Date: Saturday, February 9, 2019 - 10:00
Venue: Martin Wood Lecture Theatre, Clarendon Laboratory
Speakers
Prof Ian Shipsey
Welcome from the Head of the Physics Department
Podcast
Prof Ian Shipsey delivers the welcome speech for the Saturday Morning of Theoretical Physics
Prof Andrew Turberfield
Programming Dynamic DNA Nanosystems
Presentation (PDF)
Nanofabrication by biomolecular self-assembly can be used to create atomically precise, nanometre-scale structures. The control offered by DNA-self-assembly is spectacular: thousands of oligonucleotides can be designed to form rigid, three-dimensional complexes with defined contours and internal cavities. Each oligonucleotide has a unique sequence which defines its position in these structures; chemically modified oligonucleotides can be used to position other molecular components. Synthetic nucleic acids can also form programmable dynamic systems which compute and exhibit complex temporal behaviours. RNA can be programmed to assemble within cells, and devices formed from nucleic acids can couple to and interact with living systems. I shall survey this rapidly evolving research field and its potential to provide new tools and technologies from biophysics to manufacture to medicine. ** This talk was not recorded **
Prof Julia Yeomans FRS
Topology in Biology
Podcast Presentation (PDF)
Active systems, from cells and bacteria to flocks of birds, harvest chemical energy which they use to move and to control the complex processes needed for life. A goal of biophysicists is to construct new physical theories to understand these living systems, which operate far from equilibrium. Topological defects are key to the behaviour of certain dense active systems and, surprisingly, there is increasing evidence that they may play a role in the biological functioning of bacterial and epithelial cells.
Prof Ard Louis
Why the world is simple
The coding theorem from algorithmic information theory (AIT) - which should be much more widely taught in Physics! - suggests that many processes in nature may be highly biased towards simple outputs. Here simple means highly compressible, or more formally, outputs with relatively lower Kolmogorov complexity. I will explore applications to biological evolution, where the coding theorem implies an exponential bias towards outcomes with higher symmetry, and to deep learning neural networks, where the coding theorem predicts an Occam's razor like bias that may explain why these highly over paramterised systems work so well.
