Evolution & Design Principles in Biology :

Evolution & Design Principles in Biology:

a consequence of evolution and natural selection

Rui AlvesUniversity of [email protected]

Course Website:http://web.udl.es/usuaris/pg193845/Bioinformatics_2009/

Part I: Molecular Evolution

Theory of Evolution• Evolution is the theory that allows us to understand how organisms came to be how they are•In probabilistic terms, it is likely that all living beings today have originated from a single type of cells•These cells divided and occupied ecological niches, where they adapted to the new environments through natural selection

How did the first cell create different cells?

Neutral Mutation (e.g. by error in genome replication)

How did the first cell create different cells?

Neutral Mutation (e.g. by error in genome replication)

How did the first cell create different cells?

Neutral Mutation (e.g. by error in genome replication)

How did the first cell create different cells?

Deleterious Mutation (e.g. by error in genome replication)

How did the first cell create different cells?

Deleterious Mutation (e.g. by error in genome replication)

How did the first cell create different cells?

Deleterious Mutation (e.g. by error in genome replication)

How did the first cell create different cells?

Advantageous Mutation (e.g. by error in genome replication)

How did the first cell create different cells?

Advantageous Mutation (e.g. by error in genome replication)

And then there was sex…

Why Sex???• Asexual reproduction is quicker, easier more

offspring/individual.• Sex may limit harmful mutations– Asexual: all offspring get all mutations– Sexual: Random distribution of mutations. Those with the

most harmful ones tend not to reproduce.• Generate beneficial gene combinations– Adaptation to changing environment– Adaptation to all aspects of constant environment– Can separate beneficial mutations from harmful ones– Sample a larger space of gene combinations

New Niche/ New conditions in old niche

What drives cells to adapt?

New (better adapted) mutation

What drives cells to adapt?

How do New Genes and Proteins appear?

• Genes (Proteins) are build by combining domains• New proteins may appear either by intradomain

mutation of by combining existing domains of other proteins

Cell DivisionCell

Division …

…

The Coalescent•This model of cellular evolution has implications for molecular evolution

•Coalescent Theory:

• a retrospective model of population genetics that traces all alleles of a gene in a sample from a population to a single ancestral copy shared by all members of the population, known as the most recent common ancestor

Why is the coalescent the de facto standard today?

Alternatives?

Current sequences have evolved from the same original sequence (Coalescent)

Current sequences have converged to a similar sequence from multiple origins of life

Back of the envelop support for ?ACDEFGHIKLMNPQRSTVWY 20A EDYAHIKLMNPQRGTVWY 20

AAi AAk 0]1[ pLog

AAk AAk [ 2] 0Log p

AAi AAk [ 1] 0Log p

AAi

[ 2] 0

1 2

Log p

ptot p p

2121 pppp

Convergence2014614 121 pppptot

Divergence14 62 1p p

Which is more likely?141 ( )1Convergence p

Divergence

Back of the envelop support for divergence

About the mutational process

Point mutations:• Transitions (A↔G, C↔T) are more frequent than transversions (all other

substitutions)• In mammals, the CpG dinucleotide is frequently mutated to TG or CA (possibly

related to the fact that most CpG dinucleotides are methylated at the C-residues)• Microsatellites frequently increase or decrease in size (possibly due to polymerase

slippage during replication)Gene and genome duplications (complete or partial), may lead to:

• pseudogenes: function-less copies of genes which rapidly accumulate (mostly deleterious) mutations, useful for estimating mutation rates!

• new genes after functional diversification Chromosomal rearrangements (inversions and translocation), may lead to

• meiotic incompatibilities, speciationEstimated mutation rates:

• Human nuclear DNA: 3-5×10-9 per year• Human mitochondrial DNA: 3-5×10-8 per year• RNA and retroviruses: ~10-2 per year

Consequences of the coalescent model?

So what if we accept the coalescent model?

A1 TSRISEIRRA2 TSRISEIRRA3 TSRISEIRRA4 TSRISEIRRA5 TSRISEIRRA6 TSRISEIRRA7 PSRISEIRRA8 PKRISEVRRA9 PKRISEVRRA10 PQRISAIQRA11 PQRISAIQRA12 PQRISTIQRA13 PQRISTIQRA14 ASHLHNLQRA15 TKHLQELQREA16 TKHLQELQREA17 TKHLQELQREA18 SKHLHELQRDA19 PKNLHELQKDA20 SKRLHEVQSE

A1-6 TSRISEIRRA7 PSRISEIRRA8-9 PKRISEVRRA10-11 PQRISAIQRA12-13 PQRISTIQRA14 ASHLHNLQRA15-17 TKHLQELQRA18 SKHLHELQRA19 PKNLHELQKA20 SKRLHEVQS

So what if we accept the coalescent model?

A1-6 TSRI SEI RRA7 PSRI SEI RRA8-9 PKRI SEVRRA10-11 PQRI SAI QRA12-13 PQRI STI QRA14 ASHLHNLQRA15-17 TKHLQELQRA18 SKHLHELQRA19 PKNLHELQKA20 SKRLHEVQS

A1-6A7

A10-11A12-A13

A’1-7

A’10-13

So what if we accept the coalescent model?

A’1-7 (p-t) SRI S E I RRA8-9 P KRI S E VRRA’10-13 P QRI S(a-t)I QRA14 A SHLH N LQRA15-17 T KHLQ E LQRA18 S KHLH E LQRA19 P KNLH E LQKA20 S KRLH E VQS

4 3324 5 323

The study of sequence alignments can gives information about the evolution of the different organisms!!!!

Phylogenetic tree reconstruction, overview

Computational challenge: There is an enormous number of different topologies even for a relatively small number of sequences:

3 sequences: 1 4 sequences: 35 sequences: 15 10 sequences: 2,027,025 20 sequences: 221,643,095,476,699,771,875

Consequence: Most tree construction algorithm are heuristic methods not guaranteed to find the optimal topology.

Input data for two major classes of algorithms:1. Input data distance matrix, examples UPGMA, neighbor-joining2. Input data multiple alignment: parsimony, maximum likelihood

Distance matrix methods use distances computed from pairwise or multiple alignments as input.

Building phylogenetic trees of proteins

Genome 1

Genome 2

Genome 3

Genome …

Protein A Protein B Protein C Protein D

Protein A Protein BProtein C Protein D

Protein AProtein B Protein CProtein D

…

Distance based phylogenetic treesACTDEEGGGGSRGHI…A-TEEDGGAASRGHI…ACFDDEGGGGSRGHL……

A1

A2

A3

…

A1

A2A3

A1

5 substitutions 3 substitutionsA2

A3

8 substitutions

A2

A3

A1

3

5

Maximum likelihood phylogenetic trees

ACTDEEGGGGSRGHI…A-TEEDGGAASRGHI…ACFDDEGGGGSRGHL……

Alignment Probability of aa substitution A - E D …

A 1 0.01 0.2 0.09 …

- 0.01 1 0.0001 0.0001 …

E 0.2 0.0001 1 0.5

D 0.09 0.0001 0.5 1

…

Maximum likelihood phylogenetic trees

ACTDEEGGGGSRGHI…A-TEEDGGAASRGHI…ACFDDEGGGGSRGHL……

AlignmentA1

A2

A3

A15 substitutions

3 substitutions

A2

A3

8 substitutions

p(1,2)

p(1,3)

p(2,3)

p(2,3)>p(1,2)>p(1,3)

A1

A3

A2

A2

A3

A1

Statistical evaluation of trees: bootstrapping

1

2

54

3

76

8

Motivation: Some branching patterns in a tree may be uncertain for statistical reasons (short sequences, small number of mutational events)Goal of bootstrapping: To assess the statistical robustness for each edge of the tree.Note that each edge divides the leave nodes into two subsets. For instance, edge 7–8 divides the leaves into subsets {1,2,3} and {4,5}.However, is this short edge statistically robust ?Method: Try to generate tree from subsets of input data as follows:

• Randomly modify input MSA by eliminating some columns and replacing them by existing ones, This results in duplication of columns.

• Compute tree for each modified input MSA.• For each edge of the tree derived from the real MSA, determine the fraction

of trees derived from modified MSAs which contain an edge that divides the leaves into the same subsets. This fraction is called the bootstrap value. Edges with low bootstrap values (e.g. <0.9) are considered unreliable.

Statistical evaluation of trees: bootstrapping

Other Trees

• Use genomes• Use Enzymomes• Use whatever group of molecules are

important for a given function

Part II: Design principles

Outline

• What are design principles

How to study design principles

• Examples

What are design principles?

• Recurrent qualitative or quantitative rules that are observed in similar types of systems as a solution to a given functional problem

• Exist at different levelsNuclear Targeting Sequences

Operon

Gene 1 Gene 2 Gene 3

How can design principles emerge in molecular biology?

• Inteligent design?Not a scientific hypothesis; out of the table

• Evolution?Makes sense, but how could such regularities

emerge?

Climbing down mount improbable

• Overtime, edged stones would accumulate on the slope.

• Smooth, round, stonesaccumulate at the bottom.

Design Principles:- Smooth, roundish rocks roll down the mountain.- Edged, flat, rocks don’t.

Design principles in molecular biology

• Similarly, if a topology or set of parameters has appeared through mutation and it can be shown to create a molecular network that functionally outperforms all other possible alternatives in a given set of conditions, one can talk about a design principle for the system under those conditions.

[sensu engineering]

Index of talk

• How to identify design principles• Design principles in:– Gene expression– Metabolic networks– Signal transduction– Development

• Design principles, what are they good for?• Summary

First step, define the alternatives

Gene

Regulator

+Gene

Regulator_

X0 X1 X2 X3

X0 X1 X2 X3

First step, define the alternatives

X0 X1 X2 X3

X3

t

How strong should the feedback be?

Then, create models for each alternative

Gene

Regulator

+Gene

Regulator_

Finally:

• Compare the dynamic behavior of the models for the two or more alternatives with respect to physiologically relevant criteria.

Then, create models for each alternative

X0 X1 X2 X3

X0 X1 X2 X3

Index of talk

• How to identify design principles• Design principles in:– Gene expression– Metabolic networks– Signal transduction– Development

• Design principles, what are they good for?• Summary

The demand theory for gene expression

• Are there situations where positive regulation of gene expression outperforms negative regulation of gene expression and vice versa?

Gene

Regulator

+Gene

Regulator_

Regulating gene expression has principles

• Positive regulator:– More effective when gene product in demand for large

fraction of life cycle.– Less noise sensitive if signal is low.

• Negative regulator:– More effective when gene product in demand for small

fraction of life cycle.– Less noise sensitive if signal is high.

Gene

Regulator

+Gene

Regulator_

Genetics 149:1665; PNAS 103:3999; PNAS 104:7151;Nature 405: 590

Index of talk

• How to identify design principles• Design principles in:– Gene expression– Metabolic networks– Signal transduction– Development

• Design principles, what are they good for?• Summary

Negative overall feedback is a design principle in metabolic biosynthesis

X0 X1 X2 X3

• Negative overall feedback:– More effective in coupling production to demand.– More robust to fluctuations.

Bioinformatics 16:786; Biophysical J. 79:2290

Index of talk

• How to identify design principles• Design principles in:– Gene expression– Metabolic networks– Signal transduction– Development

• Design principles, what are they good for?• Summary

Bifunctional sensors can be a design principle in signal transduction

• Bifunctional sensor:– Performs best against cross talk

• Independent deactivator:– Better integrator of signals

Mol. Microbiol. 48:25; Mol. Microbiol. 68: 1196

Signal

SensorSensor

EfectorEfectorDeactivator

Effect

Index of talk

• How to identify design principles• Design principles in:– Gene expression– Metabolic networks– Signal transduction– Development

• Design principles, what are they good for?• Summary

Gene

Regulator_

Design principles in development

Gene

Regulator

+

High demand, low signal

Signal

+

High demand, high signal

Low demand, high signal

Low demand, low signal

Signal _

Genetics 149:1665; PNAS 103:3999; PNAS 104:7151;Nature 405: 590

Index of talk

• How to identify design principles• Design principles in:– Gene expression– Metabolic networks– Signal transduction– Development

• Design principles, what are they good for?• Summary

Biological design principles are good to understand why biology works as it does

• Biological design principles may connect molecular determinants to functional effectiveness.

Heat shock

Expr

essio

n of

im

port

ant g

enes

time

Grow

th ra

te

time

BMC Bioinformatics 7:184

Underlying assumption

• Evolution of molecular networks can be treated as modules.

• Work in the group of Uri Alon suggests that– networks evolving to meet simultaneous goals

evolve in a modular fashion– Networks evolving to meet a single goal evolve

globally• Modularity seems like a reasonable first

assumptionPNAS 102:13773; PLOS Comp Biol 4:e1000206;BMC Evol biol 7: 169

The good news about function

• Sometimes, you get stuff for free!!!

• For example:– networks that are responsive to signals, just because

they are responsive may have inbuilt buffering of noise.

– Functions that are associated with marginally stable proteins are favored because due to the large dimensions of sequence space most randomly selected sequences have a structure that is marginally stable.

PNAS 100:14463; PNAS 103:6435; Proteins 46:105

How can biological design principles be applied?

• Design of molecular circuits with specific behaviors!!

Stable Systems

Unstable systems

Oscilations

Bistable systems

Cell 113: 597; PLoS Comput Biol. 5:e1000319; PNAS 106: 6435

Index of talk

• How to identify design principles• Design principles in:– Gene expression– Metabolic networks– Signal transduction– Development

• Design principles, what are they good for?• Summary

Summary

• Design principles can be found in molecular networks.

• Such principles can sometimes be connected to selection for function effectiveness.

• Even in the absence of such a connection, if they are valid they can be used to build biological circuits with specific behaviors.