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Results for “gene models”

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PubMed1987

Oscillations and multiple steady states in a cyclic gene model with repression.

In this paper we study the cyclic gene model with repression considered by H. T. Banks and J. M. Mahaffy. Roughly, the model describes a biochemical feedback loop consisting of an integer number G of single gene reaction sequences in series. The model leads to a system of functional differential equations. We show that there is a qualitative difference in the dynamics between even and odd G if the feedback repression is sufficiently large. For even G, multiple stable steady states can coexist while for odd G, periodic orbits exist.

Feedback↗

PubMed2005

A six-gene model for differentiating benign from malignant thyroid tumors on the basis of gene expression.

BACKGROUND: Thyroid nodules are common; fine-needle aspirations commonly are read as indeterminate, necessitating surgery to exclude carcinoma. We developed a 6-gene array-based predictor model to diagnose benign versus malignant thyroid lesions. In this study, we verified whether quantitative reverse transcription-polymerase chain reaction (qRT-PCR) using this model reliably can differentiate benign from malignant thyroid nodules. METHODS: Molecular profiles of benign (follicular adenomas, hyperplastic nodules) and malignant tumors (papillary thyroid carcinomas, follicular variants of papillary thyroid carcinomas) were analyzed using qRT-PCR from our 6-gene model (kit, Hs.296031, Hs.24183, LSM7, SYNGR2, C21orf4). The gold standard was standard pathologic criteria. A diagnosis-predictor model was built by using the training samples and was then used to predict the class of 10 additional samples analyzed as unknowns. RESULTS: Our predictor model using 47 training samples correctly predicted 9/10 unknowns. One sample diagnosed as benign by standard histologic criteria was diagnosed as malignant by our model (sensitivity 75%; specificity, 100%; positive predictive value, 100%; negative predictive value, 85.7%). CONCLUSIONS: Molecular diagnosis with our 6-gene model can differentiate between benign and malignant thyroid tumors with high sensitivity and specificity. In combination, these genetic markers may be a reliable test to preoperatively diagnose the malignant potential of thyroid nodules.

Adenoma↗

PubMed2005

Modelling gene networks at different organisational levels.

Approaches to modelling gene regulation networks can be categorized, according to increasing detail, as network parts lists, network topology models, network control logic models, or dynamic models. We discuss the current state of the art for each of these approaches. There is a gap between the parts list and topology models on one hand, and control logic and dynamic models on the other hand. The first two classes of models have reached a genome-wide scale, while for the other model classes high throughput technologies are yet to make a major impact.

Algorithms↗

PubMed1998

Methods for the detection of non-random base substitution in virus genes: models of synonymous nucleotide substitution in picornavirus genes.

A substantial fraction of phylogenetic divergence between closely related RNA virus genes is generally accounted for by synonymous (non-amino acid changing) point mutation. Viral evolution may be a complicated phenomena, governed by many different processes. However in this study we ask whether there are any properties in the patterns of synonymous nucleotide substitutions in three different Picornavirus genes that permit the process of accumulation of synonymous point mutation in these genes to be distinguished from some of the simplest most basic evolutionary models. We conclude that while the observed patterns in the occurrence of synonymous point substitution are consistent with those predicted by a model in which base mutation is equi-probable along a gene, and the probability of synonymous substitution determined only by local codon usage, some patterns in the actual nucleotides exchanged remain to be explained.

Amino Acid Substitution↗

PubMed2025

Gene model for the ortholog of Ilp4 in Drosophila eugracilis.

Gene Model for Insulin-like peptide 4 (Ilp4) in the D. eugracilis (DeugGB2) assembly (GCA_000236325.2). The characterization of this ortholog was carried out as part of a larger, ongoing dataset designed to explore the evolution of the insulin/insulin-like growth factor signaling (IIS) pathway across the genus Drosophila, utilizing the Genomics Education Partnership gene annotation protocol within Course-based Undergraduate Research Experiences.

Bioinformatics↗

PubMed2025

Gene model for the ortholog of dock in Drosophila ananassae.

Gene model for the ortholog of dreadlocks ( dock ) in the May 2011 (Agencourt dana_caf1/DanaCAF1) Genome Assembly (GenBank Accession: GCA_000005115.1 ) of Drosophila ananassae . This ortholog was characterized as part of a developing dataset to study the evolution of the Insulin/insulin-like growth factor signaling pathway (IIS) across the genus Drosophila using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.

Journal Article↗

PubMed2025

Gene model for the ortholog of Glys in Drosophila ananassae.

Gene model for the ortholog of Glycogen synthase (Glys) in the May 2011 (Agencourt dana_caf1/DanaCAF1) Genome Assembly (GenBank Accession: GCA_000005115.1) of Drosophila ananassae. This ortholog was characterized as part of a developing dataset to study the evolution of the Insulin/insulin-like growth factor signaling pathway (IIS) across the genus Drosophila using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.

Bioinformatics↗

PubMed2026

Gene model for the ortholog of Pi3K21B in Drosophila ananassae.

Gene model for the ortholog of Phosphatidylinositol 3-kinase 21B ( Pi3K21B ) in the May 2011 (Agencourt dana_caf1/DanaCAF1) Genome Assembly (GenBank Accession: GCA_000005115.1 ) of Drosophila ananassae . This ortholog was characterized as part of a developing dataset to study the evolution of the Insulin/insulin-like growth factor signaling pathway (IIS) across the genus Drosophila using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.

Journal Article↗

PubMed2025

Gene model for the ortholog of Pi3K21B in Drosophila eugracilis.

Gene model for the ortholog of Phosphatidylinositol 3-kinase 21B (Pi3K21B) in the D. eugracilis May 2021 (Stanford ASM1815383v1/DeugRefSeq2) Genome Assembly (GenBank Accession: GCF_018153835.1) of Drosophila eugracilis. This ortholog was characterized as part of a developing dataset to study the evolution of the Insulin/insulin-like growth factor signaling pathway (IIS) across the genus Drosophila using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.

Journal Article↗

PubMed2025

Gene model for the ortholog of foxo in Drosophila sechellia.

Gene model for the ortholog of forkhead box, sub-group O (foxo) in the May 2011 (Broad dsec_caf1/DsecCAF1) Genome Assembly (GenBank Accession: GCA_000005215.1) of Drosophila sechellia. This ortholog was characterized as part of a developing dataset to study the evolution of the Insulin/insulin-like growth factor signaling pathway (IIS) across the genus Drosophila using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.

Bioinformatics↗

PubMed2026

Gene Model for the ortholog of Ilp2 in Drosophila ananassae.

Gene model for the ortholog of Insulin-like peptide 2 ( Ilp2 ) in the D. ananassae May 2011 (Agencourt dana_caf1/DanaCAF1) Genome Assembly (GenBank Accession: GCA_000005115.1 ) of Drosophila ananassae . This ortholog was characterized as part of a developing dataset to study the evolution of the Insulin/insulin-like growth factor signaling pathway (IIS) across the genus Drosophila using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.

Journal Article↗

PubMed2001

Symbolic dynamics and computation in model gene networks.

We analyze a class of ordinary differential equations representing a simplified model of a genetic network. In this network, the model genes control the production rates of other genes by a logical function. The dynamics in these equations are represented by a directed graph on an n-dimensional hypercube (n-cube) in which each edge is directed in a unique orientation. The vertices of the n-cube correspond to orthants of state space, and the edges correspond to boundaries between adjacent orthants. The dynamics in these equations can be represented symbolically. Starting from a point on the boundary between neighboring orthants, the equation is integrated until the boundary is crossed for a second time. Each different cycle, corresponding to a different sequence of orthants that are traversed during the integration of the equation always starting on a boundary and ending the first time that same boundary is reached, generates a different letter of the alphabet. A word consists of a sequence of letters corresponding to a possible sequence of orthants that arise from integration of the equation starting and ending on the same boundary. The union of the words defines the language. Letters and words correspond to analytically computable Poincare maps of the equation. This formalism allows us to define bifurcations of chaotic dynamics of the differential equation that correspond to changes in the associated language. Qualitative knowledge about the dynamics found by integrating the equation can be used to help solve the inverse problem of determining the underlying network generating the dynamics. This work places the study of dynamics in genetic networks in a context comprising both nonlinear dynamics and the theory of computation. (c) 2001 American Institute of Physics.

Journal Article↗

PubMed2003

Modeling gene expression measurement error: a quasi-likelihood approach.

BACKGROUND: Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution) or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale). RESULTS: Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood). Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic) variance structure of the data. As the quasi-likelihood behaves (almost) like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye) effects. CONCLUSIONS: The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also improved the power of tests to identify differential expression.

Calibration↗

PubMed2005

Histone acetylation, chromatin remodelling, transcription and nucleotide excision repair in S. cerevisiae: studies with two model genes.

We describe the technology and two model systems in yeast designed to study nucleotide excision repair (NER) in relation to transcription and chromatin modifications. We employed the MFA2 and MET16 genes as models. How transcription-coupled (TCR) and global genome repair (GGR) operate at the transcriptionally active and/or repressed S. cerevisiae MFA2 locus, and how this relates to nucleosome positioning are considered. We discuss the role of the Gcn5p histone acetyltransferase, also associated with MFA2's transcriptional activation, in facilitating efficient NER at the transcriptionally active and inactive genes. The effect of Gcn5p's absence in reducing NER was local and UV stimulates Gcn5p-mediated histone acetylation at the repressed MFA2 promoter. After UV irradiation Swi2p is partly responsible for facilitating access to restriction of DNA in the cores of the nucleosomes at the MFA2 promoter. The data suggest similarities between chromatin remodelling for NER and transcription, yet differences must exist to ensure this gene remains repressed in alpha cells during NER. For MET16, we consider experiments examining chromatin structure, transcription and repair in wild type and cbf1Delta cells under repressing or derepressing conditions. Cbf1p is a sequence specific DNA binding protein required for MET16 chromatin remodelling and transcription.

Acetylation↗

PubMed2005

FlyBase: genes and gene models.

FlyBase (http://flybase.org) is the primary repository of genetic and molecular data of the insect family Drosophilidae. For the most extensively studied species, Drosophila melanogaster, a wide range of data are presented in integrated formats. Data types include mutant phenotypes, molecular characterization of mutant alleles and aberrations, cytological maps, wild-type expression patterns, anatomical images, transgenic constructs and insertions, sequence-level gene models and molecular classification of gene product functions. There is a growing body of data for other Drosophila species; this is expected to increase dramatically over the next year, with the completion of draft-quality genomic sequences of an additional 11 Drosophila species.

Animals↗

PubMed1992

Two-locus mitochondrial and nuclear gene models for mitochondrial disorders.

Stimulated by a large pedigree with a cochlear form of deafness, for which we considered a two-locus mitochondrial and nuclear gene model, we have extended the classic methods of segregation analysis to these classes of two-locus disorders. Based on the unique maternal transmission pattern of the mitochondria, we demonstrate that utilization of the maternal line pedigree allows us to simplify the various two-locus mitochondrial models to "one nuclear locus" models. Classifying the nuclear families into different independent groups by the mother's phenotypes allows us to estimate the nuclear gene frequency in one group and to use this estimate as the expected value to test the fitness of the model on the other group. In addition, if we restrict the analysis to specific subsets of the mating type(s), we can also test the model on specific groups of nuclear families without estimating the gene frequency. Goodness-of-fit tests can be performed on pooled sibship data as well as individual sibship data. These methods of analysis should assume increasing importance as more disorders with features of mitochondrial inheritance are identified.

Cell Nucleus↗

PubMed1999

Good parent and good genes models of handicap evolution.

We previously studied a good genes handicap model in which male quality was heritable and improved offspring viability. We extend our analysis to species in which males provide direct benefits (e.g. parental care, better resources, the absence of contagious diseases). Male quality now affects female fitness by increasing female reproductive success. For this good parent handicap to work, the male signal must have condition-dependent expression. The equilibrium strength of female preference is controlled by the product of signal transmission efficiency, phenotypic variance of male quality and the effectiveness of male quality in improving female reproductive success. The equilibrium resulting from the good parent handicap has exactly the same form as with the good genes handicap. This allows us to compare the relative importance of these two forces in the evolution of female preferences. The handicap models (both good genes and good parent) also show cyclic evolution, as happens with the pure Fisherian model. However, we predict that the handicap process is often strong enough to lead to a stable equilibrium. This leads to the conclusion that cyclic evolution is less likely to occur for handicap than Fisherian traits.

Animals↗

PubMed1997

Linkage mapping in experimental crosses: the robustness of single-gene models.

The robustness of parametric linkage mapping against model misspecification is considered in experimental breeding designs, with a focus on localization of the gene. By examining the expected LOD across the genome, it is shown that single-gene models are quite robust, even for polygenic traits. However, when the marker map is of low resolution, linked polygenes can give rise to an apparent "ghost" gene, mapped to an incorrect interval. The results apply equally well to quantitative traits or qualitative (categorical) traits. The results are derived for backcross populations, with a discussion of extensions to intercross populations and relative-pair mapping in humans.

Crosses, Genetic↗