Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @Article{Colins:2017:pone,
-
author = "Andrea Colins and Ziomara P. Gerdtzen and
Marco T. Nunez and J. Cristian Salgado",
-
title = "Mathematical Modeling of Intestinal Iron Absorption
Using Genetic Programming",
-
journal = "PLOS one",
-
year = "2017",
-
volume = "12",
-
number = "1",
-
pages = "e0169601",
-
month = jan # " 10",
-
keywords = "genetic algorithms, genetic programming",
-
DOI = "doi:10.1371/journal.pone.0169601",
-
size = "24 pages",
-
abstract = "Iron is a trace metal, key for the development of
living organisms. Its absorption process is complex and
highly regulated at the transcriptional, translational
and systemic levels. Recently, the internalization of
the DMT1 transporter has been proposed as an additional
regulatory mechanism at the intestinal level,
associated to the mucosal block phenomenon. The
short-term effect of iron exposure in apical uptake and
initial absorption rates was studied in Caco-2 cells at
different apical iron concentrations, using both an
experimental approach and a mathematical modelling
framework. This is the first report of short-term
studies for this system. A non-linear behaviour in the
apical uptake dynamics was observed, which does not
follow the classic saturation dynamics of traditional
biochemical models. We propose a method for developing
mathematical models for complex systems, based on a
genetic programming algorithm. The algorithm is aimed
at obtaining models with a high predictive capacity,
and considers an additional parameter fitting stage and
an additional Jack-knife stage for estimating the
generalization error. We developed a model for the iron
uptake system with a higher predictive capacity than
classic biochemical models. This was observed both with
the apical uptake dataset used for generating the model
and with an independent initial rates dataset used to
test the predictive capacity of the model. The model
obtained is a function of time and the initial apical
iron concentration, with a linear component that
captures the global tendency of the system, and a
non-linear component that can be associated to the
movement of DMT1 transporters. The model presented in
this paper allows the detailed analysis, interpretation
of experimental data, and identification of key
relevant components for this complex biological
process. This general method holds great potential for
application to the elucidation of biological mechanisms
and their key components in other complex systems.",
- }
Genetic Programming entries for
Andrea Justina Colins Rodriguez
Ziomara P Gerdtzen
Marco Tulio Nuñez Gonzalez
J Cristian Salgado Herrera
Citations