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Several strategies were investigated to address the three previously mentioned limitations. The first attempt was to study the effect length and conformation of polyethylene glycol (PEG) on DN stability. DNs were also coated with PEG-lipid and human serum albumin (HSA) and their stealth efficiencies were compared. The findings reveal that both PEGylation and albumin coating enhance low salt stability, increase resistance towards nuclease action and reduce uptake of DNs by macrophages. Any protective coating around a DN increases its hydrodynamic radius, which is a crucial parameter influencing their clearance. Keeping this in mind, intrinsically stable DNs that can survive low salt concentration without any polymer coating were built. Several DNA compaction agents and DNA binders were screened to stabilize DNs in low magnesium conditions. Among them arginine, lysine, bis-lysine and hexamine cobalt showed the potential to enhance DN stability.
This thesis also presents a sensitive assay, the Proximity Ligation Assay (PLA), for the estimation of DN stability with time. It requires very simple modifications on the DNs and it can yield precise results from a very small amount of sample. The applicability of PLA was successfully tested on several DNs ranging from a simple wireframe tetrahedron to a 3D origami and the protocol to collect in vivo samples, isolate the DNs and measure their stability was developed.
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The dissertation I present here specifically addresses the use of the fNCAA L-(7-hydroxycoumarin-4-yl)ethylglycine (7-HCAA) in protein-based biosensors. I demonstrate 7-HCAA’s ability to act as a Förster resonance energy transfer (FRET) acceptor with tryptophan as the FRET donor in a single protein containing multiple tryptophans. I the describe efforts to elucidate—through both spectroscopic and structural characterization—interactions within a 7-HCAA containing protein that governs 7-HCAA fluorescence. Finally, I present a top-down computational design strategy for incorporating 7-HCAA into proteins that takes advantage of previously described interactions. These reports show the applicability of 7-HCAA and the wider class of fNCAAs as a whole for their use of rationally designed biosensors.
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This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.
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Pathways of Distinction Analysis of Liver Cancer Data: Genetic Differences Between Males and Females
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Swinging arms are a key functional component of multistep catalytic transformations in many naturally occurring multi-enzyme complexes. This arm is typically a prosthetic chemical group that is covalently attached to the enzyme complex via a flexible linker, allowing the direct transfer of substrate molecules between multiple active sites within the complex. Mimicking this method of substrate channelling outside the cellular environment requires precise control over the spatial parameters of the individual components within the assembled complex. DNA nanostructures can be used to organize functional molecules with nanoscale precision and can also provide nanomechanical control. Until now, protein–DNA assemblies have been used to organize cascades of enzymatic reactions by controlling the relative distance and orientation of enzymatic components or by facilitating the interface between enzymes/cofactors and electrode surfaces. Here, we show that a DNA nanostructure can be used to create a multi-enzyme complex in which an artificial swinging arm facilitates hydride transfer between two coupled dehydrogenases. By exploiting the programmability of DNA nanostructures, key parameters including position, stoichiometry and inter-enzyme distance can be manipulated for optimal activity.
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We develop a general framework to analyze the controllability of multiplex networks using multiple-relation networks and multiple-layer networks with interlayer couplings as two classes of prototypical systems. In the former, networks associated with different physical variables share the same set of nodes and in the latter, diffusion processes take place. We find that, for a multiple-relation network, a layer exists that dominantly determines the controllability of the whole network and, for a multiple-layer network, a small fraction of the interconnections can enhance the controllability remarkably. Our theory is generally applicable to other types of multiplex networks as well, leading to significant insights into the control of complex network systems with diverse structures and interacting patterns.