Computational Systems Biology in Disease Modeling and Control, Review and Perspectives

Abstract

Omics-based approaches have become increasingly influential in identifying disease mechanismsand drug responses. Considering that diseases and drug responses are co-expressed andregulated in the relevant omics data interactions, the traditional way of grabbing omics datafrom single isolated layers cannot always obtain valuable inference. Also, drugs have adverseeffects that may impair patients, and launching new medicines for diseases is costly. To resolvethe above difficulties, systems biology is applied to predict potential molecular interactions byintegrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combinedwith known drug reactions, the resulting models improve medicines’ therapeutical performanceby re-purposing the existing drugs and combining drug molecules without off-target effects.Based on the identified computational models, drug administration control laws are designedto balance toxicity and efficacy. This review introduces biomedical applications and analysesof interactions among gene, protein and drug molecules for modeling disease mechanisms anddrug responses. The therapeutical performance can be improved by combining the predictiveand computational models with drug administration designed by control laws. The challengesare also discussed for its clinical uses in this work.

Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives

“…The analysis of biological network can provide clinically actionable knowledge for disease diagnosis, prognosis, and treatment [14]. The analysis of a network can be classified into two categories: the static analysis of networks and the analysis of network dynamic [15].…”

Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives

“…The analysis of biological network can provide clinically actionable knowledge for disease diagnosis, prognosis, and treatment [14]. The analysis of a network can be classified into two categories: the static analysis of networks and the analysis of network dynamic [15].…”