Metabolic reprogramming underlies cavefish muscular endurance despite loss of muscle mass and contractility.

Physical inactivity is a scourge to human health, promoting metabolic disease and muscle wasting. Interestingly, multiple ecological niches have relaxed investment into physical activity, providing an evolutionary perspective into the effect of adaptive physical inactivity on tissue homeostasis. One such example, the Mexican cavefish Astyanax mexicanus, has lost moderate-to-vigorous activity following cave colonization, reaching basal swim speeds ~3.7-fold slower than their river-dwelling counterpart. This change in behavior is accompanied by a marked shift in body composition, decreasing total muscle mass and increasing fat mass. This shift persisted at the single muscle fiber level via increased lipid and sugar accumulation at the expense of myofibrillar volume. Transcriptomic analysis of laboratory-reared and wild-caught cavefish indicated that this shift is driven by increased expression of pparγ-the master regulator of adipogenesis-with a simultaneous decrease in fast myosin heavy chain expression. Ex vivo and in vivo analysis confirmed that these investment strategies come with a functional trade-off, decreasing cavefish muscle fiber shortening velocity, time to maximal force, and ultimately maximal swimming speed. Despite this, cavefish displayed a striking degree of muscular endurance, reaching maximal swim speeds ~3.5-fold faster than their basal swim speeds. Multi-omic analysis suggested metabolic reprogramming, specifically phosphorylation of Pgm1-Threonine 19, as a key component enhancing cavefish glycogen metabolism and sustained muscle contraction. Collectively, we reveal broad skeletal muscle changes following cave colonization, displaying an adaptive skeletal muscle phenotype reminiscent to mammalian disuse and high-fat models while simultaneously maintaining a unique capacity for sustained muscle contraction via enhanced glycogen metabolism.

The metabolome of Mexican cavefish shows a convergent signature highlighting sugar, antioxidant, and Ageing-Related metabolites.

Insights from organisms, which have evolved natural strategies for promoting survivability under extreme environmental pressures, may help guide future research into novel approaches for enhancing human longevity. The cave-adapted Mexican tetra, Astyanax mexicanus, has attracted interest as a model system for metabolic resilience, a term we use to denote the property of maintaining health and longevity under conditions that would be highly deleterious in other organisms (Figure 1). Cave-dwelling populations of Mexican tetra exhibit elevated blood glucose, insulin resistance and hypertrophic visceral adipocytes compared to surface-dwelling counterparts. However, cavefish appear to avoid pathologies typically associated with these conditions, such as accumulation of advanced-glycation-end-products (AGEs) and chronic tissue inflammation. The metabolic strategies underlying the resilience properties of A. mexicanus cavefish, and how they relate to environmental challenges of the cave environment, are poorly understood. Here, we provide an untargeted metabolomics study of long- and short-term fasting in two A. mexicanus cave populations and one surface population. We find that, although the metabolome of cavefish bears many similarities with pathological conditions such as metabolic syndrome, cavefish also exhibit features not commonly associated with a pathological condition, and in some cases considered indicative of an overall robust metabolic condition. These include a reduction in cholesteryl esters and intermediates of protein glycation, and an increase in antioxidants and metabolites associated with hypoxia and longevity. This work suggests that certain metabolic features associated with human pathologies are either not intrinsically harmful, or can be counteracted by reciprocal adaptations. We provide a transparent pipeline for reproducing our analysis and a Shiny app for other researchers to explore and visualize our dataset.

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.

A compiler for biological networks on silicon chips.

The explosive growth in semiconductor integrated circuits was made possible in large part by design automation software. The design and/or analysis of synthetic and natural circuits in living cells could be made more scalable using the same approach. We present a compiler which converts standard representations of chemical reaction networks and circuits into hardware configurations that can be used to simulate the network on specialized cytomorphic hardware. The compiler also creates circuit-level models of the target configuration, which enhances the versatility of the compiler and enables the validation of its functionality without physical experimentation with the hardware. We show that this compiler can translate networks comprised of mass-action kinetics, classic enzyme kinetics (Michaelis-Menten, Briggs-Haldane, and Botts-Morales formalisms), and genetic repressor kinetics, thereby allowing a large class of models to be transformed into a hardware representation. Rule-based models are particularly well-suited to this approach, as we demonstrate by compiling a MAP kinase model. Development of specialized hardware and software for simulating biological networks has the potential to enable the simulation of larger kinetic models than are currently feasible or allow the parallel simulation of many smaller networks with better performance than current simulation software.

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.

Tellurium: An extensible python-based modeling environment for systems and synthetic biology.

Here we present Tellurium, a Python-based environment for model building, simulation, and analysis that facilitates reproducibility of models in systems and synthetic biology. Tellurium is a modular, cross-platform, and open-source simulation environment composed of multiple libraries, plugins, and specialized modules and methods. Tellurium is a self-contained modeling platform which comes with a fully configured Python distribution. Two interfaces are provided, one based on the Spyder IDE which has an accessible user interface akin to MATLAB and a second based on the Jupyter Notebook, which is a format that contains live code, equations, visualizations, and narrative text. Tellurium uses libRoadRunner as the default SBML simulation engine which supports deterministic simulations, stochastic simulations, and steady-state analyses. Tellurium also includes Antimony, a human-readable model definition language which can be converted to and from SBML. Other standard Python scientific libraries such as NumPy, SciPy, and matplotlib are included by default. Additionally, we include several user-friendly plugins and advanced modules for a wide-variety of applications, ranging from complex algorithms for bifurcation analysis to multidimensional parameter scanning. By combining multiple libraries, plugins, and modules into a single package, Tellurium provides a unified but extensible solution for biological modeling and analysis for both novices and experts. AVAILABILITY: tellurium.analogmachine.org.

Tellurium notebooks-An environment for reproducible dynamical modeling in systems biology.

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.

Synthetic Biology: Engineering Living Systems from Biophysical Principles.

Synthetic biology was founded as a biophysical discipline that sought explanations for the origins of life from chemical and physical first principles. Modern synthetic biology has been reinvented as an engineering discipline to design new organisms as well as to better understand fundamental biological mechanisms. However, success is still largely limited to the laboratory and transformative applications of synthetic biology are still in their infancy. Here, we review six principles of living systems and how they compare and contrast with engineered systems. We cite specific examples from the synthetic biology literature that illustrate these principles and speculate on their implications for further study. To fully realize the promise of synthetic biology, we must be aware of life's unique properties.

phraSED-ML: A paraphrased, human-readable adaptation of SED-ML.

MOTIVATION: Model simulation exchange has been standardized with the Simulation Experiment Description Markup Language (SED-ML), but specialized software is needed to generate simulations in this format. Text-based languages allow researchers to create and modify experimental protocols quickly and easily, and export them to a common machine-readable format. RESULTS: phraSED-ML language allows modelers to use simple text commands to encode various elements of SED-ML (models, tasks, simulations, and results) in a format easy to read and modify. The library can translate this script to SED-ML for use in other softwares. AVAILABILITY: phraSED-ML language specification, libphrasedml library, and source code are available under BSD license from http://phrasedml.sourceforge.net/ .

Guidelines for Reproducibly Building and Simulating Systems Biology Models.

OBJECTIVE: Reproducibility is the cornerstone of the scientific method. However, currently, many systems biology models cannot easily be reproduced. This paper presents methods that address this problem. METHODS: We analyzed the recent Mycoplasma genitalium whole-cell (WC) model to determine the requirements for reproducible modeling. RESULTS: We determined that reproducible modeling requires both repeatable model building and repeatable simulation. CONCLUSION: New standards and simulation software tools are needed to enhance and verify the reproducibility of modeling. New standards are needed to explicitly document every data source and assumption, and new deterministic parallel simulation tools are needed to quickly simulate large, complex models. SIGNIFICANCE: We anticipate that these new standards and software will enable researchers to reproducibly build and simulate more complex models, including WC models.

Toward Community Standards and Software for Whole-Cell Modeling.

OBJECTIVE: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells. METHODS: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language. RESULTS: Our analysis revealed several challenges to representing WC models using the current standards. CONCLUSION: We, therefore, propose several new WC modeling standards, software, and databases. SIGNIFICANCE: We anticipate that these new standards and software will enable more comprehensive models.

libRoadRunner: a high performance SBML simulation and analysis library.

MOTIVATION: This article presents libRoadRunner, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language (SBML). SBML is the most widely used standard for representing dynamic networks, especially biochemical networks. libRoadRunner is fast enough to support large-scale problems such as tissue models, studies that require large numbers of repeated runs and interactive simulations. RESULTS: libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings, and interactively through its Python interface. Its Python Application Programming Interface (API) is similar to the APIs of MATLAB ( WWWMATHWORKSCOM: ) and SciPy ( HTTP//WWWSCIPYORG/: ), making it fast and easy to learn. libRoadRunner uses a custom Just-In-Time (JIT) compiler built on the widely used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a variety of processors, making it appropriate for solving extremely large models or repeated runs. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and non-linear algebraic equations) including several SBML extensions (composition and distributions). It offers multiple deterministic and stochastic integrators, as well as tools for steady-state analysis, stability analysis and structural analysis of the stoichiometric matrix. AVAILABILITY AND IMPLEMENTATION: libRoadRunner binary distributions are available for Mac OS X, Linux and Windows. The library is licensed under Apache License Version 2.0. libRoadRunner is also available for ARM-based computers such as the Raspberry Pi. http://www.libroadrunner.org provides online documentation, full build instructions, binaries and a git source repository. CONTACTS: hsauro@u.washington.edu or somogyie@indiana.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.