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Step 1: data retrieval |
| Regulation types | Resources |
|
| Transcriptional gene regulation | TRED (http://rulai.cshl.edu/cgi-bin/TRED/tred.cgi?process=home): a database that provides an integrated repository for both cis- and transregulatory elements in mammals
TRANSFAC (http://www.gene-regulation.com/pub/databases.html): a database that collects eukaryotic transcriptional regulation, comprising data on TFs, their target genes, and binding sites
The UCSC table browser (http://genome.ucsc.edu/): a popular web-based tool for querying the UCSC Genome Browser annotation tables
HTRIdb (http://www.lbbc.ibb.unesp.br/htri/): an open-access database for experimentally verified human transcriptional regulation interactions
MIR@NT@N (http://maia.uni.lu/mironton.php/): an integrative resource based on a metaregulation network model including TFs, miRNAs, and genes
PuTmiR (http://www.isical.ac.in/~bioinfo_miu/TF-miRNA.php): a database of predicted TFs for human miRNAs
TransmiR (http://202.38.126.151/hmdd/mirna/tf/): a database of validated TF-miRNA interactions
miRGen 2.0 (http://diana.cslab.ece.ntua.gr/mirgen/): a database of miRNA genomic information and regulation |
|
| Posttranscriptional gene regulation | miRecords (http://mirecords.biolead.org/): a resource for animal miRNA-target interactions
Tarbase (http://www.microrna.gr/tarbase/): a database that stores detailed information for each miRNA-gene interaction, the experimental validation methodologies, and their outcomes
miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/): a database that collects validated miRNA-target interactions by manually surveying the pertinent literature
miRWalk (http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/): a comprehensive database that provides information on miRNAs from human, mouse, and rat, on their predicted as well as validated binding sites in target genes |
|
| Protein-protein interaction | HPRD (http://www.hprd.org/): a centralized platform to visually depict and integrate information pertaining to domain architecture, posttranslational modifications, interaction networks, and disease association for each protein in the human proteome
STRING (http://string-db.org/): a database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations
MPPI (http://mips.helmholtz-muenchen.de/proj/ppi/): a collection of manually curated high-quality PPI data collected from the scientific literature by expert curators
DIP (http://dip.doe-mbi.ucla.edu/dip/Main.cgi): a catalog of experimentally determined interactions between proteins
IntAct (http://www.ebi.ac.uk/intact/main.xhtml): a platform that provides a database system and analysis tools for molecular interaction data
Reactome (http://www.reactome.org/): an open-source, open access, manually curated, and peer-reviewed pathway database |
|
| GO annotation | Amigo GO (http://amigo.geneontology.org/cgi-bin/amigo/go.cgi): the official GO browser and search engine
miR2Disease (http://www.mir2disease.org/): a manually curated database that aims at providing a comprehensive resource of miRNA deregulation in various human diseases
miRCancer (http://mircancer.ecu.edu): a miRNA-cancer association database constructed by text mining on the literature
PhenomiR (http://mips.helmholtz-muenchen.de/phenomir/): a database that provides information about differentially expressed miRNAs in diseases and other biological processes
miRGator (http://mirgator.kobic.re.kr/): a novel database and navigator tool for functional interpretation of miRNAs
miRó (http://ferrolab.dmi.unict.it/miro): a web-based knowledge base that provides users with miRNA-phenotype associations in humans |
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Step 2: network construction and visualization |
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(i) Visualize regulatory interactions in platforms such as CellDesigner and Cytoscape that support standardized data formats |
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(ii) Calculate confidence scores for assessing reliability of interactions in gene regulatory networks |
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Step 3: model construction and calibration |
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(i) Formulate equations using rate equations |
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(ii) Fix parameter values using available biological information |
(iii) Estimate the other unknown and immeasurable parameter values using optimization methods which can minimize the distance
between model simulations and experimental data such as time course qRT-PCR and western blot data |
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Step 4: model validation and analysis |
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(i) Design new experiments and generate new data to verify the calibrated model |
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(ii) Study complex properties and behavior of the system |
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