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SUMMARY:Identifying cell-to-cell variability using mathematical and statis
tical modelling - Dr Alex Browning (Dept of Mathematics\, University of Ox
ford)
DTSTART;VALUE=DATE-TIME:20221111T140000Z
DTEND;VALUE=DATE-TIME:20221111T150000Z
UID:https://talks.ox.ac.uk/talks/id/958ea283-6cef-4f68-bbb1-20b75cd1e255/
DESCRIPTION:Cell-to-cell variability is often a primary source of variabil
ity in experimental data. Yet\, it is common for mathematical analysis of
biological systems to neglect biological variability by assuming that mode
l parameters remain fixed between measurements. In this two-part talk\, I
present new mathematical and statistical tools to identify cell-to-cell va
riability from experimental data\, based on mathematical models with rando
m parameters. First\, I identify variability in the internalisation of mat
erial by cells using approximate Bayesian computation and noisy flow cytom
etry measurements from several million cells. Second\, I develop a computa
tionally efficient method for inference and identifiability analysis of ra
ndom parameter models based on an approximate moment-matched solution cons
tructed through a multivariate Taylor expansion. Overall\, I show how anal
ysis of random parameter models can provide more precise parameter estimat
es and more accurate predictions with minimal additional computational cos
t compared to traditional modelling approaches.\nSpeakers:\nDr Alex Browni
ng (Dept of Mathematics\, University of Oxford)
LOCATION:Mathematical Institute (L3)\, Woodstock Road OX2 6GG
TZID:Europe/London
URL:https://talks.ox.ac.uk/talks/id/958ea283-6cef-4f68-bbb1-20b75cd1e255/
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DESCRIPTION:Talk:Identifying cell-to-cell variability using mathematical a
nd statistical modelling - Dr Alex Browning (Dept of Mathematics\, Univers
ity of Oxford)
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