The effect of target secondary structure on microarray data quality

DNA? microarrays? have? become? an? invaluable? high? throughput? biotechnology? method,? which? allows? a? parallel? investigation? of? thousands? of? cellular? events? in? a? single?experiment.?The?principle?behind?the?technology?is?very?simple:?fluorescently? labeled? single? stranded? target? molecules? bind? to? their? specific? probes? deposited? on? the? microarray? surface.? However,? the? microarray? data? rarely? represent? a? yes? or? no? answer? to? a? biological? community,? but? rather? provide? a? direction? for? further? investigation.? There? is? a? complicated? quantitative? relationship? between? a? detected? spot? signal? and? the? amount? of? target? present? in? the? unknown? mixture.? We? hypothesize? that? physical? characteristics? of? probe? and? target? molecules? complicate? the?binding?reaction?between?target?and?probe.?To?test?this?hypothesis,?we?designed? a? controlled? microarray? experiment? in? which? the? amount? and? stability? of? the? secondary? structure? present? in? the? probe-binding? regions? of? target? as? biophysical? properties? of? nucleic? acids? varies? in? a? known? way.? ? Based? on? computational? simulations? of? hybridization,? we? hypothesize? that? secondary? structure? formation? in? the? target? can? result? in? considerable? interference? with? the? process? of? probe-target? binding.? ? This? interference? will? have? the? effect? of? lowering? the? spot? signal? intensity.?? We? simulated? hybridization? between? probe? and? target? and? analyzed? the? simulation? data? to? predict? how? much? the? microarray? signal? is? affected? by? folding? of? the? target? molecule,? for? the? purpose? of? developing? a? new? generation? of? microarray? design? and? analysis?software.

Detection of Pathogens in Water Using Micro and Nano-Technology

Detection of Pathogens in Water Using Micro and Nano-Technology aims to promote the uptake of innovative micro and nano-technological approaches towards the development of an integrated, cost-effective nano-biological sensor useful for security and environmental assays.  The book describes the concerted efforts of a large European research project and the achievements of additional leading research groups. The reported knowledge and expertise should support in the innovation and integration of often separated unitary processes. Sampling, cell lysis and DNA/RNA extraction, DNA hybridisation detection micro- and nanosensors, microfluidics, together also with computational modelling and risk assessment can be integrated in the framework of the current and evolving European regulations and needs. The development and uptake of molecular methods is revolutionizing the field of waterborne pathogens detection, commonly performed with time-consuming cultural methods. The molecular detection methods are enabling the development of integrated instruments based on biosensor that will ultimately automate the full pathway of the microbiological analysis of water

Detection of Pathogens in Water Using Micro and Nano-Technology

Detection of Pathogens in Water Using Micro and Nano-Technology aims to promote the uptake of innovative micro and nano-technological approaches towards the development of an integrated, cost-effective nano-biological sensor useful for security and environmental assays.  The book describes the concerted efforts of a large European research project and the achievements of additional leading research groups. The reported knowledge and expertise should support in the innovation and integration of often separated unitary processes. Sampling, cell lysis and DNA/RNA extraction, DNA hybridisation detection micro- and nanosensors, microfluidics, together also with computational modelling and risk assessment can be integrated in the framework of the current and evolving European regulations and needs. The development and uptake of molecular methods is revolutionizing the field of waterborne pathogens detection, commonly performed with time-consuming cultural methods. The molecular detection methods are enabling the development of integrated instruments based on biosensor that will ultimately automate the full pathway of the microbiological analysis of water

Pairing statistics and melting of random DNA oligomers: Finding your partner in superdiverse environments

Understanding of the pairing statistics in solutions populated by a large number of distinct solute species with mutual interactions is a challenging topic, relevant in modeling the complexity of real biological systems. Here we describe, both experimentally and theoretically, the formation of duplexes in a solution of random-sequence DNA (rsDNA) oligomers of length L = 8, 12, 20 nucleotides. rsDNA solutions are formed by 4L distinct molecular species, leading to a variety of pairing motifs that depend on sequence complementarity and range from strongly bound, fully paired defectless helices to weakly interacting mismatched duplexes. Experiments and theory coherently combine revealing a hybridization statistics characterized by a prevalence of partially defected duplexes, with a distribution of type and number of pairing errors that depends on temperature. We find that despite the enormous multitude of inter-strand interactions, defectless duplexes are formed, involving a fraction up to 15% of the rsDNA chains at the lowest temperatures. Experiments and theory are limited here to equilibrium conditions

Hierarchical assembly is more robust than egalitarian assembly in synthetic capsids

Self-assembly of complex and functional materials remains a grand challenge in soft material science. Efficient assembly depends on a delicate balance between thermodynamic and kinetic effects, requiring fine-tuning affinities and concentrations of subunits. By contrast, we introduce an assembly paradigm that allows large error-tolerance in the subunit affinity and helps avoid kinetic traps. Our combined experimental and computational approach uses a model system of triangular subunits programmed to assemble into T=3 icosahedral capsids comprising 60 units. The experimental platform uses DNA origami to create monodisperse colloids whose 3D geometry is controlled to nanometer precision, with two distinct bonds whose affinities are controlled to kBT precision, quantified in situ by static light scattering. The computational model uses a coarse-grained representation of subunits, short-ranged potentials, and Langevin dynamics. Experimental observations and modeling reveal that when the bond affinities are unequal, two distinct hierarchical assembly pathways occur, in which the subunits first form dimers in one case, and pentamers in another. These hierarchical pathways produce complete capsids faster and are more robust against affinity variation than egalitarian pathways, in which all binding sites have equal strengths. This finding suggests that hierarchical assembly may be a general engineering principle for optimizing self-assembly of complex target structures

Methods to improve gene signal : Application to cDNA microarrays

Microarrays are high throughput biological assays that allow the screening of thousands of genes for their expression. The main idea behind microarrays is to compute for each gene a unique signal that is directly proportional to the quantity of mRNA that was hybridized on the chip. A large number of steps and errors associated with each step make the generated expression signal noisy. As a result, microarray data need to be carefully pre-processed before their analysis can be assumed to lead to reliable and biologically relevant conclusions. This thesis focuses on developing methods for improving gene signal and further utilizing this improved signal for higher level analysis. To achieve this, first, approaches for designing microarray experiments using various optimality criteria, considering both biological and technical replicates, are described. A carefully designed experiment leads to signal with low noise, as the effect of unwanted variations is minimized and the precision of the estimates of the parameters of interest are maximized. Second, a system for improving the gene signal by using three scans at varying scanner sensitivities is developed. A novel Bayesian latent intensity model is then applied on these three sets of expression values, corresponding to the three scans, to estimate the suitably calibrated true signal of genes. Third, a novel image segmentation approach that segregates the fluorescent signal from the undesired noise is developed using an additional dye, SYBR green RNA II. This technique helped in identifying signal only with respect to the hybridized DNA, and signal corresponding to dust, scratch, spilling of dye, and other noises, are avoided. Fourth, an integrated statistical model is developed, where signal correction, systematic array effects, dye effects, and differential expression, are modelled jointly as opposed to a sequential application of several methods of analysis. The methods described in here have been tested only for cDNA microarrays, but can also, with some modifications, be applied to other high-throughput technologies. Keywords: High-throughput technology, microarray, cDNA, multiple scans, Bayesian hierarchical models, image analysis, experimental design, MCMC, WinBUGS.Tarkastellaan menetelmiä, joilla voidaan parantaa geneetisiä signaaleja ja hyödyntää vahvistetun signaalin käyttöä myöhemmissä analyyseissä

Feature selection and modelling methods for microarray data from acute coronary syndrome

Acute coronary syndrome (ACS) represents a leading cause of mortality and morbidity worldwide. Providing better diagnostic solutions and developing therapeutic strategies customized to the individual patient represent societal and economical urgencies. Progressive improvement in diagnosis and treatment procedures require a thorough understanding of the underlying genetic mechanisms of the disease. Recent advances in microarray technologies together with the decreasing costs of the specialized equipment enabled affordable harvesting of time-course gene expression data. The high-dimensional data generated demands for computational tools able to extract the underlying biological knowledge. This thesis is concerned with developing new methods for analysing time-course gene expression data, focused on identifying differentially expressed genes, deconvolving heterogeneous gene expression measurements and inferring dynamic gene regulatory interactions. The main contributions include: a novel multi-stage feature selection method, a new deconvolution approach for estimating cell-type specific signatures and quantifying the contribution of each cell type to the variance of the gene expression patters, a novel approach to identify the cellular sources of differential gene expression, a new approach to model gene expression dynamics using sums of exponentials and a novel method to estimate stable linear dynamical systems from noisy and unequally spaced time series data. The performance of the proposed methods was demonstrated on a time-course dataset consisting of microarray gene expression levels collected from the blood samples of patients with ACS and associated blood count measurements. The results of the feature selection study are of significant biological relevance. For the first time is was reported high diagnostic performance of the ACS subtypes up to three months after hospital admission. The deconvolution study exposed features of within and between groups variation in expression measurements and identified potential cell type markers and cellular sources of differential gene expression. It was shown that the dynamics of post-admission gene expression data can be accurately modelled using sums of exponentials, suggesting that gene expression levels undergo a transient response to the ACS events before returning to equilibrium. The linear dynamical models capturing the gene regulatory interactions exhibit high predictive performance and can serve as platforms for system-level analysis, numerical simulations and intervention studies

New optical super-resolution imaging approaches involving DNA nanotechnology

With recent advances in optical super-resolution microscopy, biological structures can be imaged with single-nanometre resolution using visible light. One implementation thereof, DNA-PAINT (Point Accumulation for Imaging in Nano-scale Topography), is based on the highly specific and transient binding of fluorescently labelled oligonucleotides, the "imager strands", to complementary strands with which the targets are labelled, the "docking strands". The imager-docking binding events are detected as fluorescence blinking and can be localised with single-nanometre precision. From the set of localised events a super-resolution image can be assembled. DNA-PAINT has multiple advantages over other imaging methods, e.g. high photon yields resulting in high resolution, a free choice of fluorophores while being effectively free from photobleaching, straightforward implementation on a conventional fluorescence microscope and the possibility of temporally multiplexed and quantitative imaging. In this thesis, a test sample based on functionalised microspheres is developed, which allows for optimisation of various DNA-PAINT imaging parameters and for the characterisation and testing of new variations and modifications of DNA-PAINT. One such method which was developed for this thesis, Quencher-Exchange-PAINT, facilitates temporally multiplexed imaging, which is based on the sequential exchange of imager strands targeting different docking strands. The exchange step is replaced by addition of competitive quencher-strands, allowing for rapid, low-crosstalk imager exchange even in biological samples with limited diffusion. Additionally, Proximity-Dependent PAINT is introduced, which enables the imaging of the nanoscale distribution of protein pairs by interaction of two proximity probes which activates DNA-PAINT type binding. The technique is demonstrated both on the microsphere assay as well as in biological samples. Finally, approaches for enhancing the signal-to-noise ratio are explored

KINETICS OF POLYMER CYCLIZATION REACTION AND NOVEL COVALENT DNA CROSS-LINKING ASSAYS

In this dissertation I first do an extensive review of polymer cyclization kinetics. Different theories of polymer cyclization kinetics, their assumptions and their predictions are presented along with the predictions of computer simulations. In addition, the experimental results for synthetic and biological polymers are summarized