ArticleJournal of biological dynamics2011
A dynamic model for functional mapping of biological rhythms.
Article in Journal of biological dynamics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits.Journal of applied genetics · 2017Article
- Mapping complex traits as a dynamic system.Physics of life reviews · 2015Review
- Article
- Towards a comprehensive picture of the genetic landscape of complex traits.Briefings in bioinformatics · 2014Article
- Systems mapping of metabolic genes through control theory.Advanced drug delivery reviews · 2013Article
- Stochastic modeling of systems mapping in pharmacogenomics.Advanced drug delivery reviews · 2013Article
- Systems mapping of HIV-1 infection.BMC genetics · 2012Article
- Dynamic modeling of genes controlling cancer stem cell proliferation.Frontiers in genetics · 2012Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
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Abstract
Functional mapping is a statistical method for mapping quantitative trait loci (QTLs) that regulate the dynamic pattern of a biological trait. This method integrates mathematical aspects of biological complexity into a mixture model for genetic mapping and tests the genetic effects of QTLs by comparing genotype-specific curve parameters. As a way of quantitatively specifying the dynamic behavior of a system, differential equations have proven to be powerful for modeling and unraveling the biochemical, molecular, and cellular mechanisms of a biological process, such as biological rhythms. The equipment of functional mapping with biologically meaningful differential equations provides new insights into the genetic control of any dynamic processes. We formulate a new functional mapping framework for a dynamic biological rhythm by incorporating a group of ordinary differential equations (ODE). The Runge-Kutta fourth order algorithm was implemented to estimate the parameters that define the system of ODE. The new model will find its implications for understanding the interplay between gene interactions and developmental pathways in complex biological rhythms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.