[2024]Digital Global Overview Report 2024 Meltwater.pdf
Wagner chapter 5
1. Book club
Andreas Wagner,
The Origins of Evolutionary Innovations
Chapter 5
Book club presented by G. M. Dall'Olio,
Pompeu Fabra, IBE-CEXS
2. Reminder:
Genotype network
A genotype network is a set of genotypes that have the same
phenotype, and are connected by single pairwise differences
Green = same phenotype = a genotype network
Note: genotype network == neutral network
3. Chapter 5:
The Origins of Evolutionary
Innovations
This chapter makes some conclusions from the 4
preceding chapters
Under which common principle do metabolic
networks, regulatory circuits and protein/RNA
folds evolve?
Which are the basics of a theory of Innovation?
4. Many more genotypes than
phenotypes
Metabolic networks:
2 ^ S genotypes (S: number of known reactions)
2 ^ C phenotypes (C: number of carbon sources)
Regulatory Networks:
3 ^ N ^ 2 genotypes (3: activation, repression, no
interaction; N: number of reactions)
2 ^ S phenotypes (S: number of genes)
Protein molecules:
20 ^ S genotypes (S: length of sequence)
10 ^ 4 phenotypes (lattice protein folds)
5. Genotypes can vary a lot,
without altering the
phenotype
In metabolic networks, organisms can differ for
75% of reactions, but still have the same
phenotype
Some regulatory circuits can be completely
different but still have the same functions
(examples of GAL4 in C.albicans/S.cerevisiae,
etc..)
Proteins with different sequences can have the same
fold (e.g. globins, etc..)
6. Genotypes can vary a lot,
without altering the
phenotype
Same fold but different sequence (genotype
Distance = 1.0):
http://eterna.cmu.edu/
7. The same phenotype can be
achieved by many
genotypes
A corollary of the previous two slides is that the
same phenotype can be achieved by many
genotypes
Why should a phenotype be reachable by more than
one genotype? (open question)
8. Robustness of a genotype
network
The robustness of a biological system is its ability
to withstand changes without altering the
phenotype
Not only within a genotype network. It is also
important that the neighbors of points in a
genotype network have “neutral” phenotypes
e.g. the neighbor of a genotype must be viable
9. The genotype-phenotype
function
The genotypephenotype function is a function that
allows to predict the phenotype of certain
genotype
Flux balance analysis in metabolic networks
Structure prediction in sequence networks
...
10. Definitions: The Genotype-
Phenotype-Map
The method of representing all genotypes as a Hamming graph and defining neutral
networks is also called “GenotypePhenotypeMap”
I am not sure about who invented the method, but it is well described in [1]
[1] Stadler, B.M. et al., 2001. The topology of the possible: formal spaces underlying patterns of evolutionary change.
Journal of theoretical biology, 213(2), pp.241-74.
11. The genotype space is huge
For a protein of length 10, there are 20^10 possible
sequences
It is difficult for humans to imagine how much the
genotype space is big
12. Big genotype networks can
be still small compared to
the genotype space
A given RNA structure can be generated by
5*10^22 sequences
Yet, this is only a tiny fraction of the genotype
space
13. Big genotype networks are
favored by evolution
Imagine that a given biological function can be
carried out by two different phenotypes:
Phenotype 1 has a big genotype network
Phenotype 2 has a small genotype network
Selection will be more likely to find Phenotype 1,
just because there are more genotypes that
produce it
14. Small and big genotype
networks
The two purple
phenotypes have a
selective advantage
over white ones
However, evolution is
more likely to find
the light phenotype,
because its genotype
network is bigger
15. Phenotypes with small
genotype networks can be
important
We said that big genotype networks are more likely
to be found by evolution
However, in nature we can observe phenotypes
with small genotype networks
16. Phenotypes involved in
multiple functions can still
have big genotype networks
Some systems can carry out more than one
biological function
For example, many metabolisms can survive on both
glucose and mannose
The genotype network of these systems would be
the intersection of the genotype networks that
carry each of the functions
Yet, these genotype networks are still big
17. Intersection of genotype
networks
Yellow → can 0....0 ….. ….. ….. ….. …..
survive on 0....1 ….. ….. ….. ….. …..
Glucose as sole 0...10 ….. ….. ….. ….. …..
0..1.0 ….. ….. …..
carbon source 0.1..0 ….. ….. …..
Blue → can survive 0..... ….. ….. …..
on Alanine as ….. ….. …..
….. ….. …..
sole carbon
….. ….. …..
source ….. ….. ….. …..
Green → ….. ….. ….. ….. ….. …..
intersection ….. ….. ….. ….. ….. …..
….. ….. ….. ….. ….. …..
18. Connectivity and broadness
of genotype networks
Two important properties of genotype networks are
the connectivity and the broadness
These two properties are important in the search for
innovations
19. A poorly connected
genotype set
Fig a shows a set of notconnected
genotype networks
They all have the same phenotype,
but are not connected
In this situation, populations can not
explore the genotype space
efficiently, because they don't
have a way to “jump” between
genotype networks
(recombination and
chromosomal
arrangements will be
discussed later)
20. A well connected but
localized genotype network
Fig b shows a well connected
genotype network
However, this network is clustered,
and all its nodes are close
It is difficult for a population to find
Innovations, because there is no
way to get close to them
21. A connected and broad
genotype network
Fig c represents a well connected and
broad genotype network
This is the ideal situation for finding
innovations
A population can explore the
genotype space without having to
“jump”
23. Genotype networks are
highly interwoven
Genotype networks are usually close in the space
Many organisms can survive on multiple carbon
sources
It is possible to convert RNA structures by changing
few aminoacids
24. Genotype networks are
highly interwoven
Yellow → can 0....0 ….. ….. ….. ….. …..
survive on 0....1 ….. ….. ….. ….. …..
Glucose as sole 0...10 ….. ….. ….. ….. …..
0..1.0 ….. ….. …..
carbon source 0.1..0 ….. ….. …..
Blue → can survive 0..... ….. ….. …..
on Alanine as ….. ….. …..
….. ….. …..
sole carbon
….. ….. …..
source ….. ….. ….. …..
Green → ….. ….. ….. ….. ….. …..
intersection ….. ….. ….. ….. ….. …..
….. ….. ….. ….. ….. …..
25. The theory of innovation
In this chapter, Wagner formalizes the framework
of “genotypephenotypemaps” for studying how
innovations can be found
It also describe some important properties that a
system must have in order to reach innovations
26. The theory of Innovations
Innovation is combinatorial in nature
Genotypephenotypemaps allow to explore the
nature of innovations
Genotypes have many neighbors with the same
phenotype
Many or all genotypes with the same phenotype are
connected in genotype networks
27. The theory of Innovations
Genotype networks of different phenotypes are
different in size
Typical genotype networks traverse a large part of
genotype space
Different neighborhoods of a genotype network
contain different phenotypes
28. Pros of this theory of
innovation
Genotype networks can explain how population
explore the genotype space, without altering the
phenotype
This framework is valid for metabolic networks,
regulatory circuits and sequences
Captures the combinatorial nature of innovation
It allows to simulate that a problem can be solved
through different solutions
e.g. different metabolic networks can survive on
glucose
29. Cons of this theory of
Innovation
Difficult to get to phenotypes that are highly
innovative, but have a tiny genotype network
Difficult to study systems where genotype networks
are not connected or localized
The method doesn't work if there are more
phenotypes than genotypes (phenotipic plasticity)
Immunity systems tend to have more phenotypes
than genotypes
30. Take Home messages
We have seen some properties that are common for
the evolution of metabolic networks, regulatory
circuits and sequences
The framework of genotypephenotypemaps can
be used to explore how innovations are found
31. There are many more
genotypes than phenotypes
A common property of the systems studied in the
previous chapters is that there are more genotypes
than phenotypes