Chapter 7 Living things

Create, transform, move

Ways of growing

Relations and invariants

Aging and mortality

Butterflies of the soul

Intelligence and consciousness

Referencias y comentarios:

· The frontiers between biology and physics and mathematics is described in “Teaching biological physics”, Raymond E. Goldstein, Philip C. Nelson and Thomas R. Powers, Physics Today, page 46, March 2005; “The biological frontier of physics”, Rob Phillips and Stephen Quake, Physics Today, page 38, May 2006; “Mathematical adventures in biology”, Michael W. Deem, Physics Today, page 42, January 2007; “How in the 20th century physicists, chemists and biologists answered the question: what is life?”, Valentin P. Reutov and Alan N. Schechter, Physics-Uspekhi 53, 377 (2010). See also James Attwater and Philipp Holliger, “Origins of life: The cooperative gene”, Nature, News & Views 17 October 2012, commenting on the possible relevance of cooperation among molecules to the transition from inanimate chemistry to biology thus allowing for life on Earth. I avoid here a traditional discussion on which the reader can be informed in “How physics can inspire biology”, by Alexei Kornyshev, Physics World, page 16, July 2009, and further bibliography and comments in this issue. See also “The impact of physics on biology and medicine”, by Harold Varmus, www.cancer.gov/aboutnci/director/speeches/impact-of-physics-1999.

· See “Differential diffusivity of nodal and lefty underlies a reaction-diffusion patterning system”, Patrick Müller et al., Science 336, 721 (2012)

· See examples in "Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation", by Shigeru Kondo and Takashi Miura, Science 329, 1616 (2010).

· The Belousov-Zhabotinsky reaction is described in www.ux.uis.no/~ruoff/BZ_phenomenology.html, www.chem.leeds.ac.uk/chaos/pic_gal.html, www.uni-regensburg.de/Fakultaeten/nat_Fak_IV/Organische_ Chemie/Didaktik/Keusch/D-oscill-e.htm, and online.redwoods.cc.ca.us/instruct/darnold/deproj/Sp98/Gabe/. The properties and consequences of reaction-diffusion equations are discussed with detail in the book by Marro and Dickman referenced in Chapter 1.

· A classic here is On growth and form, by D’Arcy W. Thompson (Cambridge University Press 1992; first edited in 1917), where it is first argued, against the Darwinian orthodoxy of the time, that structure originates before function and that growth can be explained through mathematics and physics. Alan Turing latter demonstrated the key role of chemical instabilities simply described by a set of coupled reaction–diffusion equations, as mentioned above; see www.turing.org.uk/turing/scrapbook/morph.html. See also “The genetic basis of organic form”, Annals of the New York Academy of Sciences 71, 1223 (1958) and the book The problem of organic form (Yale University Press 1963) both by Edmund W. Sinnott, and The shaping of life: The generation of biological pattern, by Lionel G. Harrison (Cambridge University Press, New York 2011).

· This follows “Lattice models of biological growth”, David A. Young and Ellen M. Corey, Physical Review A 41, 7024 (1990) which also describes extensions of the method.

· This and similar forms are illustrated in web.comhem.se/solgrop/3dtree.htm. See also Evolutionary Dynamics - Exploring the Equations of Life, Martin A. Nowak (Harvard University Press 2006) and focus.aps.org/story/v22/st12.

· According to “A general model for ontogenetic growth”, Geoffrey B. West, James H. Brown and Brian J. Enquist, Nature 413, 628 (2001). See also the comment “All creatures great and small” in page 342 of the same volume of Nature; and “Life’s Universal Scaling Laws”, G.B. West and J.H. Brown, Physics Today, page 36, September 2004. The fractal nature of trees is described in www.afrc.uamont.edu/zeideb/pdf/model/98fractalCanJ.pdf. The comment “Why Leaves Aren’t Trees”, Physical Review Focus 25, 4 (2010), physics.aps.org/story/v25/st4 concerns three related papers in the issue of 29 January 2010, volume 104, of Physical Review Letters. Also interesting is “Network Allometry”, by Jayanth R. Banavar et al., in Abstracts of Papers of the American Chemical Society 29, 1508 (2002).

· This follows “Scaling and power-laws in ecological systems”, Pablo A. Marquet et al., The Journal of Experimental Biology 208, 1749 (2005).

· For a general idea and details regarding the advances in the study of correlation between diet and longevity, see: “Extending healthy life span — From yeast to humans”, Luigi Fontana, Linda Partridge, Valter D. Longo, Science 328, 321 (2010); “A conserved ubiquitination pathway determines longevity in response to diet restriction”, Andrea C. Carrano, Zheng Liu, Andrew Dillin and Tony Hunter, Nature 460, 396 (2009); “Amino-acid imbalance explains extension of lifespan by dietary restriction in Drosophila”, Richard C. Grandison, Matthew D. W. Piper and Linda Partridge, Nature 462, 1061 (2009); www.sanford.duke.edu/centers/pparc/.

· “The Penna Model for Biological Aging and Speciation”, Suzana Moss de Oliveira et al., Computing in Science & Engineering, page 74, May-June 2004.

· “Exact law of live nature”, Mark Ya. Azbel, in Modeling Cooperative Behavior in the Social Sciences, edited by Pedro L. Garrido, Joaquín Marro and Miguel A. Muñoz (American Institute of Physics, New York 2005).

· On Ramón y Cajal: www.redaragon.com/cultura/ramonycajal/biografia.asp; www.aragob.es/culytur/rcajal/index-fr.htm; cajal.unizar.es/sp/textura/default.html; www.repatologia.com/index_autores.asp?autor=6063; and Cajal on the cerebral cortex: an annotated translation of the complete writings, edited by Javier Defelipe and Edward G. Jones (Oxford University Press, New York 1988). On the brain, also en.wikipedia.org/wiki/brain and its links such as en.wikipedia.org/wiki/portal:neuroscience.

· On the modeling of the brain structure and functions, including the “standard model”, see: Daniel J. Amit, Modeling Brain Functions (Cambridge University Press 1989); Tamás Geszti, Physical Models of Neural Networks (World Scientific, Singapore 1990); Pierre Peretto, An Introduction to the Modeling of Neural Networks (Cambridge Univ. Press 1992). For a comment on how recent theoretical and computational studies have contributed to our present understanding on how the brain works, see “Theory and Simulation in Neuroscience”, Wulfram Gerstner, Henning Sprekeler, and Gustavo Deco, Science 338, 60 (2012).

· Warren S. McCulloch and Walter Pitts, “A logical calculus of the ideas immanent in nervous activity”, Bulletin of Mathematical Biophysics 5, 115 (1943); John J. Hopfield, “Neurons with graded response have collective computational properties like those of two-state neurons”, Proceedings of the National Academy of Sciences of the U.S.A. 81, 3088 (1984); Mark E.J. Newman, “The structure and function of complex networks”, SIAM Reviews 45, 167 (2003).

· For more details, see “Evolving networks and the development of neural systems”, Samuel Johnson, J. Marro and Joaquín J. Torres, Journal of Statistical Mechanics: Theory and Experiment P03003 (2010).

· “Algorithms for identification and categorization”, in Modeling Cooperative Behavior in the Social Sciences, Pedro L. Garrido et al. (American Institute of Physics, New York 2005).

· For generalizations of the standard model and applications: “Effect of Correlated Fluctuations of Synapses in the Performance of Neural Networks”, Physical Review Letters 81, 2827 (1998) and “Neural networks in which synaptic patterns fluctuate with time”, Journal of Statistical Physics 94, 837 (1999), J. Marro et al.; “Switching between memories in a neural automata with synaptic noise”, Neurocomputing 58, pages 67 and 229 (2004), Jesús M. Cortés et al.; “Effects of static and dynamic disorder on the performance of neural automata”, Biophysical Chemistry 115, 285 (2005), Joaquín J. Torres et al.; “Effects of fast presynaptic noise in attractor neural networks”, Neural Computation 18, 614 (2004); “Control of neural chaos by synaptic noise”, Biosystems 87, 186 (2007), Jesús M. Cortés et al.; “Complex behavior in a network with time-dependent connections and silent nodes”, J. Marro et al., Journal of Statistical Mechanics: Theory and Experiment P02017 (2008).

· “Chaotic hopping between attractors in neural networks”, J. Marro et al., Neural Networks 20, 230 (2007).

· On instabilities and critical conditions: “When instability makes sense”, Peter Ashwin and Marc Timme, Nature 436, 36 (2005); “Are our senses critical?”, Dante R. Chialvo, Nature Physics 2, 301 (2006); “Optimal dynamical range of excitable networks at criticality”, Osame Kinouchi and Mauro Copelli, Nature Physics 2, 348 (2006).

· On scale invariance in the brain: “Scale-free brain functional networks”, Víctor M. Eguíluz et al., Physical Review Letters 94, 018102 (2005); “Functional optimization in complex excitable networks”, Samuel Johnson et al., Europhysics Letters 83, 46006 (2008).

Nota: véanse las referencias y enlaces que se incluyen en las dispositivas del curso, que a menudo completan las anteriores.