Research in the Ideker Lab focuses on using genome-scale measurements (genomic, proteomic, and metabolomic) to construct computer-aided models of cellular processes and disease. These models have the potential to revolutionize biology and medicine by providing a comprehensive blueprint of normal and diseased cell functions and by allowing researchers to simulate the effects of drugs on cells long before they are tested in humans.
The long-term objective of the Ideker Laboratory is to create artificially intelligent models of cancer and other diseases for translation of patient data to precision diagnosis and treatment. We seek to advance this goal by addressing fundamental questions in systems biology and bioinformatics, including: What are the genetic and molecular networks that promote cancer, and how can we best chart these? How do we use knowledge of these networks in intelligent systems for translation of genotype to phenotype?
Network models have the potential to revolutionize biology and medicine by providing a comprehensive blueprint of normal and diseased cell functions. They also provide a framework for drug development, interpretation of patient data and, ultimately, simulating the effects of a particular drug on a particular patient. To build these network models, we combine innovative computational approaches with an experimental laboratory for interaction mapping and molecular profiling. As a result we are producing a suite of network-based methods that can be applied to a wide range of biomedical problems.
Recent research efforts are described in the following pages. Although the methods themselves are general, the majority of our work is applied to mapping DNA repair and damage response networks and in using such networks for cancer diagnosis and treatment. We also have applied our methods to cell fate decisions, viral infection and other research topics, primarily in collaboration with others.
Research Focus Keywords: Systems Biology, Computational Biology & Data Science, Genomics, Machine Learning, Integrative Biology, Cancer, Neuropsychiatric disease