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Recent Highlights

20/02/2017 New papers submitted: Designing the Optimal Bit: Balancing Energetic Cost, Speed and Reliability, and What we learn from the learning rate.

PhD students Abhishek Deshpande and Rory Brittain have submitted their first papers as part of the group. Abhishek has investigated the fundamental question of how to construct systems that can store one bit of data reliably, and yet can be switched rapidly and at low energetic cost. Intiguingly, he has found that friction plays a complex role in this optimization, and that optimal bits exhibit moderate friction (which is something of a surprise, since friction is usually viewed as wasteful).

Rory has explored a recently-defined quantity called the "learning rate", which quantifies the degree to which a downstream system responds to changes in an upstream system to maintain correlations (or information) between the two. Despite this definition, the interpretation of the learning rate is not straightforward. Indeed, although it has previously been used as a metric for the performance of sensors, Rory has shown that this is not necessarily a sensible idea; at some level, the learning rate quantifies how hard a sensor is "trying", but not how effective it actually is. 

Functional molecular systems

Biochemical networks within cells achieve remarkable functionality, including sensing, signalling, information-processing and replication. We aim to understand the fundamental physical principles that set the scope of this behaviour, allowing development of engineering principles for artificial analogs of these systems.

Since these systems of interest typically involve small numbers of molecules, randomness plays an important role. This work often touches on the deep connections between two bodies of work that deal explicitly with randomness: information theory, which describes the role of randomness in communication, and statistical mechanics, through which randomness is related to thermodynamics.

In this work we collaborate closely with the biochemical networks group of Pieter Rein ten Wolde in Amsterdam, Nick Jones' systems and signals group and Guy Bart-Stan's control engineering synthetic biology group.

Relevant Publications

  1. Brittain RA, Jones NS, Ouldridge TE, What we learn from the learning rateSubmitted
  2. Ouldridge TE, 2017, The importance of thermodynamics for molecular systems, and the importance of molecular systems for thermodynamicsSubmitted.
  3. Ouldridge TE, ten Wolde PR, 2016, Fundamental costs in the production and destruction of persistent polymer copiesSubmitted.
  4. McGrath T, Jones NS, ten Wolde PR, Ouldridge TE, 2017, Biochemical machines for the interconversion of mutual information and workPhys. Rev. Lett.: 118:028101.
  5. ten Wolde PR, Becker NB, Ouldridge TE, Mugler A, 2015, Fundamental Limits to Cellular SensingJ. Stat. Phys.: 162,1395-1424.
  6. Ouldridge TE, Govern CC, ten Wolde PR, 2015, The thermodynamics of computational copying in biochemical systemSubmitted.
  7. Ouldridge TE, ten Wolde PR, 2014, The robustness of proofreading to crowding-induced pseudo-processivity in the MAPK pathwayBiophysical Journal: 107,2425-2435.

Coarse-grained modelling of DNA

The elegant selectivity of Watson-Crick base-pairing makes DNA an extremely useful tool for the construction of nanoscale objects and machines. Stable structures and mechanical cycles can be programmed into a system of single strands by careful choice of the sequences of bases. I'm particularly interested in using nucleic acids to design artificial analogs of complex cellular systems, to enable careful exploration of the design principles and engineering possibilities.

Despite the experimental successes, there is no clear theoretical description of the processes involved. We have developed a nucleotide-level coarse grained model of DNA, oxDNA, which is detailed enough to capture the essential physics of assembly processes, yet simple enough to be applicable over long time scales. Code, user guides and examples for simulating the model can be downloaded from this site.

The oxDNA model was developed in the Doye / Louis groups in Oxford. It has since been applied in collaboration with the Turberfield group in Oxford, the Winfree group in Caltech and the Nir group at the Ben-Gurion University of Negev, as well as being used independently by other researchers.

Even with oxDNA, it is still not practical to simulate the formation of very large structures. A collaboration with the Turberfield and Kwiatkowska groups in Oxford has led to a less detailed model that can describe the formation of DNA origami structures.

Relevant Publications

  1. Snodin BEK, Romano F, Rovigatti L, Ouldridge TE, Louis AA, and Doye JPK, 2016, Direct Simulation of the Self-Assembly of a Small DNA Origami, ACS Nano: 10,1724-1737.
  2. Dunn KE, Dannenberg F, Ouldridge TE, Kwiatkowska M, Turberfield AJ, Bath J, 2015, Guiding the folding pathway of DNA origami, Nature: 525,82-86.
  3. Dannenberg F, Dunn KE, Bath J, Kwiatkowska M, Turberfield AJ, Ouldridge TE, 2015, Modelling DNA Origami Self-Assembly at the Domain Level, J. Chem. Phys.: 143,165102.
  4. Snodin BEK, Randisi F, Mosayebi M, Sulc P, Schreck JS, Romano F, Ouldridge TE, Tsukanov R, Nir E, Louis AA, Doye JPK, 2015, Introducing improved structural properties and salt dependence into a coarse-grained model of DNA, J. Chem. Phys.: 142,234901.
  5. Schreck JS, Ouldridge TE, Romano F, Sulc P, Shaw L, Louis AA, Doye JPK, 2015, DNA hairpins primarily promote duplex melting rather than inhibiting hybridization, Nucleic Acids Research: 43,6181-6190.
  6. Mosayebi M, Louis AA, Doye JPK, Ouldridge TE, 2015, Force-induced rupture of a DNA duplex, ACS Nano: 9,11993-12003.
  7. Machinek RR, Ouldridge TE, Haley NE, Bath J, Turberfield AJ, 2014, Programmable energy landscapes for kinetic control of DNA strand displacement, Nature Communications: 5,5324.
  8. Doye JPK, Ouldridge TE, Louis AA, Romano F, Sulc P, Matek C, Snodin BEK, Rovigatti L, Schreck JS, Harrison RM, Smith WPJ, 2013, Coarse-graining DNA for simulations of DNA nanotechnology, Physical Chemistry Chemical Physics: 15,20395-20414.
  9. Srinivas N, Ouldridge TE, Sulc P, Schaeffer JM, Yurke B, Louis AA, Doye JPK, Winfree E, 2013, On the biophysics and kinetics of toehold-mediated DNA strand displacement, Nucleic Acids Research: 41,10641-10658.
  10. Ouldridge TE, Sulc P, Romano F, Doye JPK, Louis AA, 2013, DNA hybridization kinetics: Zippering, internal displacement and sequence dependence, Nucleic Acids Research: 41,8886-8895.
  11. Ouldridge TE, Hoare RL, Louis AA, Doye JPK, Bath J, Turberfield AJ, 2013, Optimizing DNA nanotechnology through coarse-grained modeling: A two-footed DNA walker, ACS Nano: 7,2479-2490.
  12. Sulc P, Romano F, Ouldridge TE, Rovigatti L, Doye JPK, Louis AA, 2012, Sequence-dependent thermodynamics of a coarse-grained DNA model, Journal of Chemical Physics: 137,135101.
  13. Ouldridge TE, Louis AA, Doye JPK, 2011, Structural, mechanical, and thermodynamic properties of a coarse-grained DNA model, Journal of Chemical Physics: 134,085101.
  14. Ouldridge TE, Louis AA, Doye JPK, 2010, DNA nanotweezers studied with a coarse-grained model of DNA, Physical Review Letters: 104,178101.

Thermodynamics of small systems

Thermodynamics, the science of heat and energy transfer, emerged as a field in the 19th century, motivated by the need to describe the engines that powered the industrial revolution. One of the challenges of modern science is to adapt and extend the theory to describe microscopic systems in which fluctuations play a key role. Biological and biologically-inspired systems are a key arena for these new ideas, due both to the need to understand natural molecular analogues of the engines and processes that we are familiar with at much larger length scales, and the possibility of developing artificial devices ourselves.

Not only does thermodynamics provide understanding of biological systems, but the study of real biophysical devices in turn provides us with a deeper understanding of the thermodynamic principles at play. In particular, the natural diffusive behaviour of biomolecules allows us to study complex systems that do not require external manipulation to function.

Relevant Publications

  1. Deshpande A, Gopalkrishnan M, Ouldridge TE, Jones NS  Designing the Optimal Bit: Balancing Energetic Cost, Speed and ReliabilitySubmitted
  2. Brittain RA, Jones NS, Ouldridge TE, What we learn from the learning rateSubmitted
  3. Ouldridge TE, 2017, The importance of thermodynamics for molecular systems, and the importance of molecular systems for thermodynamicsSubmitted.
  4. Ouldridge TE, ten Wolde PR, 2016, Fundamental costs in the production and destruction of persistent polymer copiesSubmitted.
  5. McGrath T, Jones NS, ten Wolde PR, Ouldridge TE, 2017, Biochemical machines for the interconversion of mutual information and workPhys. Rev. Lett.: 118:028101.
  6. Ouldridge TE, Govern CC, ten Wolde PR, 2015, The thermodynamics of computational copying in biochemical systemSubmitted.

Simulation tools and algorithms

Our work often involves systems that are too complex to be treated analytically. This means that simulations are a key tool in our research, and we are interested in simulation techniques and analysis tools for systems involving biomolecular reactions.

In this work we collaborate with the Doye / Louis groups in Oxford, the biochemical networks group of Pieter Rein ten Wolde in Amsterdam, Michael Tretyakov in Nottingham and Ruslan Davidchack in Leicester.

Relevant Publications

  1. Vijaykumar A, Ouldridge TE, ten Wolde PR, Bolhuis PG 2016, Multiscale simulations of anisotropic particles combining Brownian Dynamics and Green's Function Reaction DynamicsSubmitted.
  2. Davidchack RL, Ouldridge TE, Tretyakov MV, 2015, New Langevin and Gradient Thermostats for Rigid Body DynamicsJournal of Chemical Physics: 142:144114.
  3. Ouldridge TE, 2012, Inferring bulk self-assembly properties from simulations of small systems with multiple constituent species and small systems in the grand canonical ensembleJournal of Chemical Physics: 137,144105.
  4. Ouldridge TE, Louis AA, Doye JPK, 2010, Extracting bulk properties of self-assembling systems from small simulationsJournal of Physics: Condensed Matter: 22,104102.