Cancer not a result of mutations.

Paul Davies has an interesting homepage. He has also a recent article, Rethinking cancer in Physics World.
"Here you can find a description of my scientific work, information about my books and articles, and details of my media activities. If you want to go BEYOND and visit my new research center, it's here. If you want to see my work as PI of ASU's Center for Convergence of Physical Science and Cancer Biology, it's here. "

The Center for Convergence of Physical Science and Cancer Biology at ASU is receiving about $1.7 million in funding for each of the first two years of a five-year proposal to tackle the root causes of cancer on a conceptual level by asking questions such as:

• How do cancer cells behave as physical objects?
• How do the physical properties of cells change as a function of cancer progression?
• Can cancer provide insights into the nature of life in general?
• How do cancer cells relate to cells in their surrounding environment?
• Can we find new ways of controlling cancer based on the forces in their microenvironment?
• Does quantum mechanics play a role in cancer?
• Can cancer biologists learn anything from astrobiology?

“A more subtle approach to really understand cancer cells is to regard them as physical objects rather than as enemies to be destroyed. Cancer is a fascinating manifestation of an endlessly fascinating subject, namely life. The traditional approach to cancer is it is a disease to be cured,” says Principal Investigator Paul Davies. “But we don’t have to cure cancer. All we have to do is to find ways of preventing it from taking over and destroying the body of the host. Most cancer researchers are taking a ‘follow the genes’ approach. We want to complement that by a ‘follow the physics’ approach.

What really regulates the genes? What open or pack the genome?

Dr. Stuart Lindsay and his team studying the epigenetic control of cancer:

It is NOT the genome, he points out many times in this video. Stuart Lindsey : Two main projects in the lab focus on the molecular mechanisms that control gene expression, how chromatin (the DNA-protein complex that makes chromosomes) is altered when a model gene regulation system is "turned on" by a signaling process in a cell, and on electron transfer in molecules involved in energy metabolism.

Lindsay Lab, Single Molecule Biophysics, Molecular Electronics and Condensed Matter Physics.

• Darwinian Nanoscience Leiden 01/31/07
• Interfacing Molecules to Electronic Materials (AAAS Washington DC) (2/18/05)
• Is there more to life than physics? (4/27/04)

Molecular vision test site. Wiev and explore biomolecules.

Physical science in oncology has got a new journal too, PS-OC, "News from the Collaborative Network of Physical Sciences-Oncology Centers". In late 2009, the NCI launched the Physical Sciences-Oncology Centers (PSOC) Program by awarding twelve leading institutions from across the United States in an effort to more effectively engage and integrate the physical science community in cancer research as well as bring a fresh perspective and understanding to the issues of cancer. Its first ed. appeared now in jan. "We begin our newsletter with the Trans-Network Perspective that highlights collaborations between trans-disciplinary researchers across the PS-OC Network, the cornerstone of the PS-OC Program.

Cancer is Complex...But Is It Simple?
By Timothy Newman, professor of Physics and Director of the Center for Biological Physics. The computer model is adapted from the highly successful sub-cellar element model of embryogenesis. Timothy Newman has a research background in the theory of non-equilibrium systems and stochastic processes. His current interests lie in biological modeling, with particular emphasis on computational modeling of multicellular structures and development.


X.s view of a cell cluster generated using the sub-cellular element model. The Subcellular Element Model (ScEM) has proven to be an excellent tool for simulating three-dimensional multicellular dynamics. Briefly, each cell is described by a few hundred "subcellular elements" which represent the nodes of a coarse-grained cytoskeleton. Elements are visco-elastically coupled with short-range interactions. Neighboring cells interact through short-ranged interactions between peripheral elements on each cell. This algorithm allows a computationally efficient means to simulate three-dimensional cell shape and deformations. Despite the simplicity of its underlying framework, the ScEM has been shown to reproduce the basic rheological properties of cells on times scales greater than ~ 0.1s (Sandersius and Newman 2008). We are developing new modules for ScEM, building on the basic biomechanical foundation of the model. In particular, we are modeling active cell dynamics (e.g. polarization, cytoskeletal rearrangement) in order to capture important features of cell movement within tissue. Parallelization of the ScEM is in progress, which will allow us to go beyond our current system size limitations of ~1000 cells simulated for ~10 hours biological time (~24 hours of single CPU time). Using the 5000 node Saguaro cluster at ASU, we hope in the near future to simulate ~100,000 cells over biological times of days (embryos) to months (tumors).

Pictures and movies here.

From the journal, p.11: A turbulent sea appears as a bewildering chaotic dance to our eyes, but microscopically is nothing more than Newton’s laws of mechanics applied endlessly to collections of water molecules. Turbulence, despite its daunting reputation, is “simple complexity” in that the complex macro-scale is an emergent outcome of relatively simple and well understood microscopic interactions. A leaf, which arises from the multi-scale metabolism of light energy, and intricate networks of gene and protein interactions, is “complex complexity”. These simple examples illustrate the fundamental difference between the physical and life sciences. What makes living systems so different, and so special? One answer is that, in contrast to non-living systems, they don’t appear to optimize anything. Systems in thermodynamic equilibrium (closed) minimize their “free energies.” In fact, as shown by Ludwig Boltzmann, the properties of any isolated system in thermodynamic equilibrium can be calculated by maximizing its “entropy”. What is an “action” or “free energy” or “entropy”? And if the system is open? In development, a fertilized egg runs ancient genetic codes to transform itself into an organism replete with beautiful architectural details with which we are familiar. It is the ultimate antithesis of maximum entropy, channeling massive energy fluxes to build systemic order from nothing. By contrast, cancer breaks down order and architecture. It disrupts genetic programs. It is almost a thermodynamic process, by which disorder is re-established and entropy is ultimately maximized.

It would also be a mistake to tar all of biology with the same brush - it may not all be complex complexity. Perhaps some simple complexity is in the mix. There is good reason to think that the complexity of cancer may be of the simple variety. Why so? Because cancer may not be genetically hardwired unlike, for example, embryonic development.

Young scientists (Trans-Network Award winners Bryan Smith and Chris Hale) realized that little has thus far been done in living animals. A non-invasive technique that could be applied in living animals could become a vital tool in investigating cancer mechanics.

Biological Physics and Condensed Matter Theory.
Michael F. Thorpe. He has a research background in condensed matter theory, and in recent years has developed the mathematical theory of flexibility and mobility for use in glassy networks, and also in crystalline materials with disorder, such as zeolites and manganites. His most recent work has been in biological physics. The flexible regions in proteins and protein complexes are determined from the x-ray crystallographic structure. These are used to determine dynamical pathways between different protein conformations using geometric simulation techniques. Thorpe homepage: The role of theory -workshop,

Leverhulme Lectures at Imperial College, London (Fall 2009) The flexibility and mobility of frameworks
Lecture 1 Why are some structures stable and others flexible? pptx (33MB)
Lecture 2 Flexibility and materials pptx (12MB)
Lecture 3 Flexibility and biological function pptx (112MB)

ACS news - Flexweb has much information too.

Flexibility in biomolecules. Modeling the affect of a carbon-carbon constraint on network rigidity. outlined various methods that can be used to study flexibility and the associated motion in biomolecules. Much of the work here has been previously published, and so is only summarized.

More from this exciting research here. Membranes, conformational physics, soft matter protein electrostatics, protein folding, nanobiophysics, electrontransfer in DNA, solid state physics in biomatter, photosynthesis, etc.

Some interesting references:
S. A. Sandersius, M. Chuai, C. J. Weijer, and T. J. Newman, Correlating Tissue Topology and Cell Behavior in Embryonic Epithelia. under review (2010).

Daniel W. Farrell, Ming Lei and M. F. Thorpe (2010) Comparison of pathways from geometric targeting method and targeted molecular dynamics in Nitrogen Regulatory Protein C Journal of Molecular Biology (submitted).

Dan Farrell, Maria Kurnikova, Tatyana Mamonova and M.F. Thorpe.Geometric Pathways for Free Energy calculations in DHFR. Journal of Physical Chemistry (to be submitted)

Adam M.R. de Graff and M.F. Thorpe (2010) The long-wavelength limit of the structure factor of amorphous silicon and vitreous silica Acta Cryst. A 66, 22-31.

Vitaliy Kapko, M.M.J. Treacy, M.F. Thorpe and S.D. Guest. On the Collapse of Locally Isostatic Networks. Proc. Roy . Soc. A, 465, 3517-3530, (2009).

A. Raj and A. van Oudenaarden, Nature, nurture, or chance: Stochastic gene expression and its consequences, Cell 135 (2008).

A. J. McKane, J. D. Nagy, T. J. Newman, and M. Stefanini, Amplified Biochemical Oscillations in Cellular Systems. Journal of Statistical Physics, 128, 165:191 (2007).

T. J. Newman, Modeling multi-cellular systems using sub-cellular elements. Mathematical Biosciences and Engineering 2, 611:622 (2005).

A. J. McKane and T. J. Newman, Stochastic models of population dynamics and their deterministic analogs. Physical Review E 70, 041902 (2004)

J. Molofsky, J. Bever, J. Antonovics, and T. J. Newman, Negative Frequency Dependence and the Importance of Spatial Scale. Ecology 83, 21-27 (2002).

T. J. Newman, J. Antonovics, and H. M. Wilbur, Population dynamics with a refuge: fractal basins and the suppression of chaos. Theoretical Population Biology 62, 121:128 (2002).