Herbert M. Sauro

This study quantifies how dose-response relationships in signaling systems affect information transmission under various noise distributions and identifies design principles for improved signaling fidelity.

Antimony 3 updates the human-readable modeling language to support SBML Level 3 features, enabling unified model definitions with kinetic reactions, uncertainty, annotations, and visualizations.

This study evaluates Bayesian Metabolic Control Analysis (BMCA) using synthetic networks, finding it requires flux and enzyme data for accurate predictions and fails to reliably infer elasticity values and allosteric regulation.

The paper updates and corrects SED-ML files for 1055 models in BioModels to improve reproducibility and verification across simulation engines.

SBMLNetwork provides a framework for standards-based visualization of biochemical models by automating the generation of SBML-compliant visualization data.

This paper develops a novel evolutionary algorithm for generating mass-action chemical reaction networks with oscillatory behavior, using speciation and demonstrating it through a Julia package.

The paper investigates how biochemical reaction networks perform signal integration and integral feedback control, finding that these capabilities overlap but are not always mutually exclusive.

AMAS predicts annotations for SBML model elements using reference databases and similarity scores, achieving 80-95% accuracy for species and reactions.

This paper analyzes the frequency response and sensitivity of biochemical cascades with varying phosphorylation layers and states.

The paper generates a deep phosphoproteomics dataset of the EGFR-MAPK pathway in MCF10A cells and integrates it with protein databases to study kinase signaling.

SBMate evaluates annotation quality in systems biology models using coverage, consistency, and specificity metrics on 1,000 BioModels repository models.

The paper develops a method to generate synthetic signaling networks that mimic real biological networks in topology and dynamics, enabling testing of network inference algorithms.

The paper introduces libsbmljs, a JavaScript/WebAssembly library for handling SBML models in web environments, enabling browser-based tools and simulations without server-side processing.

The paper introduces an asynchronous, generalized island method for accelerating biochemical kinetic model fitting, achieving super-linear speedups and improved fit quality.

The paper introduces a portable software library, libStructural, for structural analysis of reaction networks, supporting conservation analysis, flux dependency analysis, and elementary modes enumeration.

This paper infers reaction networks from perturbation data using an evolutionary strategy that evolves network topologies and parameters to match experimental data.

Tellurium Notebook enables reproducible and reusable dynamical models in systems biology by integrating community standards and literate coding practices.

The paper develops a C-based reusable software library implementing the Gillespie Direct Method for stochastic simulation of chemical reactions.

The paper shows that genetic regulation at the population level depends on cell-to-cell variability in regulatory factor concentrations and the 5' UTR of the regulator gene.

Tellurium provides a Python-based platform for systems biology modeling, simulation, and reproducibility using SBML and other standards.

This paper introduces libSBNW, a library for generating and editing SBML layout information, with C and Python APIs and open-source availability.

SimpleSBML simplifies SBML model creation and editing in Python by providing one-command operations for adding model elements and supporting SBML levels 2 and 3.

This paper controls E. coli gene expression noise by analyzing synthetic gene systems and identifying key factors influencing protein copy number variability.

The paper describes modifications to Antimony to support SBML distributions and provides best practices for their use.

This paper designs self-optimizing integrated metabolic and gene regulatory networks through in silico evolution to adapt to environmental changes.