Programming Cell-Free Biosensors with DNA Strand Displacement Circuits

ABSTRACTCell-free biosensors are emerging as powerful platforms for monitoring human and environmental health. Here, we expand the capabilities of biosensors by interfacing their outputs with toehold-mediated strand displacement circuits, a dynamic DNA nanotechnology that enables molecular computation through programmable interactions between nucleic acid strands. We develop design rules for interfacing biosensors with strand displacement circuits, show that these circuits allow fine-tuning of reaction kinetics and faster response times, and demonstrate a circuit that acts like an analog-to-digital converter to create a series of binary outputs that encode the concentration range of the target molecule being detected. We believe this work establishes a pathway to create “smart” diagnostics that use molecular computations to enhance the speed, robustness and utility of biosensors.

Optical absorption properties of metal–organic frameworks: solid state versus molecular perspective

The challenges of the description of excited states in MOF crystals are addressed by periodic and molecular computations.

Quantum mechanics II–many body systems

Chapter 23 develops formalism relevant to atomic and molecular electronic structure. A review of the product Ansatz, the Slater determinant, and atomic configurations is followed by applications to small atoms. Then the self-consistent Hartree-Fock method is introduced and applied to larger atoms. Molecular structure is addressed by introducing an adiabatic separation of scales and the construction of molecular orbitals. The use of specialized bases for molecular computations is also discussed. Density functional theory and its application to complicated molecules is introduced and the local density approximation and the Kohn-Sham procedure for solving the functional equations are explained. Techniques for moving beyond the local density approximation are briefly reviewed.

Molecular computations with competitive neural networks that exploit linear and nonlinear kinetics

We show how to exploit enzymatic saturation -an ubiquitous nonlinear effects in biochemistry- in order to process information in molecular networks. The networks rely on the linearity of DNA strand displacement and the nonlinearity of enzymatic replication. We propose a pattern-recognition network that is compact and should be robust to leakage.

pH controllable photocurrent switching and molecular half-subtractor calculations based on a monolayer composite film of a dinuclear RuII complex and graphene oxide

A monolayer film composed of graphene oxide and a dinuclear Ru(ii) complex, acts as multiple logic gates performing half-subtractor molecular computations by using pH and potential as inputs and photocurrent as output.