|Notification||May 15, 2018|
|Camera-ready||May 25, 2018|
|Workshop||July 19, 2018|
Controlling Assembly and Function of DNA Nanostructures and Molecular Machinery
Andrew J. Turberfield, University of Oxford
The programmability of DNA and RNA base pairing has enabled the creation of a very wide range of synthetic nanostructures: it is possible to synthesize synthetic oligonucleotides such that a target structure, by design the most stable assembly product, forms spontaneously when these molecular components are mixed. More sophisticated design techniques can be used to control the kinetics as well as the thermodynamics of the interactions between nucleic acid molecules, creating the potential to improve yields through design of assembly pathways and allowing the construction of dynamic systems that process information and of synthetic molecular machinery. Techniques of simulation and verification are important in understanding and designing these increasingly complex systems. I shall present a broad review of this rapidly developing research field, with particular emphasis on our work on DNA origami assembly pathways, kinetic control of strand displacement reactions, molecular motors, and molecular machinery for the control of chemical synthesis.
Uncovering the Biological Programs that Govern Development
Sara-Jane Dunn, Microsoft Research
The developmental process by which complex tissues, organs and organisms develop begins with pluripotency: the ability of so-called ‘naïve’ embryonic stem cells to generate the full spectrum of adult cell types, as well as the germline. Understanding how these cells differentiate to diverse fate-restricted lineages is key both to understand the biological programs that govern development, but also to utilise the power of these cells for regenerative medicine. Fate decisions arise as the consequence of a complex interplay between regulatory factors, and while experiments have revealed critical genes and possible interactions between them, our understanding of stem cell decision-making remains fragmentary. Against this backdrop, automated reasoning provides a powerful strategy to navigate this complexity and to derive interaction networks that are consistent with experimental ‘specifications’. These networks can subsequently be used to formulate predictions of untested behaviour that guide experiment and inform model refinement. In this talk, I will describe such a reasoning methodology, which has been applied to investigate stem cell pluripotency through an iterative computational and experimental strategy. Furthermore, I will show how this approach has generated insight into how fate-restricted cells can be ‘reprogrammed’ to the embryonic stem-like state.