dynamic processes in cells(a systems approach to biology)
jeremy gunawardenadepartment of systems biology
harvard medical school
lecture 516 september 2014
2. evolution, modularity & weak linkage
“With these facts, here far too briefly and imperfectly given ... I can see no very great difficulty (not more than in the case of many other structures) in believing that natural selection has converted the simple apparatus of an optic nerve merely coated with pigment and invested by transparent membrane, into an optical instrument as perfect as is possessed by any member of the great Articulate class.”
Charles Darwin, On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life, John Murray, London, 1859
the evolution of complexity – by Darwin
how can complex functionality emerge in nature?
“To suppose that the eye, with all its inimitable contrivances ... could have been formed by natural selection, seems, I freely confess, absurd in the highest possible degree.”
arthropods
the evolution of complexity – in the modern era
(*) Nilsson, Pelger, “A pessimistic estimate of the time required for an eye to evolve”, Proc Roy Soc Lond B, 256:53-8 1994; (+) Mark Ridley, Evolution, 2nd ed, Blackwell Science, 1996
d = normalised photo-receptor diameter for constant S/Nf = focal length of lens; P = distance from lens to retina#steps = #1% changes required in all geometric structures
Based on the principles outlined above, we made a model sequence of which representative stages are presented in figure 2 . (*)
Nilsson and Pelger allowed the shape of the model eye to change at random, in steps no more than 1% change at a time ... The model eye then evolved in the computer, with each new generation formed from the optically superior eyes in the previous generation; changes that made the optics worse were rejected, as selection would reject them in nature. (+)
physics and optics of visual acuity
wishful thinking?
the complexity of evolution
the eye is not like a camera – we do not perceive with our eyes but with our brain – and it is only through the brain that the organism acquires a fitness advantage
how does evolution avoid the need for multiple changes – to both eye and brain – in order to gain a fitness advantage?
“The chance that higher life forms might have emerged in this way is comparable to the chance that a tornado sweeping through a junkyard might assemble a Boeing 747 from the materials therein.”
Fred Hoyle, Mathematics of Evolution, Acorn Enterprises 1999
X ✓
1877-1947
rediscovery of Mendelian mathematics
“To THE EDITOR OF SCIENCE: I am reluctant to intrude in a discussion concerning matters of which I have no expert knowledge, and I should have expected the very simple point which I wish to make to have been familiar to biologists. ... Mr. Yule is reported to have suggested, as a criticism of the Mendelian position, that if brachydactyly is dominant 'in the course of time one would expect, in the absence of counteracting factors, to get three brachydactylous persons to one normal'. It is not difficult to prove, however, that such an expectation would be quite groundless.”
G H Hardy, “Mendelian proportions in a mixed population”, Science, 28:49-50 1908.
much of our evidence for evolution and the mechanisms underlying it, rests on the mathematical theory (of population genetics) that emerged from the rediscovery of Mendelian genetics.
shortness of digits
“The field of population genetics is technically demanding and it is well known that most biologists abhor all things mathematical.” *
* Michael Lynch, “The frailty of adaptive hypotheses for the origins of organismal complexity”, PNAS 104:8597-604 2007
Hardy's calculation
under random mating in an infinite population with non-overlapping generations, in the absence of selection, mutation, migration, etc, the next generation looks like
two alleles (a, A) at a single locus
genotypes
alleles
genotypes
alleles
probabilities
the genotype frequencies do not change, provided so that they become stable after at most one generation
C Stern, “The Hardy-Weinberg law”, Science, 97:137-38 1943.
the first mathematical theory in biology
1892-19641889-19881890-1962
genotypes development, growth, reproduction
fitness is assumed to be a function of the genotype, not the phenotype. this drastic and “unrealistic” assumption provided the basis for an extremely successful theory
Sean Rice, Evolutionary Theory: Mathematical and Conceptual Foundations, Sinauer Associates, Massachusetts, 2004
genotypes
population genetics: how do allele frequencies change in populations under the influence of selection, mutation, migration, population structure, etc?
fitness
wright – in small populations, random processes (drift, mutation, recombination) dominate over selection. deleterious alleles can become fixed, while beneficial ones can be lost. average fitness may decrease.
fisher – in large populations, even weak positive selection will lead to an allele becoming fixed in the population, while deleterious alleles are quickly lost. average fitness increases monotonically to an optimum.
population genetics
Eugene Koonin, The Logic of Chance: The Nature and Origin of Biological Evolution, FT Press Science, New Jersey, 2012
the neo-darwinian (“modern”) synthesis
1902-19841904-20051900-1975
the “neo-darwinian synthesis” built upon population genetics to explain (a) the data from field experiments on natural populations of animals and plants; (b) the emergence of species and phyla; and (c) the fossil record
“Nothing in biology makes sense except in the light of evolution” *
* Theodosius Dobzhansky, Genetics and the Origin of Species, Columbia University Press 1982. Reprint of 1937 original.
Ernst Mayr, “What was the evolutionary synthesis?”, Tree, 8:31-4 1993.
the synthesis strongly emphasised fisher's restatement of darwin's perspective of positive selection acting on small variations in large populations. this increasingly led to the view that all biological novelty was adaptive and came from positive selection
can be spectacularly misleading
in fact, on the contrary, there is deep conservation of certain genes and their protein functions between evolutionarily very distant organisms
Lutz, Lu, Eichele, Miller, Kaufman, “Rescue of Drosophila labial null mutant by the chicken ortholog Hoxb-1 demonstrates that the function of Hox genes is phylogenetically conserved”, Genes & Dev 10:176-84 1996
“Much that has been learned about gene physiology makes it evident that the search for homologous genes is quite futile except in very close relatives.”
Ernst Mayr, Animal Species and Evolution, Harvard University Press, 1963
“evo-devo” or misunderstanding?
“Since the Modern Synthesis, most expositions of the evolutionary process have focused on microevolutionary mechanisms. Millions of biology students have been taught the view (from population genetics) that ‘evolution is change in gene frequencies.’ Isn’t that an inspiring theme? This view forces the explanation toward mathematics and abstract descriptions of genes, and away from butterflies and zebras ... The evolution of form is the main drama of life’s story, both as found in the fossil record and in the diversity of living species. So, let’s teach that story. Instead of ‘change in gene frequencies,’ let’s try ‘evolution of form is change in development’.”
“Even ignoring the fact that most species are unicellular and differentiated mainly by metabolic features, this statement illustrates two fundamental misunderstandings. Evolutionary biology is not a story-telling exercise, and the goal of population genetics is not to be inspiring, but to be explanatory. ... Nothing in evolution makes sense except in the light of population genetics.”
Sean Carroll, Endless Forms Most Beautiful: The New Science of Evo-Devo, W W Norton & Co, 2005
Michael Lynch, “The frailty of adaptive hypotheses for the origins of organismal complexity”, PNAS 104:8597-604 2007, commenting on Sean Carroll's comment
the evolution of complexity
it has MODULES capable of some degree of independent reuse, modification and reorganisation and, furthermore, these modules are not tightly wired together but, rather, are WEAKLY LINKED, thereby allowing natural selection to act on individual modules and for complexity to evolve
the evidence from molecular developmental biology suggests, in contrast to population genetics, that the “black box” must have specific properties in order for complexity to evolve
modularity in primate heads
Daniel Lieberman, The Evolution of the Human Head, Harvard University Press, 2011, Chapter 4, “Modular Growth of the Head”
chimps and humans have very different heads but identical “modules”
heads are highly integrated: breathing, eating, seeing, hearing, smelling, talking, thinking ...
hierarchical flexible modularity
modules at a lower level are reorganised to make modules at a higher level
cellular modularity
the cell is the high point of biological modularity
1810-1882 1804-1881
Henry Harris, The Birth of the Cell, Yale University Press, 1999
1821-1902
“omnis cellula e cellula” – every cell comes from another cell
1821-1902
1801-1858
Helmholtz
Eric Davidson, Douglas Erwin, “Gene regulatory networks and the evolution of animal body plans”, Science 311:796-800 2006
putative kernel module for heart specification, which is conserved across animals
network modularity
molecular modularity
John Gerhart & Marc Kirschner, “The theory of facilitated variation”, PNAS 104:8582-9 2007
metabolism
cytoskeleton
signalling
Hox genes
genome & proteome modularity
Yuh, Bolouri, Davidson, “cis-regulatory logic in the endo-16 gene”, Development 128:617-29 2001
flexible re-arrangement
pyruvate kinase
exons
domains
linking modules together – in engineering
hierarchical encapsulation and hiding of internal state, with inter-module communication taking place through agreed interfaces
module 1 module 2
independent internal
variables
A?A!
x y
agreed interface
independent internal
variables
constrained interfaces allow de-constrained innovation within modules
strong linkage
linking modules together – in biology
John Gerhart & Marc Kirschner, “The theory of facilitated variation”, PNAS 104:8582-9 2007
module 1 module 2
conserved components
x y
conserved components
modules are coupled by weak-linkage mechanisms, that operate through an intermediate or by variation/selection rather than by instruction
de-constrained interfaces allow conserved components to evolve new functionality within modules while maintaining overall system behaviour
enhances neutrality and facilitates phenotypic variation on which selection can act
weak linkage
weak linkage
weak linkage in development
weak linkage by learning – the visual centres of the brain accept whatever the eyes send them and learn from these images, allowing eye and brain to evolve independently
weak linkage by independence – bone, muscle, nerves and vasculature adapt to what each other are doing
polydactlyhomeotic mutations
similar developmental learning may underlie other forms of sensory physiology (*)
(*) Dale Purves, Brains: How They Seem to Work, FT Press, 2010
weak linkage by variation/selection – stem cells
signal
instruction
variation/selection
signal
differentiation
differentiation
Kalmar, Lim, Hayward, Munoz-Descalzo, Nichols, Garcia-Ojalvo, Martinez-Arias, “Regulated fluctuations in Nanog expression mediate cell fate decisions in embryonic stem cells”, PLoS Biol 7:e1000149 2009
primed cells
strong linkage
weak linkage
both mechanisms may co-exist to different degrees in different contexts
substrate
protein allostery
Monod, Changeux, Jacob, “Allosteric proteins and cellular control systems”, J Mol Biol 6:306-29 1963
weak linkage by variation/selection – allostery
instruction – competitive inhibition of enzyme activity
variation/selection
strong linkage
weak linkage
active conformation
inactive conformation
inhibitor
inhibitoractivator
weak linkage in PTM
p53
protein-protein interaction can be strongly linked through arranged surfaces or weakly-linked through post-translational modification of an intermediate
upstream “writers”
downstream “readers”
PTM “code” in the distribution of
modification states
Prabakaran, Lippens, Steen, Gunawardena, “Post-translational modification: Nature's escape from genetic imprisonment and the basis for dynamic information encoding”, WIREs Syst Biol Med 4:564-83 2012
weak linkage by intermediation
X-OH Y-H+ X-Y H20+
dehydrating condensations consume energy and must be coupled to energy-producing reactions
producersconsumers
“barter economy” “invention of money”
str
on
g lin
kag
e
weak lin
kag
e
weak linkage in gene expression
strong linkage
gene transcription requires both catalytic production of mRNA and regulation of transcription by cellular processes acting through transcription factors
direct activation/inhibition of Pol II catalysis
indirect binding to DNA and recruitment of factors that activate/inhibit transcription
combinatorial complexity
emerges in gene regulation
weak linkage
Pol II becomes more complicated
weak linkage facilitates evolutionary change
Li, Johnson, “Evolution of transcription networks – lessons from yeast”, Curr Biol 20:R746-53 2010; Tsong, Tuch, Li, Johnson, “Evolution of alternative transcriptional circuits with identical logic”, Nature 443:415-20 2006
a-specific genes
asgs OFF by default
asgs ON by default
the double challenge for systems biology
1. how do we identify modules and their weak linkages and determine their functioning in physiology and the phenotype?
2. how do we account for the evolution of modules and their weak linkages? how do we reconcile population genetics with the evolution of complexity?
Marc Kirschner & John Gerhart, The Plausibility of Life: Resolving Darwin's Dilemma, Yale University Press 2006; John Gerhart & Marc Kirschner, “The theory of facilitated variation”, PNAS 104:8582-9 2007
Michael Lynch, The Origins of Genome Architecture, Sinauer Associates 2007; Michael Lynch, “The frailty of adaptive hypotheses for the origins of organismal complexity”, PNAS 104:8597-604 2007
“module” means different things
phylogenetically conserved process TCA cycle
sub-graph that is statistically over-represented (motif)
feed-forward loop
sub-graph (community) that is more strongly connected internally than externally
ribosome
Alon, “Network motifs: theory and experimental approaches”, Nature Rev Genetics, 8:450-61 2007; Wagner, Pavlicev, Cheverud, “The road to modularity”, Nature Rev Genetics, 8:921-31 2007; Newman, Girvan, “Community structure in social and biological networks”, PNAS 99:7821-6 2002.
duplicated process MAP kinase cascade
set of features with stronger correlations between themselves than with other features
head structures
independent sub-part with distinct identity cell
computer simulations of evolution towards a fixed goal typically do not yield modular architectures but if goals change periodically in a modular fashion, the “internal model” can become modular
Force, Cresko, Pickett, Proulx, Amemiya, Lynch, “The origin of subfunctions and modular gene regulation”, Genetics, 170:433-46 2005
multicellular organisms have much smaller effective population sizes than most unicellular lineages, so that neutral, non-adaptive mechanisms become more important and these can lead to modular architectures
how did modules evolve?
by adaptation:
by neutral processes:
Kashtan, Alon, “Spontaneous evolution of modularity and network motifs”, PNAS, 102:13773-8 2005
