions. The distribution of EMD throughout the heart remains, however, unknown because current experimental techniques are unable to evaluate three-dimensional cardiac electromechanical behavior. The goal of this study was to determine the three-dimensional EMD distributions in the intact ventricles for sinus rhythm (SR) and epicardial pacing (EP) by using a new, to our knowledge, electromechanical model of the rabbit ventricles that incorporates a biophysical representation of myofilament dynamics. Furthermore, we aimed to ascertain the mechanisms that underlie the specific three-dimensional EMD distributions. The results revealed that under both conditions, the three-dimensional EMD distribution is nonuniform. During SR, EMD is longer at the epicardium than at the endocardium, and is greater near the base than at the apex. After EP, the three-dimensional EMD distribution is markedly different; it also changes with the pacing rate. For both SR and EP, late-depolarized regions were characterized with significant myofiber prestretch caused by the contraction of the early-depolarized regions. This prestretch delays myofiber-shortening onset, and results in a longer EMD, giving rise to heterogeneous three-dimensional EMD distributions.

Verification of cardiac mechanics software: benchmark problems and solutions for testing active and passive material behaviour

Models of cardiac mechanics are increasingly used to investigate cardiac physiology. These models are characterized by a high level of complexity, including the particular anisotropic material properties of biological tissue and the actively contracting material. A large number of independent simulation codes have been developed, but a consistent way of verifying the accuracy and replicability of simulations is lacking. To aid in the verification of current and future cardiac mechanics solvers, this study provides three benchmark problems for cardiac mechanics. These benchmark problems test the ability to accurately simulate pressure-type forces that depend on the deformed objects geometry, anisotropic and spatially varying material properties similar to those seen in the left ventricle and active contractile forces. The benchmark was solved by 11 different groups to generate consensus solutions, with typical differences in higher-resolution solutions at approximately 0.5%, and consistent results between linear, quadratic and cubic finite elements as well as different approaches to simulating incompressible materials. Online tools and solutions are made available to allow these tests to be effectively used in verification of future cardiac mechanics software.

A spatially detailed myofilament model as a basis for large-scale biological simulations

The availability of increased computing power will make possible new classes of biological models that include detailed representations of proteins and protein complexes with spatial interactions. We develop such a model of the interaction of actin and myosin within one pair of thick and thin filaments in the cardiac sarcomere. The model includes explicit representations of actin, myosin, and regulatory proteins. Although this is not an atomic-scale model, as would be the case for molecular dynamics simulations, the model seeks to represent spatial interactions between protein complexes that are thought to produce characteristic cardiac muscle responses at larger scales. While the model simulates the microscopic scale, when model results are extrapolated to larger structures, the model recapitulates complex, nonlinear behavior such as the steep calcium sensitivity of developed force in muscle structures. By bridging spatial scales, the model provides a plausible and quantitative explanation for several unexplained phenomena observed at the tissue level in cardiac muscles. Model execution entails Monte-Carlo-based simulations of Markov representations of calcium regulation and actin-myosin interactions. While most of the results presented here are preliminary, we suggest that this model will be suitable to serve as a basis for larger-scale simulations of multiple fibers assembled into larger sarcomere structures.

sbv IMPROVER Diagnostic Signature Challenge

The sbv IMPROVER (systems biology verification—Industrial Methodology for Process Verification in Research) process aims to help companies verify component steps or tasks in larger research workflows for industrial applications. IMPROVER is built on challenges posed to the community that draws on the wisdom of crowds to assess the most suitable methods for a given research task. The Diagnostic Signature Challenge, open to the public from Mar. 5 to Jun. 21, 2012, was the first instantiation of the IMPROVER methodology and evaluated a fundamental biological question, specifically, if there is sufficient information in gene expression data to diagnose diseases. Fifty-four teams used publically available data to develop prediction models in four disease areas: multiple sclerosis, lung cancer, psoriasis, and chronic obstructive pulmonary disease. The predictions were scored against unpublished, blinded data provided by the organizers, and the results, including methods of the top performers, presented at a conference in Boston on Oct. 2–3, 2012. This paper offers an overview of the Diagnostic Signature Challenge and the accompanying symposium, and is the first article in a special issue of Systems Biomedicine, providing focused reviews of the submitted methods and general conclusions from the challenge. Overall, it was observed that optimal method choice and performance appeared largely dependent on endpoint, and results indicate the psoriasis and lung cancer subtypes sub-challenges were more accurately predicted, while the remaining classification tasks were much more challenging. Though no one approach was superior for every sub-challenge, there were methods, like linear discriminant analysis, that were found to perform consistently well in all.

Making the most of it: pathway reconstruction and integrative simulation using the data at hand

Abstract Pathway reconstruction is a fundamental task in systems biology toward an ultimate goal of full-scale in silico simulations. The data for such reconstructions is mostly lacking, but collection is underway for some model organisms. However, biological specificity might limit the ability to extrapolate findings. High-throughput data and methods might alleviate these problems, but only coarse or limited reconstructions are now possible. Inclusion of multiple data sources may improve the situation but remains a challenge.

Influence of metabolic dysfunction on cardiac mechanics in decompensated hypertrophy and heart failure

Alterations in energetic state of the myocardium are associated with decompensated heart failure in humans and in animal models. However, the functional consequences of the observed changes in energetic state on mechanical function are not known. The primary aim of the study was to quantify mechanical/energetic coupling in the heart and to determine if energetic dysfunction can contribute to mechanical failure. A secondary aim was to apply a quantitative systems pharmacology analysis to investigate the effects of drugs that target cross-bridge cycling kinetics in heart failure-associated energetic dysfunction. Herein, a model of metabolite- and calcium-dependent myocardial mechanics was developed from calcium concentration and tension time courses in rat cardiac muscle obtained at different lengths and stimulation frequencies. The muscle dynamics model accounting for the effect of metabolites was integrated into a model of the cardiac ventricles to simulate pressure-volume dynamics in the heart. This cardiac model was integrated into a simple model of the circulation to investigate the effects of metabolic state on whole-body function. Simulations predict that reductions in metabolite pools observed in canine models of heart failure can cause systolic dysfunction, blood volume expansion, venous congestion, and ventricular dilation. Simulations also predict that myosin-activating drugs may partially counteract the effects of energetic state on cross-bridge mechanics in heart failure while increasing myocardial oxygen consumption. Our model analysis demonstrates how metabolic changes observed in heart failure are alone sufficient to cause systolic dysfunction and whole-body heart failure symptoms.

sbv IMPROVER diagnostic signature challenge: Scoring strategies

Evaluating the performance of computational methods to analyze high throughput data are an integral component of model development and critical to progress in computational biology. In collaborative-competitions, model performance evaluation is crucial to determine the best performing submission. Here we present the scoring methodology used to assess 54 submissions to the IMPROVER Diagnostic Signature Challenge. Participants were tasked with classifying patients’ disease phenotype based on gene expression data in four disease areas: Psoriasis, Chronic Obstructive Pulmonary Disease, Lung Cancer, and Multiple Sclerosis. We discuss the criteria underlying the choice of the three scoring metrics we chose to assess the performance of the submitted models. The statistical significance of the difference in performance between individual submissions and classification tasks varied according to these different metrics. Accordingly, we consider an aggregation of these three assessment methods and present the approaches considered for aggregating the ranking and ultimately determining the final overall best performer.

Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer's disease clinical trials

Digital technology is transforming the development of drugs for Alzheimers disease and was the topic of the Alzheimers Associations Research Roundtable on its May 23–24, 2017 meeting. Research indicates that wearable devices and unobtrusive passive sensors that enable the collection of frequent or continuous, objective, and multidimensional data during daily activities may capture subtle changes in cognition and functional capacity long before the onset of dementia. The potential to exploit these technologies to improve clinical trials as both recruitment and retention tools as well as for potential end points was discussed. The implications for the collection and use of large amounts of data, lessons learned from other related disease areas, ethical concerns raised by these new technologies, and regulatory issues were also covered in the meeting. Finally, the challenges and opportunities of these new technologies for future use were discussed.

Error estimates and communication overhead in the computation of the bidomain equations on the distributed memory parallel Blue Gene/L supercomputer

Increasing biophysical detail in multi physical, multiscale cardiac model will demand higher levels of parallelism in multi-core approaches to obtain fast simulation times. As an example of such a highly parallel multi-core approaches, we develop a completely distributed bidomain cardiac model implemented on the IBM Blue Gene/L architecture. A tissue block of size 50 times 50 times 100 cubic elements based on ten Tusscher et al. (2004) cell model is distributed on 512 computational nodes. The extracellular potential is calculated by the Gauss-Seidel (GS) iterative method that typically requires high levels of inter-processor communication. Specifically, the GS method requires knowledge of all cellular potentials at each of its iterative step. In the absence of shared memory, the values are communicated with substantial overhead. We attempted to reduce communication overhead by computing the extracellular potential only every 5th time step for the integration of the cell models. We also investigated the effects of reducing inter-processor communication to every 5th, 10th, 50th iteration or no communication within the GS iteration. While technically incorrect, these approximation had little impact on numerical convergence or accuracy for the simulations tested. The results suggest some heuristic approaches may further reduce the inter-processor communication to improve the execution time of large-scale simulations.