Mathematical approaches in studying <i><b>bicoid</b></i> gene

Ghodsi, Zara; Hassani, Hossein; McGhee, Kevin A.

doi:10.1007/s40484-015-0058-6

Cited by 7 publications

(13 citation statements)

References 69 publications

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“…3 illustrates the output from this effort. It is evident that the SSA method provides a relatively smooth signal line with correlation below 0.10 which credits the satisfactory level of separation between noise and signal using SSA [27].…”

Section: Datamentioning

confidence: 55%

“…SSA is a powerful non-parametric method and has been previously applied for signal extraction of gene expression profiles [24], [25], [26], [27]. The basic SSA method consists of two complementary stages: decomposition and reconstruction [44].…”

Section: Causality Detection and Noise Filtering Techniquesmentioning

confidence: 99%

“…1, the profile achieved by fluorescence antibodies technique is highly volatile and in such cases, establishing a cause-and-effect relationship is more challenging and demands applying a noise filtering step prior to causation studies. In order to overcome these issues, among several noise filtering techniques, we have applied Singular Spectrum Analysis (SSA) which is a powerful method and has recently transformed itself into a valuable tool for gene expression signal extraction (see, for example, [24], [25], [26], [27]). …”

Section: Introductionmentioning

confidence: 99%

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Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

Ghodsi

Huang

Hassani³

2017

Genomics Data

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In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.

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Section: Datamentioning

confidence: 55%

Section: Causality Detection and Noise Filtering Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

Ghodsi

Huang

Hassani³

2017

Genomics Data

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“…In this study, the expression profiles were extracted from the nuclear intensities of %10 longitudinal and are unprocessed for any noise reduction methods. Similar to [8,19,20], we set to work with one-dimensional gene expression data. Hence, the second spatial coordinate (dorsoventral axis) has not been considered.…”

Section: Real Datamentioning

confidence: 99%

Evaluating the analytical distribution of bicoid gene expression profile

Hassani¹,

Ghodsi

2017

Meta Gene

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Segmentation in Drosophila melanogaster starts with a key maternal input known as bicoid gene. The initial positional information provided by this gene induces the sequential activation of segmentation network. Therefore, an accurate mathematical model describing the gene expression profile of bicoid gene expects to provide essential insights into the gene cross-regulations presented in that network. The significantly stochastic, highly volatile and non-normal nature of the bicoid gene expression profile encouraged us to look for the best distribution function describing this profile. We exploit the use of fifty-four different powerful and widely-used distributions and conclude that FatigueLife(3P) fits the data more accurately than the other distributions. The reliability and validity of the results are evaluated via both simulation studies and empirical evidence thereby adding more confidence and value to the findings of this research.

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“…Many other reviews provide detailed information on the other related aspects of the system [8][9][10][11][12][13]. The review is organized as the following: we first demonstrate that developmental patterning is highly dynamic.…”

Section: Introductionmentioning

confidence: 98%

Noise transmission during the dynamic pattern formation in fly embryos

Yang

et al. 2018

Quant. Biol.

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Background: Developmental patterning is highly reproducible and accurate at the single-cell level during fly embryogenesis despite the gene expression noise and external perturbations such as the variation of the embryo length, temperature and genes. To reveal the underlying mechanism, it is very important to characterize the noise transmission during the dynamic pattern formation. Two hypotheses have been proposed. The "channel" scenario requires a highly reproducible input and an accurate interpretation by downstream genes. In contrast, the "filter" scenario proposes a noisy input and a noise filter via the cross-regulation of the downstream network. It has been under great debates which scenario the fly embryogenesis follows. Results: The first 3-h developmental patterning of fly embryos is orchestrated by a hierarchical segmentation gene network, which rewires upon the maternal to zygotic transition. Starting from the highly reproducible maternal gradients, the positional information is refined to the single-cell precision through the highly dynamical evolved zygotic gene expression profiles. Thus the fly embryo development might strictly fit into neither the originally proposed "filter" nor "channel" scenario. The controversy that which scenario the fly embryogenesis follows could be further clarified by combining quantitative measurements and modeling. Conclusions: Fly embryos have become one of the perfect model systems for quantitative systems biology studies. The underlying mechanism discovered from fly embryogenesis will deepen our understanding of the noise control of the gene network, facilitate searching for more efficient and safer methods for cell programming and reprogramming, and have the great potential for tissue engineering and regenerative medicine.Keywords: pattern formation; gene regulatory network; noise; embryogenesis; Drosophila Author summary: It is intriguing how the development of organisms is highly reproducible and accurate despite the external and internal noise. The key to solve this mystery is to explore the noise transmission during development and discover the underlying mechanism. The developmental system could generate precise inputs and outputs in each step, or gradually generate a final precise output by filtering a noisy input according to the "channel" or "filter" scenario, respectively. The quantitative studies on the early development of the fruit fly's embryos show that the developmental process is highly dynamic and its noise transmission may employ a hybridized strategy.

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Mathematical approaches in studying bicoid gene

Cited by 7 publications

References 69 publications

Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

Evaluating the analytical distribution of bicoid gene expression profile

Noise transmission during the dynamic pattern formation in fly embryos

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