The World of Programming Languages

Marcotty, Michael; Ledgard, Henry

doi:10.1007/978-1-4612-4692-3

Cited by 14 publications

(4 citation statements)

References 29 publications

(38 reference statements)

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“…The nomenclature for the new markers followed the Bakus-Naur form described in Marcotty and Ledgard [55]: <3-letter institute code>_<single letter marker assay type designator>< MARKER ASSAY NAME>. The institute code "cnu" represents Chungnam National University, and the single letter marker assay type designators are those published by De Vicente et al [56].…”

Section: Marker Development and Genotypingmentioning

confidence: 99%

Quantitative Trait Locus Mapping of Clubroot Resistance and Plasmodiophora brassicae Pathotype Banglim-Specific Marker Development in Brassica rapa

Choi

Chhapekar

et al. 2020

IJMS

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Clubroot resistance is an economically important trait in Brassicaceae crops. Although many quantitative trait loci (QTLs) for clubroot resistance have been identified in Brassica, disease-related damage continues to occur owing to differences in host variety and constant pathogen variation. Here, we investigated the inheritance of clubroot resistance in a double haploid population developed by crossing clubroot resistant and susceptible lines “09CR500” and “09CR501”, respectively. The resistance of “09CR500” to Plasmodiophora brassicae pathotype “Banglim” was controlled as a single dominant gene, with the segregation of resistance and susceptibility being nearly 1:1. PbBrA08Banglim was identified as having a logarithm of odds value of 7.9–74.8, and a phenotypic variance of 26.0–97.1% with flanking marker “09CR.11390652” in A08. After aligning QTL regions to the B. rapa reference genome, 11 genes were selected as candidates. PbBrA08Banglim was located near Crr1, CRs, and Rcr9 loci, but differences were validated by marker analysis, gene structural variations, and gene expression levels, as well as phenotypic responses to the pathotype. Genotyping using the “09CR.11390652” marker accurately distinguished the Banglim-resistance phenotypes in the double haploid population. Thus, the developed marker will be useful in Brassica breeding programs, marker-assisted selection, and gene pyramiding to identify and develop resistant cultivars.

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Section: Marker Development and Genotypingmentioning

confidence: 99%

Quantitative Trait Locus Mapping of Clubroot Resistance and Plasmodiophora brassicae Pathotype Banglim-Specific Marker Development in Brassica rapa

Choi

Chhapekar

et al. 2020

IJMS

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“…Backus-Naur Form is a formal notation for describing the syntax of a context-free grammar as a set of production rules that consist of terminals and nonterminals [27]. Nonterminals form the left-hand side of production rules while both terminals and nonterminals can form the right-hand side.…”

Section: A Grammar For Petri Net Models In Backus-naur Formmentioning

confidence: 99%

An Improved Grammatical Evolution Strategy for Hierarchical Petri Net Modeling of Complex Genetic Systems

Moore

Hahn

2004

Lecture Notes in Computer Science

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Abstract. DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems. Understanding the hierarchical relationships in the genotype-phenotype mapping is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This strategy uses an evolutionary computation approach called grammatical evolution for symbolic manipulation and optimization of Petri net models. We previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of complex genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. However, the modeling strategy was generally not successful when extended to modeling nonlinear interactions between three DNA sequence variations. In the present study, we evaluate a modified grammar for building Petri net models of biochemical systems that are consistent with high-order genetic models of disease susceptibility. The results indicate that our hierarchical model-building approach is capable of identifying perfect Petri net models when an appropriate grammar is used.

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“…Sentences created by our grammar are systems of coupled nonlinear differential equations. We use a formal notation for describing the syntax of a context-free grammar as a set of production rules that consist of terminals (model elements) and nonterminals (the production rules themselves) [7, 8, 19]. For simplicity, only (var) nonterminals are used in the Figure 1 illustration.…”

Section: Grammatical Immunoglobulin Hypermutationmentioning

confidence: 99%

Grammatical Immune System Evolution for Reverse Engineering Nonlinear Dynamic Bayesian Models

McKinney

Tian

2008

Cancer Inform

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An artificial immune system algorithm is introduced in which nonlinear dynamic models are evolved to fit time series of interacting biomolecules. This grammar-based machine learning method learns the structure and parameters of the underlying dynamic model. In silico immunogenetic mechanisms for the generation of model-structure diversity are implemented with the aid of a grammar, which also enforces semantic constraints of the evolved models. The grammar acts as a DNA repair polymerase that can identify recombination and hypermutation signals in the antibody (model) genome. These signals contain information interpretable by the grammar to maintain model context. Grammatical Immune System Evolution (GISE) is applied to a nonlinear system identification problem in which a generalized (nonlinear) dynamic Bayesian model is evolved to fit biologically motivated artificial time-series data. From experimental data, we use GISE to infer an improved kinetic model for the oxidative metabolism of 17β-estradiol (E2), the parent hormone of the estrogen metabolism pathway.

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The World of Programming Languages

Cited by 14 publications

References 29 publications

Quantitative Trait Locus Mapping of Clubroot Resistance and Plasmodiophora brassicae Pathotype Banglim-Specific Marker Development in Brassica rapa

Quantitative Trait Locus Mapping of Clubroot Resistance and Plasmodiophora brassicae Pathotype Banglim-Specific Marker Development in Brassica rapa

An Improved Grammatical Evolution Strategy for Hierarchical Petri Net Modeling of Complex Genetic Systems

Grammatical Immune System Evolution for Reverse Engineering Nonlinear Dynamic Bayesian Models

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