Evolutionary dynamics of populations with genotype-phenotype map

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1 Evolutionary dynamics of populations with genotype-phenotype map Esther Ibáñez Marcelo, Tomas Alarcon Cor Biomat 2013: Mathematics of Planet Earth CENTRE DE RECERCA MATEMÀTICA June 2013 Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

Map between genotype-phenotype is not one-to-one: many genotypes are compatible

2 Complexity of the genotype-phenotype map There is a complex relation between genotype and phenotype. Map between genotype-phenotype is not one-to-one: many genotypes are compatible with the same phenotype. Try to relate genotype and phenotype networks. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

3 Complexity of the genotype-phenotype map There is a complex relation between genotype and phenotype. Map between genotype-phenotype is not one-to-one: many genotypes are compatible with the same phenotype. Try to relate genotype and phenotype networks. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

4 Complexity of the genotype-phenotype map There is a complex relation between genotype and phenotype. Map between genotype-phenotype is not one-to-one: many genotypes are compatible with the same phenotype. Try to relate genotype and phenotype networks. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

5 Complexity of the genotype-phenotype map There is a complex relation between genotype and phenotype. Map between genotype-phenotype is not one-to-one: many genotypes are compatible with the same phenotype. Try to relate genotype and phenotype networks. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

6 Problems from the evolutionary point of view While genes are passed on from one generation to the next, selective pressure acts at the level of phenotypes. Models assigning fitness values to genotypes or genes (mutant variants, different alleles,...) have been proposed, but, in general, is not a valid approach. A more appropriate approach consists of considering models that take into account the genotype and the phenotype, or a model where selection acts at the level of phenotypes. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

7 Problems from the evolutionary point of view While genes are passed on from one generation to the next, selective pressure acts at the level of phenotypes. Models assigning fitness values to genotypes or genes (mutant variants, different alleles,...) have been proposed, but, in general, is not a valid approach. A more appropriate approach consists of considering models that take into account the genotype and the phenotype, or a model where selection acts at the level of phenotypes. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

8 Problems from the evolutionary point of view While genes are passed on from one generation to the next, selective pressure acts at the level of phenotypes. Models assigning fitness values to genotypes or genes (mutant variants, different alleles,...) have been proposed, but, in general, is not a valid approach. A more appropriate approach consists of considering models that take into account the genotype and the phenotype, or a model where selection acts at the level of phenotypes. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

9 Problems from the evolutionary point of view While genes are passed on from one generation to the next, selective pressure acts at the level of phenotypes. Models assigning fitness values to genotypes or genes (mutant variants, different alleles,...) have been proposed, but, in general, is not a valid approach. A more appropriate approach consists of considering models that take into account the genotype and the phenotype, or a model where selection acts at the level of phenotypes. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

10 Model GRNs & Genotype & Phenotype GRNs are modelled as graphs: nodes correspond to the genes (g i ) regulatory interactions between them as directed edges GRN and associated weights define a genotype. Each node has associated a state variable g(t) = (g i (t)) = ±1, i = 1,..., N (N =number of genes) The final state of the dynamics g( ) define the phenotype. Each regulatory edge has an associated weight w ij = ±1, 0. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

11 Model GRNs & Genotype & Phenotype GRNs are modelled as graphs: nodes correspond to the genes (g i ) regulatory interactions between them as directed edges GRN and associated weights define a genotype. Each node has associated a state variable g(t) = (g i (t)) = ±1, i = 1,..., N (N =number of genes) The final state of the dynamics g( ) define the phenotype. Each regulatory edge has an associated weight w ij = ±1, 0. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

12 Model GRNs & Genotype & Phenotype GRNs are modelled as graphs: nodes correspond to the genes (g i ) regulatory interactions between them as directed edges GRN and associated weights define a genotype. Each node has associated a state variable g(t) = (g i (t)) = ±1, i = 1,..., N (N =number of genes) The final state of the dynamics g( ) define the phenotype. Each regulatory edge has an associated weight w ij = ±1, 0. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

13 Model GRNs & Genotype & Phenotype GRNs are modelled as graphs: nodes correspond to the genes (g i ) regulatory interactions between them as directed edges GRN and associated weights define a genotype. Each regulatory edge has an associated weight w ij = ±1, 0. Each node has associated a state variable g(t) = (g i (t)) = ±1, i = 1,..., N (N =number of genes) The final state of the dynamics g( ) define the phenotype. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

14 Model GRNs & Genotype & Phenotype GRNs are modelled as graphs: nodes correspond to the genes (g i ) regulatory interactions between them as directed edges GRN and associated weights define a genotype. Each regulatory edge has an associated weight w ij = ±1, 0. Each node has associated a state variable g(t) = (g i (t)) = ±1, i = 1,..., N (N =number of genes) The final state of the dynamics g( ) define the phenotype. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

15 Model GRNs & Genotype & Phenotype GRNs are modelled as graphs: nodes correspond to the genes (g i ) regulatory interactions between them as directed edges Each regulatory edge has an associated weight w ij = ±1, 0. GRN and associated weights define a genotype. Each node has associated a state variable g(t) = (g i (t)) = ±1, i = 1,..., N (N =number of genes) The final state of the dynamics g( ) define the phenotype. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

16 Model GRNs & Genotype & Phenotype GRNs are modelled as graphs: nodes correspond to the genes (g i ) regulatory interactions between them as directed edges Each regulatory edge has an associated weight w ij = ±1, 0. GRN and associated weights define a genotype. Each node has associated a state variable g(t) = (g i (t)) = ±1, i = 1,..., N (N =number of genes) The final state of the dynamics g( ) define the phenotype. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

17 GRN Genotypes + g(0) initial condition + DYNAMICS PHENOTYPE g( ) (final state) Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

18 Genotypes GRN + g(0) initial condition + DYNAMICS PHENOTYPE g( ) (final state) Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

19 Genotypes GRN 1 mutation + g(0) initial condition + DYNAMICS PHENOTYPE g( ) (final state) Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

20 Genotypes GRN 1 mutation + g(0) initial condition + DYNAMICS PHENOTYPE g( ) (final state) Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

21 GRN Genotypes + g(0) initial condition PHENOTYPE g( ) (final state) 1 mutation + DYNAMICS 1 mutation g( ) Phenotype Genotypes Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

22 GRN Genotypes + g(0) initial condition PHENOTYPE g( ) (final state) Phenotype 1 g( ) 1 mutation + DYNAMICS Phenotype 2 g( ) Genotypes Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

23 Dynamics of gene regulatory networks Dynamics 1 I i (t) = N j=1 w ijg j (t), N = #genes 2 g i (t + 1) = { 1 if Ii (t) 0 if I i (t) < 0 The final state of the dynamics g( ) define the phenotype. Not always it has a final state, we can obtain cycles of different lengths. Viability conditions: Discard or accept some final states depending on the length of the final state cycle. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

24 GRN Genotypes + g(0) initial condition + DYNAMICS g( ) (final state) BIPARTITE NETWORK 1 mutation g( ) Phenotype Genotypes Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

25 Population dynamics Population dynamics: Multi-type Wright-Fisher model. Start with a fix population, chose one random for proliferation, remove another randomly. Evolutionary dynamics: Each individual picked up for proliferation is assigned a probability of mutation: change one sign in the genotype matrix. Selective pressure: If a mutation induces a non-viable 1 phenotype, discard and go through another Wright-Fisher step. 1 We are going to work with viabilities 5, 7, 10. It means that we are accepting, as a final state, cycles until length 5, 7, or 10. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

26 Representation of the genotype-phenotype space Pseudo-bipartite graph Nodes: Genotypes Phenotypes (grey circles) Edges: Genotype-Genotype (if only 1 mutation between them) Genotype-Phenotype g(0) = [1, 1, 1] g( ) = [1, 1, 1] Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

27 Robustness and evolvability Robustness: measure the resilience to mutations. Retain the same phenotype upon mutations in genotype. Robustness is equivalent to clustering 2. Evolvability: measure the ability for adaptation. Also called innovation. Evolvability is equivalent to percolation 3. Neutral network: join all genotypes which the same phenotype. Usually genotypes are connected (an edge if only 1 mutation) 2 2T (u) Clustering: c u =, where T (u) is the number of triangles deg(u)(deg(u)) through node u and deg(u) is the degree of u. 3 Percolation: point when giant component appears. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

28 Robustness and evolvability Robustness: measure the resilience to mutations. Retain the same phenotype upon mutations in genotype. Robustness is equivalent to clustering 2. Evolvability: measure the ability for adaptation. Also called innovation. Evolvability is equivalent to percolation 3. Neutral network: join all genotypes which the same phenotype. Usually genotypes are connected (an edge if only 1 mutation) F1 F1 F2 F3 F2 F3 2 2T (u) Clustering: c u =, where T (u) is the number of triangles deg(u)(deg(u)) through node u and deg(u) is the degree of u. Esther 3 Percolation: Ibáñez Marcelo point (CRM) when giantgenotype-phenotype component appears. map June / 13

29 Robustness and evolvability Robustness: measure the resilience to mutations. Retain the same phenotype upon mutations in genotype. Robustness is equivalent to clustering 2. Evolvability: measure the ability for adaptation. Also called innovation. Evolvability is equivalent to percolation 3. Neutral network: join all genotypes which the same phenotype. Usually genotypes are connected (an edge if only 1 mutation) 2 2T (u) Clustering: c u =, where T (u) is the number of triangles deg(u)(deg(u)) through node u and deg(u) is the degree of u. 3 Percolation: point when giant component appears. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

30 Robustness and evolvability Robustness: measure the resilience to mutations. Retain the same phenotype upon mutations in genotype. Robustness is equivalent to clustering 2. Evolvability: measure the ability for adaptation. Also called innovation. Evolvability is equivalent to percolation 3. Neutral network: join all genotypes which the same phenotype. Usually genotypes are connected (an edge if only 1 mutation) Two robust phenotypes without innovation Innovation 2 2T (u) Clustering: c u =, where T (u) is the number of triangles deg(u)(deg(u)) through node u and deg(u) is the degree of u. 3 Percolation: point when giant component appears. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

Canalisation and convergence Canalisation: assimilation of a change that after some generations can survive without the mutation imposed before.

31 Canalisation and convergence Canalisation: assimilation of a change that after some generations can survive without the mutation imposed before. Also called, buffering of the genotype or genetic assimilation. Convergence: Different species of different lineages ahieve the same biological traits. We relate to the fact that two or more different unrelated genotypes produce the same phenotype. Convergence is equivalent to Hamming distance between genotypes (g 1, g 2 ). 4 4 Hamming distance: number of mutations that we need to go from one genotype to another, d(g 1, g 2 ), we only have to count the differencies between the corresponding matrix associated to each genotype. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

32 Canalisation and convergence Canalisation: assimilation of a change that after some generations can survive without the mutation imposed before. Also called, buffering of the genotype or genetic assimilation. Convergence: Different species of different lineages ahieve the same biological traits. We relate to the fact that two or more different unrelated genotypes produce the same phenotype. Convergence is equivalent to Hamming distance between genotypes (g 1, g 2 ). 4 4 Hamming distance: number of mutations that we need to go from one Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

33 Canalisation and convergence Canalisation: assimilation of a change that after some generations can survive without the mutation imposed before. Also called, buffering of the genotype or genetic assimilation. Convergence: Different species of different lineages ahieve the same biological traits. We relate to the fact that two or more different unrelated genotypes produce the same phenotype. Convergence is equivalent to Hamming distance between genotypes (g 1, g 2 ). 4 4 Hamming distance: number of mutations that we need to go from one genotype to another, d(g 1, g 2 ), we only have to count the differencies between the corresponding matrix associated to each genotype. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

34 Some Results and Conclusions Strong relation between clustering and degree in the bipartite network (in phenotypes). Canalisation and evolvability coevolve. GRN parameters affect percolation (evolvability). Hamming distance measure convergence (different genotypes with a high distance can have the same phenotype). Robustness Esther (high Ibáñez Marcelo clustering) (CRM) emerges Genotype-Phenotype in phenotypes map associated17-21 to June genotypes / 13

35 Some Results and Conclusions Strong relation between clustering and degree in the bipartite network (in phenotypes). Canalisation and evolvability coevolve. GRN parameters affect percolation (evolvability). Hamming distance measure convergence (different genotypes with a high distance can have the same phenotype). Robustness (high clustering) emerges in phenotypes associated to genotypes that exhibit high degrees of similarity. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

36 Some Results and Conclusions Strong relation between clustering and degree in the bipartite network (in phenotypes). Canalisation and evolvability coevolve. GRN parameters affect percolation (evolvability). Hamming distance measure convergence (different genotypes with a high distance can have the same phenotype). Robustness (high clustering) emerges in phenotypes associated to genotypes Esther that Ibáñez exhibit Marcelo (CRM) high degreesgenotype-phenotype of similarity. map June / 13

37 Some Results and Conclusions Strong relation between clustering and degree in the bipartite network (in phenotypes). Canalisation and evolvability coevolve. GRN parameters affect percolation (evolvability). Hamming distance measure convergence (different genotypes with a high distance can have the same phenotype). Robustness (high clustering) emerges in phenotypes associated to genotypes Esther that Ibáñez exhibit Marcelo (CRM) high degreesgenotype-phenotype of similarity. map June / 13

38 Some Results and Conclusions Strong relation between clustering and degree in the bipartite network (in phenotypes). Canalisation and evolvability coevolve. GRN parameters affect percolation (evolvability). Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

39 Some Results and Conclusions Strong relation between clustering and degree in the bipartite network (in phenotypes). Canalisation and evolvability coevolve. GRN parameters affect percolation (evolvability). Hamming distance measure convergence (different genotypes with a high distance can have the same phenotype). Robustness (high clustering) emerges in phenotypes associated to genotypes that exhibit high degrees of similarity. Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

40 Thanks for your attention Esther Ibáñez Marcelo (CRM) Genotype-Phenotype map June / 13

Genetic Engineering and Creative Design

Genetic Engineering and Creative Design Background genes, genotype, phenotype, fitness Connecting genes to performance in fitness Emergent gene clusters evolved genes MIT Class 4.208 Spring 2002 Evolution