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 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 Article Commentary: Rough Set Soft Computing Cancer Classification and Network: One Stone, Two Birds

2010 ◽  
Vol 9 ◽  
pp. CIN.S4874 ◽  
Author(s):  
Yue Zhang
Keyword(s):  
Soft Computing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cancer Classification ◽  
Soft Computing Method ◽  
Direct Gene ◽  
Gene Regulatory ◽  
Variable Precision Rough Sets ◽  
Computing Method ◽  
Selection Of

Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article.

scholarly journals Machine learning methods to reverse engineer dynamic gene regulatory networks governing cell state transitions

2018 ◽  
Author(s):  
P. Tsakanikas ◽  
D. Manatakis ◽  
E. S. Manolakos
Keyword(s):  
Machine Learning ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Most Probable Number ◽  
State Transitions ◽  
Gene Expressions ◽  
Dynamic Interactions ◽  
Probable Number ◽  
Cell State ◽  
Gene Regulatory

ABSTRACTDeciphering the dynamic gene regulatory mechanisms driving cells to make fate decisions remains elusive. We present a novel unsupervised machine learning methodology that can be used to analyze a dataset of heterogeneous single-cell gene expressions profiles, determine the most probable number of states (major cellular phenotypes) represented and extract the corresponding cell sub-populations. Most importantly, for any transition of interest from a source to a destination state, our methodology can zoom in, identify the cells most specific for studying the dynamics of this transition, order them along a trajectory of biological progression in posterior probabilities space, determine the "key-player" genes governing the transition dynamics, partition the trajectory into consecutive phases (transition "micro-states"), and finally reconstruct causal gene regulatory networks for each phase. Application of the end-to-end methodology provides new insights on key-player genes and their dynamic interactions during the important HSC-to-LMPP cell state transition involved in hematopoiesis. Moreover, it allows us to reconstruct a probabilistic representation of the “epigenetic landscape” of transitions and identify correctly the major ones in the hematopoiesis hierarchy of states.

scholarly journals Premature Ovarian Insufficiency: Past, Present, and Future

2021 ◽  
Vol 9 ◽  
Author(s):  
Seung Joo Chon ◽  
Zobia Umair ◽  
Mee-Sup Yoon
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Genetic Basis ◽  
Ovarian Function ◽  
Premature Ovarian Insufficiency ◽  
Ovarian Insufficiency ◽  
Non Coding Rna ◽  
Gene Regulatory ◽  
Near Future ◽  
Selection Of

Premature ovarian insufficiency (POI) is the loss of normal ovarian function before the age of 40 years, a condition that affects approximately 1% of women under 40 years old and 0.1% of women under 30 years old. It is biochemically characterized by amenorrhea with hypoestrogenic and hypergonadotropic conditions, in some cases, causing loss of fertility. Heterogeneity of POI is registered by genetic and non-genetic causes, such as autoimmunity, environmental toxins, and chemicals. The identification of possible causative genes and selection of candidate genes for POI confirmation remain to be elucidated in cases of idiopathic POI. This review discusses the current understanding and future prospects of heterogeneous POI. We focus on the genetic basis of POI and the recent studies on non-coding RNA in POI pathogenesis as well as on animal models of POI pathogenesis, which help unravel POI mechanisms and potential targets. Despite the latest discoveries, the crosstalk among gene regulatory networks and the possible therapies targeting the same needs to explore in near future.

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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