University of Oxford Crest

Dr. Eric Werner

Contact: eric.werner@dpag.ox.ac.uk

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Title: Cancer Networks: A general theoretical and computational framework for
understanding cancer

Author: Eric Werner


Contact: eric.werner at oarf.org


Abstract

We present a general computational theory of cancer and its developmental dynamics. The theory is based on a theory of the architecture and function of developmental control networks which guide the formation of multicellular organisms. Cancer networks are special cases of developmental control networks. Cancer results from transformations of normal developmental networks. Our theory generates a natural classification of all possible cancers based on their network architecture. Each cancer network has a unique topology and semantics and developmental dynamics that result in distinct clinical tumor phenotypes. We apply this new theory with a series of proof of concept cases for all the basic cancer types. These cases have been computationally modeled, their behavior simulated and mathematically described using a multicellular systems biology approach. There are fascinating correspondences between the dynamic developmental phenotype of computationally modeled in silico cancers and natural in vivo cancers. The theory lays the foundation for a new research paradigm for understanding and investigating cancer. The theory of cancer networks implies that new diagnostic methods and new treatments to cure cancer will become possible.  


Key words: cancer networks, cene, cenome, developmental control networks, stem cells, stem cell networks, cancer stem cells, stochastic stem cell networks, stochastic cancer stem cell networks, metastases hierarchy, linear networks, exponential networks, geometric cancer networks, cell signaling, cell communication networks, cancer communication networks, systems biology, computational biology, multiagent systems, muticellular modeling, simulation, cancer modeling, cancer simulation

Research

in silico Mulicellular Development( Eric Werner)

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