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Spin Glasses, Biological Evolution Dynamics, Cancer, and New Materials Bob Austin Dept. of Physics Princeton University Two parts to this crazy talk: •My materials science theory can shed new light on biology, maybe •The challenge to materials scienc to create truly complex 3-D designed structures. I. A Failed War The National Cancer Institute (NC just finished a series of 3 Workshops on bringing the hard physical sciences into oncology (study of cancer), and not in the usual way of building new ways to detect cancer, but to understand cancer Why? Aren’t we making fabulous ression was typical: surgery, chemo, remissio wed by relapse after 2 years, which was fatal. e old story. t happened to the War on cancer? Where is the ory? II. Darwin and Evolution: problem and examine the mathematic and biological foundations o evolution and natural selectio you can soon feel yourself becoming dangerously close the ranks of excommunicate scientists who wonder: how well do we really understand the dynamics of evolution under stress? This I think is one of our gre n random, point mutations explain the dible rates of evolution observed in Natu diverse beaks of Darwin’s finches were o s keys to natural selection. But, it was a s rvation. But Peter and Rosemary Grant Princeton) found that there was much more to Darwin’s finches then even he hought. evoid of human interference into natural selection, s documented some 13 species of "Darwin's Finch ing: that is flightless that cohabits with e iguanas (the vampire finch) ves on blood that is entirely arian; es and traced their elaborate lineage, ling them to document the changes that idual species make, primarily to their bea action to the environment. ng prolonged drought, for instance, beak become longer and sharper, to reach the st of seeds. is the problem: we are talking about sands of birds, not millions. We are talki t beaks that change over periods of year mics of Darwin’s finches and the small population size pose a severe problem for this kind of a model. mma’’. simply put, it says that the number g of rations needed to “fix” a mutation is abo ulation of this magic number 300 is rathe y, but it is roughly ln(1/su). rse problem is then optimizing (fixing) 10 s: now we are talking about 300,000 rations. That’s too long. t Darwin (actually the Grants) saw pid evolution on his little islands. hy? hink 2 things are at work: ess drives evolution (and stress ves cancer) in a non-linear way. mall heterogenous populations drive olution (and drives the evolution of II. Proteins, Energy landscapes and spin glasses. things happen: system freezes into a distribution of sta e is no one protein conformation, but a f gy landscape of them. n the frozen distribution of states the ponse function” of the system is a powe an exponential. he groundwork for rough landscape mod logy: ostats rather than water baths as do logists. proteins have a distribution of conforma es, it turns out that the probability distrib ction (pdf) of the activation energies, g(H notation, must be an exponential in Hba hematically yield a power law in the tails t is the origin of these probability distrib ions? pin glass is a system of coupled spins frustrated interactions. - + + ? eresting properties (hard to prove alytically even for simplest systems): Spin system has a freezing point, like otein. ollective spin “metapopulations”: energy ndscape. een spin metapopulations. gle inequality, can be used for minimizing path HKUST building labyrinths ) for spin space populations, “stronger” ality: d: ese y: ct: etric space: nsider a sphere of radius R which ntains all spin populations whose utual distance is less then R. en: Any other sphere must be either ntained within R or be disjoint from you can’t share populations tween different spheres. us, you have speciation in spinasses, and in biology. V. Fitness landscapes and spin glasses. dscape by a metapopulation is ually a subject of physics interest. general field is called spin-glass sics, and it is about the flow of ormation”. third Workshop that the NCI nsored was called “Coding, Decodin nsfer and Translation of information cer. w variables: local number of individuals with a “quas ecies” genome homology i. = “interbreeding” between quasi-species d j, which you can also view of genomic rrangements on a metascale (not SNPs). roenvironment is the local fitness aroun quasi-species. In the valley: stress. rugged field of this character, selection w y carry the species to the nearest peak, bu may be innumerable other peaks which a er but which are separated by "valleys." problem of evolution as I see it is that of a hanism by which the species may continu ts way from lower to higher peaks in such In order that this may occur, there must b e trial and error mechanism on a grand sc h the species may explore the region can design fitness landscapes micr ia “learned” to grow more slowly and exploit stress abitats. In this sense we see the evolution of resis population sizes in microhabitats in evolution dynamics. elective advantage s to “fix” (dominate) i ulation of N individuals? is, the larger the population, the longer i s to fix a mutation. Large populations bu ution rates, and is connected to the powe ys. , evolution can proceed more rapidly if y k up a population N into sub-populations mbers, and let them interbreed at rate m is is a very strange and important result says that the combinatorics (entropy!) of rogenous genome gives rise to slower fi of a trait with increasing populations size different then, say, radioactive decay, w e mean lifetime for 1/2 the atoms to decay independent of the number of atoms. ce we can define distances and rlaps amongst genomes using uencing and mapping, evolution amics has become quantitative. It n’t with Darwin. lution on a fitness landscape thus omes a biased random walk problem re the distance between genomes c measured, with mutations acting as V. Cancer and spin glasses: the delicate balance between rapid evolution, freezing, and melting ibution of the little metapopulations s that move evolution forward rapidl t Wright called: e trial and error mechanism on a nd scale by which the species ma ore the region surrounding the ll portion of the field which it upies.” he selection s is too small, you umulate o may bad mutations and the popul es a “mutational meltdown”. nk of the Royal Families of Europe a nce Charles. us, you need a finite selection press ntain a stable small population. he metapopulation size n is too sma y local maxima in local fitness you e a “complexity catastrophe” uffman) and the system is stuck in a glass frozen state even at finite perature: n distance <d> to local maximum go og(n), so always near local maximum a catastrophic genomic delocalizatio ike that expression because it soun ike quantum mechanics). nomic delocalization means that the exist no stable solutions to populati equations, no quasi-species, and the systems wanders through sequence space. s occurs at an error threshold u* giv s is an important result; here is an analogy between phase nsitions in physics and genomic abilities in genomics. u* = melting perature. here is “danger” in too small a per. re is a dual purpose to stress (= ction): ess drives natural selection and ution. No stress, no evolution. ess causes mutations, and changes eased stress = increased mutations ally, stress drives individuals within anisms to cheat, to defect, to gain al fitness at the expense of others. s is the province of Game Theory, it really has no analogy that I am re of to physical systems, but may crucially ortant in biology. s is why biology may be deeper in a Optimal Solution Nash Equilibrium The role of deliberate mutational diversit wall Wright’s “trial and error mechanism grand scale”) is to accelerate evolution possible that the germline roductive/stem cell) genomic diversity o anisms is generated deliberately in rapid lving organisms....and that cancer is a essary byproduct of rapid evolution whic not be removed because of the fundame ability of the individuals to cheat in a price of high evolution rates of course er: the heterogenous genomes and esale genomic rearrangements neces apid evolution means that occasionally em will lose control, and that’s OK at th ies level, just bad for you. Thus, cancer IS necessary for high ates of evolution and is not a disease. Think of it as Windows Vista. VI. New Materials to Build New Ecologies in Biology The “death galaxy” my design for a physics-based-ecolo which will show all t features of what we doing wrong in canc therapy. Chemical coupling is through t (10 micron) polymer barrier. g): I rapidly evolve bacterial resistance ntibiotic in my “death galaxy” using s of biology and materials science ics? ut this is just 2-D! We need to build omplex 3-D ecologies to really emula tual biological ecologies. elf-assembly of micropatterned 3-D ructures is not something I do, but hers are doing. avis Garcias, Johns Hopkins QuickTime™ and a MPEG-4 Video decompressor are needed to see this picture. Materials science will be driven by biological questions and we will learn how to design 3-D construction of extremely complex shapes with defined purposes. At some point, we may begin to rival Nature in her fantastic design of “one-off” complex structures. Thanks!!!