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SUMMARY:Bayes Reading Group:  Nicholas Cowie (Technical University of Denm
 ark)
DTSTART:20250411T150000Z
DTEND:20250411T160000Z
DTSTAMP:20260410T145400Z
UID:indico-event-4053@indico.flatironinstitute.org
DESCRIPTION:Discussion Lead: Nicholas Cowie (Technical University of Den
 mark)\nTopic: Non-parametric models of cell metabolism\nAbstract: Metabo
 lic networks are often modelled as systems of ordinary differential equati
 ons with the form dx/dt = f(x\, e\, θ)\, where x are metabolite concentra
 tions\, e are enzyme concentrations and θ is the set of hyperparameters. 
 The previous gold standard for non-parametric metabolic models uses a sing
 le reference experiment to calculate dx/dt and is only locally accurate. P
 redictions about metabolite concentrations and reaction rates are therefor
 e highly influenced by the choice of the reference experiment. We propose 
 a reproducing kernel Hilbert space framework that uses a set of inducing e
 xperiments – eliminating the need to identify the best reference conditi
 on and creating a more accurate representation of metabolism. This present
 ation evaluates how well this approach works when modelling a small metabo
 lic pathway by comparing predictions made by this method to the previous g
 old standard. Furthermore\, we will show how this method better approximat
 es aspects of enzyme behaviour that break the assumptions underlying stand
 ard parametric models.\n\nhttps://indico.flatironinstitute.org/event/4053/
LOCATION:3rd Floor Conference Room  (162 Fifth Avenue)
URL:https://indico.flatironinstitute.org/event/4053/
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