Open Access
Engineering multistate DNA molecules: a tunable thermal band‐pass filter
Author(s) -
Rose John A.,
Komiya Ken,
Kobayashi Satoshi
Publication year - 2016
Publication title -
micro and nano letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.25
H-Index - 31
ISSN - 1750-0443
DOI - 10.1049/mnl.2016.0345
Subject(s) - nanodevice , sigmoid function , filter (signal processing) , computer science , construct (python library) , scope (computer science) , thermal , melting temperature , materials science , biological system , algorithm , nanotechnology , physics , thermodynamics , artificial intelligence , biology , artificial neural network , composite material , computer vision , programming language
Engineering of biopolymer ‘partner folds’ that exist in competitive equilibrium with the native state to produce exotic behaviours remains a relatively unexplored area of molecular engineering. Previously, a temperature‐sensitive DNA nanodevice that operates by harnessing such a partner fold to implement a thermal band‐pass filter was proposed, modelled, and experimentally validated. Due to its peculiar hill‐shaped efficiency profile, which differs markedly from the sigmoidal melting curves of simple DNA hairpins, this device could be used to implement temperature‐specific control of other molecular machines, and thus represents a promising biotechnological advance. However, no effort was made to examine the detailed dependencies of the peak temperature T † , width Δ T 50 , and maximum efficiency ε max on the stabilities of device components. In this work, closed‐form expressions for T † and ln ε max are derived and validated. The functional behaviours of these expressions are then examined and harnessed to construct an efficient algorithm for producing designs with target T † and Δ T 50 values and optimised ε max , thereby establishing the feasibility of algorithmic device design. Method effectiveness is validated via production of a target filter, with detailed simulations of device behaviour. Finally, a discussion is presented regarding model effectiveness, extension, and scope.