Merging genomic control networks and soil-plant-atmosphere-continuum models

Welch, Stephen M.; Roe, Judith L.; Das, Sanjoy; Dong, Zhanshan; He, Rui; Kirkham, M.B.

doi:10.1016/j.agsy.2004.07.019

Cited by 23 publications

(23 citation statements)

References 91 publications

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“…Recently, a "bottom" up approach has been applied [5,6,7] that models gene interactions directly at the expression level. Small groups of one to four interacting genes can synthesize a wide variety of signal processing functions including Boolean logic gates, linear arithmetic units, delays, differentiators, integrators, oscillators, coincidence detectors, and bi-stable devices [8]. This is consistent with the apparent small-scale modularity of gene networks [9].…”

Section: Gene Regulatory Network Modelssupporting

confidence: 60%

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Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models

Koduru¹,

Das²,

Welch³

et al. 2004

Genetic and Evolutionary Computation – GECCO 2004

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Abstract. Hybrid algorithms that combine genetic algorithms with the Nelder-Mead simplex algorithm have been effective in solving certain optimization problems. In this article, we apply a similar technique to estimate the parameters of a gene regulatory network for flowering time control in rice. The algorithm minimizes the difference between the model behavior and real world data. Because of the nature of the data, a multiobjective approach is necessary. The concept of fuzzy dominance is introduced, and a multi-objective simplex algorithm based on this concept is proposed as a part of the hybrid approach. Results suggest that the proposed method performs well in estimating the model parameters.

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Section: Gene Regulatory Network Modelssupporting

confidence: 60%

“…In [25] the levels of messenger RNA was measured every 3h under short-days (SD, 9h) and longdays (LD, 15h) for HEADING DATE 1 (Hd1), an important flowering time control gene in rice (Oryza sativa). In [8] the authors modeled this data with the equation(s):…”

Section: Gene Regulatory Network Modelsmentioning

confidence: 99%

Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models

Koduru¹,

Das²,

Welch³

et al. 2004

Genetic and Evolutionary Computation – GECCO 2004

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“…Although, traditionally, physiological methods have been used to predict phenotypes for different crop cultivars as they respond to variable environmental inputs (Sinclair and Seligman, 1996;Hammer et al, 2004), the most recent trend is to estimate phenotypic outcomes by directly modeling the underlying gene and gene interactions at the expression level (Welch et al, 2003(Welch et al, , 2005a. In these cases, genes are typically modeled using Boolean logic, linear units, oscillators and so on (Welch et al, 2005b;Ravasz et al, 2002).…”

Section: Analysis Of Grns: Historical Perspectivementioning

confidence: 99%

Post-GWAS: where next? More samples, more SNPs or more biology?

2013

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The power of genome-wide association studies (GWAS) rests on several foundations: (i) there is a significant amount of additive genetic variation, (ii) individual causal polymorphisms often have sizable effects and (iii) they segregate at moderate-tointermediate frequencies, or will be effectively 'tagged' by polymorphisms that do. Each of these assumptions has recently been questioned. (i) Why should genetic variation appear additive given that the underlying molecular networks are highly nonlinear? (ii) A new generation of relatedness-based analyses directs us back to the nearly infinitesimal model for effect sizes that quantitative genetics was long based upon. (iii) Larger effect causal polymorphisms are often low frequency, as selection might lead us to expect. Here, we review these issues and other findings that appear to question many of the foundations of the optimism GWAS prompted. We then present a roadmap emerging as one possible future for quantitative genetics. We argue that in future GWAS should move beyond purely statistical grounds. One promising approach is to build upon the combination of population genetic models and molecular biological knowledge. This combined treatment, however, requires fitting experimental data to models that are very complex, as well as accurate capturing of the uncertainty of resulting inference. This problem can be resolved through Bayesian analysis and tools such as approximate Bayesian computation-a method growing in popularity in population genetic analysis. We show a case example of anterior-posterior segmentation in Drosophila, and argue that similar approaches will be helpful as a GWAS augmentation, in human and agricultural research.

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“…Arabidopsis thaliana has also been instrumental in differential equation modeling of selected plant gene networks (e.g., Locke et al 2005;Welch et al 2005b). Although current models contain only a few genes, they still provide useful insights.…”

Section: Molecular Genetic Tools and The Decomposition Of Quantitativmentioning

confidence: 99%

Resolving the genetic basis of invasiveness and predicting invasions

et al. 2006

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Considerable effort has been invested in determining traits underlying invasiveness. Yet, identifying a set of traits that commonly confers invasiveness in a range of species has proven elusive, and almost nothing is known about genetic loci affecting invasive success. Incorporating genetic model organisms into ecologically relevant studies is one promising avenue to begin dissecting the genetic underpinnings of invasiveness. Molecular biologists are rapidly characterizing genes mediating developmental responses to diverse environmental cues, i.e., genes for plasticity, as well as to environmental factors likely to impose strong selection on invading species, e.g., resistance to herbivores and competitors, coordination of life-history events with seasonal changes, and physiological tolerance of heat, drought, or cold. Here, we give an overview of molecular genetic tools increasingly used to characterize the genetic basis of adaptation and that may be used to begin identifying genetic mechanisms of invasiveness. Given the divergent traits that affect invasiveness, "invasiveness genes" common to many clades are unlikely, but the combination of developmental genetic advances with further evolutionary studies and modeling may provide a framework for identifying genes that account for invasiveness in related species.

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Merging genomic control networks and soil-plant-atmosphere-continuum models

Cited by 23 publications

References 91 publications

Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models

Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models

Post-GWAS: where next? More samples, more SNPs or more biology?

Resolving the genetic basis of invasiveness and predicting invasions

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