An efficient gene regulatory network inference algorithm for early Drosophila melanogaster embryogenesis

AbstractThe spatial patterns of gene expression in early Drosophila melanogaster embryogenesis have been studied experimentally and theoretically to reveal the molecular basis of morphogenesis. In particular, the gene regulatory network (GRN) of gap genes has been investigated through mathematical modeling and simulation. Although these simulation-based approaches are useful for describing complex dynamics and have revealed several important regulations in spatial patterning, they are computationally intensive because they optimize GRN with iterative simulation. Recently, the advance of experimental technologies is enabling the acquisition of comprehensive spatial expression data, and an efficient algorithm will be necessary to analyze such large-scale data. In this research, we developed an efficient algorithm to infer the GRN based on a linear reaction-diffusion model. First, we qualitatively analyzed the GRNs of gap genes and pair-rule genes based on our algorithm and showed that two mutual repressions are fundamental regulations. Then, we inferred the GRN from gap gene data, and identified asymmetric regulations in addition to the two mutual repressions. We analyzed the effect of these asymmetric regulations on spatial patterns, and showed that they have the potential to adjust peak position. Our algorithm runs in sub-second time, which is significantly smaller than the runtime of simulation-based approaches (between 8 and 160 h, for exmaple). Neverthe-less, our inferred GRN was highly correlated with the simulation-based GRNs. We also analyzed the gap gene network of Clogmia albipunctata and showed that different mutual repression regulations might be important in comparison with those of Drosophila melanogaster. As our algorithm can infer GRNs efficiently and can be applied to several different network analysis, it will be a valuable approach for analyzing large-scale data.

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Large Scale Data Centers Simulation Based on Baseline Test Model

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) ◽

10.1109/ipdpsw.2018.00018 ◽

2018 ◽

Cited By ~ 1

Author(s):

Fei Lei ◽

Lei Yu ◽

Bing Shao ◽

Fei Teng ◽

Bo Zhou

Keyword(s):

Large Scale ◽

Data Centers ◽

Test Model ◽

Large Scale Data ◽

Simulation Based ◽

Baseline Test ◽

Scale Data

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Assessing Complex Patterns of Student Resources and Behavior in the Large Scale

The Annals of the American Academy of Political and Social Science ◽

10.1177/0002716219844963 ◽

2019 ◽

Vol 683 (1) ◽

pp. 233-249 ◽

Cited By ~ 3

Author(s):

Knut Neumann ◽

Horst Schecker ◽

Heike Theyßen

Keyword(s):

Large Scale ◽

Reliability And Validity ◽

Performance Tests ◽

Automated Scoring ◽

Large Scale Data ◽

Assessment Design ◽

Simulation Based ◽

And Behavior ◽

Physics Experiments ◽

Scale Data

Large-scale assessments still focus on those aspects of students’ competence that can be evaluated using paper-and-pencil tests (or computer-administered versions thereof). Performance tests are considered costly due to administration and scoring, and, more importantly, they are limited in reliability and validity. In this article, we demonstrate how a sociocognitive perspective provides an understanding of these issues and how, based on this understanding, an argument-based approach to assessment design, interpretation, and use can help to develop comprehensive, yet reliable and valid, performance-based assessments of student competence. More specifically, we describe the development of a computer-administered, simulation-based assessment that can reliably and validly assess students’ competence to plan, perform, and analyze physics experiments at a large scale. Data from multiple validation studies support the potential of adopting a sociocognitive perspective and assessments based on an argument-based approach to design, interpretation, and use. We conclude by discussing the potential of simulations and automated scoring methods for reliable and valid performance-based assessments of student competence.

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Gene Expression Maps in Plants: Current State and Prospects

Plants ◽

10.3390/plants8090309 ◽

2019 ◽

Vol 8 (9) ◽

pp. 309 ◽

Cited By ~ 2

Author(s):

Anna V. Klepikova ◽

Aleksey A. Penin

Keyword(s):

Regulatory Networks ◽

Large Scale ◽

Developmental Stages ◽

Large Scale Data ◽

Current State ◽

Genome Wide ◽

Plant Transcriptome ◽

Gene Regulatory ◽

Genome Wide Expression ◽

Scale Data

For many years, progress in the identification of gene functions has been based on classical genetic approaches. However, considerable recent omics developments have brought to the fore indirect but high-resolution methods of gene function identification such as transcriptomics, proteomics, and metabolomics. A transcriptome map is a powerful source of functional information and the result of the genome-wide expression analysis of a broad sampling of tissues and/or organs from different developmental stages and/or environmental conditions. In plant science, the application of transcriptome maps extends from the inference of gene regulatory networks to evolutionary studies. However, only some of these data have been integrated into databases, thus enabling analyses to be conducted without raw data; without this integration, extensive data preprocessing is required, which limits data usability. In this review, we summarize the state of plant transcriptome maps, analyze the problems associated with the combined analysis of large-scale data from various studies, and outline possible solutions to these problems.

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