Dr. Moritz Gerstung

Current Research Projects

Somatic Evolution of Cancer

Genetic intra-tumor diversity. In this interdisciplinary project, supported by an iPhD grant from SystemsX.ch, we study the genetic diversity inside of a tumor with next generation sequencing technologies. This project involves an in-house collaboration with Dr. Christian Beisel from the Epigenetics Group, and with Prof. Holger Moch from the University Hospital Zurich (software).

Genetic progression of cancer. We recently developed graphical models termed Hidden Conjunctive Bayesian Networks for learning the order of mutations during cancer progression (paper, software).

Integrative Genomics

Cancer genetics and transcriptomics. In a collaboration with Dr. Manoj Mahimkar from ACTREC, India, and Dr. Alejandro Schäffer from NIH, we characterizes oral squamous cell carcinoma combining data from gene expression microarrays and array CGH technologies.

Epigenetics of D. Melanogaster. In a collaboration with Dr. Christian Beisel from from the Epigenetics Group, we integrate ChIP-seq and other data types for a comprehensive understanding of epigenetic regulation.

Research Interests

My research interests are broad, including:

• Next Generation Sequencing: NGS allows for studying genetic and epigenetic processes at unprecedented resolution. The resulting wealth of data also presents fascinating challenges for computational biology and statistics.
• Machine Learning and High Dimensional Statistics: Today there exists an enormous mass of biological data and more is on the way. I'm interested in statistical and computational methods for extracting the interesting bits from the big pile.
  
• Evolutionary Dynamics: We use population genetics and evolutionary game theory models to describe the somatic evolution of cancer and link them to experimental results from NGS and other high-throughput technologies.
  
• Stochastic Processes:  Both the accumulation of evolutionary dynamics of cancer and cellular signalling can be modeled as stochastic processes.
  
• Complex networks: In biology everything interacts; complex networks are a formal way to account for this. Cancer can be seen as a perturbation of those networks by mutations in signaling proteins.