Discover and read the best of Twitter Threads about #mathonco
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Very excited to present my first article, "Mechanistic modeling of metastatic relapse in early-stage breast cancer (eBC) to investigate the biological impact of prognostic biomarkers" !
(supervision @SBenzekry)
#cancer #mathonco #mechanisticModeling #nlme
doi.org/10.1016/j.cmpb…
(supervision @SBenzekry)
#cancer #mathonco #mechanisticModeling #nlme
doi.org/10.1016/j.cmpb…
Our model is (voluntarily) simple and describes the metastatic process through 2 mathematical parameters: alpha (growth) and mu (dissemination), to be confronted to distant metastasis-free survival (DMFS) data 
We studied 3 clinical datasets, 2 datasets with routine data (Bergonie, n = 591 and AP-HM, n = 167) and the latter from public databases (IPC, n= 676).
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
(1/n) Excited that final version of work led by @D4N__ studying evolutionary repeatability and collateral sensitivity is out. #mathevo #clecliniccancer nature.com/articles/s4146… @CCLRI @NatureComms @CWRUSOM
(2/n) This collab between @CompSciOxford @CCLRI @CWRUSOM & @MoffittPSOC is an extension of @D4N__ ‘s initial theory paper that proposed ‘steering evolution’ as a method of extending the use of existing drugs to control infection. #AntibioticResistance journals.plos.org/ploscompbiol/a…
(3/n) using the fitness landscape metaphor, we reasoned that instances of collateral sensitivity need not be repeatable, as evolutionary branch points could lead to multiple peaks - which could have different sensitivity profiles.
