AI Article Synopsis
- - Mathematical modeling of regulatory networks can uncover insights at a system level, but current tools are often complex and not user-friendly for hypothesis testing or biological interpretation.
- - A new approach using a Matlab toolbox called FALCON has been developed to simplify the exploration of regulatory networks by contextually linking them with biological data, allowing for effective parameter analysis and model investigations.
- - FALCON is available for free on GitHub under the GPLv3 license, and it requires Matlab and the Optimization Toolbox for operation, with full documentation and resources provided online.
Article Abstract
Motivation: Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically.
Results: We have developed a computational approach to contextualize logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes.
Availability And Implementation: FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON. FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox.
Contact: thomas.sauter@uni.lu.
Supplementary Information: Supplementary data are available at Bioinformatics online.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860161 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btx380 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Overexpression of lncRNA LINC00294 Induces Cell Cycle Arrest and Apoptosis in Colorectal Cancer by Regulating the miR-499a-5p/LARP4B Axis.
J Biochem Mol Toxicol
January 2025
Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Increasing long noncoding RNAs (lncRNAs) have been found to participate in regulating the progression of colorectal cancer (CRC), which is a common gastrointestinal malignancy. Here, the specific role and mechanisms of lncRNA LINC00294 were investigated in CRC. The expression levels of LINC00294, miR-499a-5p, and La-related protein 4B (LARP4B) in CRC cells (HCT116 and SW620) and tissues were assessed by RT-qPCR.
View Article and Find Full Text PDFIntegrated metabolomics and transcriptomics analysis provides insights into biosynthesis and accumulation of flavonoids and glucosinolates in different radish varieties.
Curr Res Food Sci
November 2024
Shandong Provincial Key Laboratory of Test Technology on Food Quality and Safety, Institute of Quality Standard and Testing Technology for Agro-Products, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
Radish is an important vegetable worldwide, with wide medicinal functions and health benefits. The quality of radish, strongly affected by phytochemicals like flavonoids and glucosinolates, are quite different depending on the radish varieties. However, the comprehensive accumulation profiles of secondary metabolites and their molecular regulatory mechanisms in different radish cultivars remain unclear thus far.
View Article and Find Full Text PDFMachine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune-Related Biomarkers in Periodontitis.
Mediators Inflamm
December 2024
Department of Stomatology, School of Stomatology, The Third Affiliated Hospital, Xi'an Medical University, Xi'an, China.
This study aimed to investigate the molecular mechanisms of periodontitis and identify key immune-related biomarkers using machine learning and Mendelian randomization (MR). Differentially expressed gene (DEG) analysis was performed on periodontitis datasets GSE16134 and GSE10334 from the Gene Expression Omnibus (GEO) database, followed by weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules. Various machine learning algorithms were utilized to construct predictive models, highlighting core genes, while MR assessed the causal relationships between these genes and periodontitis.
View Article and Find Full Text PDFBibliometric and visual analysis of human microbiome-breast cancer interactions: current insights and future directions.
Front Microbiol
December 2024
Department of Breast Surgery and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Sichuan, China.
The composition of the gut microbiome differs from that of healthy individuals and is closely linked to the progression and development of breast cancer. Recent studies have increasingly examined the relationship between microbial communities and breast cancer. This study analyzed the research landscape of microbiome and breast cancer, focusing on 736 qualified publications from the Web of Science Core Collection (WoSCC).
View Article and Find Full Text PDFBioinformatics Analysis of Biomarkers and Therapeutic Targets Related to Necroptosis in Intervertebral Disc Degeneration.
Biomed Res Int
December 2024
Department of Orthopedics, The First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan 650032, China.
Necroptosis is a critical process in intervertebral disc degeneration (IDD). This research is aimed at identifying key genes regulating necroptosis in IDD to provide a theoretical basis for early diagnosis and treatment. Transcriptome data from patients with IDD and normal samples were obtained from the GSE34095 and GSE124272 datasets of the Gene Expression Omnibus (GEO) public database.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!