PMID- 32049328
OWN - NLM
STAT- MEDLINE
DCOM- 20201029
LR  - 20240328
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 36
IP  - 10
DP  - 2020 May 1
TI  - Bayesian inference using qualitative observations of underlying continuous 
      variables.
PG  - 3177-3184
LID - 10.1093/bioinformatics/btaa084 [doi]
AB  - MOTIVATION: Recent work has demonstrated the feasibility of using non-numerical, 
      qualitative data to parameterize mathematical models. However, uncertainty 
      quantification (UQ) of such parameterized models has remained challenging because 
      of a lack of a statistical interpretation of the objective functions used in 
      optimization. RESULTS: We formulated likelihood functions suitable for performing 
      Bayesian UQ using qualitative observations of underlying continuous variables or 
      a combination of qualitative and quantitative data. To demonstrate the resulting 
      UQ capabilities, we analyzed a published model for immunoglobulin E (IgE) 
      receptor signaling using synthetic qualitative and quantitative datasets. 
      Remarkably, estimates of parameter values derived from the qualitative data were 
      nearly as consistent with the assumed ground-truth parameter values as estimates 
      derived from the lower throughput quantitative data. These results provide 
      further motivation for leveraging qualitative data in biological modeling. 
      AVAILABILITY AND IMPLEMENTATION: The likelihood functions presented here are 
      implemented in a new release of PyBioNetFit, an open-source application for 
      analyzing Systems Biology Markup Language- and BioNetGen Language-formatted 
      models, available online at www.github.com/lanl/PyBNF. SUPPLEMENTARY INFORMATION: 
      Supplementary data are available at Bioinformatics online.
CI  - (c) The Author(s) 2020. Published by Oxford University Press. All rights reserved. 
      For permissions, please e-mail: journals.permissions@oup.com.
FAU - Mitra, Eshan D
AU  - Mitra ED
AD  - Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos 
      National Laboratory, Los Alamos, NM 87545, USA.
FAU - Hlavacek, William S
AU  - Hlavacek WS
AD  - Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos 
      National Laboratory, Los Alamos, NM 87545, USA.
LA  - eng
GR  - P50 GM085273/GM/NIGMS NIH HHS/United States
GR  - R01 GM111510/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
MH  - Bayes Theorem
MH  - Likelihood Functions
MH  - *Software
MH  - *Systems Biology
MH  - Uncertainty
PMC - PMC7214020
EDAT- 2020/02/13 06:00
MHDA- 2020/10/30 06:00
PMCR- 2021/05/01
CRDT- 2020/02/13 06:00
PHST- 2019/08/29 00:00 [received]
PHST- 2020/01/08 00:00 [revised]
PHST- 2020/02/03 00:00 [accepted]
PHST- 2020/02/13 06:00 [pubmed]
PHST- 2020/10/30 06:00 [medline]
PHST- 2020/02/13 06:00 [entrez]
PHST- 2021/05/01 00:00 [pmc-release]
AID - 5734648 [pii]
AID - btaa084 [pii]
AID - 10.1093/bioinformatics/btaa084 [doi]
PST - ppublish
SO  - Bioinformatics. 2020 May 1;36(10):3177-3184. doi: 10.1093/bioinformatics/btaa084.