skip to content »

Structural and Computational Biology and Molecular Biophysics

Houston, Texas

A BCM research lab.
Structural and Computational Biology & Molecular Biophysics
not shown on screen

Marek Kimmel, Ph.D.

Marek Kimmel, Ph.D.Professor, Statistics

Rice University


Ph.D., Control Technology, Silesian Technical University, Gliwice, Poland

Research Interests:

Principal interests are stochastic modeling of genome dynamics, in particular in cancer progression, statistical and population genetics, biostatistics and bioinformatics. Examples of projects include:

  • Interaction of genetic and environmental causes in genesis of lung cancer.
  • Modeling metabolic pathways in airways inflammation.
  • Analysis of Single Nucleotide Polymorphism (SNP) data at cancer-related loci.

Selected Publications:

  • Kimmel M and Corey S. Stochastic Hypothesis of Transition from Inborn Neutropenia to AML: Interactions of Cell Population Dynamics and Population Genetics. Front Oncol, 3:89 (2013). PubMed
  • Goldwasser DL and Kimmel M. Small median tumor diameter at cure threshold (<20 mm) among aggressive non-small cell lung cancers in male smokers predicts both chest X-ray and CT screening outcomes in a novel simulation framework. Int J Cancer, 132(1):189-97 (2012). PubMed
  • Hicks S, Wheeler DA, Plon SE and Kimmel M. Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum Mutat, 32(6):661-8 (2011). PubMed
  • Kimmel M and Mathaes M. Modeling neutral evolution of Alu elements using a branching process. BMC Genomics, 11: Suppl 1:S11 (2010). PubMed
  • Goldwasser DL and Kimmel M. Modeling excess lung cancer risk among screened arm participants in the Mayo Lung Project. Cancer, 116(1):122-31, (2010). PubMed
  • Wu X, Strome ED, Meng Q, Hastings PJ, Plon SE and Kimmel M. A robust estimator of mutation rates. Mutat Res, 661(1-2):101-9, (2009) PubMed
  • Chen BY, Bryant DH, Cruess AE, Bylund JH, Fofanov VY, Kristensen DM, Kimmel M Lichtarge O, and Kavraki LE. Composite motifs integrating multiple protein structures increase sensitivity for function prediction. Comput Syst Bioinformatics Conf, 6:343-55 (2007). PubMed

For more publications, see listing on PubMed.

Contact Information:

Department: Statistics
Address: Statistics MS138
2097 Duncan Hall
6100 S. Main St.
Houston, TX 77005-1892
Phone: 713-348-5255
Fax: 713-348-5476
Additional Links: Stochastic Models in Cell Biology, Genetics, and Evolution

E-mail this page to a friend