Design and analysis of DNA strand displacement devices using probabilistic model checking

Designing correct, robust DNA devices is difficult because of the many possibilities for unwanted interference between molecules in the system. DNA strand displacement has been proposed as a design paradigm for DNA devices, and the DNA strand displacement (DSD) programming language has been developed as a means of formally programming and analysing these devices to check for unwanted interference. We demonstrate, for the first time, the use of probabilistic verification techniques to analyse the correctness, reliability and performance of DNA devices during the design phase. We use the probabilistic model checker prism, in combination with the DSD language, to design and debug DNA strand displacement components and to investigate their kinetics. We show how our techniques can be used to identify design flaws and to evaluate the merits of contrasting design decisions, even on devices comprising relatively few inputs. We then demonstrate the use of these components to construct a DNA strand displacement device for approximate majority voting. Finally, we discuss some of the challenges and possible directions for applying these methods to more complex designs

Generation of DNA Oligomers with Similar Chemical Kinetics via in-silico Optimization

Networks of interacting DNA oligomers are useful for applications such as biomarker detection, targeted drug delivery, information storage, and photonic information processing. However, differences in the chemical kinetics of hybridization reactions, referred to as kinetic dispersion, can be problematic for some applications. Here, it is found that limiting unnecessary stretches of Watson-Crick base pairing, referred to as unnecessary duplexes, can yield exceptionally low kinetic dispersions. Hybridization kinetics can be affected by unnecessary intra-oligomer duplexes containing only 2 base-pairs, and such duplexes explain up to 94% of previously reported kinetic dispersion. As a general design rule, it is recommended that unnecessary intra-oligomer duplexes larger than 2 base-pairs and unnecessary inter-oligomer duplexes larger than 7 base-pairs be avoided. Unnecessary duplexes typically scale exponentially with network size, and nearly all networks contain unnecessary duplexes substantial enough to affect hybridization kinetics. A new method for generating networks which utilizes in-silico optimization to mitigate unnecessary duplexes is proposed and demonstrated to reduce in-vitro kinetic dispersions as much as 96%. The limitations of the new design rule and generation method are evaluated in-silico by creating new oligomers for several designs, including three previously programmed reactions and one previously engineered structure

Probability 1 computation with chemical reaction networks

The computational power of stochastic chemical reaction networks (CRNs) varies significantly with the output convention and whether or not error is permitted. Focusing on probability 1 computation, we demonstrate a striking difference between stable computation that converges to a state where the output cannot change, and the notion of limit-stable computation where the output eventually stops changing with probability 1. While stable computation is known to be restricted to semilinear predicates (essentially piecewise linear), we show that limit-stable computation encompasses the set of predicates ϕ:N→{0,1} in Δ^0_2 in the arithmetical hierarchy (a superset of Turing-computable). In finite time, our construction achieves an error-correction scheme for Turing universal computation. We show an analogous characterization of the functions f:N→N computable by CRNs with probability 1, which encode their output into the count of a certain species. This work refines our understanding of the tradeoffs between error and computational power in CRNs

Emerging Approaches to DNA Data Storage: Challenges and Prospects

With the total amount of worldwide data skyrocketing, the global data storage demand is predicted to grow to 1.75 × 1014GB by 2025. Traditional storage methods have difficulties keeping pace given that current storage media have a maximum density of 103GB/mm3. As such, data production will far exceed the capacity of currently available storage methods. The costs of maintaining and transferring data, as well as the limited lifespans and significant data losses associated with current technologies also demand advanced solutions for information storage. Nature offers a powerful alternative through the storage of information that defines living organisms in unique orders of four bases (A, T, C, G) located in molecules called deoxyribonucleic acid (DNA). DNA molecules as information carriers have many advantages over traditional storage media. Their high storage density, potentially low maintenance cost, ease of synthesis, and chemical modification make them an ideal alternative for information storage. To this end, rapid progress has been made over the past decade by exploiting user-defined DNA materials to encode information. In this review, we discuss the most recent advances of DNA-based data storage with a major focus on the challenges that remain in this promising field, including the current intrinsic low speed in data writing and reading and the high cost per byte stored. Alternatively, data storage relying on DNA nanostructures (as opposed to DNA sequence) as well as on other combinations of nanomaterials and biomolecules are proposed with promising technological and economic advantages. In summarizing the advances that have been made and underlining the challenges that remain, we provide a roadmap for the ongoing research in this rapidly growing field, which will enable the development of technological solutions to the global demand for superior storage methodologies

Probability 1 computation with chemical reaction networks

The computational power of stochastic chemical reaction networks (CRNs) varies significantly with the output convention and whether or not error is permitted. Focusing on probability 1 computation, we demonstrate a striking difference between stable computation that converges to a state where the output cannot change, and the notion of limit-stable computation where the output eventually stops changing with probability 1. While stable computation is known to be restricted to semilinear predicates (essentially piecewise linear), we show that limit-stable computation encompasses the set of predicates ϕ:N→{0,1} in Δ^0_2 in the arithmetical hierarchy (a superset of Turing-computable). In finite time, our construction achieves an error-correction scheme for Turing universal computation. We show an analogous characterization of the functions f:N→N computable by CRNs with probability 1, which encode their output into the count of a certain species. This work refines our understanding of the tradeoffs between error and computational power in CRNs

2008 GREAT Day Program

SUNY Geneseo’s Second Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1002/thumbnail.jp

Hope College Abstracts: 16th Annual Celebration of Undergraduate Research and Creative Performance

The 16th Annual Celebration of Undergraduate Research and Creative Performance was held on April 21, 2017 in the Richard and Helen DeVos Fieldhouse at Hope College and featured student-faculty collaborative research projects. This program is a record reflective of those projects between the 2016-2017 academic year

Controlling superparamagnetic particles with dynamic magnetic fields generated by a Helmholtz-coil system

Tese de mestrado em Engenharia Física, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2010The aim of this work was the creation of a novel system of magnetic tweezers which is surface free and makes possible the massive parallel measurement of macromolecule characteristics. The system is able to magnetically control superparamagnetic microparticles which control macromolecules, DNA strands or cells attached to them in a surface free environment, i.e. without the use of surfaces to which the objects are bond. This allows the system composed by a pair of beads and macromolecule to oat freely in the solution, allowing parallel and inside cell measurements. The system constructed is composed of two pairs of water cooled Helmholtz coils controlled by an electronic circuit and software. The system is mounted on an inverted fluorescence microscope. The magnetic forces acting on deferent types of particles were calculated and fully simulated. This allowed the optimization of the coils' parameters. In the present document we explain the physical concepts behind the behavior of the magnetic particles, the details of the design, fabrication and specifications of the control system and at the end we show and discuss some qualitative experiments made with the system.O objectivo deste trabalho foi a criação de um novo sistema de pinças magnéticas que funciona livre de superfícies e possibilita a medição paralela (ou massiva) de caracter ísticas de macromoléculas. O sistema é capaz de controlar magneticamente micropart ículas superparamagnéticas que controlam macromoléculas, pequenas sequências de DNA ou células a elas acopladas num ambiente livre de superfícies, o que significa sem o uso de um sistema físico para os fixar. Isto permite ao sistema composto por um par de partículas magnéticas e macromolécula flutuar livremente na solução, permitindo medições paralelas e dentro de células. O sistema é composto por dois pares de bobinas de Helmholtz arrefecidas a água, controladas por um sistema electrónico e um programa especificamente projectados para a função. O sistema está montado num microscópio invertido com capacidade para microscopia de fluorescência. As forças magnéticas que actuam nos diferentes tipos de micropartículas magnéticas foram calculadas e simuladas o que permitiu uma optimiza ção dos parâmetros das bobinas. No presente documento explicamos os conceitos físicos intervenientes no comportamento das partículas magnéticas, os detalhes do desenho, construção e especificações do sistema de controlo e no fim mostramos e discutimos algumas experiências qualitativas

Designing Scalable Biological Interfaces

This thesis presents the analysis and design of biological interfacing technologies in light of a need for radical improvements in scalability. It focuses primarily on structural and functional neural data acquisition, but also extends to other problems including genomic editing and nanoscale spatial control. Its main contributions include analysis of the physical limits of large-scale neural recording, experimental development of a screening platform for ion-dependent molecular recording devices, characterization of the design space for molecularly-annotated neural connectomics, and new designs for high-speed genome engineering and bio-nano-fabrication. Articulating governing principles and roadmaps for these domains has contributed to the initiation of multi-institutional projects that are strategically targeted towards scalability