To impart directionality to the motions of a molecular mechanism, one must overcome the random thermal forces that are ubiquitous on such small scales and in liquid solution at ambient temperature. In equilibrium without energy supply, directional motion cannot be sustained without violating the laws of thermodynamics. Under conditions away from thermodynamic equilibrium, directional motion may be achieved within the framework of Brownian ratchets, which are diffusive mechanisms that have broken inversion symmetry1–5. Ratcheting is thought to underpin the function of many natural biological motors, such as the F1F0-ATPase6–8, and it has been demonstrated experimentally in synthetic microscale systems (for example, to our knowledge, first in ref. 3) and also in artificial molecular motors created by organic chemical synthesis9–12. DNA nanotechnology13 has yielded a variety of nanoscale mechanisms, including pivots, hinges, crank sliders and rotary systems14–17, which can adopt different configurations, for example, triggered by strand-displacement reactions18,19 or by changing environmental parameters such as pH, ionic strength, temperature, external fields and by coupling their motions to those of natural motor proteins20–26. This previous work and considering low-Reynolds-number dynamics and inherent stochasticity27,28 led us to develop a nanoscale rotary motor built from DNA origami that is driven by ratcheting and whose mechanical capabilities approach those of biological motors such as F1F0-ATPase.
Doing scientific work always involves a lot of focus and scrutiny, since producing a scientific result requires several levels of depth of analysis, all of which must be as accurate and as reproducible as possible. All this required scrutiny should be naturally translated into the codebase of the scientific project. One should strive for a code that is doing what it is supposed to, is reproducible, doesn't break over time, is sufficiently clear of bugs, and with simulation results that are appropriately labelled, and more. The challenges associated with carrying out scientific work should not be made any worse by the difficulties of managing the codebase and resulting data/simulations. An unfortunate but likely outcome of this stress is that scientific codebases tend to be sloppy: folders are not organized, there is no version control, data are not provenanced properly, most scripts break over time, and the whole project is very hard, if not impossible, to reproduce. We have created the software DrWatson to make the process of scientific project management easier. In this paper we will describe how DrWatson results in an efficient scientific workflow, taking time away from project management and giving it to doing science.
We present a method for both cross estimation and iterated time series prediction of spatio temporal dynamics based on reconstructed local states, PCA dimension reduction, and local modelling using nearest neighbour methods. The effectiveness of this approach is shown for (noisy) data from a (cubic) Barkley model, the Bueno-Orovio-Cherry-Fenton model, and the Kuramoto-Sivashinsky model.
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