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Physics of Life (2022) / Chapter Skim
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4 How Do Living Systems Navigate Parameter Space?
Pages 158-180

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From page 158...
... The physicists' approach to describing any particular functional mechanism invites us to see that mechanism as one possibility drawn from a large space of alternatives. In this view we think not about one system but about a distribution or ensemble of systems, much as in the statistical physics of disordered systems.
From page 159...
... Learning 166 Formalizing and quantifying Statistical physics Neural networks in colloquial ideas of learning of inference; phase artificial intelligence; in animals, humans, and transitions and "aha;" principles of machines. connecting molecular machine learning; complexity to learning natural language algorithms.
From page 160...
... In some cases, these adaptive dynamics can be traced down to the dynamics of particular ion channels, which have long time scales of inactivation after opening during an action potential. Similarly, at different stages of the auditory system neurons adapt to the dynamic range and temporal statistics of incoming sounds.
From page 161...
... (C) Modulation of the rate of action potentials in response to sinusoidal variations of current or current variance.
From page 162...
... One important example of this is the simple conversion of nutrients into growth or biomass, where the relation between the number of enzyme molecules and the conversion rate is determined by the classical chart of biochemical reactions in core metabolism. A more subtle example is the electrical activity of neurons, which depends on the numbers of different ion channel proteins through equations that are known very precisely.
From page 163...
... This immediately suggests that cells could tune their pat tern of electrical activity by increasing or decreasing the synthesis of particular ion channels in response to changing internal calcium concentration. Real molecular mechanisms will be sensitive to concentration averaged over some limited time, and by using mechanisms that have different averaging windows the cell could achieve even more fine-grained control.
From page 164...
... The plane that best divides silent from tonic activity Whereas the plot of gmaxNa, gmaxCa, and gmaxA re FIGURE 4.2 Possible electrical dynamics of a neuron as a function has a unit normal vector a  {aNa, aC a, aA, aKC a, aKd}  {0.22, of the number global mapofofion channels the activity states of the neuron, indiv in the membrane. At left,0.73, the0.64, neuron uses 0.01, 0.10}seven different and is offset from types of channels, the origin by 0.02 and behaviorcannot conductances is mapped predict the state of activity of along the normal direction.
From page 165...
... It has been suggested that systems with this sort of behavior form a well-defined class of "sloppy models," neither robust nor finely tuned but with a full spectrum of parameter sensitivities. There is a substantial effort underway to understand the origins of this behavior, its connections to other ideas in statistical physics, and its implications for the dynamics of adaptation.
From page 166...
... (1) Fundamentally, sloppiness involves an extraordinarily singular coordinate transforma tion in parameter space between the bare parameters natural in biology (e.g., binding affinities and rate constants)
From page 167...
... The biological physics community entered the subject through models for networks of neurons, as described in Chapter 3. Statistical Physics Approaches to Learning The functions that are accomplished by a neural network are determined by the strengths of connections or "synaptic weights," among all the neurons in the network.
From page 168...
... . Under steady conditions, the distribution of pitch across multiple examples of a single note gives a measure of the bird's own tolerance for errors, in the language of statistical physics models for learning in networks.
From page 169...
... Experimental studies that have tracked synaptic dynamics in the live mammalian brain suggest that the adult neocortex, which is thought to store some memories for the adult lifetime, has different subsets of synapses with different lifetimes, in the spirit of these theoretical considerations. Perspective The study of learning has had many independent lives: in psychology and ani mal behavior; in mathematics and computer science; and in neurobiology, genetics,
From page 170...
... Schematic Cascade Model Synaptic Plasticity When conditions are such as to trigger strengthening of the synapse, system transitionsainto transition blue to state There are two levels of synaptic strength, weak (brown) and strong + state 1; conversely weakening of the synapse causes a transition into gold state 1.
From page 171...
... This makes evolutionary optimization a key simplifying principle throughout biology: Biological systems have purpose and function to the extent that evolution can measure and select for those properties, and they navigate to highly constrained regions of parameter space to do so. The biological physics community began to engage more deeply with these issues at the start of the 21st century, starting by developing theories for evolutionary dynamics in the simplest possible contexts.
From page 172...
... At the start of the 21st century it was realized that in these problems the evolution of the population as a whole is driven by the tail of the distribution of high-fitness individuals, con necting to other statistical physics problems; even the overall rate of evolutionary progress toward higher growth rates has a subtle dependence on population size, mutation rates, and the selective advantage of each mutation. More recently it has been possible to bring similar rigor to the analysis of much more complex evolu tionary scenarios; examples include spatial structure, fluctuating environments, and perhaps most importantly the interactions between evolutionary and ecological dynamics that create and maintain diverse communities of organisms.
From page 173...
... In other species, genome-based investigations of evolutionary history have led to insights on population structure, local adaptation, and speciation. In microbial and viral populations, we can track evolutionary changes by se quencing populations as they adapt to the human gut or as viral epidemics spread across the world.
From page 174...
... This experiment originated in the evolutionary biology com munity, and has been used as the basis for important insights into the evolution of novelty, the interactions between ecological and evolutionary dynamics, the evolution of mutation rates, and many other questions. It has also generated a rich data set (including a fossil record of samples frozen every 500 generations)
From page 175...
... Theorists from the physics of living systems community have also worked to extend models of evolutionary dynamics to include important additional features, such as the role of recombination within linear genomes, the emergence of eco logical interactions, and the effects of spatial structure. These same theorists have established connections with experimental groups to test these theoretical ideas (or, in several cases, conducted experiments themselves)
From page 176...
... This expression allowed us to visualize In the late 2010s, the exploration of antibody diversity was revolutionized by a population might not necessarily reflect positive selection but, the cells in the absence of any IPTG in the medium. Measure instead, recent range expansions, which have probably occurred ments of the exponential growth rate of both ancestral strains, the possibility of using DNA sequencing to make deep surveys of antibody diversity in many species (11, 12)
From page 177...
... This is an especially interesting example because it makes explicit how the biological physics community has been able to tame the variability across organ isms -- in this case, humans -- even to the point of showing how parameter-free predictions for the distribution of this variability arise from more fundamental theoretical considerations. The examples of evolution in the immune system discussed so far involve the analysis of snapshots of the system at single moments in time.
From page 178...
... FIGURE 4.8 The biological physics community pioneered DNA sequencing experiments that revo lutionized the exploration of antibody diversity. Statistical physics models provide a framework for inferring the parameters of recombination, deletion, and insertion events.
From page 179...
... There is a growing body of both theoretical and experimental work that aims to predict and measure, in quantitative detail, how populations create and maintain genetic variation, how efficiently natural selection can operate, and how predictable and repeatable adaptation will be. These advances are now contribut ing to understanding the somatic evolution of cancers, the operation of adaptive immune systems, the spread of viral epidemics, and more (Chapters 6 and 7)


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