The systems biology simulation core algorithm

System biology analysis of endosulfan biodegradation in bacteria and its effect in other living systems: modeling and simulation studies

Endosulfan is a broadly applied cyclodiene insecticide which has been in use across 80 countries since last 5 decades. Owing to its recalcitrant nature, endosulfan residues have been reported from air, water and soil causing toxicity to various non-target organisms. Microbial decontamination of endosulfan has been reported previously by several authors. In the current study, we have evaluated the pathways of endosulfan degradation and its hazardous impact on other living beings including insects, humans, plants, aquatic life and environment by in-silico methods. For establishment of the endosulfan metabolism in different ecosystems, cell designer was employed. The established model was thereafter assessed and simulated to understand the biochemical and physiological metabolism of the endosulfan in various systems of the network. Topological investigation analysis of the endosulfan metabolism validated the presence of 207 nodes and 274 edges in the network. We have concluded that biomagnification of the endosulfan generally occurs in the various elements of the ecosystem. Dynamics study of endosulfan degrading enzymes suggested the important role of monooxygenase I, II and hydrolase in endosulfan bioremediation. Endosulfan shows toxicity in human beings, fishes and plants, however it is biodegraded by the microbes. To date, there are no reports of in- silico analysis of bioremediation of endosulfan and its hazardous effects on the environment. Thus, this report can be important in terms of modelling and simulation of biodegradation network of endosulfan and similar compounds and their impact on several other systems.Communicated by Ramaswamy H. Sarma.

SBML to bond graphs: From conversion to composition

The Systems Biology Markup Language (SBML) is a popular software-independent XML-based format for describing models of biological phenomena. The BioModels Database is the largest online repository of SBML models. Several tools and platforms are available to support the reuse and composition of SBML models. However, these tools do not explicitly assess whether models are physically plausible or thermodynamically consistent. This often leads to ill-posed models that are physically impossible, impeding the development of realistic complex models in biology. Here, we present a framework that can automatically convert SBML models into bond graphs, which imposes energy conservation laws on these models. The new bond graph models are easily mergeable, resulting in physically plausible coupled models. We illustrate this by automatically converting and coupling a model of pyruvate distribution to a model of the pentose phosphate pathway.

SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.

The systems biology simulation core library

SUMMARY

Studying biological systems generally relies on computational modeling and simulation, e.g., model-driven discovery and hypothesis testing. Progress in standardization efforts led to the development of interrelated file formats to exchange and reuse models in systems biology, such as SBML, the Simulation Experiment Description Markup Language (SED-ML) or the Open Modeling EXchange format. Conducting simulation experiments based on these formats requires efficient and reusable implementations to make them accessible to the broader scientific community and to ensure the reproducibility of the results. The Systems Biology Simulation Core Library (SBSCL) provides interpreters and solvers for these standards as a versatile open-source API in JavaTM. The library simulates even complex bio-models and supports deterministic Ordinary Differential Equations; Stochastic Differential Equations; constraint-based analyses; recent SBML and SED-ML versions; exchange of results, and visualization of in silico experiments; open modeling exchange formats (COMBINE archives); hierarchically structured models; and compatibility with standard testing systems, including the Systems Biology Test Suite and published models from the BioModels and BiGG databases.

AVAILABILITY AND IMPLEMENTATION

SBSCL is freely available at https://draeger-lab.github.io/SBSCL/ and via Maven Central.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Publishing reproducible dynamic kinetic models

Publishing repeatable and reproducible computational models is a crucial aspect of the scientific method in computational biology and one that is often forgotten in the rush to publish. The pressures of academic life and the lack of any reward system at institutions, granting agencies and journals means that publishing reproducible science is often either non-existent or, at best, presented in the form of an incomplete description. In the article, we will focus on repeatability and reproducibility in the systems biology field where a great many published models cannot be reproduced and in many cases even repeated. This review describes the current landscape of software tooling, model repositories, model standards and best practices for publishing repeatable and reproducible kinetic models. The review also discusses possible future remedies including working more closely with journals to help reviewers and editors ensure that published kinetic models are at minimum, repeatable. Contact: hsauro@uw.edu.

Modeling and simulation of atrazine biodegradation in bacteria and its effect in other living systems

Atrazine is the most commonly used herbicide worldwide in the agricultural system. The increased environmental concentration of the atrazine showed the toxic effects on the non-target living species. Biodegradation of the atrazine is possible with the bacterial systems. The present study investigated biodegradation potential of atrazine degrading bacteria and the impact of atrazine on environmental systems. Model of atrazine fate in ecological systems constructed using the cell designer. The used model further analyzed and simulated to know the biochemistry and physiology of the atrazine in different cellular networks. Topological analysis of the atrazine degradation confirmed the 289 nodes and 300 edges. Our results showed that the overall biomagnification of the atrazine in the different environmental systems. Atrazine is showing toxic effects on humans and plants, whereas degraded by the bacterial systems. To date, no one has analyzed the complete degradation and poisonous effects of the atrazine in the environment. Therefore, this study is useful for overall system biology based modeling and simulation analysis of atrazine in living systems.Communicated by Ramaswamy H. Sarma.

SBML2Modelica: integrating biochemical models within open-standard simulation ecosystems

MOTIVATION

SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g. enable in silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models. Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems.

RESULTS

In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2-compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialized SBML simulators.

AVAILABILITY AND IMPLEMENTATION

SBML2Modelica is written in Java and is freely available for non-commercial use at https://bitbucket.org/mclab/sbml2modelica.

Insights into zinc-sensing metalloregulator 'Zur' deciphering mechanism of zinc transportation in Bacillus spp. by modeling, simulation and molecular docking

To comprehend the molecular mechanism of zinc transportation by bacteria tends to be a very complicated and time-consuming method. To date, fragmented and scanty studies are available about the mechanism of zinc transportation at molecular level. So, the present study scrutinizes in silico pathways of zinc fractions transportation, specifically in Bacillus spp. stimulating dynamic performance of zinc. For this, the constructed model reveals Zur to be the prime regulatory transport protein maintaining bacterial survivability at fluctuation in zinc concentrations, thereby attaining zinc homeostasis. Topology for hub nodes displays appropriate evidence of the molecular basis of bacterial zinc imports and exports. Further, the molecular docking reveals interaction of Zur protein with the zinc ligands (ZnCO₃ and ZnSO₄). By validation of binding affinity, binding energy and docking score via Autodock Vina and X-Score, the ZnSO₄ compound was found to possess excellent stability in the active pocket site of Zur, stating Zur-ZnSO₄ complex to be the most potential. Owing to which, the Zur-ZnSO₄ complex was selected and subjected to molecular dynamics simulation, revealing RMSD, RG, RMSF, SASA and interaction energy for 20 ns trajectory period. Henceforth,the study provides novel insight into revealing the unrecognized Zur protein pathway, assisting zinc transportation, besides retaining best interaction with ZnSO₄ ligand. This is the first system biology where molecular docking and molecular dynamics simulation-based investigation decipher the role of Zur transport protein system and interaction of its amino acids with zinc ligands in a simpler and economical form via in silico techniques.Communicated by Ramaswamy H. Sarma.

Modeling of the jasmonate signaling pathway in Arabidopsis thaliana with respect to pathophysiology of Alternaria blight in Brassica

The productivity of Oilseed Brassica, one of the economically important crops of India, is seriously affected by the disease, Alternaria blight. The disease is mainly caused by two major necrotrophic fungi, Alternaria brassicae and Alternaria brassicicola which are responsible for significant yield losses. Till date, no resistant source is available against Alternaria blight, hence plant breeding methods can not be used to develop disease resistant varieties. Jasmonate mediated signalling pathway, which is known to play crucial role during defense response against necrotrophs, could be strengthened in Brassica plants to combat the disease. Since scanty information is available in Brassica-Alternaria pathosystems at molecular level therefore, in the present study efforts have been made to model jasmonic acid pathway in Arabidopsis thaliana to simulate the dynamic behaviour of molecular species in the model. Besides, the developed model was also analyzed topologically for investigation of the hubs node. COI1 is identified as one of the promising candidate genes in response to Alternaria and other linked components of plant defense mechanisms against the pathogens. The findings from present study are therefore informative for understanding the molecular basis of pathophysiology and rational management of Alternaria blight for securing food and nutritional security.

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/ .

SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks

BACKGROUND

The size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have simplified the network reconstruction process, but building kinetic models for these systems is still a manually intensive task. Appropriate kinetic equations, based upon reaction rate laws, must be constructed and parameterized for each reaction. The complex test-and-evaluation cycles that can be involved during kinetic model construction would thus benefit from automated methods for rate law assignment.

RESULTS

We present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made by the algorithm can be influenced in order to assign the desired type of rate law to each reaction. This algorithm is implemented in the software package SBMLsqueezer 2. In addition, this program contains an integrated connection to the kinetics database SABIO-RK to obtain experimentally-derived rate laws when desired.

CONCLUSIONS

The described approach fills a heretofore absent niche in workflows for large-scale biochemical kinetic model construction. In several applications the algorithm has already been demonstrated to be useful and scalable. SBMLsqueezer is platform independent and can be used as a stand-alone package, as an integrated plugin, or through a web interface, enabling flexible solutions and use-case scenarios.

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.

Quantitative and logic modelling of molecular and gene networks

Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.

BioModels: Content, Features, Functionality, and Use

BioModels is a reference repository hosting mathematical models that describe the dynamic interactions of biological components at various scales. The resource provides access to over 1,200 models described in literature and over 140,000 models automatically generated from pathway resources. Most model components are cross-linked with external resources to facilitate interoperability. A large proportion of models are manually curated to ensure reproducibility of simulation results. This tutorial presents BioModels' content, features, functionality, and usage.

SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology

The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspired optimization techniques can find appropriate sets of parameters that calibrate a model to experimentally obtained time series data. We present SBMLsimulator, a tool that combines the Systems Biology Simulation Core Library for dynamic simulation of biochemical models with the heuristic optimization framework EvA2. SBMLsimulator provides an intuitive graphical user interface with various options as well as a fully-featured command-line interface for large-scale and script-based model simulation and calibration. In a parameter estimation study based on a published model and artificial data we demonstrate the capability of SBMLsimulator to identify parameters. SBMLsimulator is useful for both, the interactive simulation and exploration of the parameter space and for the large-scale model calibration and estimation of uncertain parameter values.

Improving collaboration by standardization efforts in systems biology

Collaborative genome-scale reconstruction endeavors of metabolic networks would not be possible without a common, standardized formal representation of these systems. The ability to precisely define biological building blocks together with their dynamic behavior has even been considered a prerequisite for upcoming synthetic biology approaches. Driven by the requirements of such ambitious research goals, standardization itself has become an active field of research on nearly all levels of granularity in biology. In addition to the originally envisaged exchange of computational models and tool interoperability, new standards have been suggested for an unambiguous graphical display of biological phenomena, to annotate, archive, as well as to rank models, and to describe execution and the outcomes of simulation experiments. The spectrum now even covers the interaction of entire neurons in the brain, three-dimensional motions, and the description of pharmacometric studies. Thereby, the mathematical description of systems and approaches for their (repeated) simulation are clearly separated from each other and also from their graphical representation. Minimum information definitions constitute guidelines and common operation protocols in order to ensure reproducibility of findings and a unified knowledge representation. Central database infrastructures have been established that provide the scientific community with persistent links from model annotations to online resources. A rich variety of open-source software tools thrives for all data formats, often supporting a multitude of programing languages. Regular meetings and workshops of developers and users lead to continuous improvement and ongoing development of these standardization efforts. This article gives a brief overview about the current state of the growing number of operation protocols, mark-up languages, graphical descriptions, and fundamental software support with relevance to systems biology.

Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, and attended by a total of 96 people. This edition pioneered a first day devoted to modeling approaches in biology, which attracted a broad audience of scientists thanks to a panel of renowned speakers. During subsequent days, discussions were held on many subjects including the introduction of new features in the various COMBINE standards, new software tools that use the standards, and outreach efforts. Significant emphasis went into work on extensions of the SBML format, and also into community-building. This year’s edition once again demonstrated that the COMBINE community is thriving, and still manages to help coordinate activities between different standards in computational systems biology.

Modeling and simulation using CellDesigner

In silico modeling and simulation are effective means to understand how the regulatory systems function in life. In this chapter, we explain how to build a model and run the simulation using CellDesigner, adopting the standards such as SBML and SBGN.

GRN2SBML: automated encoding and annotation of inferred gene regulatory networks complying with SBML

GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple object access protocol (SOAP)-based application programming interface of BioMart Central Portal and minimum information required in the annotation of models registry. Additionally, we provide an R-package, which processes the output of supported inference algorithms and automatically passes all required parameters to GRN2SBML. Therefore, GRN2SBML closes a gap in the processing pipeline between the inference of gene regulatory networks and their subsequent analysis, visualization and storage.

AVAILABILITY

GRN2SBML is freely available under the GNU Public License version 3 and can be downloaded from http://www.hki-jena.de/index.php/0/2/490.

SUPPLEMENTARY INFORMATION

General information on GRN2SBML, examples and tutorials are available at the tool's web page.