Understanding how cancer initiates, grows and migrates has been a  fundamental topic of biomedical research in the past decades and is still the object of intense scientific activity. While traditionally cancer research has relied heavily in the traditional tools of biologists, such as biochemistry and genetics, it is recently becoming apparent that an approach based on physical sciences could provide a new perspective on the problem bringing to the field new ideas and tools.

Although contributions to cancer grounded on computational models are starting to emerge, they are mostly ignored by mainstream cancer research. We believe that this is mostly due to language and cultural barriers that could be overcome by suitable initiatives bridging the gap between these different disciplines. The present workshop is a step in this direction: we plan to bring together researchers in computational physics and cancer biology to discuss possible new pathways to export methods and ideas from physics to cancer. The idea is to invite computational physicists, applied mathematicians studying cancer modeling, statistical physicists working in bioinformatics, and more traditional cancer biologists.  We are involving leading scientists in cancer research with the twofold aim to expose them to new methods coming from computational physics and obtain a state-of-art description of open problems in the field. We have contacted already some researchers from different countries in Europe and USA, coming from different backgrounds, and we have received many positive responses.

Understanding how cancer initiates, grows and migrates has been a  fundamental topic of biomedical research in the past decades and is still the object of intense scientific activity. While traditionally cancer research has relied heavily in the traditional tools of biologists, such as biochemistry and genetics, it is recently becoming apparent that an approach based on physical sciences could provide a new perspective on the problem bringing to the field new ideas and tools.
Although contributions to cancer grounded on computational models are starting to emerge, they are mostly ignored by mainstream cancer research. We believe that this is mostly due to language and cultural barriers that could be overcome by suitable initiatives bridging the gap between these different disciplines. The present workshop is a step in this direction: we plan to bring together researchers in computational physics and cancer biology to discuss possible new pathways to export methods and ideas from physics to cancer. The idea is to invite computational physicists, applied mathematicians studying cancer modeling, statistical physicists working in bioinformatics, and more traditional cancer biologists.  We are involving leading scientists in cancer research with the twofold aim to expose them to new methods coming from computational physics and obtain a state-of-art description of open problems in the field. We have contacted already some researchers from different countries in Europe and USA, coming from different backgrounds, and we have received many positive responses.

A major problem that confounds the analysis of tumor progression is the presence of an heterogeneous cell population. It has been suggested that in some tumors the heterogeneity is organized hierarchically, with cancer stem cells at the top of the structure [1]. Another major problem is that it is practically impossible to follow the progression of a tumor in a single patient: we have only limited information at specific times and for different patients. Physicists have developed a set of quantitative tools, both computational and theoretical, to deal with dynamically evolving heterogeneous systems  that could be used to guide improve our understanding of cancer and develop novel strategies to fight it. A similar contribution from physicists could give further strength to the field of  “cancer systems biology” that is still in its infancy.

Important contributions to cancer research could come from different fields of computational physics, including, among others, biophysics, soft-condensed matter and the statistical mechanics of complex systems.  Soft-condensed matter studies materials such as colloids and polymers that have a direct relevance for biology and the distance between the two fields is rapidly shrinking.  Methods derived from the statistical mechanics of complex non-equilibrium systems have been applied to a wide variety of biological problems, ranging from protein folding [2], the analysis of genetic data  to the spreading of epidemics [3], but applications to cancer are more rare: they range from numerical  models for genetic data [4] and mutations [5] to cellular automata describing tumor growth [6]. Several other problems in cancer research could be tackled with computational models: Angiogenesis seems analogous to fractal growth phenomena that have been studied by physicists for decades [7]; physicists can model how cancer progression is influenced by mechanical properties of tissues [8]; statistical mechanics of non-equilibrium systems could provide new insight on cancer growth and metastasis (see for instance a recent model of melanoma growth leading to contour instabilities [9]). Computational methods that are promising for cancer include:  finite element models for tissue mechanics, fractal growth models, cellular automata, molecular dynamics and more.