A top-down approach to mechanistic biological modeling: application to the single-chain antibody folding pathway.

A top-down approach to mechanistic modeling of biological systems is presented and exemplified with the development of a hypothesis-driven mathematical model for single-chain antibody fragment (scFv) folding in Saccharomyces cerevisiae by mediators BiP and PDI. In this approach, model development starts with construction of the most basic mathematical model--typically consisting of predetermined or newly-elucidated biological behavior motifs--capable of reproducing desired biological behaviors. From this point, mechanistic detail is added incrementally and systematically, and the effects of each addition are evaluated. This approach follows the typical progression of experimental data availability in that higher-order, lumped measurements are often more prevalent initially than specific, mechanistic ones. It also necessarily provides the modeler with insight into the structural requirements and performance capabilities of the resulting detailed mechanistic model, which facilitates further analysis. The top-down approach to mechanistic modeling identified three such requirements and a branched dependency-degradation competition motif critical for the scFv folding model to reproduce experimentally observed scFv folding dependencies on BiP and PDI and increased production when both species are overexpressed and promoted straightforward prediction of parameter dependencies. It also prescribed modification of the guiding hypothesis to capture BiP and PDI synergy.