Computational Biology

My name is Jitao David Zhang. I am a biologist and bioinformatician by training, with working experience and wide interests in related fields including statistics (especially linear models, fixed effect models and Bayesian networks), pattern recognition and machine learning, and software engineering. Following is a brief CV.

  • I received B.Sc. in Biology of Peking University in 2006. There I was trained to be a molecular biologist familiar with experiments in laboratory. Interests in bioinformatics and biostatistics never faded due to fascinations about mathematics and computer programming since my early childhood.
  • Between 2006 and 2008, I was a ZMBH-EMBL-DKFZ Fellow enrolled in the Internal Molecular and Cellular Biology Program organized unitedly by the three institutes. I received the M.Sc. degree in bioinformatics and computational biology in 2008 with the work on the in-silico solution of a high-throughput screening to identify human MAPK pathway modulators. I became a frequent user of R and Bioconductor. At the same time I worked as HiWi student in the HUSAR Bioinformatics group in DKFZ. There I was trained in several topics in bioinformatics, including sequence analysis, classification using support vector machines, and NGS analysis.
  • Between August 2007 and Feburary 2008, I received the Marie-Curie-Fellowship from EU and worked as a computational biologist under the supervision of Dr. Wolfgang Huber in European Bioinformatics Institute, Cambridge, UK. There I was trained for advanced skills in R programming. The experience inspired me shortly after the visit to create my first Bioconductor package KEGGgraph.
  • Between 2008 and 2011 he worked as a pre-doc in the Division of Molecular Genome Analysis led by PD Dr. Stefan Wiemann, in German Cancer Research Center (DKFZ), Heidelberg, Germany. January 2011 he received his Ph.D. title with the thesis titled Computational and statistical approaches to profile human cancer-related gene regulatory networks. My work contributed to the understanding how microRNAs, a class of small regulatory RNAs, regulates cell proliferation, differentiation, invasion and apoptosis in breast cancer and gastrointestinal cancer. Together with biologists, pathologists and computer scientists, I learned many new possibilities to help wet-lab biologists solve their questions with computational and statistical tools.
  • In February 2011, I joined F. Hoffmann-La Roche as a computational biologist. Here I work in a highly competitive team, and support a variety of projects with bioinformatics, computational biology, and machine learning techniques.

Translational Computational Biology

Computational Biology is defined by WikiPedia as an interdisciplinary field that applies the techniques of computer science, applied mathematics and statistics to address biological problems.  My scientific interest lies in applying computational biology and biostatistic approaches in translational oncological research. My work generally targets two questions: MoA analysis of drug targeting and drug resistance, as well as novel prognostic biomarker identification. To thie end, I develop algorithms, design statistical models and implement software to profile human cancer-related signaling transduction and gene regulatory networks. I collaborate very closely with biology experimentalists, bioinformaticians/computer scientists as well as clinicians internal and external of the division. 

Reseach topics

  • Computational approaches for quantitative cell-based high-throughput screenings (HTS) to profile signaling pathways with oncological relevance
    • Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer [Molecular System Biology,Associated NEWS AND NEWS]
    • miR-200bc/429 family regulating cell proliferation, cell cycle and invasion differetially from miR-200a/141 by targeting PLCG1 [Oncogene]
    • miR-200c regulates breast cancer cell migration and invasion by multiple targeting [Molecular and Cellular Biology]
    • microRNA-375 reverses both tamoxifen resistance and accompanying EMT-like properties in breast cancer [Oncogene Open Access]
    • Human microRNA mimics library screening to identify NFkB-pathway modulating microRNAs [Oncogene Open Access]
    • Real-time screening of cell proliferation upon RNAi perturbation with the RTCA system [PLoS One Free Text]
    • Genome-wide RNAi screening to identify MAPK pathway modulators (publication in preparation)
    • Sub-genome RNAi screening to identify miR-21 modulators (publication in preparation) and miR-31/miR-155 modulators
  • Clinical studies and MoA studies
    • Multiple-layer analysis and data integration of signal transduction and molecular pathology of GIST in retrospective clinical studies [Pathologe]: mRNA, small RNA [Journal of Pathology], methylation and proteomics (collaboration with Dr. Florian Haller, Uni-Klinikum Freiburg). [Posterpreisträger 2010]
    • Bladder cancer: diagonosis value and functional relevance of novel biomarkers with molecular genomics and pathway analysis (AACR 102nd meeting poster, publication in preparation, collaboration with LIFE GmbH and BROAD Institute).
    • Breast cancer: Novel tyrosine kinase inhibitor (TKI) MoA analysis in mouse model and drug resistance mechanism analysis (publication in preparation, collaboration with Dr. Hynes from FMI Basel/Novartis)
  • Data mining of biological pathways and networks in public domains
    • KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor. Read paper in Bioinformatics. [PubMed][Bioconductor]
    • RpsiXML: Bridging PSI-MI standarized protein-protein interaction data with computational environment R. [Bioconductor]
    • Integrative analysis and machine learning techniques to transform a large-scale in-silico toxicogenomic library a non-clinical safety evaluation platform.
  • Statistical analysis of quantitative high-throughput cell-based assays
    • ddCt: Statistical analysis pipeline of qRT-PCR experiments. [GenomExpress 3.09 (GER)][Bioconductor][application note in Biochemica] (collaboration with Roche Penzberg)
    • RTCA: Software toolkit for data import, analysis and visualization of the real-time cell analyzer (RTCA) system (xCELLigence(R) Roche).[application note in Biochemica].[Bioconductor]
    • r-opencv: Interface to computer vision library OpenCV in R.[Project page]
    • flowDeconvolutor (Internal): Automatic analysis of DNA-staining cell-cycle experiments with flow cytometry.
    • ScratchIt (Internal): Analysis of would-healing assay (aka. scratch assay) with computer vision tools.
    • BioVision: Bioinformatic image and video analysis software suite with r-opencv as backend.
  • Bayesian phylogenetic analysis
  • Identification and functional profiling of biological network motifs
    • Dissertation Computational and statistical approaches to profile human cancer-related gene regulatory networks, Universität Heidelberg 2011.