Abstract:
:Cell classifiers are genetic logic circuits that transduce endogenous molecular inputs into cell-type-specific responses. Designing classifiers that achieve optimal differential response between specific cell types is a hard computational problem because it involves selection of endogenous inputs and optimization of both biochemical parameters and a logic function. To address this problem, we first derive an optimal set of biochemical parameters with the largest expected differential response over a diverse set of logic circuits, and second, we use these parameters in an evolutionary algorithm to select circuit inputs and optimize the logic function. Using this approach, we design experimentally feasible microRNA-based circuits capable of perfect discrimination for several real-world cell-classification tasks. We also find that under realistic cell-to-cell variation, circuit performance is comparable to standard cross-validation performance estimates. Our approach facilitates the generation of candidate circuits for experimental testing in therapeutic settings that require precise cell targeting, such as cancer therapy.
journal_name
Cell Systjournal_title
Cell systemsauthors
Mohammadi P,Beerenwinkel N,Benenson Ydoi
10.1016/j.cels.2017.01.003subject
Has Abstractpub_date
2017-02-22 00:00:00pages
207-218.e14issue
2eissn
2405-4712issn
2405-4720pii
S2405-4712(17)30003-0journal_volume
4pub_type
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