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How to build and analyze biological networks?

How to build and analyze biological networks?

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Several Challenges Associated with Building and Analyzing Biological Networks

Biological networks are models of the interactions between biological components, such as genes, RNAs, proteins, and metabolites, which can be categorized into several types, including transcription factor binding networks, metabolic networks, protein-protein interaction networks, and signaling networks, etc.

However, building and analyzing biological interaction networks is a challenging task requiring experimental and computational biology expertise. One of the major challenges is dealing with the high level of noise and false positives that can be present in high-throughput data. Another is identifying the directionality of interactions, as many experimental techniques do not distinguish between activating and inhibitory interactions. Additionally, biological networks can be highly dynamic and context-dependent, meaning that the interactions and functions of components may vary depending on the cellular environment or physiological state.

Representation of the six levels of abstraction in biological systems.Representation of the six levels of abstraction in biological systems. (Boucher B and Jenna S, 2013)

Methods of Transcription Factor Binding Networks Analysis

Building a transcription factor binding network involves identifying the transcription factors (TFs) that bind to specific DNA sequences, and then determining how these TFs interact with each other and with other proteins to regulate gene expression.

Systematic analysis of binding of transcription factors to noncoding variants.Systematic analysis of binding of transcription factors to noncoding variants.(Yan J, et al., 2021)

Here are some general steps to follow for building and analyzing a transcription factor binding network:

  • Identify potential transcription factor binding sites: One of the first steps in building a transcription factor binding network is to identify the potential binding sites for the TFs of interest. This can be done using various computational tools that predict TF binding sites based on DNA sequence data, such as motif scanning programs.
  • Validate predicted binding sites: Once potential binding sites have been identified, it is important to experimentally validate whether these sites are actually bound by the TFs in question. This can be done using techniques such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) or quantitative PCR (qPCR).
  • Collect Data on TF-TF interactions: To build a TF binding network, it is important to gather data on how TFs interact with each other. This can be done using techniques such as co-immunoprecipitation (co-IP) or yeast two-hybrid assays.
  • Map Regulatory Connections: Once data on TF binding and interactions has been collected, it is possible to map the regulatory connections between TFs and their target genes. This can be done using bioinformatic tools that integrate data on TF binding, gene expression, and protein-protein interactions.
  • Analyze Network Properties: Finally, once the TF binding network has been constructed, it is important to analyze its properties. This can include calculating various network metrics such as degree distribution, clustering coefficient, and betweenness centrality, as well as identifying key regulatory nodes (e.g. TFs that regulate many other TFs or target genes).

Methods of Protein-Protein Interaction Networks Analysis

Building and analyzing protein-protein interaction networks (PPINs) can be a challenging but rewarding task. Here are the general steps to follow:

  • Collect protein-protein interaction data: There are various ways to collect interaction data, including high-throughput experimental methods such as yeast two-hybrid screening or co-immunoprecipitation followed by mass spectrometry, and low-throughput methods such as literature curation. Databases such as STRING, BioGRID, and IntAct contain curated and predicted protein-protein interactions that can be used as a starting point.
  • Preprocess the data: The collected interaction data should be cleaned and standardized. For example, redundant interactions or self-interactions should be removed, and protein names and identifiers should be standardized to avoid errors due to different naming conventions.
  • Build the network of PPI: The protein-protein interaction data can be represented as a graph, where nodes represent proteins and edges represent interactions. Various software tools are available to create and visualize the network, such as Cytoscape, Gephi, or NetworkX in Python.
  • Analyze the network: Once the network is built, various network analysis methods can be applied to gain insights into the topology and properties of the network. Common measures used for this purpose include degree distribution, clustering coefficient, betweenness centrality, and community detection.
  • Interpret the results: By combining interaction data with other types of biological data, such as gene expression or protein localization, it is possible to predict the function of uncharacterized proteins based on their interaction partners. By interpreting the network analysis results, it is possible to gain a deeper understanding of how the protein interactions contribute to biological processes and identify potential targets for drug development.

Protein interaction network.Protein interaction network. (Celaj A et al., 2017)

Methods of Metabolic Interaction Networks Analysis

The construction and analysis of metabolic networks using genomic and biochemical data have led to significant advances in our understanding of the underlying biological processes in various organisms. Moreover, topological analysis of metabolic networks across diverse organisms has revealed conserved network structures and properties.

Global metabolic interaction network of the human gut microbiota.Global metabolic interaction network of the human gut microbiota. (Sung J et al., 2017)

  • Flux Balance Analysis (FBA): an approach for studying biochemical networks, specifically the genome-scale metabolic network reconstructions that have been developed over the past decade, which contain all of the known metabolic reactions in an organism and the genes that encode each enzyme. FBA calculates the flow of metabolites through the metabolic network, using linear programming techniques to optimize a cellular objective such as maximizing growth rate or maximizing the production of a specific metabolite. By constraining the flux of certain reactions or metabolites, FBA can also simulate various environmental conditions or genetic perturbations and predict how these changes will affect the metabolic network and cellular behavior.
  • Pathway Analysis of Global Metabolomic Profiles: involves the integration of experimental metabolomics data with pathway information from databases such as KEGG, MetaCyc, and Reactome. Metabolic pathway analysis aims to identify metabolic pathways associated with a particular phenotype or biological condition, which includes pathway enrichment analysis, pathway topology analysis, and pathway network analysis. Pathway enrichment analysis identifies pathways that are overrepresented in a set of differentially expressed metabolites, while pathway topology analysis takes into account the connectivity and centrality of individual metabolites within a pathway. Pathway network analysis integrates multiple pathways and their interactions to provide a systems-level view of metabolic regulation and network rewiring. Learn about our Global Metabolomic Profiles data analysis.
  • Network Topology Analysis: used to study the organization of connections among the metabolites and reactions in a metabolic network using graph theory. Based on the construction of a graph or network representation of the metabolic network, where nodes represent metabolites or reactions, and edges represent the connections between them, network topology analysis provides insight into the functional properties of the network, such as its robustness and efficiency, by analyzing its structural features, such as connectivity, modularity, and centrality.
  • Metabolic Control Analysis (MCA): aims to quantitatively determine the control that individual or groups of enzymes exert over the flux through a particular pathway or network, and to analyze the response of the system to changes in nutrient levels or other factors that affect the activity of specific enzymes by a mathematical and theoretical framework. It has also been applied to study metabolic disorders and diseases, as well as to optimize metabolic pathways for biotechnological applications.
  • Machine Learning: as a powerful approach for analyzing metabolic interaction networks, machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment, or verify models that can be used to predict behavior or properties of systems. It can be divided into several steps: classification or prediction of samples into different groups; development of predictive models for disease diagnosis or prognosis; integration of metabolomics data with other omics data; identification of metabolic pathways or network features that are associated with specific biological processes or disease states.

Methods of Genetic Networks Analysis

Genetic interaction networks can provide insights into how genes and proteins work together to carry out biological processes at different levels of organization, and how perturbations in these interactions can lead to disease. We can identify key genes and pathways that are involved in complex traits and diseases, and develop targeted therapies based on this information.

Integration of reference interactomes with multi-omics data and phenotypes (GWAS) to obtain context specific interactomes.Integration of reference interactomes with multi-omics data and phenotypes (GWAS) to obtain context specific interactomes. (Hawe J S et al., 2019)

  • Epistasis Analysis: Epistasis refers to the interaction between different genes or proteins. Epistasis analysis involves measuring the effect of mutations in multiple genes on a particular phenotype, and using this information to infer genetic interactions. Quantitative trait locus (QTL) mapping is a type of epistasis analysis that involves measuring the variation in a phenotype across a population of individuals and correlating this variation with differences in genetic markers between individuals. GWAS (genome-wide association studies), another method used in genetic network analysis, scan the entire genome of individuals to identify genetic variants that are associated with a particular trait or disease.
  • Synthetic Lethality Analysis: Synthetic lethality occurs when the simultaneous loss of function of two genes leads to cell death, while the loss of function of either gene alone does not. Synthetic lethality analysis involves screening for pairs of genes that exhibit synthetic lethality, which can help identify genetic interactions.
  • High-Throughput Screening: High-throughput screening involves systematically testing the effect of different gene or protein combinations on a particular phenotype using large-scale experimental techniques, such as RNA interference or CRISPR-Cas9 gene editing. For more information about our bioinformatics solution of CRISPR gene editing.
  • Network Inference: Network inference involves using computational algorithms to infer genetic interaction networks from large-scale datasets, such as gene expression profiles or protein-protein interaction data.

Network Integration

Network integration of different types of data can provide a more comprehensive view of biological systems, allowing to identify key players and regulatory mechanisms that would not have been apparent from individual data sets. Network-based integration help to fill in gaps in knowledge by combining data from different sources and identifying relationships between genes or proteins (or phenotypes) that were previously unknown.

Illustration of the concept of different network inference methods.Illustration of the concept of different network inference methods. (Hawe J S et al., 2019)

  • Co-Expression Networks: This method uses gene expression data to identify groups of genes that are co-regulated and form a functional module.
  • Bayesian Networks: This method uses probabilistic models to infer causal relationships among different biological variables, such as genes, proteins, and metabolites.
  • Graph-Based Methods: This method constructs a graph that represents the relationships between different biological molecules and uses graph theory to analyze the properties of the network.
  • Matrix Factorization: This method uses linear algebra techniques to decompose a high-dimensional omics data matrix into lower-dimensional components that capture different aspects of the data, such as biological pathways, modules, or cellular processes.
  • Machine Learning-Based Methods: This method uses various machine learning algorithms, such as random forests, support vector machines, and deep neural networks, to integrate omics data and predict biological outcomes, such as disease progression, drug response, and patient survival.

References

  1. Boucher B, Jenna S. Genetic interaction networks: better understand to better predict. Frontiers in genetics, 2013, 4: 290.
  2. Hawe J S, Theis F J, Heinig M. Inferring interaction networks from multi-omics data[J]. Frontiers in genetics, 2019, 10: 535.
  3. Yan J, Qiu Y, Ribeiro dos Santos A M, et al. Systematic analysis of binding of transcription factors to noncoding variants[J]. Nature, 2021, 591(7848): 147-151.
  4. Celaj A, Schlecht U, Smith J D, et al. Quantitative analysis of protein interaction network dynamics in yeast[J]. Molecular Systems Biology, 2017, 13(7): 934.
  5. Sung J, Kim S, Cabatbat J J T, et al. Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis[J]. Nature communications, 2017, 8(1): 15393.
  6. Hawe J S, Theis F J, Heinig M. Inferring interaction networks from multi-omics data[J]. Frontiers in genetics, 2019, 10: 535.
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