Dataset Information

Celiku_Friedenberg_2022 -- Probabilistic Model Checking of Cancer Metabolism

ABSTRACT: We provide a model of glycolysis and TCA cycle in cancers in which IDH is encountered both in its wild type form as well as mutated and capable of catalyzing an onco-metabolite. The model is constructed using the Probabilistic Symbolic Model Checker (PRISM,https://www.prismmodelchecker.org/) specification language, and properties are specified using a temporal-property specification language. The reaction rates are instantiated using both experimentally derived metabolic flux rate data (under normoxia and hypoxia conditions) as well as derived through a combination of these experimental rates and relative enzyme activity levels estimated using mRNA expression data from patient samples. We provide shell scripts for invoking the freely-available automatic model checker for reasoning about the specified properties.

SUBMITTER: Orieta Celiku

PROVIDER: MODEL2207180001 | BioModels | 2022-07-18

REPOSITORIES: BioModels

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Publications

Probabilistic model checking of cancer metabolism.

Friedenberg Meir D MD Lita Adrian A Gilbert Mark R MR Larion Mioara M Celiku Orieta O

Scientific reports 20221107 1

Cancer cell metabolism is often deregulated as a result of adaption to meeting energy and biosynthesis demands of rapid growth or direct mutation of key metabolic enzymes. Better understanding of such deregulation can provide new insights on targetable vulnerabilities, but is complicated by the difficulty in probing cell metabolism at different levels of resolution and under different experimental conditions. We construct computational models of glucose and glutamine metabolism with focus on the ...[more]

PMID: 36344581

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Project description:Small RNAs (sRNAs) are hypothesized to contribute to hybrid vigor because they maintain genome integrity, contribute to genetic diversity, and control gene expression. We used Illumina sequencing to assess how sRNA populations vary between two maize inbred lines (B73, Mo17) and their hybrid. We sampled sRNAs from the seedling shoot apex and the developing ear, two rapidly growing tissues that program the greater growth of maize hybrids. We found that parental differences in siRNAs primarily originate from repeat regions. Although the maize genome contains greater number and complexity of repeats compared to Arabidopsis or rice, we confirmed that like these simpler plant genomes, 24-nt siRNAs whose abundance differs between maize parents also show a trend of downregulation following hybridization. Surprisingly, hybrid vigor is fully maintained when 24-nt siRNAs are globally reduced by mutation of the RNA-dependent RNA polymerase2 (RDR2) encoded by modifier of paramutation1 (mop1). We also discovered that 21-22nt siRNAs derived from a number of distinct retrotransposon families differentially accumulate between B73 and Mo17 as well as their hybrid. Thus, maize possesses a novel source of genetic variation for regulating both transposons and genes at a genomic scale, which may contribute to its high degree of observed heterosis. sRNA libraries were derived from RNA isolated from the seedling shoot apex and developing ear tissues from B73, Mo17, B73xMo17 and Mo17xB73. The shoot apex was chosen because it is enriched for meristematic tissue where cell proliferation occurs, rates of organ initiation are determined, and organ size is specified. The developing ear was examined because it is enriched in meristematic tissue and is undergoing rapid growth, and also because the mature ear shows the highest degree of heterosis. Total RNA was isolated and separated on a 15% TBE-Urea polyacrylamide gel. Using a 10-bp ladder, the sRNA fraction representing 10-40-bp was excised. sRNA libraries were prepared according to Lu et al. (2007) or manufacturer's instructitions (Illumina). A combination of Perl scripts and FASTX toolkit scripts were used to remove adapters, collapse identical sequences and count reads per sequence. Supplementary processed data text files contain the distinct sRNA sequences for all of the genotypes analyzed in that experiment. Abundance (reads per million) was calculated for each distinct sequence by dividing the number of reads of distinct sRNA in a library by the total number of sRNA reads for that library and multiplying this by 1 million. Genome builds: B73 genome, maizesequence.org release 4a.53 (October, 2009); Mo17 whole genome shotgun clones.

Dataset Information

Celiku_Friedenberg_2022 -- Probabilistic Model Checking of Cancer Metabolism

Publications

Probabilistic model checking of cancer metabolism.

Similar Datasets

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