Using both qualitative and quantitative data in parameter identification for systems biology models

Eshan D. Mitra, Raquel Dias, Richard G Posner, William S. Hlavacek

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.

Original languageEnglish (US)
Article number3901
JournalNature Communications
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2018

Fingerprint

parameter identification
Systems Biology
biology
Identification (control systems)
Yeasts
yeast
Yeast
Uncertainty
Cell Cycle
Phenotype
phenotype
Chemical activation
Cells
activation
scalars
cycles
output
estimates
profiles

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Cite this

Using both qualitative and quantitative data in parameter identification for systems biology models. / Mitra, Eshan D.; Dias, Raquel; Posner, Richard G; Hlavacek, William S.

In: Nature Communications, Vol. 9, No. 1, 3901, 01.12.2018.

Research output: Contribution to journalArticle

@article{ed0613eb8328494bbc125167068e70f9,
title = "Using both qualitative and quantitative data in parameter identification for systems biology models",
abstract = "In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.",
author = "Mitra, {Eshan D.} and Raquel Dias and Posner, {Richard G} and Hlavacek, {William S.}",
year = "2018",
month = "12",
day = "1",
doi = "10.1038/s41467-018-06439-z",
language = "English (US)",
volume = "9",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",

}

TY - JOUR

T1 - Using both qualitative and quantitative data in parameter identification for systems biology models

AU - Mitra, Eshan D.

AU - Dias, Raquel

AU - Posner, Richard G

AU - Hlavacek, William S.

PY - 2018/12/1

Y1 - 2018/12/1

N2 - In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.

AB - In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.

UR - http://www.scopus.com/inward/record.url?scp=85053828802&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053828802&partnerID=8YFLogxK

U2 - 10.1038/s41467-018-06439-z

DO - 10.1038/s41467-018-06439-z

M3 - Article

VL - 9

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 3901

ER -