Towards a comprehensive assessment of QSP models: what would it take?

CYTOCON DB: A versatile database of human cell and molecule concentrations for accelerating model development

“…QSP models typically consist of tens‐to‐hundreds of state variables, parameters, equations, and initial conditions. Parameterization of such models is a laborious and rate‐limiting process that often requires extensive mining of literature followed by data extraction 9,10 . A key issue for accessing human data from literature is the enormous heterogeneity in experimental protocols and assay conditions across studies that necessitate development of domain expertise and time‐intensive manual conversion of measurements and units by QSP modelers to ensure consistency.…”

“…Parameterization of such models is a laborious and rate‐limiting process that often requires extensive mining of literature followed by data extraction. 9 , 10 A key issue for accessing human data from literature is the enormous heterogeneity in experimental protocols and assay conditions across studies that necessitate development of domain expertise and time‐intensive manual conversion of measurements and units by QSP modelers to ensure consistency. CYTOCON DB provides a solution to this issue via an expert manual curation process that results in readily available, annotated, and organized data in standard units.…”

Teaching computational systems biology with an eye on quantitative systems pharmacology at the undergraduate level: Why do it, who would take it, and what should we teach?

“…1) Parameter estimation in systems biology (Poyton et al, 2006;Gutenkunst et al, 2007;Ashyraliyev et al, 2009;Liepe et al, 2014;Meyer et al, 2014;Degasperi et al, 2017) 2) The intrinsic difficulty of estimating parameters in biology (Gutenkunst et al, 2007) 3) An enormous number of excellent optimization textbooks exist. The classic textbook (Gill et al, 1981) is suggested as a gentle introduction to basic concepts of optimization 4) On model assessment (Androulakis, 2022)…”

“…Furthermore, the possibility of incorporating mathematical and computational modeling in business and regulatory decisions is what builds momentum for deploying further systems approaches ( Androulakis, 2022 ). Recently, the FDA made the development of computational modeling a key priority in supporting regulatory decision-making ( US FDA, 2011 ), aiming to facilitate faster, cheaper, and better pathways to market ( Drager and Palsson, 2014 ).…”