Stimulus design for model selection and validation in cell signaling.

Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Joshua F Apgar, Jared E Toettcher, Drew Endy, Forest M White, Bruce Tidor
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/fab8e059031a4b60914c1ca14447329d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fab8e059031a4b60914c1ca14447329d
record_format	dspace
spelling	oai:doaj.org-article:fab8e059031a4b60914c1ca14447329d2021-11-25T05:41:26ZStimulus design for model selection and validation in cell signaling.1553-734X1553-735810.1371/journal.pcbi.0040030https://doaj.org/article/fab8e059031a4b60914c1ca14447329d2008-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18282085/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus-response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody-ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models.Joshua F ApgarJared E ToettcherDrew EndyForest M WhiteBruce TidorPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 2, p e30 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Joshua F Apgar Jared E Toettcher Drew Endy Forest M White Bruce Tidor Stimulus design for model selection and validation in cell signaling.
description	Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus-response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody-ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models.
format	article
author	Joshua F Apgar Jared E Toettcher Drew Endy Forest M White Bruce Tidor
author_facet	Joshua F Apgar Jared E Toettcher Drew Endy Forest M White Bruce Tidor
author_sort	Joshua F Apgar
title	Stimulus design for model selection and validation in cell signaling.
title_short	Stimulus design for model selection and validation in cell signaling.
title_full	Stimulus design for model selection and validation in cell signaling.
title_fullStr	Stimulus design for model selection and validation in cell signaling.
title_full_unstemmed	Stimulus design for model selection and validation in cell signaling.
title_sort	stimulus design for model selection and validation in cell signaling.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/fab8e059031a4b60914c1ca14447329d
work_keys_str_mv	AT joshuafapgar stimulusdesignformodelselectionandvalidationincellsignaling AT jaredetoettcher stimulusdesignformodelselectionandvalidationincellsignaling AT drewendy stimulusdesignformodelselectionandvalidationincellsignaling AT forestmwhite stimulusdesignformodelselectionandvalidationincellsignaling AT brucetidor stimulusdesignformodelselectionandvalidationincellsignaling
_version_	1718414555966603264

Stimulus design for model selection and validation in cell signaling.

Ejemplares similares