We introduce the notion of a Cancer Hybrid Automaton, a formalism to model the progression of cancers through discrete phenotypes (e.g., so-called hallmarks). The classification of various cancers using multiple stages has become common in the biology literature, but primarily as an organizing principle, and not as an executable formalism. The precise computational model developed in this talk aims to exploit this untapped potential, namely, through automatic verification of progression models (e.g., consistency, causal connections, etc.), classification of unreachable or unstable states (e.g., “anti-hallmarks”) and computer-generated (individualized or universal) therapy plans. The basic algorithm builds on a phenomenological approach, and as such does not need to model the biochemistry underlying the progression. Rather, it abstractly models transition timings between various stages as well as the effects of drugs and clinical tests, and thus allows formalization of temporal statements about the progression as well as notions of timed therapies. The model proposed here is ultimately based on hybrid automata (with multiple clocks), for which relevant verification and planning algorithms exist in the literature. We also describe how more precise causal progression models could be built from TCGA (The Cancer Genome Atlas) data.
Bhubaneswar (Bud) Mishra is a professor of computer science and mathematics at NYU's Courant Institute of Mathematical Sciences, professor of human genetics at Mt. Sinai School of Medicine, and a professor of cell biology at NYU School of Medicine. He currently leads several groups working in biotechnology, bioinformatics, biomedicine, cyber security, data privacy, data sciences and a new design of the Internet. Prof. Mishra has a degree in Physics from Utkal University, in Electronics and Communication Engineering from IIT, Kharagpur, and MS and PhD degrees in Computer Science from Carnegie-Mellon University. He has industrial experience in Computer Science (ATTAP, Genesis Media, SeQster, and Tartan Laboratories), Finance (Instadat,LLC, PRF, LLC, and Tudor Investment), Robotics and Bio- and Nanotechnologies (Abraxis, Bioarrays, InSilico, MRTechnology and OpGen). He is an author of a textbook on algorithmic algebra and more than two hundred archived publications. He has advised and mentored more than 40 graduate students and post-docs in the areas of computer science, robotics and control engineering, applied mathematics, finance, biology and medicine. He is an inventor of Optical Mapping and Sequencing (SMASH), Array Mapping, Copy-Number Variation Mapping, Cancer Therapy Design, Model Checker for circuit verification, Robot Grasping and Fixturing, Reactive Robotics, Nanotechnology for DNA profiling, Causality Analysis and Personal Private Data Markets. He is a fellow of IEEE, ACM and AAAS, a Distinguished Alumnus of IIT-Kgp, and a NYSTAR Distinguished Professor. From 2001-04, he was a professor at the Watson School of Biological Sciences, Cold Spring Harbor Lab; currently he is a QB visiting scholar at Cold Spring Harbor Lab.