Applied Systems Biology Core

Matthias Kretzler, MD
ASBC Director


The emerging field of systems biology aims to integrate the constantly growing amount of complex biological information to provide a comprehensive description of regulatory events. This approach has been greatly facilitated by the rapid development of molecular medicine allowing the generation of large-scale data sets from small-scale patient samples.

Systems genetics approaches can also be employed to define regulatory dependencies between genotypic risk, molecular profiles, and phenotypic parameters.

The Applied Systems Biology Core (ASBC) helps Research Investigators to link the large-scale data sets generated by ongoing research projects in their specific research interest. They are then able to link the clinical, genetic, and molecular information with the specific mechanism of renal function and failure that are of interest to the investigator.

Main Objectives of the ASBC: Empowering the renal community to employ genome-scale information to:



The ASBC is comprised of 3 different services.


Service 1: Interactive shared data mining services:

  • Introduction to and training in system biology tools
  • Genome scale data mapping and analyses (transcripts, proteins, metabolites, pathways)
  • Integration of multi-omic data sets to identify cross cutting mechanisms 


Service 2: Development and support of web based search engine: 



  • User-friendly tool to link gene expression with renal function and failure
  • No deep bioinformatics skills necessary
  • Fast results with direct human relevance
  • More data at your fingertips
    • Supplementary clinical information from study authors
    • Standardized clinical metadata



  • Nephrocell is a web application for querying gene expression levels across our collection of human kidney and human kidney organoid single-cell datasets.


Service 3: Support for study specific data analysis platforms



  • Cohort specific data mining tools using tranSMART for dynamic data mining across a wide range of data sets
  • CPROBE-specific tranSMART instance for the Kidney Center


When using any core services, users agree to cite the George M. O’Brien Michigan Kidney Translational Core Center, funded by NIH/NIDDK grant 2P30-DK-081943 in any publications and/or presentations resulting from the use of these core services. 

For more information or a service, please contact Joseph Tychewicz  at
To request services please click here.

Recent Publications

Wernisch S, Pennathur S. Application of differential mobility-mass spectrometry for untargeted human plasma metabolomic analysis. Anal Bioanal Chem. 2019 Apr 2. doi: 10.1007/s00216-019-01719-z. [Epub ahead of print] PubMed PMID: 30941479.

Hinder LM, Sas KM, O'Brien PD, Backus C, Kayampilly P, Hayes JM, Lin CM, Zhang H, Shanmugam S, Rumora AE, Abcouwer SF, Brosius FC 3rd, Pennathur S, Feldman EL. Mitochondrial uncoupling has no effect on microvascular complications in type 2 diabetes. Sci Rep. 2019 Jan 29;9(1):881. doi: 10.1038/s41598-018-37376-y. PubMed PMID: 30696927; PubMed Central PMCID: PMC6351661.

Mathew AV, Pennathur S. Response to Comment on Mathew et al. Therapeutic Lifestyle Changes Improve HDL Function by Inhibiting Myeloperoxidase-Mediated Oxidation in Patients With Metabolic Syndrome. Diabetes Care 2018;41:2431-2437. Diabetes Care. 2019 Feb;42(2):e26-e27. doi: 10.2337/dci18-0053. PubMed PMID: 30665969; PubMed Central PMCID: PMC6341290.

Harder JL, Menon R, Otto EA, Zhou J, Eddy S, Wys NL, O'Connor C, Luo J, Nair V, Cebrian C, Spence JR, Bitzer M, Troyanskaya OG, Hodgin JB, Wiggins RC, Freedman BS, Kretzler M; European Renal cDNA Bank (ERCB); Nephrotic Syndrome Study Network (NEPTUNE). Organoid single cell profiling identifies a transcriptional signature of glomerular disease. JCI Insight. 2019 Jan 10;4(1). pii: 122697. doi: 10.1172/jci.insight.122697. [Epub ahead of print] PubMed PMID: 30626756; PubMed Central PMCID: PMC6485369.

Bria CRM, Afshinnia F, Skelly PW, Rajendiran TM, Kayampilly P, Thomas TP, Andreev VP, Pennathur S, Kim Ratanathanawongs Williams S. Asymmetrical flow field-flow fractionation for improved characterization of human plasma lipoproteins. Anal Bioanal Chem. 2019 Jan;411(3):777-786. doi: 10.1007/s00216-018-1499-3. Epub 2018 Nov 23. PubMed PMID: 30470915; PubMed Central PMCID: PMC6451313.