arXiv subjects

Joseph L. Hellerstein

Publications and source records attributed to Joseph L. Hellerstein.

4 recordsLinked to original sources

arXiv2026

An interactive simulator for integrating biochemical models with experimental data

Biological models based on mathematical simulations (e.g., ordinary differential equations) are hugely beneficial for understanding biological processes and predicting the outcomes of experiments. Such models are built using a variety of sophisticated modeling tools. Unfortunately, the complexity of these tools is often a barrier to use by experimental biologists. To improve the usability and impact of biological models, we have created a desktop platform (DeskIridium) and a web-based simulator (WebIridium, hosted as a GitHub page) that, in combination, provide features that promote ease of use by experimental biologists, including: human-readable editing of models via the Antimony syntax; sliders for adjusting parameter values while simultaneously displaying simulation results in real time; zero-install distribution; AI chat integration; and generation of standalone Python implementations of SBML models to support reproducibility. The desktop version uses the well-established libroadrunner simulation package; the web-based application uses an Emscripten translated version of COPASI. Both simulators are SBML-compatible, allowing users to easily import previously built models or edit existing models using the easy-to-use antimony language. We used modern AI methods for software development and share the lessons we learned. Binaries, source code, and the web interface are available at: https://github.com/sys-bio/IridiumSimulator and https://github.com/sys-bio/WebIridium.

q-bio.MN↗

arXiv2023

Adapting Modeling and Simulation Credibility Standards to Computational Systems Biology

Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as form of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrow domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models.

q-bio.MN↗

arXiv2022

BioSimulators: a central registry of simulation engines and services for recommending specific tools

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML, and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

q-bio.QM↗

arXiv2016

PerfEnforce: A Dynamic Scaling Engine for Analytics with Performance Guarantees

In this paper, we present PerfEnforce, a scaling engine designed to enable cloud providers to sell performance levels for data analytics cloud services. PerfEnforce scales a cluster of virtual machines allocated to a user in a way that minimizes cost while probabilistically meeting the query runtime guarantees offered by a service level agreement. With PerfEnforce, we show how to scale a cluster in a way that minimally disrupts a user's query session. We further show when to scale the cluster using one of three methods: feedback control, reinforcement learning, or perceptron learning. We find that perceptron learning outperforms the other two methods when making cluster scaling decisions.

cs.DB↗