Using views of Systems Biology Cloud: application for model building

2010 ◽  
Vol 130 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Oliver Ruebenacker ◽  
Michael Blinov
Keyword(s):  
Systems Biology ◽  
Model Building ◽  
Cloud Application

scholarly journals Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Claudio Angione
Keyword(s):  
Systems Biology ◽  
Precision Medicine ◽  
Model Building ◽  
Research Field ◽  
Point Of View ◽  
Patient Specific ◽  
Metabolic Modelling ◽  
Metabolic Systems ◽  
A Cell ◽  
Metabolic Models

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient’s disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, andin silicoclinical trials.

scholarly journals BioKC: a collaborative platform for systems biology model curation and annotation

2020 ◽  
Author(s):  
Carlos Vega ◽  
Valentin Grouès ◽  
Marek Ostaszewski ◽  
Reinhard Schneider ◽  
Venkata Satagopam
Keyword(s):  
Systems Biology ◽  
Model Building ◽  
Biomedical Knowledge ◽  
Standard Data ◽  
Collaborative Tools ◽  
Important Concern ◽  
New Findings ◽  
Systems Biology Markup Language ◽  
Collaborative Platform ◽  
Biological Entities

Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever increasing growth of domain literature. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).

scholarly journals A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

2021 ◽  
Author(s):  
João P. G. Santos ◽  
Kadri Pajo ◽  
Daniel Trpevski ◽  
Andrey Stepaniuk ◽  
Olivia Eriksson ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Systems Biology ◽  
Model Building ◽  
Web Based ◽  
Analysis Process ◽  
Single Scale ◽  
Custom Made ◽  
Phases Of Development ◽  
Pathway Models

AbstractNeuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB® scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model.

scholarly journals Tellurium: A Python Based Modeling and Reproducibility Platform for Systems Biology

2016 ◽  
Author(s):  
Kiri Choi ◽  
J. Kyle Medley ◽  
Caroline Cannistra ◽  
Matthias König ◽  
Lucian Smith ◽  
...  
Keyword(s):  
Systems Biology ◽  
Synthetic Biology ◽  
Computational Models ◽  
Model Building ◽  
Stochastic Simulations ◽  
Superior Performance ◽  
Markup Language ◽  
Development Environment ◽  
Dimensional Parameter ◽  
Simulation Engine

AbstractIn this article, we present Tellurium, a powerful Python-based integrated environment designed for model building, analysis, simulation and reproducibility in systems and synthetic biology. Tellurium is a modular, cross-platform, and open-source integrated development environment (IDE) composed of multiple libraries, plugins, and specialized modules and methods. Tellurium ensures exchangeability and reproducibility of computational models by supporting SBML (Systems Biology Markup Language), SED-ML (Simulation Experiment Description Markup Language), the COMBINE archive, and SBOL (Synthetic Biology Open Language). Tellurium is a self-contained modeling platform which comes with a fully configured Python distribution independent of other local Python installations on the target machine. The main interface is based on the Spyder IDE which has a highly accessible user interface akin to MATLAB (https://www.mathworks.com/). Tellurium uses libRoadRunner as the default SBML simulation engine due to its superior performance, scalability and ease of integration. libRoadRunner 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 visualization tools to complex algorithms for bifurcation analysis and multi-dimensional parameter scanning. By combining multiple libraries, plugins, and modules into a single package, Tellurium provides a unified but extensible solution for biological modeling and simulation.

Abstract 391: Modeling of the Angiotensin Peptide Processing Network

Hypertension ◽  
2012 ◽  
Vol 60 (suppl_1) ◽  
Author(s):  
John H Schwacke ◽  
John Christian Spainhour ◽  
John M Arthur ◽  
Michael G Janech ◽  
Juan Carlos Q Velez
Keyword(s):  
Systems Biology ◽  
Model Building ◽  
Deviance Information Criterion ◽  
Peptide Fragmentation ◽  
Ion Current ◽  
Computational Systems Biology ◽  
Peptide Processing ◽  
Systems Model ◽  
Processing Network

New insights into the intrarenal renin-angiotensin system (RAS) have modified the traditional view of the system. Nonetheless, the complexity of this network of angiotensin (Ang) peptides and peptidases is not completely understood. We hypothesized that a computational systems biology approach, applied to peptidomic data, could elucidate the network of enzymatic conversions and the resulting net stimulatory signal. To that effect, we built and refined a Bayesian network model and a dynamic systems model of the Ang peptide fragmentation utilizing a database of MALDI-TOF mass spectra from experiments conducted in mouse podocytes exposed to exogenous Ang substrates, augmented by new experiments guided by findings from the archive. A model-building process suggested novel steps, three of which were confirmed in vitro. When 1 μM Ang 2-10 was added to cell media, 102 ± 17 nM Ang 2-7 was detected at 4 hours, decreasing to 30 ± 12 nM in the presence of a neprilysin (NEP) inhibitor, supporting the model-predicted cleavage of Ang 2-10 by NEP. In addition, using Ang 1-9 as substrate, we observed Ang 2-9 production (15.3 ± 3% total Ang ion current at 2 hours) that decreased to 4.6 ± 0.5% during co-treatment with an aminopeptidase A (APA) inhibitor, supporting the model-predicted cleavage of Ang 1-9 by APA. Similarly, incubation of Ang 1-7 showed evidence of Ang 2-7 (28.1 ± 2.2% total Ang ion current at 2 hours) with undetectable amounts in cell-free controls. These data concur with the well-recognized robust degrading capacity of NEP and APA localized in glomerular podocytes. The performance of our model improved after inclusion of the newly discovered steps by Deviance Information Criterion. Although the products from these novel reactions are not known to be bioactive, the cleavages shunt Ang substrates away from formation of known bioactive peptides. In conclusion, our results demonstrate that systems biology methods are effective in identifying novel steps in the Ang peptide processing network, expanding our understanding of the RAS. Besides, the study illustrates the potential for mining archives of peptidomic data. Further exploration of the Ang network using this strategy will likely improve the study of the intricacies of the RAS, with potential clinical impact.

scholarly journals A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

2020 ◽  
Author(s):  
João P.G. Santos ◽  
Kadri Pajo ◽  
Daniel Trpevski ◽  
Andrey Stepaniuk ◽  
Olivia Eriksson ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Systems Biology ◽  
Model Building ◽  
Web Based ◽  
Process Systems ◽  
Analysis Process ◽  
Single Scale ◽  
Custom Made ◽  
Phases Of Development ◽  
Pathway Models

AbstractNeuroscience incorporates knowledge from a range of scales from molecular dynamics to neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Systems biology, for instance, is among the more standardized fields. However, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implement custom-made MATLAB® scripts to perform parameter estimation and sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. By this we can simulate the same model in three principally different simulators, describing different aspects of the system, and with a smooth conversion between the different model formats.

scholarly journals libsbmljs — Enabling Web–Based SBML Tools

2019 ◽  
Author(s):  
J Kyle Medley ◽  
Joseph Hellerstein ◽  
Herbert M Sauro
Keyword(s):  
Systems Biology ◽  
Model Building ◽  
Source Code ◽  
Stochastic Simulations ◽  
Web Based ◽  
Server Side ◽  
Link Type ◽  
Software Distribution ◽  
Api Documentation ◽  
The Web

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.

Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

2019 ◽  
Vol 42 ◽  
Author(s):  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  
...  
Keyword(s):  
Systems Biology ◽  
High Resolution ◽  
Experimental Design ◽  
Integrated Systems ◽  
Microbiome Research ◽  
Constructive Criticism

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

Systems Biology

2009 ◽  
Author(s):  
Bernhard O. Palsson ◽  
Marc Abrams
Keyword(s):  
Systems Biology
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