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Iglesias-Martinez, Luis F.
Preferred name
Iglesias-Martinez, Luis F.
Official Name
Iglesias-Martinez, Luis F.
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Now showing 1 - 2 of 2
- PublicationAn Integrated Global Analysis of Compartmentalized HRAS Signaling(Elsevier, 2019-03-12)
; ; ; ; ; ; ; ; ; Modern omics technologies allow us to obtain global information on different types of biological networks. However, integrating these different types of analyses into a coherent framework for a comprehensive biological interpretation remains challenging. Here, we present a conceptual framework that integrates protein interaction, phosphoproteomics, and transcriptomics data. Applying this method to analyze HRAS signaling from different subcellular compartments shows that spatially defined networks contribute specific functions to HRAS signaling. Changes in HRAS protein interactions at different sites lead to different kinase activation patterns that differentially regulate gene transcription. HRAS-mediated signaling is the strongest from the cell membrane, but it regulates the largest number of genes from the endoplasmic reticulum. The integrated networks provide a topologically and functionally resolved view of HRAS signaling. They reveal distinct HRAS functions including the control of cell migration from the endoplasmic reticulum and TP53-dependent cell survival when signaling from the Golgi apparatus.Scopus© Citations 32 444 - PublicationBGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer researchReconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.
380Scopus© Citations 31