Pathway and Network Analysis

Pathway and network analysis techniques can help address the challenge in interpreting proteomics results. Analysis of proteomic data at the pathway level has become increasingly popular. For pathway analysis, we refer to data analysis that aims to identify activated pathways or pathway modules from functional proteomic data. Biological pathways can be viewed as signaling pathways, gene regulatory pathways, and metabolic pathways, all of which are curated carefully in reputable scientific publications. Pathway analysis can help organize a long list of proteins onto a short list of pathway knowledge maps, making it easy to interpret molecular mechanisms underlying these altered proteins or their expressions. For network analysis, we refer to data analysis that build, overlay, visualize, and infer protein interaction networks from functional Proteomics and other systems biology data. Network analysis usually requires the use of graph theory, information theory, or Bayesian theory. Different from pathway analysis, network analysis aims to use comprehensive network wiring diagram derived both from prior experimental sources and new in silico prediction to gain systems-level biological meanings.

Overall solutions

CD ComputaBio will build a signal pathway regulation network based on the mutual regulation relationship between the pathways in which all differential genes participate at the same time, and study the signal transduction and regulation process between various signal pathways from a systematic perspective. We can find the regulatory mechanism between the core signaling pathways and important signaling pathways affected by the experiment.

What kinds of data is used for such analysis?

Gene expression data
Microarrays
RNA-seq
Metabolomics data
Single nucleotide
Polymorphisms (SNPs)

Pathway analysis gets used often, and often in different ways：

–  physical interaction networks (e.g., protein–protein interactions)
–  kinetic simulation of pathways
–  steady-state pathway analysis (e.g., flux-balance analysis)
–  inference of pathways from expression and sequence data

Services items

Project name	Complex network analysis service
Our services	Process Data: Derive a list of differentially expressed genes from RNA sequencing data. Identify Pathways: Identify enriched pathways using over-representation analysis or Gene Set Enrichment Analysis. Visualize: Create an Enrichment Map displaying the landscape of pathways. Build the network: Investigate and visualize functional interaction among genes in hit pathways. Predict gene function: Predict the function of a gene or gene set. Discover the Regulons: iRegulon - sequence based discovery of the TF, the targets and the motifs/tracks from a set of genes.
Cycle	Depends on the time you need to simulate and the time required for the system to reach equilibrium.
Product delivery mode	The simulation results provide you with the raw data and analysis results of molecular dynamics.
Price	Inquiry

CD ComputaBio provides corresponding pathway and network analysis services. Our residue interaction service has proven to be very useful for understanding the biochemical basis of physiological events at different stages of drug development (even in different fields such as materials science). The CD ComputaBio team has worked in this field for more than a decade and published his findings in top scientific journals. If you need pathway and network analysis services, please feel free to contact us.