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BEGIN:VEVENT
DTSTAMP:20260427T084628Z
DTSTART;VALUE=DATE-TIME:20241118T130000
DTEND;VALUE=DATE-TIME:20241118T140000
SUMMARY:Mike Chappell\, ÌÇÐÄTV
TZID:Europe/London
UID:20241118-8a1785d791c69c560191cd6200db16c4@warwick.ac.uk
CREATED:20241112T214657Z
DESCRIPTION:Time: 13.00-14.00 Seminar location: A2.05B There will be an i
 nformal sandwich lunch outside D2.02 at 12.30. To join this meeting onli
 ne click here. Title: Structural Identifiability Analysis: An Important 
 Tool in Systems Modelling Abstract: For many systems (certainly those in
  biology\, medicine and pharmacology) the mathematical models that are g
 enerated invariably include state variables that cannot be directly meas
 ured and associated model parameters\, many of which may be unknown\, an
 d which also cannot be measured. For such systems there is also often li
 mited access for inputs or perturbations. These limitations can cause im
 mense problems when investigating the existence of hidden pathways or at
 tempting to estimate unknown parameters and this can severely hinder mod
 el validation. It is therefore highly desirable to have a formal approac
 h to determine what additional inputs and/or measurements are necessary 
 in order to reduce or remove these limitations and permit the derivation
  of models that can be used for practical purposes with greater confiden
 ce. Structural identifiability arises in the inverse problem of inferrin
 g from the known\, or assumed\, properties of a system a suitable model 
 structure and estimates for the corresponding rate constants and other m
 odel parameters. Structural identifiability analysis considers the uniqu
 eness of the unknown model parameters from the input-output structure co
 rresponding to proposed experiments to collect data for parameter estima
 tion (under an assumption of the availability of continuous\, noise-free
  observations). This is an important\, but often overlooked\, theoretica
 l prerequisite to experiment design\, system identification and paramete
 r estimation\, since estimates for unidentifiable parameters are effecti
 vely meaningless. If parameter estimates are to be used to inform about 
 intervention\, inhibition or control strategies\, or other critical deci
 sions\, then it is essential that the parameters be uniquely identifiabl
 e. Numerous techniques for performing a structural identifiability analy
 sis on linear parametric models exist and this is a well-understood topi
 c. In comparison\, there are relatively few techniques available for non
 linear systems (the Taylor series approach\, similarity transformation-b
 ased approaches\, differential algebra techniques and the more recent ob
 servable normal form approach and symmetries approaches) and significant
  (symbolic) computational problems can arise\, even for relatively simpl
 e models in applying these techniques. In this talk an introduction to s
 tructural identifiability analysis will be provided demonstrating the ap
 plication of the techniques available to both linear and nonlinear param
 eterised systems. A brief overview of current research in this field wil
 l also be provided. Bio: Professor Michael Chappell | School of Engineer
 ing | University of ÌÇÐÄTV
LOCATION:A2.05B
CATEGORIES:WCPM
LAST-MODIFIED:20241112T214657Z
ORGANIZER;CN=Sarah Jarratt:
END:VEVENT
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