Kuttippurathu L, Juskeviciute E, Dippold RP, Hoek JB, Vadigepalli R. A novel comparative pattern analysis approach identifies chronic alcohol mediated dysregulation of transcriptomic dynamics during liver regeneration. BMC Genomics. DOI: 10.1186/s12864-016-2492-x

This paper describes a new data analysis approach named COMPACT, which is particularly suited for identifying key patterns in time series genomics data sets. 

Cook D, Ogunnaike BA, Vadigepalli R. (2015) Systems analysis of non-parenchymal cell modulation of liver repair across multiple regeneration modes. BMC Syst Biol. 2015 Oct 22;9:71. doi: 10.1186/s12918-015-0220-9. PubMed PMID: 26493454; PubMed Central PMCID: PMC4618752.

This paper describes a systems dynamics model of liver regeneration. Analysis of the model led to a new insight that altered non-parenchymal cell activation is sufficient to account for the deficient regeneration observed in multiple disease cases.

Anderson WD, Makadia HK, Greenhalgh AD, Schwaber JS, David S, Vadigepalli R. (2015) Computational modeling of cytokine signaling in microglia. Mol Biosyst. 2015 Oct  6. [Epub ahead of print] PubMed PMID: 26440115.

This paper describes a new model of a cytokine regulatory network in the microglia that led to an apparently counterintuitive prediction that knock out of the “anti-inflammatory” cytokine IL-10 can lead to a suppressed inflammatory response due to interaction between two kinetically imbalanced negative feedback connections. The results were validated by new experimental findings.

DeCicco D, Zhu H, Brureau A, Schwaber JS, Vadigepalli R. (2015) MicroRNA network changes in the brain stem underlie the development of hypertension. Physiol Genomics. 2015 Sep;47(9):388-99. doi: 10.1152/physiolgenomics.00047.2015. Epub 2015 Jun 30. PubMed PMID: 26126791; PubMed Central PMCID: PMC4556940. Editorial Highlight; APSselect for September 2015.

This study identified a new set of microRNAs that were upregulated in the brainstem during the onset of hypertension. These microRNAs were predicted to disinhibit neuroinflammatory processes and angiotensin II signaling, the two key processes driving the development of hypertension.

Park J, Brureau A, Kernan K, Starks A, Gulati S, Ogunnaike B, Schwaber J, Vadigepalli R. (2014) Inputs drive cell phenotype variability. Genome Res. 2014 Jun;24(6):930-41. doi: 10.1101/gr.161802.113. Epub 2014 Mar 26. PubMed PMID: 24671852; PubMed Central PMCID: PMC4032857.

This work revealed new findings that challenged the then popular paradigm that single cell gene expression variability is largely due to stochastic processes and manifests as ‘noise’. Based on measuring ~75 genes each in hundreds of single neurons, we found that the variability is highly ordered along a gradient based on inputs to individual cells, yielding a continuum of neuronal phenotypes.