Automated Design of Synthetic Cell Classifier Circuits Using a Two-Step Optimization Strategy.

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 Syst

journal_title

Cell systems

authors

Mohammadi P,Beerenwinkel N,Benenson Y

doi

10.1016/j.cels.2017.01.003

subject

Has Abstract

pub_date

2017-02-22 00:00:00

pages

207-218.e14

issue

2

eissn

2405-4712

issn

2405-4720

pii

S2405-4712(17)30003-0

journal_volume

4

pub_type

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