Advances in genomic technology have led to the discovery of numerous genes whose expression di ers between cellular conditions; however, genes do not act in isolation, rather they act together in complex networks that drive cellular function. By considering the interactions between genes (and gene products), one gains a more in-depth understanding of the underlying cellular mechanisms. Estimation of these gene regulatory networks is necessary to understand cellular mechanisms, detect di erences between cell types, and predict cellular response to interventions. Cancer progression has been shown to produce drastic changes in genetic networks critical to normal cellular function. Some oncogenic mutations produce self-sustaining alterations in the network structure such that removal of the original mutation does not restore normal cellular function. This suggests that identifying the original oncogenic mutation may not be sucient for a targeted intervention; rather, a detailed understanding of the gene regulatory networks present in both normal and malignant cells may be necessary. Gene perturbation experiments are the primary tool to investigate gene regulatory networks and predict cel- lular response to interventions. Unfortunately, current network estimation algorithms are unable to adequately reconstruct gene networks from expression data. This is not surprising given that most network estimation algo- rithms function modularly and disregard uncertainty in previous steps. The overall goals of the proposed research are: (1) to improve the estimation of gene regulatory networks from perturbation experiments, by using methods that explicitly model and incorporate uncertainty in each step of the process, and (2) to use these estimated networks to predict cellular response to intervention. My long term goal is to pursue independent research into complex cellular networks drawing on the elds of statistics, systems biology, and genetics. This Award will provide support to obtain the expertise required to address the proposed research aims and transition to an independent research career. This will be accomplished through a combination of coursework, mentorship, and research experience. Of particular importance is continuing my education in molecular biology and cancer genomics through formal coursework and instruction in genomic laboratory techniques. This will provide the background necessary to work closely with biomedical investigators developing statistical methodology that addresses cutting-edge challenges in genomic research. Regular interaction with my mentors and collaborators { experts in Statistics, Computational Biology, Biomedical Genetics, and Cancer Research { will provide a rich environment in which I can obtain the necessary skills to successfully transition to independent research.
The proposed statistical methodology will result in an improved understanding of gene networks, particularly self- sustaining networks in cancer cells. Detailed knowledge of these networks has the potential to guide treatments designed to preferentially target malignant cells.
| Nassiri, Isar; McCall, Matthew N (2018) Systematic exploration of cell morphological phenotypes associated with a transcriptomic query. Nucleic Acids Res 46:e116 |
| Rosenberg, Avi Z; Wright, Carrie; Fox-Talbot, Karen et al. (2018) xMD-miRNA-seq to generate near in vivo miRNA expression estimates in colon epithelial cells. Sci Rep 8:9783 |
| McCall, Matthew N; Kim, Min-Sik; Adil, Mohammed et al. (2017) Toward the human cellular microRNAome. Genome Res 27:1769-1781 |
| Komisarof, Justin; McCall, Matthew; Newman, Laurel et al. (2017) A four gene signature predictive of recurrent prostate cancer. Oncotarget 8:3430-3440 |
| McCall, Matthew N; Illei, Peter B; Halushka, Marc K (2016) Complex Sources of Variation in Tissue Expression Data: Analysis of the GTEx Lung Transcriptome. Am J Hum Genet 99:624-635 |
| Xie, Qing Yan; Almudevar, Anthony; Whitney-Miller, Christa L et al. (2016) A microRNA biomarker of hepatocellular carcinoma recurrence following liver transplantation accounting for within-patient heterogeneity. BMC Med Genomics 9:18 |
| McCall, Matthew N; Baras, Alexander S; Crits-Christoph, Alexander et al. (2016) A benchmark for microRNA quantification algorithms using the OpenArray platform. BMC Bioinformatics 17:138 |
| Cherry, Jonathan D; Olschowka, John A; O'Banion, M Kerry (2015) Arginase 1+ microglia reduce A? plaque deposition during IL-1?-dependent neuroinflammation. J Neuroinflammation 12:203 |
