Biology subjects

Sauro, H. M.

Publications and source records attributed to Sauro, H. M..

17 recordsLinked to original sources

bioRxiv2026

Effects of dose-response relationships on cellular information transmission

Cell signaling systems can transmit information with high precision, despite relying on complex reaction networks and inherently noisy biochemical reactions. To better understand how they achieve this precision, this work focuses on how cells can tune their quantitative steady-state dose-response relationships to improve signaling fidelity. Using a combination of analytic and numerical approaches, we quantify the maximum information that can be transmitted in single-step and multi-step signaling systems under several biologically realistic noise distributions: Gaussian, gamma, constant coeffcient of variation, and binomial. We find that signaling systems transmit more information if their dose-response curves have low baselines, large dynamic ranges, and curve shapes that are well matched to the natural distribution of input signals. For multi-step systems, linear transfer functions typically improve signaling fidelity for all noise levels, consistent with previous work on "dose-response alignment". We also find that switchlike transfer functions transmit a maximum of one bit and are robust to low levels of noise, but fail rapidly with high levels of noise. Simulations of toy signaling systems corroborate these results. Together, these findings provide insight into some cellular design principles that have arisen through evolution and that are widely observed in nature.

systems biology↗

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↗

bioRxiv2025

Speciated evolution of oscillatory mass-action chemical reaction networks

Evolutionary algorithms, a class of optimization techniques inspired by biological evolution, have emerged as powerful tools for the optimization of complex systems, including the evolution of mass-action chemical reaction networks. This work explores the application of evolutionary algorithms in this domain, presenting a novel approach inspired by neural network evolution methodologies. A key feature of the algorithm is speciation, which separates candidate reaction networks into groups based on their similarity, which maintains diversity and protects innovations. Crossover has also been shown to be an effective means of improving evolutionary success in other domains. However, crossover of mass-action networks is tested and found to be detrimental to the evolutionary process. This work goes beyond theoretical exploration by offering a practical contribution in the form of a user-friendly software module. This module encapsulates the newly devised algorithm, enabling researchers and practitioners to readily apply the speciation-based approach in their own investigations of mass-action chemical reaction networks. Author summaryEvolutionary algorithms are an optimization technique inspired by biological evolution. They can be used to solve complex multi-dimensional problems for which analytic solutions are infeasible. We developed a novel evolutionary algorithm for use with mass-action chemical reaction networks. This algorithm implements two features of biological evolution, speciation and crossover, in an effort to generate chemical reaction networks with specific behaviors. Here, this novel algorithm is demonstrated by generating chemical reaction networks whose chemical species oscillate in time. This algorithm is encapsulated in a julia package and is publicly available as ReactionNetworkEvolution.jl.

systems biology↗

bioRxiv2025

Evaluating the Limitations of Bayesian Metabolic Control Analysis

Bayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCAs ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering and emphasizing the need for methodological refinements. Author summaryUnderstanding how enzymes control metabolic pathways is crucial for optimizing biomanufacturing and synthetic biology applications. Bayesian Metabolic Control Analysis (BMCA) is a promising computational method that integrates Bayesian inference with metabolic control analysis to estimate key control parameters, even in cases with limited experimental data. However, the accuracy and limitations of BMCA remain unclear. In this study, we systematically evaluate BMCA using three synthetic metabolic networks to determine how different types of physiological data impact its predictive performance. We find that BMCA requires flux and enzyme concentration data for accurate predictions, while external metabolite concentrations contribute little. Additionally, BMCA fails to predict elasticity values beyond a magnitude of 1.5 and reliably infer allosteric regulation, even when strong regulatory interactions exist. In addition, BMCA does not accurately rank metabolic control points, which may limit its utility in identifying key enzymes in engineered pathways. Our work provides practical insights into when and how BMCA can be applied, guiding future research in metabolic modeling and control analysis.

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↗

bioRxiv2020

Generating Synthetic Signaling Networks for in Silico Modeling Studies

Predictive models of signaling pathways have proven to be difficult to develop. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. To meet this need, we developed a method for generating artificial signaling networks that were reasonably realistic and thus could be treated as ground truth models. These synthetic models could then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. We defined the reaction degree and reaction distance to measure the topology of reaction networks, especially to consider enzymes. To determine whether our generated signaling networks displayed meaningful behavior, we compared them with signaling networks from the BioModels Database. This comparison indicated that our generated signaling networks had high topological similarities with BioModels signaling networks with respect to the reaction degree and distance distributions. In addition, our synthetic signaling networks had similar behavioral dynamics with respect to both steady states and oscillations, suggesting that our method generated synthetic signaling networks comparable with BioModels and thus could be useful for building network evaluation tools. HighlightsO_LIWe provided a Julia script to generate synthetic signaling networks. C_LIO_LIWe defined reaction degree and distance to measure the reaction network topology. C_LIO_LIWe provided the Python scripts to calculate the reaction network topology. C_LIO_LIThe synthetic signaling networks had topological similarities with the BioModels. C_LIO_LIThe synthetic signaling networks had dynamic similarities with the BioModels. C_LI

systems biology↗

bioRxiv2019

Accelerated Biochemical Kinetic Model Fitting via the Asynchronous, Generalized Island Method

Mechanistic kinetic models of biological pathways are an important tool for understanding biological systems. Constructing kinetic models requires fitting the parameters to experimental data. However, parameter fitting on these models is a non-convex, non-linear optimization problem. Many algorithms have been proposed to addressing optimization for parameter fitting including globally convergent, population-based algorithms. The computational complexity of the this optimization for even modest models means that parallelization is essential. Past approaches to parameter optimization have focused on parallelizing a particular algorithm. However, this requires re-implementing the algorithm usinga distributed computing framework, which requires a significant investment of time and effort. There are two major drawbacks of this approach: First, the choice of best algorithm may depend on the model. Given the large variety of optimization algorithms available, it is difficult to re-implement every potentially useful algorithm. Second, when new advances are made in a given optimization algorithm, the parallel implementation must be updated to take advantage of these advantages. Thus, there is a continual burden placed on the parallel implementation. The drawbacks of re-implementing algorithms lead us to a different approach to parallelizing parameter optimization. Instead of parallelizing the algorithms themselves, we run many instances of the algorithm on single cores. This provides great flexibility as to the choice of algorithms by allowing us to reuse previous implementations. Also, it does not require the creation and maintenance of parallel versions of optimization algorithms. This approach is known as the island method. To our knowledge, the utility of the island method for parameter fitting in systems biology has not been previously demonstrated. For the parameter fitting problem, we allow islands to exchange information about their \"best\" solutions so that all islands leverage the discoveries of the few. This turns out to be avery effective in practice, leading to super-linear speedups. That is, if a single processor finds the optimal value of parameters in time t, then N processors exchanging information in this way find the optimal value much faster than t/N. We show that the island method is able to consistently provide good speedups for these problems. We also benchmark the island method against a variety of large, challenging kinetic models and show that it is able to consistently improve the quality of fit in less time than a single-threaded implementation.Our software is available at https://github.com/sys-bio/sabaody under a Apache 2.0 license.\n\nContactmailto:medjk@comcast.net

systems biology↗

bioRxiv2019

libsbmljs - Enabling Web--Based SBML Tools

The SBML standard is used in a number of online repositories for storing systems biology models, yet there is currently no Web-capable JavaScript library that can read and write the SBML format. This is a severe limitation since the Web has become a universal means of software distribution, and the graphical capabilities of modern web browsers offer a powerful means for building rich, interactive applications. Also, there is a growing developer population specialized in web technologies that is poised to take advantage of the universality of the web to build the next generation of tools in systems biology and other fields. However, current solutions require server- side processing in order to support existing standards in modeling. We present libsbmljs, a JavaScript / WebAssembly library for Node.js and the Web with full support for all SBML extensions. Our library is an enabling technology for online SBML editors, model-building tools, and web-based simulators, and runs entirely in the browser without the need for any dedicated server resources. We provide NPM packages, an extensive set of examples, JavaScript API documentation, and an online demo that allows users to read and validate the SBML content of any model in the BioModels and BiGG databases. We also provide instructions and scripts to allow users to build a copy of libsbmljs against any libSBML version. Although our library supports all existing SBML extensions, we cover how to add additional extensions to the wrapper, should any arise in the future. To demonstrate the utility of this implementation, we also provide a demo at https://libsbmljsdemo.github.io/ with a proof-of-concept SBML simulator that supports ODE and stochastic simulations for SBML core models. Our project is hosted at https://libsbmljs.github.io/, which contains links to examples, API documentation, and all source code files and build scripts used to create libsbmljs. Our source code is licensed under the Apache 2.0 open source license.

systems biology↗

bioRxiv2018

Inferring Reaction Networks using Perturbation Data

In this paper we examine the use of perturbation data to infer the underlying mechanistic dynamic model. The approach uses an evolutionary strategy to evolve networks based on a fitness criterion that measures the difference between the experimentally determined set of perturbation data and proposed mechanistic models. At present we only deal with reaction networks that use mass-action kinetics employing uni-uni, bi-uni, uni-bi and bi-bi reactions. The key to our approach is to split the algorithm into two phases. The first phase focuses on evolving network topologies that are consistent with the perturbation data followed by a second phase that evolves the parameter values. This results in almost an exact match between the evolved network and the original network from which the perturbation data was generated from. We test the approach on four models that include linear chain, feed-forward loop, cyclic pathway and a branched pathway. Currently the algorithm is implemented using Python and libRoadRunner but could at a later date be rewritten in a compiled language to improve performance. Future studies will focus on the impact of noise in the perturbation data on convergence and variability in the evolved parameter values and topologies. In addition we will investigate the effect of nonlinear rate laws on generating unique solutions.

systems biology↗

bioRxiv2018

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

scientific communication and education↗

bioRxiv2018

A Portable Structural Analysis Library for Reaction Networks

The topology of a reaction network can have a significant influence on the networks dynamical properties. Such influences can include constraints on network flows and concentration changes or more insidiously result in the emergence of feedback loops. These effects are due entirely to mass constraints imposed by the network configuration and are important considerations before any dynamical analysis is made. Most established simulation software tools usually carry out some kind of structural analysis of a network before any attempt is made at dynamic simulation. In this paper we describe a portable software library, libStructural, that can carry out a variety of popular structural analyses that includes conservation analysis, flux dependency analysis and enumerating elementary modes. The library employs robust algorithms that allow it to be used on large networks with more than a two thousand nodes. The library accepts either a raw or fully labeled stoichiometry matrix or models written in SBML format. The software is written in standard C/C++ and comes with documentation and a test suite. The software is available for Windows, Mac OS X, and can be compiled easily on any Linux operating system. A language binding for Python is also available through the pip package manager making it trivial to install on any standard Python distribution. As a second example, we also create a new libStructural plugin for PathwayDesigner that allows solutions to be viewed graphically. The source code is licensed under the open source BSD license and is available on GitHub (https://github.com/sys-bio/Libstructural)

systems biology↗

bioRxiv2017

Tellurium Notebooks - An Environment for Dynamical Model Development, Reproducibility, and Reuse

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system that facilitates building reproducible dynamical models and reusing models by 1) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 2) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python-based Jupyter-like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in-line, human-readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards-compliant models and simulations, run the simulations in-line, and re-export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.\n\nAuthor summaryThere is considerable value to systems and synthetic biology in creating reproducible models. An essential element of reproducibility is the use of community standards, an often challenging undertaking for modelers. This article describes Tellurium Notebook, a tool for developing dynamical models that provides an intuitive approach to building and reusing models built with community standards. Tellurium automates embedding human-readable representations of COMBINE archives in literate coding notebooks, bringing to systems biology this strategy central to other literate notebook systems such as Mathematica. We show that the ability to easily edit this human-readable representation enables users to test models under a variety of conditions, thereby providing a way to create, reuse, and modify standard-encoded models and simulations, regardless of the users level of technical knowledge of said standards.

systems biology↗

bioRxiv2017

A Portable Software Library that Implements the Gillespie Direct Method

A common approach to simulating chemical reaction systems is to formulate the problem as a set of ordinary differential equation and solve them using standard numerical procedures. Such a formulation works well when the number of molecules involved in the simulation is large and assumes a deterministic formulation for the underling chemical kinetics. However for systems such as gene regulatory networks where the number of molecules of a specific species can be of the order of 10s of molecules, the deterministic approach fails to capture important dynamical features. At low molecular numbers stochastic events dominate the dynamics. As a result much research has been devoted to developing numerical algorithms for computing the stochastic trajectories. One of the most common approaches is based on Gillespies stochastic simulation algorithm, for example the direct method. Due to its simplicity the direct method has been implemented many times in software. However, unusually, there does not appear to be a C based reusable software library. This article describes the development of such a library. All source code is licensed under the liberal Apache 2.0 open source license and is available at Github: https://github.com/sys-bio/libStochastic

systems biology↗

bioRxiv2017

Context-Dependent Genetic Regulation

Cells process extra-cellular signals with multiple layers of complex biological networks. Due to the stochastic nature of the networks, the signals become significantly noisy within the cells and in addition, due to the nonlinear nature of the networks, the signals become distorted, shifted, and (de-)amplified. Such nonlinear signal processing can lead to non-trivial cellular phenotypes such as cell cycles, differentiation, cell-to-cell communication, and homeostasis. These nonlinear pheno-types, when observed at the cell population levels, can be quite different from the single-cell level observation. As one of the underlying mechanisms behind this difference, we report the interplay between nonlinearity and stochasticity in genetic regulation. Here we show that nonlinear genetic regulation, characterized at the cellular population level, can be affected by cell-to-cell variability in the regulatory factor concentrations. The observed genetic regulation at the cell population is shown to be significantly dependent on the upstream DNA sequences of the regulator, in particular, 5 untranslated region. This indicates that genetic regulation observed at the cell population level can be significantly dependent on its genetic context, and that its characterization needs a careful attention on noise propagation.\n\nOne Sentence SummaryGenetic regulation observed at the cell population level can be significantly affected by cell-to-cell variability in the regulatory factor copy numbers, indicating that the observed regulation is dependent on 5 UTR of the regulator coding gene.

synthetic biology↗