Development of Dynamic DNA Probes for High-Content in situ Proteomic Analyses

Dynamic DNA complexes are able to undergo multiple hybridization and dissociation events through a process called strand displacement. This unique property has facilitated the creation of programmable molecular detection systems and chemical logic gates encoded by nucleotide sequence. This work examines whether the ability to selective exchange oligonucleotides among different thermodynamically-stable DNA complexes can be harnessed to create a new class of imaging probes that permit fluorescent reporters to be sequentially activated (“turned on”) and erased (“turned off”). Here, dynamic DNA complexes detect a specific DNA-conjugated antibody and undergo strand displacement to liberate a quencher strand and activate a fluorescent reporter. Subsequently, incubation with an erasing complex allows the fluorophore to be stripped from the target strand, quenched, and washed away. This simple capability therefore allows the same fluorescent dyes to be used multiple times to detect different markers within the same sample via sequential rounds of fluorescence imaging. We evaluated and optimized several DNA complex designs to function efficiently for in situ molecular analyses. We also applied our DNA probes to immunofluorescence imaging using DNA-conjugated antibodies and demonstrated the ability to at least double the number of detectable markers on a single sample. Finally, the probe complexes were reconfigured to act as AND-gates for the detection of co-localized proteins. Given the ability to visualize large numbers of cellular markers using dynamic DNA probe complexes, high-content proteomic analyses can be performed on a single sample, enhancing the power of fluorescence imaging techniques. Furthermore, dynamic DNA complexes offer new avenues to incorporate DNA-based computations and logic for in situ molecular imaging and analyses

Finite Models of Splicing and Their Complexity

Durante las dos últimas décadas ha surgido una colaboración estrecha entre informáticos, bioquímicos y biólogos moleculares, que ha dado lugar a la investigación en un área conocida como la computación biomolecular. El trabajo en esta tesis pertenece a este área, y estudia un modelo de cómputo llamado sistema de empalme (splicing system). El empalme es el modelo formal del corte y de la recombinación de las moléculas de ADN bajo la influencia de las enzimas de la restricción.Esta tesis presenta el trabajo original en el campo de los sistemas de empalme, que, como ya indica el título, se puede dividir en dos partes. La primera parte introduce y estudia nuevos modelos finitos de empalme. La segunda investiga aspectos de complejidad (tanto computacional como descripcional) de los sistema de empalme. La principal contribución de la primera parte es que pone en duda la asunción general que una definición finita, más realista de sistemas de empalme es necesariamente débil desde un punto de vista computacional. Estudiamos varios modelos alternativos y demostramos que en muchos casos tienen más poder computacional. La segunda parte de la tesis explora otro territorio. El modelo de empalme se ha estudiado mucho respecto a su poder computacional, pero las consideraciones de complejidad no se han tratado apenas. Introducimos una noción de la complejidad temporal y espacial para los sistemas de empalme. Estas definiciones son utilizadas para definir y para caracterizar las clases de complejidad para los sistemas de empalme. Entre otros resultados, presentamos unas caracterizaciones exactas de las clases de empalme en términos de clases de máquina de Turing conocidas. Después, usando una nueva variante de sistemas de empalme, que acepta lenguajes en lugar de generarlos, demostramos que los sistemas de empalme se pueden usar para resolver problemas. Por último, definimos medidas de complejidad descriptional para los sistemas de empalme. Demostramos que en este respecto los sistemas de empalme finitos tienen buenas propiedades comparadosOver the last two decades, a tight collaboration has emerged between computer scientists, biochemists and molecular biologists, which has spurred research into an area known as DNAComputing (also biomolecular computing). The work in this thesis belongs to this field, and studies a computational model called splicing system. Splicing is the formal model of the cutting and recombination of DNA molecules under the influence of restriction enzymes.This thesis presents original work in the field of splicing systems, which, as the title already indicates, can be roughly divided into two parts: 'Finite models of splicing' on the onehand and 'their complexity' on the other. The main contribution of the first part is that it challenges the general assumption that a finite, more realistic definition of splicing is necessarily weal from a computational point of view. We propose and study various alternative models and show that in most cases they have more computational power, often reaching computational completeness. The second part explores other territory. Splicing research has been mainly focused on computational power, but complexity considerations have hardly been addressed. Here we introduce notions of time and space complexity for splicing systems. These definitions are used to characterize splicing complexity classes in terms of well known Turing machine classes. Then, using a new accepting variant of splicing systems, we show that they can also be used as problem solvers. Finally, we study descriptional complexity. We define measures of descriptional complexity for splicing systems and show that for representing regular languages they have good properties with respect to finite automata, especially in the accepting variant

A Framework for Computing Discrete-Time Systems and Functions using DNA

University of Minnesota Ph.D. dissertation. July 2017. Major: Electrical/Computer Engineering. Advisors: Keshab Parhi, Marc Riedel. 1 computer file (PDF); xvii, 216 pages.Due to the recent advances in the field of synthetic biology, molecular computing has emerged as a non-conventional computing technology. A broad range of computational processes has been considered for molecular implementation. In this dissertation, we investigate the development of molecular systems for performing the following computations: signal processing, Markov chains, polynomials, and mathematical functions. First, we present a \textit{fully asynchronous} framework to design molecular signal processing algorithms. The framework maps each delay unit to two molecular types, i.e., first-type and second-type, and provides a 4-phase scheme to synchronize data flow for any multi-input/multi-output signal processing system. In the first phase, the input signal and values stored in all delay elements are consumed for computations. Results of computations are stored in the first-type molecules corresponding to the delay units and output variables. During the second phase, the values of the first-type molecules are transferred to the second-type molecules for the output variable. In the third phase, the concentrations of the first-type molecules are transferred to the second-type molecules associated with each delay element. Finally, in the fourth phase, the output molecules are collected. The method is illustrated by synthesizing a simple finite-impulse response (FIR) filter, an infinite-impulse response (IIR) filter, and an 8-point real-valued fast Fourier transform (FFT). The simulation results show that the proposed framework provides faster molecular signal processing systems compared to prior frameworks. We then present an overview of how continuous-time, discrete-time and digital signal processing systems can be implemented using molecular reactions. We also present molecular sensing systems where molecular reactions are used to implement analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). These converters can be used to design mixed-signal processing molecular systems. A complete example of the addition of two molecules using digital implementation is described where the concentrations of two molecules are converted to digital by two 3-bit ADCs, and the 4-bit output of the digital adder is converted to analog by a 4-bit DAC. Furthermore, we describe implementation of other forms of molecular computation. We propose an approach to implement any first-order Markov chain using molecular reactions in general and DNA in particular. The Markov chain consists of two parts: a set of states and state transition probabilities. Each state is modeled by a unique molecular type, referred to as a data molecule. Each state transition is modeled by a unique molecular type, referred to as a control molecule, and a unique molecular reaction. Each reaction consumes data molecules of one state and produces data molecules of another state. The concentrations of control molecules are initialized according to the probabilities of corresponding state transitions in the chain. The steady-state probability of the Markov chain is computed by the equilibrium concentration of data molecules. We demonstrate our method for the Gambler’s Ruin problem as an instance of the Markov chain process. We analyze the method according to both the stochastic chemical kinetics and the mass-action kinetics model. Additionally, we propose a novel {\em unipolar molecular encoding} approach to compute a certain class of polynomials. In this molecular encoding, each variable is represented using two molecular types: a \mbox{type-0} and a \mbox{type-1}. The value is the ratio of the concentration of type-1 molecules to the sum of the concentrations of \mbox{type-0} and \mbox{type-1} molecules. With the new encoding, CRNs can compute any set of polynomial functions subject only to the limitation that these polynomials can be expressed as linear combinations of Bernstein basis polynomials with positive coefficients less than or equal to 1. The proposed encoding naturally exploits the expansion of a power-form polynomial into a Bernstein polynomial. We present molecular encoders for converting any input in a standard representation to the fractional representation, as well as decoders for converting the computed output from the fractional to a standard representation. Lastly, we expand the unipolar molecular encoding for bipolar molecular encoding and propose simple molecular circuits that can compute multiplication and scaled addition. Using these circuits, we design molecular circuits to compute more complex mathematical functions such as $e^{-x}$, $\sin (x)$, and sigmoid$(x)$. According to this approach, we implement a molecular perceptron as a simple artificial neural network

RNA folding kinetics including pseudoknots

RNA Moleküle sind ein essenzieller Bestandteil biologischer Zellen. Ihre Vielfalt an Funktionen ist eng verknüpft mit der jeweiligen Sequenz und der daraus gebildeten Struktur. Der Großteil bekannter RNA Moleküle faltet in eine bestimmte energetisch stabile Struktur, bzw. ̈hnliche suboptimale Strukturen mit der gleichen biologischen Funktion. Riboswitches hingegen, eine bestimmte Gruppe von RNA Molekülen können zwischen zwei strukturell sehr verschiedenen Konformationen wechseln, wobei eine funktional ist und die andere nicht. Die Umfaltung solcher RNA-Schalter wird normalerweise durch verschiedenste Metaboliten ausgelöst die mit der RNA interagieren. Zellen nutzen dieses Prinzip um auf Signale aus der Umwelt effizient reagieren zu können. Im Zuge der synthetischen Biologie wurde eine neue Art von RNA-Schaltern entwickelt, die statt einem bestimmten Metaboliten ein anderes RNA Molekül erkennt [1]. Dieses Prinzip ziehlt weniger darauf ab Signale aus der Umgebung wahrzunehmen, sondern ein weiteres Level an Genregulation zu ermöglichen. In dieser Abeit wird das Program RNAscout.pl präsentiert, welches die Umfaltung zwischen verschiedenen RNA Strukturen berechnet und damit die Effizienz RNA-induzierter RNA-Schalter bewerten kann. Der zugrundeliegenede Algorithmus berechnet ein Set an Zwischenzuständen die sowohl energetisch günstig, als auch strukturell ähnlich zu den beiden stabilen Riboswitch-Konformationen sind. Basierend auf diesem Umfaltungsnetzwerk werden kinetische Simulationen gezeigt, bei denen der Umfaltungsweg des RNA-Schalters vorhergesagt wird. Des Weiteren wird das Programm pk findpath vorgestellt. Der zugrundeliegende Algorithmus berechnet den besten direkten Umfaltungspfad zwischen zwei RNA Strukturen mittels einer Breitensuche. Beide Programme, RNAscout.pl und pk findpath, werden verwendet um abzuschätzen ob natürliche RNA Moleküle optimiert sind um in ihre energetisch günstigste Konformation zu falten. Im Zuge dessen werden die Programme mit existierenden Programmen des Vienna RNA package [2] verglichen.RNA molecules are essential components of living cells. Their wide range of different functions depends on the sequence of nucleotides and the corresponding structure. The majority of known RNA molecules fold into their energetically most stable conformation, as well as structurally similar suboptimal conformations that do not alter the specific task of the molecule. However, there are RNA molecules which can switch between two structurally distant conformations one of which is functional, the other is not. The best known examples are riboswitches, which usually sense various kinds of metabolites from their environment that trigger the refolding from one conformation into the other. The rather new field of synthetic biology led to the construction of an example for a new type of riboswitches, which refold upon interaction with other RNA molecules [1]. Such RNA-triggered riboswitches are not aimed at sensing the environment, but expand the repertoire of gene-regulation. Inspired by this example, we present RNAscout.pl, a new program to study refolding between two RNA conformations, which can be used to estimate the performance of RNA-triggered riboswitches. The underlying algorithm heuristically computes a set of intermediate conformations that are energetically favorable and structurally related to both stable conformations of the riboswitch. Based on this refolding network, we show kinetic simulations that support the expected refolding path for our riboswitch example. Moreover, we present pk findpath, a breadth-first search algorithm to estimate direct paths (i. e. a small subset of all possible paths) between two different RNA conformations. Both programs RNAscout.pl and pk findpath will be used to estimate whether natural RNA molecules are optimized to fold into their energetically most stable conformation. Thereby, we compare the new programs against existing programs of the Vienna RNA package [2

Journal of Telecommunications and Information Technology, 2011, nr 4

kwartalni

New Statistical Algorithms for the Analysis of Mass Spectrometry Time-Of-Flight Mass Data with Applications in Clinical Diagnostics

Mass spectrometry (MS) based techniques have emerged as a standard forlarge-scale protein analysis. The ongoing progress in terms of more sensitive machines and improved data analysis algorithms led to a constant expansion of its fields of applications. Recently, MS was introduced into clinical proteomics with the prospect of early disease detection using proteomic pattern matching. Analyzing biological samples (e.g. blood) by mass spectrometry generates mass spectra that represent the components (molecules) contained in a sample as masses and their respective relative concentrations. In this work, we are interested in those components that are constant within a group of individuals but differ much between individuals of two distinct groups. These distinguishing components that dependent on a particular medical condition are generally called biomarkers. Since not all biomarkers found by the algorithms are of equal (discriminating) quality we are only interested in a small biomarker subset that - as a combination - can be used as a fingerprint for a disease. Once a fingerprint for a particular disease (or medical condition) is identified, it can be used in clinical diagnostics to classify unknown spectra. In this thesis we have developed new algorithms for automatic extraction of disease specific fingerprints from mass spectrometry data. Special emphasis has been put on designing highly sensitive methods with respect to signal detection. Thanks to our statistically based approach our methods are able to detect signals even below the noise level inherent in data acquired by common MS machines, such as hormones. To provide access to these new classes of algorithms to collaborating groups we have created a web-based analysis platform that provides all necessary interfaces for data transfer, data analysis and result inspection. To prove the platform's practical relevance it has been utilized in several clinical studies two of which are presented in this thesis. In these studies it could be shown that our platform is superior to commercial systems with respect to fingerprint identification. As an outcome of these studies several fingerprints for different cancer types (bladder, kidney, testicle, pancreas, colon and thyroid) have been detected and validated. The clinical partners in fact emphasize that these results would be impossible with a less sensitive analysis tool (such as the currently available systems). In addition to the issue of reliably finding and handling signals in noise we faced the problem to handle very large amounts of data, since an average dataset of an individual is about 2.5 Gigabytes in size and we have data of hundreds to thousands of persons. To cope with these large datasets, we developed a new framework for a heterogeneous (quasi) ad-hoc Grid - an infrastructure that allows to integrate thousands of computing resources (e.g. Desktop Computers, Computing Clusters or specialized hardware, such as IBM's Cell Processor in a Playstation 3)