Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems1,2,3,4,5,6,7. Recently, simple molecular-scale autonomous programmable computers were demonstrated8,9,10,11,12,13,14,15 allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for ‘logical’ control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton12,13,14,15,16,17; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes18,19,20,21,22 associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.

/pdf/an-autonomous-molecular-computer-for-logical-control-of-gene-2jpatkz47q.pdf

An autonomous molecular computer for logical control of gene expression

Digital information processing in molecular systems.

Molecular beacons are DNA probes that form a stem-and-loop structure and possess an internally quenched fluorophore. When they bind to complementary nucleic acids, they undergo a conformational transition that switches on their fluorescence. These probes recognize their targets with higher specificity than probes that cannot form a hairpin stem, and they easily discriminate targets that differ from one another by only a single nucleotide. Our results show that molecular beacons can exist in three different states: bound to a target, free in the form of a hairpin structure, and free in the form of a random coil. Thermodynamic analysis of the transitions between these states reveals that enhanced specificity is a general feature of conformationally constrained probes.

https://www.pnas.org/content/pnas/96/11/6171.full.pdf

Thermodynamic Basis of the Enhanced Specificity of Structured DNA Probes

A device, system and method for molecular computing which not only includes a suitable, renewable power source, but actually is able to receive power through the performance of the computations themselves. The molecular computing machine of the present invention actually employs the free-energy difference between its input and output to accomplish a computation, preferably by using its input DNA molecule as a partial source of energy, or alternatively by using the input DNA molecule as the sole source of energy. This molecular finite automaton preferably transforms an input DNA molecule into an output DNA molecule by digesting the input as it computes.

Programmable and autonomous computing machine made of biomolecules

Almost 40 years ago, Leonard Hayflick discovered that cultured normal human cells have limited capacity to divide, after which they become senescent -- a phenomenon now known as the 'Hayflick limit'. Hayflick's findings were strongly challenged at the time, and continue to be questioned in a few circles, but his achievements have enabled others to make considerable progress towards understanding and manipulating the molecular mechanisms of ageing.

Hayflick, his limit, and cellular ageing

The maximal clique problem has been solved by means of molecular biology techniques. A pool of DNA molecules corresponding to the total ensemble of six-vertex cliques was built, followed by a series of selection processes. The algorithm is highly parallel and has satisfactory fidelity. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.

https://science.sciencemag.org/content/sci/278/5337/446.full.pdf

Shumao Liu

Papers

DNA Solution of the Maximal Clique Problem