From convergence to an iterative Optimization-Circumvention-Collapse framework in CRISPR bioengineering

CRISPR-Cas9 gene editing is constrained by unintended off-target effects (OTEs) and inefficient homology-directed repair (HDR). Although modern CRISPR engineering increasingly co-evaluates activity, specificity, repair outcome, delivery, and genomic stability, OTEs and HDR are often analyzed through distinct intervention frameworks. This review formalizes their interdependence as three levels of convergence that define a Pareto-like optimization frontier. Operationally, finite cell numbers, ex vivo editing constraints, and delivery limitations prevent sequential optimization. Mechanistically, Cas9 exposure time influences both off-target accumulation and synchronization with HDR-competent cell-cycle windows. Structurally, on-target activity, Cas9 specificity, and HDR efficiency form a mutually constraining trade-off in which no single point maximizes all three objectives. Convergence therefore extends optimization into an iterative design framework with two additional engineering responses. If the architecture of DSB-based CRISPR systems prevents the optimization frontier from intersecting the clinical acceptance region, circumvention through base- or prime-editor architectures may be preferable. Conversely, when an architecture is clinically viable but constrained by strong mechanistic coupling, collapse weakens those couplings and reshapes the frontier toward greater clinical utility. This review therefore develops an iterative optimization-circumvention-collapse framework in which convergence generates the optimization frontier, identifies mechanistic couplings that may be collapsed, and indicates when architectural dependencies should be circumvented.

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Publication Details

Journal
Journal of High School Science
Published
2026-07-16
DOI
https://doi.org/10.64336/001c.165176
Primary Topic
CRISPR and Genetic Engineering
Type
article
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article

From convergence to an iterative Optimization-Circumvention-Collapse framework in CRISPR bioengineering

Federico Filippone-Thaulero
Journal of High School Science
CRISPR and Genetic Engineering
article

From convergence to an iterative Optimization-Circumvention-Collapse framework in CRISPR bioengineering

Federico Filippone-Thaulero
article en

Abstract

CRISPR-Cas9 gene editing is constrained by unintended off-target effects (OTEs) and inefficient homology-directed repair (HDR). Although modern CRISPR engineering increasingly co-evaluates activity, specificity, repair outcome, delivery, and genomic stability, OTEs and HDR are often analyzed through distinct intervention frameworks. This review formalizes their interdependence as three levels of convergence that define a Pareto-like optimization frontier. Operationally, finite cell numbers, ex vivo editing constraints, and delivery limitations prevent sequential optimization. Mechanistically, Cas9 exposure time influences both off-target accumulation and synchronization with HDR-competent cell-cycle windows. Structurally, on-target activity, Cas9 specificity, and HDR efficiency form a mutually constraining trade-off in which no single point maximizes all three objectives. Convergence therefore extends optimization into an iterative design framework with two additional engineering responses. If the architecture of DSB-based CRISPR systems prevents the optimization frontier from intersecting the clinical acceptance region, circumvention through base- or prime-editor architectures may be preferable. Conversely, when an architecture is clinically viable but constrained by strong mechanistic coupling, collapse weakens those couplings and reshapes the frontier toward greater clinical utility. This review therefore develops an iterative optimization-circumvention-collapse framework in which convergence generates the optimization frontier, identifies mechanistic couplings that may be collapsed, and indicates when architectural dependencies should be circumvented.

Journal of High School ScienceVol. 10(3)
Rome International School (IT)
Openalex Percentile: Top 14%
CRISPR and Genetic Engineering
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