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Controlling the speed and trajectory of evolution with counterdiabatic driving -- a TWEETORIAL 0/n #evolution #mathonco #mathevo #physics #quantumcomputing biorxiv.org/content/10.110…
#Evolution is the most fundamental biological *mechanism - and drives change over time across all the kingdoms of life. We are getting better and better at *predicting* its outcomes, and *harnessing it* (directed evolution), but we haven't previously had any *control* over it 1/n
in work w\ @D4N__ we took advantage of sequential application of selective pressures in a math model to *steer evolution* & relied on taking limits (as time -> infinity) to understand where populations equilibrated in genotype space 2/n journals.plos.org/ploscompbiol/a… @PLOSCompBiol
we had a hint that these notions were applicable in experimental systems when we tried short time (order weeks) experiments in E. coli where we observed evolutionary contingencies at play 3/n nature.com/articles/s4146… @naturecomm
together with @AndriyMarusyk et al. we've extended some of these notions to cancer... and we have no reason to believe they won't generalize to any evolving system. biorxiv.org/content/10.110… 4/n
This then begs the question:

What if we could establish a degree of control over the pace and probability of a given evolutionary trajectory?

This could enable advances in #evolutionarymedicine, #directedevolution, #agriculture and #conservancy 5/n
To address this, @hinczewskilab and our group began a project using an idea from #quantumcomputing called counterdiabatic driving which allows for control of the behavior of quantum states in certain systems. which we preprinted today! 6/n biorxiv.org/content/10.110…
Alluringly, these notions have been recently shown to be portable to a physical system in the form of an optical tweezer: nature.com/articles/nphys… @NaturePhysics 7/n
Using a theoretical model of evolution (a modification of a Wright-Fisher process) on a fitness landscape we were able to derive a Fokker-Planck approximation for the genotype distribution over time. From this, a control protocol can be derived. #mathonco #mathevo 8/n
Considering a super simplified 2-d fitness landscape, this CD driving protocol (panel b) removes all evolutionary lag -- allowing for tight #control of the evolving population. 9/n
Here's a video... in red you have the CD driving protocol (top) and population (bottom) compared with a 'standard' dosing strategy (blue). #mathonco This highlights our #evolutionarycontrol protocol in a simplified system. 10/n
To see if this could work in a more 'real' situation, @emilyldolson took data from the Hartl lab that showed dose 'seascapes' - i.e. changing landscapes with changing dose - and applied this driving protocol in on a 16-dimensional genotype space (top 4 shown). and IT WORKS! 11/n
We're really excited about the possibilities of this approach in a broad range of applications in medicine and other human endeavors. We are eager for feedback, please give a read and tell us what you think! 12/n biorxiv.org/content/10.110…
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