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SUMMARY:Identifiability analysis in enzyme kinetics using profile likeliho
od
DTSTART;VALUE=DATE-TIME:20180709T094500Z
DTEND;VALUE=DATE-TIME:20180709T100000Z
DTSTAMP;VALUE=DATE-TIME:20241111T072909Z
UID:indico-contribution-241@conferences.maths.unsw.edu.au
DESCRIPTION:Speakers: Rejo Anto Britto (University of Auckland)\nThe aim o
f this research was to explore the potential of proﬁle likelihoods for i
dentiﬁability analysis in the context of real enzyme kinetics data\, col
lected ourselves.\n\nParameter identifiability concerns the question of wh
ether the type of experimental data we have collected properly determines
the parameters of our mathematical models. Identifiability issues arise be
cause not all biological variables involved in the system can be measured\
, and even those that can be measured can be structurally decoupled from s
ome of the parameters of interest. Although structural non-identifiability
is in principle an all-or-nothing concept\, in practice parameters may al
so be only weakly identified or may be practically non-identifiable given
finite data. Profile likelihood has proven to be one of the few promising
general methods of identifiability analysis that is applicable to multi-pa
rameter\, nonlinear problems\, but it has not yet been widely adopted in t
he mathematical/computational biology community. Thus we sought to further
explore its usefulness for typical models in this area\, and using real e
xperimental (as opposed to synthetic) data.\n\nIn this study\, we collecte
d data on the activity of the enzyme tissue plasminogen activator (tPA) un
der variety of scenarios\, including different initial substrate and pH le
vels. We developed a series of simple reaction kinetics models\, including
both Michaelis-Menten velocity-concentration models and full time-depende
nt ODE models\, and generated profile likelihoods under the various experi
mental conditions. For the simple Michaelis-Menten model we found that par
ameters were generally identifiable/weakly identifiable but tended to beco
me less identiﬁable (approaching practical non-identifiability) at lower
pH levels. On the other hand\, individual parameters of the full ODE mode
l of enzyme kinetics showed full structural non-identiﬁability. This led
us to consider the identifiability of targeted ‘interest’ parameters\
, motivated by the parameters in the simpler system. Using this approach w
e found that certain combinations of rate parameters\, corresponding to th
ose in the simpler Michaelis-Menten model\, were better identified in the
full model.\n\nOverall we found proﬁle likelihood to be a promising tech
nique for identiﬁability analysis of enzyme kinetics models. For complex
models\, however\, choosing targeted interest parameters appears to be es
sential to avoid structural non-identifiability. Further work is needed on
systematically motivating these interest parameters based on\, for exampl
e\, simpler models and/or model reduction procedures. In the context of tP
A kinetics\, more complex reactions involving the inhibitor neuroserpin an
d interactions of H+ ions with the enzyme should be considered.\n\nhttps:/
/conferences.maths.unsw.edu.au/event/2/contributions/241/
LOCATION:University of Sydney Holme Building/--The Refectory
URL:https://conferences.maths.unsw.edu.au/event/2/contributions/241/
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