Functional mapping of growth and development

Li, Yao; Wu, Rongling

doi:10.1111/j.1469-185x.2009.00096.x

Cited by 48 publications

(36 citation statements)

References 49 publications

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“…It is widespread opinion that the growth rate in many mammals has a sigmoid form (Li & Wu, 2010). This growth curve form is considered universal and is modelled by a number of growth functions (Barberis et al 2011).…”

Section: Growth Dynamicsmentioning

confidence: 99%

An Analytical Model of Animal Growth

Stass¹

2017

ESJ

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The purpose of this study was to explain some aspects of ontogenetic growth in pigs by analysing the relationships between variables that are significant to the development of animals. The novelty of the study is a new modelling approach to the growth problem, with the attention that has been paid to both a new set of variables, and an analytical discrete-continuous hybrid model, innovative for the field. This is a first species-specific hybrid model for animal growth formulated in discrete-time difference equation technique. The efficiency of the model is not only due to the modelling technique but also due to a set of relevant variables, especially a feed conversion coefficient, which provides a link between macro and micro physiological scales. The model is based on functional relationships between relevant variables acquired from experimental data analyses, and field observations. The concept explains some aspects of growth in pigs from 30 kg to 600 kg, which is considered the maximum individual weight for a boar, and further growth up to a species maximum weight. The model predicts that boar can reach their maximum individual weight of 600 kg when 6,40 years old and are required to consume 62,51 kg of feed to put on the last kilogram. The phenotypes that can attain their maximum individual weight go through bifurcation of the growth trajectory, a transform in the growth mode. After bifurcation, the smallest number of the phenotypes go on the growth trajectory that leads to a set of species maximum weights of over 1205 kg, and the greatest number of phenotypes continue to live until aged 24,90 years, provided their maximum weights do not change. The study includes growth rate equations, identifies species maximum weight phenotypes, and produces insight into pig longevity. The results suggest that species maximum weight growth trajectories are phenotype-dependant. A modified discrete-time difference equation technique combined with standard continuum methods is an appropriate formalism to model ontogenetic growth in animals.

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Section: Growth Dynamicsmentioning

confidence: 99%

An Analytical Model of Animal Growth

Stass¹

2017

ESJ

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“…By integrating the physiological and developmental pathways underlying phenotype formation using mathematical equations, functional mapping is equipped with a capacity to identify genes involved in rate-limiting processes and to quantify the dynamic effect pattern of these genes across time and space scales [15][16][17]. More recently, functional mapping has been extended to systems mapping by viewing a phenotype as a dynamic system [18][19][20].…”

Section: An Evolutionary View Of Phenotype Formationmentioning

confidence: 99%

Integrating Evolutionary Game Theory into Mechanistic Genotype–Phenotype Mapping

Zhu

Jiang

et al. 2016

Trends in Genetics

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“…By integrating the dynamic pathways underlying phenotypic formation using mathematical equations, this model is renovated to identify QTLs involved in rate-limiting processes and to quantify the dynamic effect pattern of these genes across a time and space scale [58,89]. More recently, functional mapping has been extended to systems mapping by viewing a phenotype as a dynamic system [46,47,176].…”

Section: Introductionmentioning

confidence: 99%

Mapping complex traits as a dynamic system

Sun

2015

Physics of Life Reviews

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Despite increasing emphasis on the genetic study of quantitative traits, we are still far from being able to chart a clear picture of their genetic architecture, given an inherent complexity involved in trait formation. A competing theory for studying such complex traits has emerged by viewing their phenotypic formation as a “system” in which a high-dimensional group of interconnected components act and interact across different levels of biological organization from molecules through cells to whole organisms. This system is initiated by a machinery of DNA sequences that regulate a cascade of biochemical pathways to synthesize endophenotypes and further assemble these endophenotypes toward the end-point phenotype in virtue of various developmental changes. This review focuses on a conceptual framework for genetic mapping of complex traits by which to delineate the underlying components, interactions and mechanisms that govern the system according to biological principles and understand how these components function synergistically under the control of quantitative trait loci (QTLs) to comprise a unified whole. This framework is built by a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function, and provides a quantitative and testable platform for assessing the multiscale interplay between QTLs and development. The method will enable geneticists to shed light on the genetic complexity of any biological system and predict, alter or engineer its physiological and pathological states.

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Functional mapping of growth and development

Cited by 48 publications

References 49 publications

An Analytical Model of Animal Growth

An Analytical Model of Animal Growth

Integrating Evolutionary Game Theory into Mechanistic Genotype–Phenotype Mapping

Mapping complex traits as a dynamic system

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