BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

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2458 Despite dramatic improvements in treatment over the past 40 years, acute lymphoblastic leukemia (ALL) remains one of the most common causes of death from disease in childhood. Glucocorticoids are among the most effective agents used in the treatment of lymphoid malignancies, and patient response to treatment is an important determinant of long-term outcome in childhood ALL. In spite of its clinical significance, the molecular basis of glucocorticoid resistance is still poorly understood. The aim of this study was to develop an experimental model system to define clinically relevant mechanisms of glucocorticoid resistance in childhood ALL. An in vivo model of childhood ALL has been developed in our laboratory, using patient biopsies established as xenografts in immune-deficient nonobese diabetic severe-combined immunodeficient (NOD/SCID) mice. This model is highly representative of the human disease (Lock et al., Blood, 99: 4100-4108, 2002). The in vivo responses of these xenografts to the glucocorticoid dexamethasone (DEX) correlated significantly with patient outcome (p 1 μM) in xenografts from six patients, five of whom died of their disease. In contrast, four DEX-sensitive xenografts (IC50 values 2-fold in sensitive xenografts within 8 hours of treatment. In contrast, Bim induction was dramatically attenuated in DEX-resistant xenografts. These results have identified a clinically significant and novel mechanism of glucocorticoid resistance in childhood ALL, which occurs downstream of receptor-ligand interactions, but upstream of the signalling pathway resulting in Bim induction and apoptosis.

Novel mechanisms of glucocorticoid resistance in childhood acute lymphoblastic leukemia.

Nanoscale robots have potential as intelligent drug delivery systems that respond to molecular triggers. Using DNA origami we constructed an autonomous DNA robot programmed to transport payloads and present them specifically in tumors. Our nanorobot is functionalized on the outside with a DNA aptamer that binds nucleolin, a protein specifically expressed on tumor-associated endothelial cells, and the blood coagulation protease thrombin within its inner cavity. The nucleolin-targeting aptamer serves both as a targeting domain and as a molecular trigger for the mechanical opening of the DNA nanorobot. The thrombin inside is thus exposed and activates coagulation at the tumor site. Using tumor-bearing mouse models, we demonstrate that intravenously injected DNA nanorobots deliver thrombin specifically to tumor-associated blood vessels and induce intravascular thrombosis, resulting in tumor necrosis and inhibition of tumor growth. The nanorobot proved safe and immunologically inert in mice and Bama miniature pigs. Our data show that DNA nanorobots represent a promising strategy for precise drug delivery in cancer therapy.

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A DNA nanorobot functions as a cancer therapeutic in response to a molecular trigger in vivo.

DNA has become one of the most extensively used molecular building blocks for engineering self-assembling materials. DNA origami is a technique that uses hundreds of short DNA oligonucleotides, called staple strands, to fold a long single-stranded DNA, which is called a scaffold strand, into various designer nanoscale architectures. DNA origami has dramatically improved the complexity and scalability of DNA nanostructures. Due to its high degree of customization and spatial addressability, DNA origami provides a versatile platform with which to engineer nanoscale structures and devices that can sense, compute, and actuate. These capabilities open up opportunities for a broad range of applications in chemistry, biology, physics, material science, and computer science that have often required programmed spatial control of molecules and atoms in three-dimensional (3D) space. This review provides a comprehensive survey of recent developments in DNA origami structure, design, assembly, and directed self-assemb...

DNA Origami: Scaffolds for Creating Higher Order Structures

Over the past decade, we have seen rapid advances in applying nanotechnology in biomedical areas including bioimaging, biodetection, and drug delivery. As an emerging field, DNA nanotechnology offers simple yet powerful design techniques for self-assembly of nanostructures with unique advantages and high potential in enhancing drug targeting and reducing drug toxicity. Various sequence programming and optimization approaches have been developed to design DNA nanostructures with precisely engineered, controllable size, shape, surface chemistry, and function. Potent anticancer drug molecules, including Doxorubicin and CpG oligonucleotides, have been successfully loaded on DNA nanostructures to increase their cell uptake efficiency. These advances have implicated the bright future of DNA nanotechnology-enabled nanomedicine. In this review, we begin with the origin of DNA nanotechnology, followed by summarizing state-of-the-art strategies for the construction of DNA nanostructures and drug payloads delivered ...

DNA Nanotechnology-Enabled Drug Delivery Systems

It was suggested more than thirty years ago that Watson-Crick base pairing might be used for the rational design of nanometre-scale structures from nucleic acids. Since then, and especially since the introduction of the origami technique, DNA nanotechnology has enabled increasingly more complex structures. But although general approaches for creating DNA origami polygonal meshes and design software are available, there are still important constraints arising from DNA geometry and sense/antisense pairing, necessitating some manual adjustment during the design process. Here we present a general method of folding arbitrary polygonal digital meshes in DNA that readily produces structures that would be very difficult to realize using previous approaches. The design process is highly automated, using a routeing algorithm based on graph theory and a relaxation simulation that traces scaffold strands through the target structures. Moreover, unlike conventional origami designs built from close-packed helices, our structures have a more open conformation with one helix per edge and are therefore stable under the ionic conditions usually used in biological assays.

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DNA rendering of polyhedral meshes at the nanoscale

Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.

/pdf/controlling-directed-protein-interaction-networks-in-cancer-11e5kznaw2.pdf

Controlling Directed Protein Interaction Networks in Cancer.

This paper surveys some known results in Watson-Crick automata theory. In particular, we concentrate on the computational power, complexity measures, decidability problems, and systems of Watson-Crick automata working together on the same input. This selection of topics is not exhaustive, reflecting the research interests of the authors. A series of open problems and questions is also included.

A Short Survey on Watson-Crick Automata.

On the descriptional complexity of Watson-Crick automata

Reversible cellular automata (RCA) are models of massively parallel computation that preserve information. They consist of an array of identical finite state machines that change their states synchronously according to a local update rule. By selecting the update rule properly the system has been made information preserving, which means that any computation process can be traced back step-by-step using an inverse automaton. We investigate the maximum range in the array that a cell may need to see in order to determine its previous state. We provide a tight upper bound on this inverse neighborhood size in the one-dimensional case: we prove that in a RCA with n states the inverse neighborhood is not wider than n–1, when the neighborhood in the forward direction consists of two consecutive cells. Examples are known where range n–1 is needed, so the bound is tight. If the forward neighborhood consists of m consecutive cells then the same technique provides the upper bound nm−1–1 for the inverse direction.

Eugen Czeizler

Papers

DNA rendering of polyhedral meshes at the nanoscale

Controlling Directed Protein Interaction Networks in Cancer.

A Short Survey on Watson-Crick Automata.

On the descriptional complexity of Watson-Crick automata

A tight linear bound on the neighborhood of inverse cellular automata