Logical computation with self-assembling electric circuits.

Inspired by self-assembled biological growth, the Circuit Tile Assembly Model (cTAM) was developed to provide insights into signal propagation, information processing, and computation in bioelectric networks. The cTAM is an abstract model that produces a family of circuits of different sizes that is amenable to exact analysis. Here, the cTAM is extended to the Boolean Circuit Tile Assembly Model (bcTAM) that implements a computationally complete set of Boolean gates through self-assembled and self-controlled growth.

Systems of axon-like circuits for self-assembled and self-controlled growth of bioelectric networks.

By guiding cell and chemical migration and coupling with genetic mechanisms, bioelectric networks of potentials influence biological pattern formation and are known to have profound effects on growth processes. An abstract model that is amenable to exact analysis has been proposed in the circuit tile assembly model (cTAM) to understand self-assembled and self-controlled growth as an emergent phenomenon that is capable of complex behaviors, like self-replication. In the cTAM, a voltage source represents a finite supply of energy that drives growth until it is unable to overcome randomizing factors in the environment, represented by a threshold.

In vitro molecular machine learning algorithm via symmetric internal loops of DNA.

Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e.

Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.

Inspired by biological systems, self-assembly aims to construct complex structures. It functions through piece-wise, local interactions among component parts and has the potential to produce novel materials and devices at the nanoscale. Algorithmic self-assembly models the product of self-assembly as the output of some computational process, and attempts to control the process of assembly algorithmically.

A DNA-based pattern classifier with in vitro learning and associative recall for genomic characterization and biosensing without explicit sequence knowledge.

Background: Genetic material extracted from in situ microbial communities has high promise as an indicator of biological system status. However, the challenge is to access genomic information from all organisms at the population or community scale to monitor the biosystem's state. Hence, there is a need for a better diagnostic tool that provides a holistic view of a biosystem's genomic status.

Assessing the detection capacity of microarrays as bio/nanosensing platforms.

Microarray is one of the most powerful detection systems with multiplexing and high throughput capability. It has significant potential as a versatile biosensing platform for environmental monitoring, pathogen detection, medical therapeutics, and drug screening to name a few. To date, however, microarray applications are still limited to preliminary screening of genome-scale transcription profiling or gene ontology analysis.

A DNA assembly model of sentence generation.

Recent results of corpus-based linguistics demonstrate that context-appropriate sentences can be generated by a stochastic constraint satisfaction process. Exploiting the similarity of constraint satisfaction and DNA self-assembly, we explore a DNA assembly model of sentence generation. The words and phrases in a language corpus are encoded as DNA molecules to build a language model of the corpus.

Independent sets of DNA oligonucleotides for nanotechnology applications.

Independent sets of DNA oligonucleotides, which only bind with their Watson-Crick complements, have potential use in self-assembly of nanostructures, since they minimize errors and inefficiency from unwanted binding. A software tool implemented a thermodynamic model for DNA duplex formation and was used to generate large independent sets of DNA oligonucleotides. The principle of the approach was experimentally verified on a sample set of oligonucleotides.

Coupled equilibrium model of hybridization error for the DNA microarray and tag-antitag systems.

In this work, a detailed coupled equilibrium model is presented for predicting the ensemble average probability of hybridization error per chip-hybridized input strand, providing the first ensemble average method for estimating postannealing microarray/TAT system error rates. Following a detailed presentation of the model and implementation via the software package NucleicPark, under a mismatched statistical zipper model of duplex formation, error response is simulated for both mean-energy and randomly encoded TAT systems versus temperature and input concentration. Limiting expressions and simulated model behavior indicate the occurrence of a transition in hybridization error response, from a logarithmically convex function of temperature for excess inputs (high-error behavior), to a monotonic, log-linear function of temperature for dilute inputs (low-error behavior), a novel result unpredicted by uncoupled equilibrium models.

Estimating the sequence complexity of a random oligonucleotide population by using in vitro thermal melting and Cot analyses.

Randomly generated oligonucleotide populations have a high potential to serve as pools for selecting non-cross-hybridizing sequences, which are useful for nanoscale self-assembly and biological and biomedical applications, as well as for DNA computing applications. In this study a nonlinear kinetic model was developed for the complexity estimation of large unknown polynucleotide populations and was experimentally verified. The model was implemented to estimate the sequence complexity of the random 20 base-pair population after in vitro renaturation experiments.

Equilibrium analysis of the efficiency of an autonomous molecular computer.

In the whiplash polymerase chain reaction (WPCR), autonomous molecular computation is implemented in vitro by the recursive, self-directed polymerase extension of a mixture of DNA hairpins. Although computational efficiency is known to be reduced by a tendency for DNAs to self-inhibit by backhybridization, both the magnitude of this effect and its dependence on the reaction conditions have remained open questions. In this paper, the impact of backhybridization on WPCR efficiency is addressed by modeling the recursive extension of each strand as a Markov chain.