In-Person EventFeb 08 - 11, 2015San Francisco, California, USA
The field of cancer systems biology has emerged to address the increasing challenge of cancer as a complex, multifactorial disease using sophisticated model-based approaches, ranging from relatively coarse genome-wide regulatory and signaling networks to detailed kinetic models of key pathways. These networks and pathways are responsible for implementing cancer-relevant mechanisms and for processing aberrant signals from the spectrum of somatic mutations and heritable variants that contribute to tumor initiation, progression, and drug sensitivity. The ability to assemble and interrogate such cancer models is the direct result of the availability of large sample-matched collections of molecular profiles from thousands of human malignancies, as made available by large consortia, such as TCGA, ICG, and TARGET, among others.
This AACR Special Conference complements and expands the topics explored by the AACR Special Conference on Translation of the Cancer Genome, with which it shares a joint day of sessions, to focus on new innovations in the translation of genomic data using model-based and other systems biology approaches. One of the conference highlights is a unique day of sessions and networking held jointly with the AACR Special Conference on Translation of the Cancer Genome. Presenters will explore new directions in these overlapping and rapidly evolving fields to promote new opportunities for transdisciplinary collaborations. As such, it will appeal to basic, translational, computational and clinical scientists from both academia and industry. Attendees may register for one or both conferences.
Attendees of the AACR Special Conference on Translation of the Cancer Genome (A) will joined by the attendees of the AACR Special Conference on Computational and Systems Biology of Cancer (B) for a keynote address and reception on Sunday, Feb. 8, then for joint sessions on Monday, Feb. 9. During the joint sessions, overlapping themes in the areas of patient stratification, big data, and network-based cancer biology will be discussed to enhance interaction between colleagues across both disciplines.