Biology subjects

Hellerstein, J. L.

Publications and source records attributed to Hellerstein, J. L..

6 recordsLinked to original sources

bioRxiv2026

Antimony 3: Extending human-readable model definitions for SBML Level 3 Core and Packages

Antimony is a human-readable language for defining and sharing models developed by the systems biology community. It enables scientists to describe biochemical networks with a simple syntax, while supporting seamless conversion to and from the Systems Biology Markup Language (SBML) community standard. Since Antimonys original release, both SBML and modeling practices have evolved significantly, creating a need to update Antimony to maintain its standards compliance and practical relevance. In this paper, we introduce Antimony 3, a comprehensive update that formalizes its cumulative improvements and extends its support for SBML Level 3 Core and Flux Balance Constraints (FBC), Distributions, Layout, and Render packages. Antimony 3 enables model specifications that combine kinetic reactions with flux balance analysis, represent uncertainty using probability distributions, add biological context through annotations, and define publication-ready visualizations, all within a unified plain-text format. Antimony 3 is delivered as a lightweight C/C++ library with a stable C API. It is available through official bindings for Python, Julia, and JavaScript/WebAssembly, as well as a cross-platform desktop GUI, which enables straightforward use across scripting environments, desktop applications, and browser-based tools. Antimony 3 is released as open-source software under the BSD 3-Clause License and is available at https://github.com/sys-bio/antimony. Author SummaryBiological models are typically stored in standardized formats that ensure compatibility across different software tools, but these formats rely on verbose, machine-readable syntax that is difficult for humans to write or inspect directly. Antimony addresses this challenge by providing an intuitive, text-based language for defining biological models that can be automatically converted to and from the Systems Biology Markup Language (SBML). Since Antimonys original release in 2009, the SBML standard and common modeling workflows have expanded significantly. We developed Antimony 3 to support these advances, enabling researchers to write a single human-readable text file that defines reaction networks, constraint-based objectives, uncertainty in parameters and initial conditions, semantic annotations linking to biological databases, and model diagrams. Antimony 3 is provided as open-source software with broad support across computational environments, making it accessible to researchers in a wide range of workflows.

systems biology↗

bioRxiv2025

SBMLNetwork: a framework for standards-based visualization of biochemical models

SBMLNetwork is an open-source software library that makes the SBML Layout and Render packages practical for standards-based visualization of biochemical models. Current tools often manage model visualization data in custom-designed, tool-specific formats and store it separately from the model itself, hindering interoperability, reproducibility, and the seamless integration of visualization with model data. SBMLNetwork addresses these limitations by building directly on the SBML Layout and Render specifications, automating the generation of standards-compliant visualization data, offering a modular implementation with broad integration support, and providing a robust API tailored to the needs of systems biology researchers. We illustrate the capabilities of SBMLNetwork across key visualization tasks, including SBGN-compliant visualization, application of predefined style templates, layout arrangement to reflect pathway logic, and integration of model data into network diagrams. These examples demonstrate how SBMLNetwork enables high-level visualization features and seamlessly translate user intent into reproducible outputs that support both structural representation and dynamic data visualization within the SBML model. SBMLNetwork is freely available at https://github.com/sys-bio/SBMLNetwork under the MIT license.

systems biology↗

bioRxiv2023

Sensitivity and Frequency Response of Biochemical Cascades

AO_SCPLOWBSTRACTC_SCPLOWSignal transduction from a cells surface to cytoplasmic and nuclear targets takes place through a complex network of interconnected pathways. Phosphorylation cycles are common components of many pathways and may take the form of a multi-layered cascade of cycles or incorporate species with multiple phosphorylation sites that effectively create a sequence of cycles with increasing states of phosphorylation. This work focuses on the frequency response and sensitivity of such systems, two properties that have not been thoroughly examined. Starting with a singularly phosphorylated single-cycle system, we compare the sensitivity to perturbation at steady-state across a range of input signal strengths. This is followed by a frequency response analysis focusing on the gain and associated bandwidth. Next, we consider a two-layer cascade of single phosphorylation cycles and focus on how the two cycles interact to produce various effects on the bandwidth and damping properties. Then we consider the (ultra)sensitivity of a doubly phosphorylated system, where we describe in detail first-order ultrasensitivity, a unique property of these systems, which can be blended with zero-order ultrasensitivity to create systems with relatively constant gain over a range of signal input. Finally, we give an in-depth analysis of the sensitivity of an n-phosphorylated system.

systems biology↗

bioRxiv2023

An Automated Model Annotation System (AMAS) for SBML Models

MotivationAnnotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. ResultsWe present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g., species, reactions) by specifying the reference database (e.g., ChEBI for species) and the match score function (e.g., string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has sub-second response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. AvailabilityOur project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license. Contacthsauro@uw.edu Supplementary informationSupplementary data are available online.

systems biology↗

bioRxiv2021

SBMate: A Framework for Evaluating Quality of Annotations in Systems Biology Models

The interests in repurposing and reusing systems biology models have been growing in recent years. Semantic annotations play an important role for this, as they provide crucial information on the meanings and functions of models. However, there are a limited number of tools that evaluate the existence or quality of such annotations. In this paper, we introduce SBMate, a python package that would serve as a framework for evaluating the quality of annotations in systems biology models. Three default metrics are provided: coverage, consistency, and specificity. Coverage checks whether annotations exist in a model. Consistency tests if the annotations are appropriate for the given model element. Finally, specificity represents how detailed the annotations are. We analyzed 1,000 curated models from the BioModels repository using the three metrics and discussed the results. Additional metrics can be easily added to extend the current version of SBMate.

systems biology↗

bioRxiv2019

Detecting and Isolating Mass Balance Errors in Reaction Based Models in Systems Biology

MotivationThe growing complexity of reaction-based models necessitates early detection and resolution of model errors. This paper addresses mass balance errors, discrepancies between the mass of reactants and products in reaction specifications. One approach to detection is atomic mass analysis, which uses meta-data to expose atomic formulas of chemical species. Atomic mass analysis isolates errors to individual reactions. However, this approach burdens modelers with expressing model meta-data and writing excessively detailed reactions that include implicit chemical species (e.g., water). Moreover, atomic mass analysis has the shortcoming of not being applicable to large molecules because of the limitations of current annotation techniques. The second approach, Linear Programming (LP) analysis, avoids using model meta-data by checking for a weaker condition, stoichiometric inconsistency. But this approach suffers from false negatives and has large isolation sets (the set of reactions implicated in the stoichiometric inconsistency).\n\nResultsWe propose alternatives to both approaches. Our alternative to atomic mass analysis is moiety analysis. Moiety analysis uses model meta-data in the form of moieties present in chemical species. Moiety analysis avoids excessively detailed reaction specifications, and can be used with large molecules. Our alternative to LP analysis is Graphical Analysis of Mass Equivalence Sets (GAMES). GAMES has a slightly higher false negative rate than LP analysis, but it provides much better error isolation. In our studies of the BioModels Repository, the average size of isolation sets for LP analysis is 55.5; for GAMES, it is 5.4. We have created open source codes for moiety analysis and GAMES.\n\nAvailability and ImplementationOur project is hosted at https://github.com/ModelEngineering/ SBMLLint, which contains examples, documentation, source code files, and build scripts used to create SBMLLint. Our source code is licensed under the MIT open source license.\n\nContact: jlheller@uw.edu\n\nSupplementary information: None.

systems biology↗