General Chemical Reaction Network Theory for Olfactory Sensing Based on G-Protein-Coupled Receptors : Elucidation of Odorant Mixture Effects and Agonist-Synergist Threshold

This work presents a general chemical reaction network theory for olfactory sensing processes that employ G-protein-coupled receptors as olfactory receptors (ORs). The theory is applicable to general mixtures of odorants and an arbitrary number of ORs. Reactions of ORs with G-proteins, both in the presence and the absence of odorants, are explicitly considered. A unique feature of the theory is the definition of an odor activity vector consisting of strengths of odorant-induced signals from ORs relative to those due to background G-protein activity in the absence of odorants. It is demonstrated that each component of the odor activity defined this way reduces to a Michaelis-Menten form capable of accounting for cooperation or competition effects between different odorants. The main features of the theory are illustrated for a two-odorant mixture. Known and potential mixture effects, such as suppression, shadowing, inhibition, and synergy are quantitatively described. Effects of relative values of rate constants, basal activity, and G-protein concentration are also demonstrated

Recent advances in biomedical simulations: a manifesto for model engineering [version 1; referees: 3 approved]

Biomedical simulations are widely used to understand disease, engineer cells, and model cellular processes. In this article, we explore how to improve the quality of biomedical simulations by developing simulation models using tools and practices employed in software engineering. We refer to this direction as model engineering. Not all techniques used by software engineers are directly applicable to model engineering, and so some adaptations are required. That said, we believe that simulation models can benefit from software engineering practices for requirements, design, and construction as well as from software engineering tools for version control, error checking, and testing. Here we survey current efforts to improve simulation quality and discuss promising research directions for model engineering

BBF RFC 112: Synthetic Biology Open Language (SBOL) Version 2.1.0

BBF RFC 112 (the SBOL 2.1.0 standard) replaces BBF RFC 108 (the SBOL 2.0 standard), as well as the minor update SBOL 2.0.1.The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information

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

Chronic Obstructive Pulmonary Disease and Lung Cancer: Underlying Pathophysiology and New Therapeutic Modalities

Chronic obstructive pulmonary disease (COPD) and lung cancer are major lung diseases affecting millions worldwide. Both diseases have links to cigarette smoking and exert a considerable societal burden. People suffering from COPD are at higher risk of developing lung cancer than those without, and are more susceptible to poor outcomes after diagnosis and treatment. Lung cancer and COPD are closely associated, possibly sharing common traits such as an underlying genetic predisposition, epithelial and endothelial cell plasticity, dysfunctional inflammatory mechanisms including the deposition of excessive extracellular matrix, angiogenesis, susceptibility to DNA damage and cellular mutagenesis. In fact, COPD could be the driving factor for lung cancer, providing a conducive environment that propagates its evolution. In the early stages of smoking, body defences provide a combative immune/oxidative response and DNA repair mechanisms are likely to subdue these changes to a certain extent; however, in patients with COPD with lung cancer the consequences could be devastating, potentially contributing to slower postoperative recovery after lung resection and increased resistance to radiotherapy and chemotherapy. Vital to the development of new-targeted therapies is an in-depth understanding of various molecular mechanisms that are associated with both pathologies. In this comprehensive review, we provide a detailed overview of possible underlying factors that link COPD and lung cancer, and current therapeutic advances from both human and preclinical animal models that can effectively mitigate this unholy relationship

Reproducible, Robust, and Reliable Biochemical Reaction Network Models for Systems Biology

Thesis (Ph.D.)--University of Washington, 2019Reproducibility, robustness, and reliability are features desired for a biochemical reaction network model. Scientific research is reproducible when the findings can be independently verified and reproducibility is crucial for the integrity of science. Unfortunately, however, scientific studies, including computational studies, are often not reproducible. It is hard to achieve robustness and reliability due to technical difficulties with experiments producing high quantity, high-quality data and inherent unidentifiability arising from multiparametric nature of biological processes. Robustness and reliability can be achieved by obtaining more data and by implementing better computational algorithms. To improve the reproducibility of biochemical reaction network models, software tools are necessary. We need novel algorithms to increase the robustness and reliability of models. Most of all, a scalable and extensible computing environment is necessary for incorporating tools and algorithms. Therefore, we build a Python-based modeling and simulation environment called Tellurium to ensure reproducibility of studies while supporting a wide array of tools to help design robust and reliable models. Tellurium is specifically designed for high-throughput studies which are necessary to deploy novel modeling algorithms. Next, software tools to improve the reproducibility of computational studies have been built and integrated into Tellurium. In particular, Python support for standards related to describing simulation experiments has been improved. Lastly, two algorithms to help construct robust and reliable mechanistic models have been designed. The algorithms actively explore the concepts of ensemble modeling and specifically utilize the data from perturbation studies. It is demonstrated that a model ensemble can provide reasonable predictions on the system of interest. The idea of the model ensemble directing future experiments toward maximal reduction of the potential topology of models in the ensemble is also explored. Once deployed, ensemble-based experiment selection is expected to close the cycle between modeling and experimental endeavors, bridging the disparity between data-driven modeling and modeling-driven data collection

sys-bio/tellurium: 2.0.13

Fix segfault when loading sed-ml such as this example with certain types of model changes

sys-bio/roadrunner: 1.4.18

Bump version to 1.4.1

Tellurium notebooks—An environment for reproducible dynamical modeling in systems biology

<div><p>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 for model authoring, simulation, and teaching that facilitates building reproducible dynamical models and reusing models by 1) providing a notebook environment which allows models, Python code, and narrative to be intermixed, 2) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 3) 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.</p></div