Matching Models Across Abstraction Levels with Gaussian Processes

Giulio Caravagna, Luca Bortolussi, Guido Sanguinetti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement, and whether such agreement holds for different parametrisations. Here we present a generally applicable statistical machine learning methodology to automatically reconcile the predictions of different models across abstraction levels. Our approach is based on defining a correction map, a random function which modifies the output of a model in order to match the statistics of the output of a different model of the same system. We use two biological examples to give a proof-of-principle demonstration of the methodology, and discuss its advantages and potential further applications.
Original languageEnglish
Title of host publicationComputational Methods in Systems Biology
Subtitle of host publication14th International Conference, CMSB 2016, Cambridge, UK, September 21-23, 2016, Proceedings
EditorsEzio Bartocci, Pietro Lio, Nicola Paoletti
Place of PublicationCham
PublisherSpringer International Publishing
Pages49-66
Number of pages18
ISBN (Electronic)978-3-319-45177-0
ISBN (Print)978-3-319-45176-3
DOIs
Publication statusPublished - 4 Sep 2016
Event14th International Conference on Computational Methods in Systems Biology - Cambridge, United Kingdom
Duration: 21 Sep 201623 Sep 2016
http://www.cl.cam.ac.uk/events/cmsb2016/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume9859
ISSN (Print)0302-9743

Conference

Conference14th International Conference on Computational Methods in Systems Biology
Abbreviated titleCMSB 2016
CountryUnited Kingdom
CityCambridge
Period21/09/1623/09/16
Internet address

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