scholarly journals SIAN: software for structural identifiability analysis of ODE models

2019 ◽  
Vol 35 (16) ◽  
pp. 2873-2874 ◽  
Author(s):  
Hoon Hong ◽  
Alexey Ovchinnikov ◽  
Gleb Pogudin ◽  
Chee Yap
Keyword(s):  
Open Source Software ◽  
A Priori ◽  
Supplementary Information ◽  
Biological Processes ◽  
Structural Identifiability ◽  
Unknown Parameters ◽  
Identifiability Analysis ◽  
Ode Models ◽  
Free Data ◽  
Continuous Noise

Abstract Summary Biological processes are often modeled by ordinary differential equations with unknown parameters. The unknown parameters are usually estimated from experimental data. In some cases, due to the structure of the model, this estimation problem does not have a unique solution even in the case of continuous noise-free data. It is therefore desirable to check the uniqueness a priori before carrying out actual experiments. We present a new software SIAN (Structural Identifiability ANalyser) that does this. Our software can tackle problems that could not be tackled by previously developed packages. Availability and implementation SIAN is open-source software written in Maple and is available at https://github.com/pogudingleb/SIAN. Supplementary information Supplementary data are available at Bioinformatics online.

EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences

2021 ◽  
Author(s):  
Ting-Hsuan Wang ◽  
Cheng-Ching Huang ◽  
Jui-Hung Hung
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
A Priori ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Comparable Accuracy ◽  
Meta Analyses ◽  
Next Generation Sequencing Ngs ◽  
Adapter Trimming ◽  
Generation Sequencing

Abstract Motivation Cross-sample comparisons or large-scale meta-analyses based on the next generation sequencing (NGS) involve replicable and universal data preprocessing, including removing adapter fragments in contaminated reads (i.e. adapter trimming). While modern adapter trimmers require users to provide candidate adapter sequences for each sample, which are sometimes unavailable or falsely documented in the repositories (such as GEO or SRA), large-scale meta-analyses are therefore jeopardized by suboptimal adapter trimming. Results Here we introduce a set of fast and accurate adapter detection and trimming algorithms that entail no a priori adapter sequences. These algorithms were implemented in modern C++ with SIMD and multithreading to accelerate its speed. Our experiments and benchmarks show that the implementation (i.e. EARRINGS), without being given any hint of adapter sequences, can reach comparable accuracy and higher throughput than that of existing adapter trimmers. EARRINGS is particularly useful in meta-analyses of a large batch of datasets and can be incorporated in any sequence analysis pipelines in all scales. Availability and implementation EARRINGS is open-source software and is available at https://github.com/jhhung/EARRINGS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Benchmarking of numerical integration methods for ODE models of biological systems

2020 ◽  
Author(s):  
Philipp Städter ◽  
Yannik Schälte ◽  
Leonard Schmiester ◽  
Jan Hasenauer ◽  
Paul L. Stapor
Keyword(s):  
Systems Biology ◽  
Numerical Integration ◽  
Computation Time ◽  
Biological Processes ◽  
Unknown Parameters ◽  
Integration Methods ◽  
Ode Models ◽  
Stability And Bifurcation Analysis ◽  
Numerical Integration Methods ◽  
The Impact

AbstractOrdinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 167 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.

scholarly journals Finding and Breaking Lie Symmetries: Implications for Structural Identifiability and Observability in Biological Modelling

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 469 ◽  
Author(s):  
Gemma Massonis ◽  
Alejandro F. Villaverde
Keyword(s):  
Nonlinear Models ◽  
Rational Functions ◽  
Lie Symmetries ◽  
Structural Identifiability ◽  
Unknown Parameters ◽  
Identifiability Analysis ◽  
Model Equations ◽  
Rational Expressions ◽  
Model Reformulation ◽  
Biological Modelling

A dynamic model is structurally identifiable (respectively, observable) if it is theoretically possible to infer its unknown parameters (respectively, states) by observing its output over time. The two properties, structural identifiability and observability, are completely determined by the model equations. Their analysis is of interest for modellers because it informs about the possibility of gaining insight into a model’s unmeasured variables. Here we cast the problem of analysing structural identifiability and observability as that of finding Lie symmetries. We build on previous results that showed that structural unidentifiability amounts to the existence of Lie symmetries. We consider nonlinear models described by ordinary differential equations and restrict ourselves to rational functions. We revisit a method for finding symmetries by transforming rational expressions into linear systems. We extend the method by enabling it to provide symmetry-breaking transformations, which allows for a semi-automatic model reformulation that renders a non-observable model observable. We provide a MATLAB implementation of the methodology as part of the STRIKE-GOLDD toolbox for observability and identifiability analysis. We illustrate the use of the methodology in the context of biological modelling by applying it to a set of problems taken from the literature.

scholarly journals BMSS2: a unified database-driven modelling tool for systematic model selection and identifiability analysis

2021 ◽  
Author(s):  
Russell Kai Jie Ngo ◽  
Jing Wui Yeoh ◽  
Gerald Horng Wei Fan ◽  
Wilbert Keat Siang Loh ◽  
Chueh Loo Poh
Keyword(s):  
Model Selection ◽  
Rational Design ◽  
System Analysis ◽  
A Priori ◽  
Model Development ◽  
Experimental Designs ◽  
Supplementary Information ◽  
Identifiability Analysis ◽  
The Right ◽  
Python Package

AbstractSummaryModelling in Synthetic Biology constitutes a powerful tool in our continuous search for improved performance with rational Design-Build-Test-Learn approach. In particular, kinetic models unravel system dynamics, enabling system analysis for guiding experimental designs. However, a systematic yet modular pipeline that allows one to identify the “right” model and guide the experimental designs while tracing the entire model development and analysis is still lacking. Here, we introduce a unified python package, BMSS2, which offers the principal tools in model development and analysis—simulation, Bayesian parameter inference, global sensitivity analysis, with an emphasis on model selection, and a priori and a posteriori identifiability analysis. The whole package is database-driven to support interactive retrieving and storing of models for reusability. This allows ease of manipulation and deposition of models for the model selection and analysis process, thus enabling better utilization of models in guiding experimental designs.Availability and ImplementationThe python package and examples are available on https://github.com/EngBioNUS/BMSS2. A web page which allows users to browse and download the models (SBML format) in MBase is also available with the link provided on GitHub.Supplementary InformationSupplementary data is available.

scholarly journals Benchmarking of numerical integration methods for ODE models of biological systems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Philipp Städter ◽  
Yannik Schälte ◽  
Leonard Schmiester ◽  
Jan Hasenauer ◽  
Paul L. Stapor
Keyword(s):  
Systems Biology ◽  
Numerical Integration ◽  
Computation Time ◽  
Biological Processes ◽  
Unknown Parameters ◽  
Integration Methods ◽  
Ode Models ◽  
Stability And Bifurcation Analysis ◽  
Numerical Integration Methods ◽  
The Impact

AbstractOrdinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 142 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models

2017 ◽  
Vol 34 (8) ◽  
pp. 1421-1423 ◽  
Author(s):  
Thomas S Ligon ◽  
Fabian Fröhlich ◽  
Oana T Chiş ◽  
Julio R Banga ◽  
Eva Balsa-Canto ◽  
...  
Keyword(s):  
Structural Identifiability ◽  
Identifiability Analysis ◽  
Structural Identifiability Analysis

scholarly journals DEBKS: A Tool to Detect Differentially Expressed Circular RNA

2020 ◽  
Author(s):  
Zelin Liu ◽  
Huiru Ding ◽  
Jianqi She ◽  
Chunhua Chen ◽  
Weiguang Zhang ◽  
...  
Keyword(s):  
Open Source ◽  
Rna Sequencing ◽  
Open Source Software ◽  
Simulated Data ◽  
Circular Rna ◽  
Host Gene ◽  
Circular Rnas ◽  
Biological Processes ◽  
Rna Seq ◽  
Disease Pathogenesis

AbstractCircular RNAs (circRNAs) are involved in various biological processes and in disease pathogenesis. However, only a small number of functional circRNAs have been identified among hundreds of thousands of circRNA species, partly because most current methods are based on circular junction counts and overlook the fact that circRNA is formed from the host gene by back-splicing (BS). To distinguish between expression originating from BS and that from the host gene, we present DEBKS, a software program to streamline the discovery of differential BS between two rRNA-depleted RNA sequencing (RNA-seq) sample groups. By applying real and simulated data and employing RT-qPCR for validation, we demonstrate that DEBKS is efficient and accurate in detecting circRNAs with differential BS events between paired and unpaired sample groups. DEBKS is available at https://github.com/yangence/DEBKS as open-source software.

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