Summary We present StochSS Live!, a web-based service for modeling, simulation, and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters, and analyze the results.
The Next-Generation Toolkit for Simulation-Driven Biological Discovery
We are pleased to announce the release of StochSS Live: Stochastic Simulation Service. StochSS Live is a complete rewrite of StochSS, our earlier Software-as-a-Service platform providing state-of-the-art tools to streamline and accelerate the development of robust quantitative models of complex biological and biochemical systems, with powerful new features including machine-learning- aided parameter inference and tools for exploring the parameter space, guided by interactive semi-supervised learning and harnessing the computational power of cloud computing. Check it out at: https://live.stochss.org/
Model building user interface
Create your model in minutes
Preview simulations on the same page
Workflows: Simulate, analyze, and visualize your model
We are pleased to announce that development has begun on the next generation of StochSS!
The next-generation of StochSS will have the following features:
Model Development Toolkit. We are developing tools to facilitate and accelerate the process of Model Development: the iterations of modeling, simulation, and experiment that are typically required to converge on the most plausible model that can explain the data. The Model Development Toolkit will address parameter estimation and quantification of uncertainty, generation and evaluation of the set of plausible models, and optimal design of experiments (prediction of which information would be most informative to validate or invalidate a model).
Model Exploration Toolkit. We are developing tools for Model Exploration: the process of exploring the parameter space to ensure that the model is robust to variations in uncertain and/or undetermined parameters, to find the regions of parameter space in which the model is capable of yielding a given behavior such as oscillations, and to discover all of the qualitatively distinct behaviors which the model can yield within the space of uncertain and/or undetermined parameters.
Expanded core capabilities. We will extend the core functional capabilities, including an updated cloud backend, to support scalable computing for model development and model exploration, and improved compatibility with other software via support for standard formats for model exchange.
StochSS 1.5 is now officially released!
Version 1.5 includes:
A new model editor: Edit models faster and easier!
Cost analysis functionality: Measure the cost of running jobs in the cloud
Cloud data reproduction: Delete cloud data and reproduce it only when it is needed
Cloud instance type selection: Configure what instance types are used for cloud computation
Details and instructions on how to obtain the code can be found on the Download page
Tutorials are available on the Documentation page Linda Petzold and Chandra Krintz – University of California Santa Barbara Per Lötstedt and Andreas Hellander – Uppsala University