Titlebook: Reverse Engineering of Regulatory Networks; Sudip Mandal Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive licens

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Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R,ology. These networks consist of transcription factors and their associations with genes they regulate. Despite the utility of deep learning methods in studying gene expression and regulation, their accessibility remains limited for biologists,  mainly due to the prerequisites of programming skills

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Computational Inference of Gene Regulatory Network Using Genome-wide ChIP-X Data,red by a phenotype during various environmental perturbations. Inferring the regulatory network from the high-throughput data needs an algorithmic approach involving statistical analysis. There are several interaction databases such as JASPAR and SwissRegulon that provide information for TFs-targets

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Reverse Engineering in Biotechnology: The Role of Genetic Engineering in Synthetic Biology,st. This chapter explores the uses, processes, and applications of genetic engineering in synthetic biology. The chapter provides a brief history and course of development of the field of synthetic biology and genetic engineering and their unbreakable association. Next, the chapter delves into mater

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Computational Inference of Gene Regulatory Network Using Genome-wide ChIP-X Data,ta for a desired set of target-TFs from the JASPAR database, and used that information to infer the network by using the . library. Further, we also mentioned the important parameters for analyzing the different properties of the network. The described procedure will be helpful in discerning the GRN based on the set of TF-gene pairs.