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Objective: Examine sequence of weakness in arm muscles from longitudinal hand-held dynamometry (HHD) data in ALS for congruence with contiguous spread of neurodegeneration along spinal cord segments. Methods: Longitudinal HHD data from the Ceftriaxone clinical trial were examined using nonlinear mixed models, assuming a logistic trajectory from normal to zero strength. Unobserved baseline normal strength of weak muscles was assumed using strength of the best-preserved muscle. A novel metric called “time from onset to midway strength” (TOMS) was estimated for each muscle group, and TOMS ratios were examined to identify sequence of weakness, overall and by onset site. Results: Shoulder flexion (SF), elbow flexion (EF), elbow extension (EE), wrist extension (WE), and first dorsal interosseous (FDI) were measured on each side. Over a median of 36 weeks, 513 subjects provided 2589 sets of HHD measures. TOMS increased sequentially in the following order: FDI, WE, SF, EF, and EE. TOMS ratios estimates with 95% CIs (adjusted for multiple comparisons) were: WE/FDI 1.32 (1.24–1.41), SF/WE 1.06 (1.01–1.10), EF/SF 1.06 (1.02–1.10), and EE/EF 1.18 (1.12–1.23). Elbow and shoulder flexors weakened sooner than did elbow extensors. The sequence of arm muscle weakness progression was similar regardless of onset site. Conclusion: Nonsegmental progression of arm muscle weakness that is similar for different onset sites favors cortical influence/network spread over contiguous spread of neurodegeneration in the spinal cord. Furthermore, this study confirms the “split elbow” pattern. TOMS and other proposed methods may have value as outcome measures in clinical research.

© 2016 Drawert et al. We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.

In this paper, we present the design and implementation of Neptune, a simple, domain-specific language based on the Ruby programming language. Neptune automates the configuration and deployment of scientific software frameworks over disparate cloud computing systems. Neptune integrates support for MPI, MapReduce, UPC, X10, StochKit, and others. We implement Neptune as a software overlay for the AppScale cloud platform and extend AppScale with support for elasticity and hybrid execution for scientific computing applications. Neptune imposes no overhead on application execution, yet significantly simplifies the application deployment process, enables portability across cloud systems, and promotes lock-in avoidance by specific cloud vendors.

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

In this paper, we present CLOUDRUNNER, a framework that extracts arbitrary programs from a source code repository (e.g. GitHub), wraps them in a web service and tasking system, and deploys them over disparate cloud infrastructures and local clusters, automating their portability. In particular, CLOUDRUNNER automatically creates and configures virtual machines so that they can execute the applications, provides a web UI with which users parameterize their applications, deploys instances of the program as background tasks, and collects the results for easy access via a browser. CLOUDRUNNER is an ideal framework for deploying scientific simulation applications portably and as such, we use it to implement StochSS - Stochastic Simulation as-a-service (now available at http://www.stochss.org). We use StochSS to evaluate CLOUDRUNNER overheads and find that they are small, consistent, and amortized for even short running applications.

In this work, we investigate tools that enable dollar cost optimization of scientific simulations using commercial clouds. We present a framework, called CloudTracker, that transparently records information from a simulation that is executed in a commercial cloud so that it may be “replayed” exactly to reproduce its results. Using the automated CloudTracker provenance and replay facilities, scientists can choose either to store the results of a simulation or to reproduce it on-demand – whichever is more cost efficient in terms of the dollar cost charged for storage and computing by the commercial cloud provider. We present a prototype implementation of CloudTracker for the Amazon AWS commercial cloud and the StochSS stochastic simulation system. Using this prototype, we analyze the storage-versus-compute cost tradeoffs for different classes of StochSS simulations when deployed and executed in AWS.

Background Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both computationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling. Results We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses U nstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the R eaction-D iffusion M aster E quation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development. Conclusions In this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. Through the combined efforts of algorithm development and improved modeling accuracy, increasingly complex biological models become feasible to study through computational methods. URDME is freely available at http://www.urdme.org.