ArticleBMC systems biology2013
Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0.
Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Single-cell level LasR-mediated quorum sensing response of Pseudomonas aeruginosa to pulses of signal molecules.Scientific reports · 2024Article
- Host-pathogen protein-nucleic acid interactions: A comprehensive review.Computational and structural biotechnology journal · 2022Review
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- Inferring transcriptional logic from multiple dynamic experiments.Bioinformatics (Oxford, England) · 2017Article
- LASSIM-A network inference toolbox for genome-wide mechanistic modeling.PLoS computational biology · 2017Article
- RNA-seq analysis of the gonadal transcriptome during Alligator mississippiensis temperature-dependent sex determination and differentiation.BMC genomics · 2016Article
- Transcriptomic Crosstalk between Fungal Invasive Pathogens and Their Host Cells: Opportunities and Challenges for Next-Generation Sequencing Methods.Journal of fungi (Basel, Switzerland) · 2016Review
- Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens.Frontiers in microbiology · 2016Article
- How to Predict Molecular Interactions between Species?Frontiers in microbiology · 2016Review
- SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks.BMC systems biology · 2015Article
- CyNetworkBMA: a Cytoscape app for inferring gene regulatory networks.Source code for biology and medicine · 2015Article
- Network Modeling Reveals Cross Talk of MAP Kinases during Adaptation to Caspofungin Stress in Aspergillus fumigatus.PloS one · 2015Article
- Computational prediction of molecular pathogen-host interactions based on dual transcriptome data.Frontiers in microbiology · 2015Article
- Integrated inference and evaluation of host-fungi interaction networks.Frontiers in microbiology · 2015Article
- A review on computational systems biology of pathogen-host interactions.Frontiers in microbiology · 2015Review
- Data- and knowledge-based modeling of gene regulatory networks: an update.EXCLI journal · 2015Review
- Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients.BMC medical genomics · 2014Article
- Getting to the edge: protein dynamical networks as a new frontier in plant-microbe interactions.Frontiers in plant science · 2014Review
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundInference of gene-regulatory networks (GRNs) is important for understanding behaviour and potential treatment of biological systems. Knowledge about GRNs gained from transcriptome analysis can be increased by multiple experiments and/or multiple stimuli. Since GRNs are complex and dynamical, appropriate methods and algorithms are needed for constructing models describing these dynamics. Algorithms based on heuristic approaches reduce the effort in parameter identification and computation time.
resultsThe NetGenerator V2.0 algorithm, a heuristic for network inference, is proposed and described. It automatically generates a system of differential equations modelling structure and dynamics of the network based on time-resolved gene expression data. In contrast to a previous version, the inference considers multi-stimuli multi-experiment data and contains different methods for integrating prior knowledge. The resulting significant changes in the algorithmic procedures are explained in detail. NetGenerator is applied to relevant benchmark examples evaluating the inference for data from experiments with different stimuli. Also, the underlying GRN of chondrogenic differentiation, a real-world multi-stimulus problem, is inferred and analysed.
conclusionsNetGenerator is able to determine the structure and parameters of GRNs and their dynamics. The new features of the algorithm extend the range of possible experimental set-ups, results and biological interpretations. Based upon benchmarks, the algorithm provides good results in terms of specificity, sensitivity, efficiency and model fit.
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