Categories of (hypothetical) biomolecules, e.g., surface receptors, are defined by their absolute and relative expression levels on target and nontarget cells. Biomolecule expression levels ranging from no expression to highest estimated expression on target and nontarget samples are included and used to model selection. (2) The law of mass action-based computational modeling is performed to optimize selection reaction parameters (e.g., numbers of target and nontarget cells) which are then (3) used in experimental selection to enrich displayed antibodies to sought categories of differentially expressed biomolecules. Enriched antibody pools are analyzed by massively parallel next generation sequencing (NGS) to provide experimental antibody enrichment signatures. The fraction of antibodies that has been enriched in a target biomolecule-dependent manner (the hit rate) is determined. (4 and 5) The law of mass action-basedin silicomodeling is used to generate predicted antibody enrichment signatures for antibodies to the different categories of biomolecules. (6) Finally, experimental and predicted enrichment signatures are matched to identify antibody sequences encoding specificity to sought categories of differentially expressed biomolecules. See alsoFigure S1.
