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SUMMARY:Structural identifiability analysis: An important tool in systems
modelling - Professor Michael Chappell (University of Warwick)
DTSTART;VALUE=DATE-TIME:20240222T120000Z
DTEND;VALUE=DATE-TIME:20240222T130000Z
UID:https://new.talks.ox.ac.uk/talks/id/182dd420-e500-4d05-a55f-c509ee06fa
a8/
DESCRIPTION:For many systems (certainly those in biology\, medicine and ph
armacology) the mathematical models that are generated invariably include
state variables that cannot be directly measured and associated model para
meters\, many of which may be unknown\, and which also cannot be measured.
For such systems there is also often limited access for inputs or pertur
bations. These limitations can cause immense problems when investigating
the existence of hidden pathways or attempting to estimate unknown paramet
ers and this can severely hinder model validation. It is therefore highly
desirable to have a formal approach to determine what additional inputs a
nd/or measurements are necessary in order to reduce or remove these limita
tions and permit the derivation of models that can be used for practical p
urposes with greater confidence.\nStructural identifiability arises in the
inverse problem of inferring from the known\, or assumed\, properties of
a biomedical or biological system a suitable model structure and estimates
for the corresponding rate constants and other model parameters. Structu
ral identifiability analysis considers the uniqueness of the unknown model
parameters from the input-output structure corresponding to proposed expe
riments to collect data for parameter estimation (under an assumption of t
he availability of continuous\, noise-free observations). This is an impo
rtant\, but often overlooked\, theoretical prerequisite to experiment desi
gn\, system identification and parameter estimation\, since estimates for
unidentifiable parameters are effectively meaningless. If parameter estim
ates are to be used to inform about intervention or inhibition strategies\
, or other critical decisions\, then it is essential that the parameters b
e uniquely identifiable. \nNumerous techniques for performing a structural
identifiability analysis on linear parametric models exist and this is a
well-understood topic. In comparison\, there are relatively few technique
s available for nonlinear systems (the Taylor series approach\, similarity
transformation-based approaches\, differential algebra techniques and the
more recent observable normal form approach and symmetries approaches) an
d significant (symbolic) computational problems can arise\, even for relat
ively simple models in applying these techniques.\nIn this talk an introdu
ction to structural identifiability analysis will be provided demonstratin
g the application of the techniques available to both linear and nonlinear
parameterised systems and to models of (nonlinear mixed effects) populati
on nature.\n\nIn this talk an introduction to structural identifiability a
nalysis will be provided demonstrating the application of the techniques a
vailable to both linear and nonlinear parameterised systems and to models
of (nonlinear mixed effects) population nature.\n\nSpeakers:\nProfessor Mi
chael Chappell (University of Warwick)
LOCATION:Mathematical Institute (L3)\, Woodstock Road OX2 6GG
TZID:Europe/London
URL:https://new.talks.ox.ac.uk/talks/id/182dd420-e500-4d05-a55f-c509ee06fa
a8/
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DESCRIPTION:Talk:Structural identifiability analysis: An important tool in
systems modelling - Professor Michael Chappell (University of Warwick)
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