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.