Creating Networks
Network representations of biological systems have been around for decades. Reconstruction of metabolic maps from early biochemical experiments started in the 1950s with Boehringer Mannheim charts. Modern reconstruction efforts like Reactome 5 , SIGNOR6, KEGG7, RECON 8, and Disease Maps 9 encompass hundreds of thousands of reactions, curated from scientific publications. Despite this herculean effort, these manually curated databases cannot keep up with the rate of scientific production given the available resources. To support manual curation efforts, multiple natural language processing (NLP) and crowd sourcing approaches to extract computable models from scientific literature have been developed10, and recent language learning models (LLMs) offer great promise in expanding these efforts11. Additionally, in the case where there is very little existing literature about a system, networks can be inferred de novo or by expanding existing models12. This approach was particularly popular in the early phases of COVID-19 pandemic, wherein many researchers used network-based approaches grounded in SARS-CoV and MERS-CoV networks to extrapolate the molecular processes governing SARS-CoV-2 biology13. When PKNs are incomplete or nonexistent, interactions captured in the data can be used to infer a network structure. For example, in the case of high throughput PPI assays the identified interactions are commonly quantified based on confidence, then filtered using a cut-off score to lessen any noise introduced by the mode of collection. The filter chosen, which may be empirically or statistically informed, can have a significant impact on the rate of false positives and negatives in the resulting network14. Finally, some high-throughput modalities such as protein co-IP experiments can be readily expressed as networks without referring to curated sources of prior knowledge. Additional layers such as drug-target relationships can then be mapped to these interaction networks, as was done during COVID-19 to nominate targets for drug repurposing15.