scholarly journals The Invariant Nature of a Morphological Character and Character State: Insights from Gene Regulatory Networks

2019 ◽  
Author(s):  
Sergei Tarasov
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Morphological Character ◽  
Markov Models ◽  
Phylogenetic Analyses ◽  
Character State ◽  
Complex Character ◽  
Transition Rate Matrix ◽  
Gene Regulatory ◽  
Hierarchical Nature

Abstract What constitutes a discrete morphological character versus character state has been long discussed in the systematics literature but the consensus on this issue is still missing. Different methods of classifying organismal features into characters and character states (CCSs) can dramatically affect the results of phylogenetic analyses. Here, I show that, in the framework of Markov models, the modular structure of the gene regulatory network (GRN) underlying trait development, and the hierarchical nature of GRN evolution, essentially remove the distinction between morphological CCS, thus endowing the CCS with an invariant property with respect to each other. This property allows the states of one character to be represented as several individual characters and vice versa. In practice, this means that a phenotype can be encoded using a set of characters or just one complex character with numerous states. The representation of a phenotype using one complex character can be implemented in Markov models of trait evolution by properly structuring transition rate matrix.

scholarly journals The Invariant Nature of a Morphological Character and Character State: Insights from Gene Regulatory Networks

2018 ◽  
Author(s):  
Sergei Tarasov
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Morphological Character ◽  
Phylogenetic Analyses ◽  
State Transitions ◽  
Character State ◽  
Complex Character ◽  
Gene Regulatory ◽  
Hierarchical Nature ◽  
Selection Of

AbstractWhat constitutes a morphological character versus character state has been long discussed in the systematics literature but the consensus on this issue is still missing. Different methods of classifying organismal features into characters and character states can dramatically affect the results of phylogenetic analyses. Here, I show that the modular structure of the gene regulatory network (GRN) underlying trait development, and the hierarchical nature of GRN evolution, essentially remove the distinction between morphological character and character state, thus endowing the character and character state with an invariant property with respect to each other. This property allows representing the states of one character as several individual characters and vice versa. In practice, this means that a phenotype can be encoded using a set of characters or just one complex character with numerous states. The representation of a phenotype using one complex character requires a selection of an appropriate penalty for the state transitions.

scholarly journals Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits

2017 ◽  
Author(s):  
Sergei Tarasov
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Markov Models ◽  
Phylogenetic Inference ◽  
Phenotypic Traits ◽  
Character State ◽  
Body Parts ◽  
Gene Regulatory ◽  
Hidden States ◽  
New Framework

AbstractModeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes – hierarchical and hidden – which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of structured Markov models with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of gene regulatory networks and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing tail color problem (that aims at coding tail when it is absent) and presents a previously unknown issue called the “two-scientist paradox”. The latter issue refers to the confounding nature of the coding of a trait and the hidden processes driving the trait’s evolution; failing to account for the hidden process may result in a bias, which can be avoided by using hidden state models. All this provides a clear guideline for coding traits into characters. This paper gives practical examples of using the new framework for phylogenetic inference and comparative analysis.

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