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SUMMARY:Strong inference in mathematical modelling: a new twist to an old
idea
DTSTART;VALUE=DATE-TIME:20180711T052000Z
DTEND;VALUE=DATE-TIME:20180711T054000Z
DTSTAMP;VALUE=DATE-TIME:20241113T045547Z
UID:indico-contribution-218@conferences.maths.unsw.edu.au
DESCRIPTION:Speakers: Vitaly Ganusov (University of Tennessee)\nWhile ther
e are many opinions on what mathematical modelling in biology is\, in esse
nce\, modelling is a mathematical tool which allows consequences to logica
lly follow from a set of assumptions. Only when this tool is applied appr
opriately\, it may allow to understand importance of specific mechanisms/a
ssumptions in biological processes. Mathematical modelling can be less us
eful or even misleading if used inappropriately\, for example\, by creatin
g a false impression of a good understanding of biological processes. It h
as been argued that the best use of mathematical models is not when a mode
l is used to confirm a hypothesis but rather when a study shows inconsiste
ncy of the model (defined by a specific set of assumptions) and data. Fol
lowing the principle of strong inference for experimental sciences propose
d by Platt\, I suggest “strong inference in mathematical modelling” as
an effective and robust way of using mathematical modelling to understand
mechanisms driving dynamics of biological systems. The major steps of str
ong inference in mathematical modelling are 1) to develop multiple alterna
tive models for the phenomenon in question\; 2) to compare the models with
available experimental data and to determine which of the models are not
consistent with the data\; 3) to determine reasons why rejected models fai
led to explain the data\, and 4) to suggest experiments which would allow
to discriminate between remaining alternative models. The use of strong i
nference is likely to provide better robustness of predictions of mathemat
ical models and it should be strongly encouraged in mathematical modelling
-based publications in the 21st century.\n\nhttps://conferences.maths.unsw
.edu.au/event/2/contributions/218/
LOCATION:University of Sydney New Law School/--106
URL:https://conferences.maths.unsw.edu.au/event/2/contributions/218/
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