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Evolution of the AMP-Activated Protein Kinase Controlled Gene Regulatory Network

Part of the Lecture Notes in Bioengineering book series (LNBE)

Abstract

Alterations in gene regulation are considered major driving forces in divergent evolution. This is reflected in different species by the variable architecture of regulatory networks controlling highly conserved metabolic pathways. While many regulatory proteins are surprisingly conserved their wiring has evolved more rapidly. This project focuses on the adaptation to nutrient limitation, which requires the activation of the conserved AMP-activated protein kinase (AMPK alias Snf1 in yeast) and its downstream effectors. The goal is to uncover basic principles of adaptation and steps in the evolutionary process associated with regulatory network rearrangement. This requires improving the prediction of gene regulation based experimental data, DNA sequence information and information theory. In this project Context Tree (CT) models and Parsimonious Context Tree (PCT) models and the corresponding algorithms for extended Context Tree Maximization (CTM) and extended Parsimonious Context Tree Maximization (PCTM) are derived, implemented, and applied. Computational predictions and experimental validation will establish an iterative cycle to improve algorithms in each cycle leading to a growing set of experimentally verified and falsified predictions, finally allowing a deeper understanding of the evolution of the transcriptional regulatory network controlling energy metabolism, one of the most fundamental processes conserved across all kingdoms of life.

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  • DOI: 10.1007/978-3-319-54729-9_9
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Publications Within the Project

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  • Eggeling R et al (2012) Gibbs sampling for parsimonious Markov models with latent variables. In: The sixth European workshop on probabilistic graphical models

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  • Eggeling R, (2013) Inhomogeneous parsimonious Markov models. In: Machine learning and knowledge discovery in databases: European conference, ECML PKDD, et al (2013) Prague, Czech Republic, 23–27 Sept 2013. Proceedings, Part I

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  • Eggeling R et al (2014) On the value of intra-motif dependencies of human insulator protein CTCF. PLoS ONE 9(1):1–12. doi:10.1371/journal.pone.0085629

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  • Eggeling R et al (2015) Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data. BMC Bioinform 16(1):375. doi:10.1186/s12859-015-0797-4

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  • Eggeling R, Koivisto M, Grosse I (2015a) Dealing with small data: on the generalisation of context trees. In: Proceedings of the 32nd international conference on machine learning. Lille, France

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  • Mehlgarten C et al (2015) Divergent evolution of the transcriptional network controlled by snf1-interacting protein sip4 in budding yeasts. PLoS ONE 10(10):1–23. doi:10.1371/journal.pone.0139464

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  • Nettling M et al (2015) DiffLogo: a comparative visualization of sequence motifs. BMC Bio 16: 387+. (17 Nov 2015), http://dx.doi.org/10.1186/s12859-015-0767-x

  • Nettling M et al (2016) Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species information. BMC Genomics 17(1). http://view.ncbi.nlm.nih.gov/pubmed/27165633

  • Nettling M, Treutler H, Cerquides J, Grosse I (2017a) Unrealistic phylogenetic trees may improve phylogenetic footprinting. Bioinformatics. doi:10.1093/bioinformatics/btx033. [Epub ahead of print]

  • Nettling M, Treutler H, Cerquides J, Grosse I (2017b) Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies. BMC Bioinformatics 18(1):141

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Correspondence to Karin D. Breunig .

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Mehlgarten, C. et al. (2018). Evolution of the AMP-Activated Protein Kinase Controlled Gene Regulatory Network . In: Bossert, M. (eds) Information- and Communication Theory in Molecular Biology. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-319-54729-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-54729-9_9

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  • Publisher Name: Springer, Cham

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