Tailored Synthetic sRNAs Dynamically Tune Multilayer Genetic Circuits.

Predictable and controllable tuning of genetic circuits to regulate gene expression, including modulation of existing circuits or constructs without the need for redesign or rebuilding, is a persistent challenge in synthetic biology. Here, we propose rationally designed new small RNAs (sRNAs) that dynamically modulate gene expression of genetic circuits with a broad range (high, medium, and low) of repression. We designed multiple multilayer genetic circuits in which the variable effector element is a transcription factor (TF) controlling downstream the production of a reporter protein. The sRNAs target TFs instead of a reporter gene, and harnessing the intrinsic RNA-interference pathway in E. coli allowed for a wide range of expression modulation of the reporter protein, including the most difficult to achieve dynamic switch to an OFF state. The synthetic sRNAs are expressed independently of the circuit(s), thus allowing for repression without modifying the circuit itself. Our work provides a frame for achieving independent modulation of gene expression and dynamic and modular control of the multilayer genetic circuits by only including an independent control circuit expressing synthetic sRNAs, without altering the structure of existing genetic circuits.

Permutation parity machines for neural cryptography.

Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

Algorithm for thermodynamically based prediction of DNA/DNA cross-hybridisation.

A careful design of DNA strands is crucial for several biological applications such as microarray techniques, Polymerase Chain Reaction (PCR), and DNA computing. For this, the important criterion under laboratory conditions is the hybridisation energy of two DNA strands. During the last decade, a thermodynamic model was developed that allows for the calculation of the DNA/DNA hybridisation energy and recently also the cross-hybridisation energy of structural motifs. Employing this model a new algorithm for the secondary structure prediction of DNA/DNA cross-hybridisation complexes called HYBGRAPH is introduced. The method is based on Gibbs free energy minimisation and the paradigm of dynamic programming.

Exploiting the features of the finite state automata for biomolecular computing.

Here, we review patents that have emerged in the field of DNA-based computing focusing thereby on the discoveries using the concept of molecular finite state automata. A finite state automaton, operating on a finite sequence of symbols and converting information from one to another, provides a basis for developing molecular-scale autonomous programmable models of biomolecular computation at cellular level. We also provide a brief overview on inventions which methodologically support the DNA-based computational approach.

An autonomous DNA model for finite state automata.

In this paper we introduce an autonomous DNA model for finite state automata. This model called sticker automaton model is based on the hybridisation of single stranded DNA molecules (stickers) encoding transition rules and input data. The computation is carried out in an autonomous manner by one enzyme which allows us to determine whether a resulting double-stranded DNA molecule belongs to the automaton's language or not.

Computational genes: a tool for molecular diagnosis and therapy of aberrant mutational phenotype.

BACKGROUND: A finite state machine manipulating information-carrying DNA strands can be used to perform autonomous molecular-scale computations at the cellular level. RESULTS: We propose a new finite state machine able to detect and correct aberrant molecular phenotype given by mutated genetic transcripts. The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced. We experimentally demostrated in in vitro translation system that a viable protein can be autonomously assembled. CONCLUSION: Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function.

Biomolecular autonomous solution of the Hamiltonian path problem via hairpin formation.

The Hamiltonian path problem is one of the famous hard combinatorial problems. We provide the first molecular-scale autonomous solution of the decision Hamiltonian path problem. It is based on the formation of secondary structures of DNA molecules.