CD Genomics provides a comprehensive and integrative approach to analyzing biological networks to better understand complex biological systems.
What is biological network analysis?
Biological network analysis is a computational approach used to study complex biological systems by analyzing the interactions and relationships among various biological components such as genes, proteins, metabolites, and other molecules. It involves the construction of network graphs that represent the nodes (biological components) and edges (interactions or relationships between them) within the system. Different techniques can be used in analyzing biology networks. However, many biological studies compare more than two networks, it requires a comparative analysis of multiple network graphs.
Network representations. (Koutrouli M et al., 2020)
Our Biological Network Analysis Service
Our custom biological network analysis solution involves the construction and analysis of network models. We help our clients in identifying key components and pathways that are involved in various biological processes, such as cell signaling, metabolism, and disease progression, promoting underlying mechanisms of diseases and developing new therapeutic strategies.
Based on our capacities of graph theory, machine learning, and statistical modeling, we build and analyze networks in system biology, including but not limited to:
Protein-Protein Interaction Networks Analysis
Protein-protein interactions (PPIs) is essential for almost every process in a cell. PPINs provide a mathematical representation of PPIs and can be used to assign putative roles to uncharacterized proteins, add detail to signaling pathways, characterize multi-molecular complexes, identify drug targets, and design new proteins. Learn more about our Proteomic Data Analysis Services.
Interactomes Data Analysis
The interactome is a complex and dynamic network of protein-protein interactions (PPIs) that occur within a cell, organism, or specific biological context. However, the interactome remains a valuable resource for understanding the complex network of PPIs that underlie cellular function.
Gene Regulatory Networks (GRNs)
GRNs represent the interactions between genes that regulate their expression. Graph theory can be used to analyze the topology of GRNs, while machine learning can be used to identify key regulatory genes or modules within the network. Statistical modeling can be used to identify significant regulatory interactions between genes and to predict the effect of perturbations on gene expression.
Sequence Similarity Networks (SSNs)
SSNs represent the evolutionary relationships between protein sequences. Graph theory can be used to identify clusters or subgroups of related sequences within the network, while machine learning can be used to predict the function or properties of uncharacterized sequences based on their sequence similarity to known sequences.
Signal Transduction Networks
Signal transduction networks represent the signaling pathways that transmit signals between cells or within a cell. Graph theory can be used to analyze the topology of these networks, while machine learning can be used to predict the effect of perturbations on the signaling pathway or to identify key regulatory nodes within the network.
Metabolic Networks
The metabolic network usually focuses on the mass flow in basic chemical pathways that generate essential components such as amino acids, sugars, and lipids, and the energy required by the biochemical reactions. As such, these networks typically present both protein and metabolite information.
lncRNA–Protein Interaction Networks
lncRNA–protein interaction networks represent the interactions between long non-coding RNAs (lncRNAs) and proteins in a cell. Graph theory can be used to analyze the topology of these networks, while machine learning can be used to identify key regulatory lncRNAs or modules within the network.
Disease Networks
Disease networks are a way of visualizing the connections between diseases and their causative genes. These networks are typically constructed using data from repositories such as the Online Mendelian Inheritance in Man (OMIM), which contains information about genetic disorders and their associated genes.
Our Biological Network Analysis Pipeline
We offer a range of network analysis services including network construction, visualization, and interpretation, as well as pathway enrichment analysis and gene set enrichment analysis. We help you interpret and use the results of our network analysis to drive your research forward.
Data Collection and QC
As a leading multiomics data analysis company, we have tremendous strength and experience in the acquisition and quality control of big data and high-throughput data. We can help you acquire data from databases or experimental data depending on your project requirements. Or you can contact our technical team to help you design experiments to obtain useful histological data if you are requesting access to data
Network Construction and Analysis
We have many years of experience in building and analyzing biological networks and can help you with analyses such as protein-protein interaction networks, gene regulation networks and metabolic networks. Our team of experts are dedicated to quickly and accurately identifying key network features and analyzing network dynamics to gain insight into biological processes.
Network Visualization
Visualization of network analysis results is an important element. Our bioinformatics platform has state-of-the-art tools to create interactive network visualizations, enabling you to explore your data in real-time with visually appealing, informative network visualizations that allow you to easily interpret complex network data.
Customized Network Analysis
Our team can work with you to develop customized analysis pipelines tailored to your specific research questions. Whether you need to analyze large-scale gene expression data, identify key pathways in a disease model, or study the interactions between specific proteins, we can help you design and execute a robust analysis strategy.
How It Works
Initial Consultation
- Meet with technical experts to understand the client's research goals and objectives
- Discuss project design and scope of work
- Determine sample preparation and sequencing requirements
- Provide an estimated schedule and budget
Project Confirmation
- If you don't have data yet, we can also provide omics sequencing services
- Project confirmation process
Data Analysis
- Perform quality control and data filtering
- Perform downstream analysis such as functional annotation, pathway analysis, or gene network analysis
Results Delivery
- Provide the client with a detailed report summarizing the analysis results
- Include visualizations of the data
- Provide recommendations for future research directions
If you're interested in learning more about our services, please inquire now or contact us directly. We're happy to answer any questions you may have and provide you with a customized quote based on your specific needs. We look forward to working with you!
References
- Koh G C K W, Porras P, Aranda B, et al. Analyzing protein–protein interaction networks[J]. Journal of proteome research, 2012, 11(4): 2014-2031.
- Koutrouli M, Karatzas E, Paez-Espino D, et al. A guide to conquer the biological network era using graph theory[J]. Frontiers in bioengineering and biotechnology, 2020, 8: 34.
