DNA Staged Self-Assembly at Temperature 1

We introduce alternate temperature 1 self-assembly constructions of an n x n square by efficiently utilizing bins and stages to achieve desirable results. These bins are able to contain a variety of tiles or supertiles, which are then mixed together in a pre-determined sequence of distinct stages. The basic 2D tile assembly model at temperature 1 uses 2n-1 tile types to construct a square. The model only utilizes one bin and occurs all in one stage. We will demonstrate how the use of bins and stages will allow for the construction of these squares more efficiently

Temperature 1 Self-Assembly: Deterministic Assembly in 3D and Probabilistic Assembly in 2D

We investigate the power of the Wang tile self-assembly model at temperature 1, a threshold value that permits attachment between any two tiles that share even a single bond. When restricted to deterministic assembly in the plane, no temperature 1 assembly system has been shown to build a shape with a tile complexity smaller than the diameter of the shape. In contrast, we show that temperature 1 self-assembly in 3 dimensions, even when growth is restricted to at most 1 step into the third dimension, is capable of simulating a large class of temperature 2 systems, in turn permitting the simulation of arbitrary Turing machines and the assembly of $n\times n$ squares in near optimal $O(\log n)$ tile complexity. Further, we consider temperature 1 probabilistic assembly in 2D, and show that with a logarithmic scale up of tile complexity and shape scale, the same general class of temperature $\tau=2$ systems can be simulated with high probability, yielding Turing machine simulation and $O(\log^2 n)$ assembly of $n\times n$ squares with high probability. Our results show a sharp contrast in achievable tile complexity at temperature 1 if either growth into the third dimension or a small probability of error are permitted. Motivated by applications in nanotechnology and molecular computing, and the plausibility of implementing 3 dimensional self-assembly systems, our techniques may provide the needed power of temperature 2 systems, while at the same time avoiding the experimental challenges faced by those systems

Approches colloïdale et bio-inspirée en nanoplasmonique

Le confinement et le guidage de l'énergie lumineuse à l'échelle nanométrique dans des composants colloïdaux requiert le contrôle précis (i) de la morphologie des nanoparticules, (ii) de leur agencement spatial dans des architectures d'ordre supérieur et (iii) du couplage entre structures plasmoniques et molécules photoactives. Ce travail de thèse explore des approches nouvelles de synthèse, essentiellement bio-inspirées, de ces trois défis. Dans un premier temps, nous avons utilisé les principes de biominéralisation pour ajuster le couplage entre plasmon et fluorophore et ainsi contrôler l'exaltation de fluorescence. La fluorescence d'ensembles finis et organisés de fluorophores (porphyrines) appelé agrégats J est modulée par leur encapsulation dans une fine couche de silice d'épaisseur contrôlée (entre 2 ± 1 nm et 12 ± 1 nm) produite par minéralisation, suivie de l'accrochage de nanoparticules d'or ou d'argent. Les agrégats J servent de template à la minéralisation silicée qui renforce alors leur stabilité mécanique, permet d'adsorption spécifique de nanoparticules métalliques et joue le rôle d'espaceur diélectrique permettant une optimisation du couplage exciton-plasmon. L'exaltation de fluorescence par les plasmons a ainsi pu être optimisée à plus de 400% et 200% par conjugaison de particules d'argent et d'or respectivement sur les agrégats J minéralisés. Notre approche colloïdale ascendante pourrait contribuer à la conception de sondes optiques pour des applications capteurs ou en imagerie mais s'inscrit aussi dans la recherche de systèmes efficaces pour le traitement de l'information optique par intégrations de structures plasmoniques cristallines et d'absorbeurs /émetteurs moléculaires. Dans un deuxième temps, nous avons exploré de nouvelles méthodes de contrôle de la morphologie de nanoparticules métalliques et de leur auto-assemblage en utilisant des protéines artificielles appelées α-Repins. Le principal avantage de ces protéines artificielles est leur grande stabilité thermique et leur structure tridimensionnelle robuste et modulable par concaténation de portions de séquence tout en permettant une variabilité de certains acides aminés. Pour la première fois, ces protéines ont été utilisées comme agents directeurs de croissance de nanoparticules d'or, ce qui nous a permis de produire des particules sphériques, prismatiques triangulaires, des nanobâtonnets par effet template des protéines de formes différentes. Dans des conditions particulières, nous avons aussi pu produire des nanoparticules fluorescentes d'or de 2-6 nm de diamètre. Par ailleurs, des paires de protéines α-Repins, sélectionnées par évolution dirigée pour leur affinité mutuelle, ont été conjuguées à des populations différentes de nanoparticules. L'auto-assemblage massif et spontané des nanoparticules est alors induit lors du mélange de population portant des protéines complémentaires. Ces résultats constituent la première étape de la construction d'une approche généralisation dans laquelle des protéines artificielles peuvent être conçues et produites pour contrôler la structure cristalline et la morphologie de particules plasmoniques ou bien induire leur couplage spécifique avec d'autres particules fonctionnelles permettant ainsi d'envisager la construction d'architectures colloïdales plasmoniques complexes.Confinement and guiding of light energy at nanoscale in devices composed of colloidal building blocks, requires a precise control of (i) the morphology of the nanoparticles, (ii) their spatial organization into larger scale architectures and (iii) the coupling between plasmonic colloid and optically active. This thesis work explores new synthetic approaches, including bio-inspired ones, of these three challenges. As a first insight, we have employed biomineralization principles to tune the plasmon-fluorophore coupling in order to control the fluorescence enhancement. The fluorescence properties of a well-organized, finite ensemble of porphyrins called J-aggregates is modulated by the templated encapsulation of silica of controlled thickness, in the range of 2 ± 1 nm to 12 ± 1 nm, and its decoration with Au and Ag nanoparticles. Porphyrin J-aggregates act as templates for the silica mineralization, while the inorganic shell first provides a mechanical stability and also becomes a template for the specific binding Au or Ag nanoparticles with a dielectric spacing for optimal exciton-plasmon coupling. The metal-enhanced fluorescence can be optimized exceeding 400% and about 200% with the conjugation of Ag and Au nanoparticles on templated J-aggregates respectively. Such bottom-up templated constructions could contribute to the design of optical probes for sensing and imaging applications but also to the efficient integration of molecular absorbers and emitters into plasmonic devices for optical information processing. In the second part we explored new methods to control the morphology of metallic nanoparticles, and their self-assembly using artificial proteins called a-Repins. The main advantages of these artificial proteins are there high thermal stability and their well-defined and robust 3D structure, which can be modulated by concatenation of a portion of the sequence while preserving some variability for some amino acid positions. The direct chemical reaction of these a-Rep proteins with Au sol results in the particles of spherical triangular, rod and wire shaped morphology where proteins acts as a template. Also fluorescent nanoclusters of size 2-6nm has been obtained when a-Rep proteins are used as a stabilizing agents. Finally, pairs of a-Rep proteins with mutual affinity have been selected by phage display and conjugated with different population of nanoparticles. Massive and spontaneous self-assembly was triggered by mixing these two particle particles populations bearing complementary proteins. These results are the first steps of the development of a versatile biomolecular toolbox in which artificial proteins can be fully designed to either control the crystallographic structure and morphology of plasmonic nanoparticles or induce their specific coupling to other functional nanoparticles therefore allowing to construct plasmonic and metamaterials colloidal architectures

Machine Learning Models for Deciphering Regulatory Mechanisms and Morphological Variations in Cancer

The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances in Deep Learning (DL), in particular, are transforming many branches of engineering, science, and technology. Several of these methodologies have already been adapted for harnessing biological datasets; however, there is still a need to further adapt and tailor these techniques to new and emerging technologies. In this dissertation, we present computational algorithms and tools that we have developed to study gene-regulation and cellular morphology in cancer. The models and platforms that we have developed are general and widely applicable to several problems relating to dysregulation of gene expression in diseases. Our pipelines and software packages are disseminated in public repositories for larger scientific community use. This dissertation is organized in three main projects. In the first project, we present Causal Inference Engine (CIE), an integrated platform for the identification and interpretation of active regulators of transcriptional response. The platform offers visualization tools and pathway enrichment analysis to map predicted regulators to Reactome pathways. We provide a parallelized R-package for fast and flexible directional enrichment analysis to run the inference on custom regulatory networks. Next, we designed and developed MODEX, a fully automated text-mining system to extract and annotate causal regulatory interaction between Transcription Factors (TFs) and genes from the biomedical literature. MODEX uses putative TF-gene interactions derived from high-throughput ChIP-Seq or other experiments and seeks to collect evidence and meta-data in the biomedical literature to validate and annotate the interactions. MODEX is a complementary platform to CIE that provides auxiliary information on CIE inferred interactions by mining the literature. In the second project, we present a Convolutional Neural Network (CNN) classifier to perform a pan-cancer analysis of tumor morphology, and predict mutations in key genes. The main challenges were to determine morphological features underlying a genetic status and assess whether these features were common in other cancer types. We trained an Inception-v3 based model to predict TP53 mutation in five cancer types with the highest rate of TP53 mutations. We also performed a cross-classification analysis to assess shared morphological features across multiple cancer types. Further, we applied a similar methodology to classify HER2 status in breast cancer and predict response to treatment in HER2 positive samples. For this study, our training slides were manually annotated by expert pathologists to highlight Regions of Interest (ROIs) associated with HER2+/- tumor microenvironment. Our results indicated that there are strong morphological features associated with each tumor type. Moreover, our predictions highly agree with manual annotations in the test set, indicating the feasibility of our approach in devising an image-based diagnostic tool for HER2 status and treatment response prediction. We have validated our model using samples from an independent cohort, which demonstrates the generalizability of our approach. Finally, in the third project, we present an approach to use spatial transcriptomics data to predict spatially-resolved active gene regulatory mechanisms in tissues. Using spatial transcriptomics, we identified tissue regions with differentially expressed genes and applied our CIE methodology to predict active TFs that can potentially regulate the marker genes in the region. This project bridged the gap between inference of active regulators using molecular data and morphological studies using images. The results demonstrate a significant local pattern in TF activity across the tissue, indicating differential spatial-regulation in tissues. The results suggest that the integrative analysis of spatial transcriptomics data with CIE can capture discriminant features and identify localized TF-target links in the tissue

Doctor of Philosophy

dissertationThermal ablation is widely used, first line local-regional therapy for unresectable hepatocellular carcinoma (HCC). Although high temperature delivered by thermal energy results in efficient coagulation necrosis in tumor cells, various factors including tumor size, shape, location, and cirrhosis can lead to un-uniform heat distribution and inefficient cell damage. As a result, the incomplete ablation causes high rates of tumor recurrence and poor survival for HCC patients. Cells that are not completely ablated can induce heat shock proteins (HSPs), which are cellular gatekeepers to protect tumor cells from thermal damage and prepare them for future neoplastic growth. Synchronous adjuvant chemotherapy targeting those cells can achieve more complete tumor abrogation and prevent future tumor recurrence. This dissertation describes a strategy to combat postablation recurrence by synchronous inhibition of heat shock protein 90 (HSP90) by thermo-responsive, elastin-like polypeptide (ELP)-based biopolymer conjugates. ELP copolymer carries high concentrations of a potent HSP90 inhibitor, geldanamycin (GA), which inhibit the induction of HSP90 and further destabilize numerous HSP90 client proteins critical for cell survival. It is hypothesized that combination of thermal ablation with concomitant inhibition of HSP90 via ELP-GA conjugates can achieve synergistic anticancer effect. Specifically, the ablation-created hyperthermia will sensitize tumor cells to be more vulnerable to the drug, which will be conjugated with high concentrations through thermally targeted, ELP-based biopolymer systems. The ELP conjugates, in turn, will reach and kill the remaining viable cells to prevent future recurrence. ELP-GA conjugates that ferry multiple GAs and rapidly respond to hyperthermia were synthesized, characterized, and evaluated for activity in HCC models. The cytotoxicity of ELP-GA conjugates was enhanced with hyperthermia treatment, and effective HSP90 inhibition was achieved in HCC cell lines. In a tumor-bearing mouse model, electrocautery-based thermal ablation offered effective destruction of tumor core and created a hyperthermia zone for targeted delivery and accumulation of ELP-GA conjugates. Results demonstrate that the combination of thermal ablation and targeted HSP90 inhibition can enhance the anticancer effect and cellular delivery of macromolecular chemotherapeutics to achieve safe, synergistic, and long-term anticancer effect with no tumor recurrence observed. The combination approach paves the way for developing molecular-targeted intervention to increase the efficacy of first-line local-regional therapies for HCC

Bacteriophages

Bacteriophages have received attention as biological control agents since their discovery and recently their value as tools has been further emphasized in many different fields of microbiology. Particularly, in drug design and development programs, phage and prophage genomics provide the field with new insights. Bacteriophages reveals information on the organisms ranging from their biology to their applications in agriculture and medicine. Contributors address a variety of topics capturing information on advancing technologies in the field. The book starts with the biology and classification of bacteriophages with subsequent chapters addressing phage infections in industrial processes and their use as therapeutic or biocontrol agents. Microbiologists, biotechnologists, agricultural, biomedical and sanitary engineers will find Bacteriophages invaluable as a solid resource and reference book