Genetic Algorithms and their Application to the Artificial Evolution of Genetic Regulatory Networks

Genetic Algorithms and their Application to the Artificial Evolution of Genetic Regulatory Networks

Part 1: Fundamentals

Biological Evolution

Biological Evolution - Example

Biological Evolution – cont‘d

Evolutionary cycle : Generation

Selection and Reproduction

Recombination (crossover)

The Evolutionary Mechanisms

Part 1: Fundamentals (2)

Evolutionary Computation

Evolutionary Computation

Optimization - Problems

Optimization - Problems

Optimization - Problems

Evolutionary cycle - revisited

EC - General properties

Genetic Algorithms

GAs - Simple Example

Simple example – f(x) = x²

F(x) = x² - Start Population

F(x) = x² - Selection

F(x) = x² - Selection

F(x) = x² - Selection

F(x) = x² - Recombination

F(x) = x² - Recombination

F(x) = x² - Mutation

F(x) = x² - End

F(x) = x² - Animation

Genetic algorithms

Coding and Mapping

Genetic coding and mapping

Mapping – Example

Mapping – Example cont‘d

Mapping – Example cont‘d

Mapping – Example cont‘d

Mapping – Example cont‘d

Mapping – Example cont‘d

Selection - Roulette-Wheel

Selection - Elitism

Selection - Tournament

Genetic variability operators

Recombination/Crossover

Fitness function

Fitness space or landscape

Fitness space or landscape

Fitness landscapes contd.

Fitness space – Good design

Fitness space - Bad design

Fitness space – Mediocre design

Dynamic fitness landscape

Integrating problem knowledge

Design decisions

Hints for the parameter search

Wrapping up Part 1

GA- Summary cont'd

More recent inputs from Biology

Part 2: Modelling GRNs

Evolutionary cycle - again

Evolutionary cycle - again

Decoding, Evaluating, Comparing

Decoding, Evaluating, Comparing

Decoding, Evaluating, Comparing

Decoding, Evaluating, Comparing

(Artificial) Genetic Regulatory Networks (aGRN)

GRN-theory – Central Dogma

GRN-theory – Central Dogma

Control of gene expression

Control of gene expression

Control of gene expression

Control of gene expression

Dynamics: Petri-net notation

Dynamics: Petri-net notation

Symbols: Arrows

Dynamics: what happens?

Dynamics: what happens?

Dynamics: what happens?

Dynamics: what happens?

Dynamics: what happens?

Dynamics: what happens?

Dynamics: what happens?

Dynamics: what happens?

Worth remembering (1)

Worth remembering (2)

Combining control and dynamics

Creating a dynamic GRN model - Questions (1)

Creating a dynamic GRN model – Questions (2)

Basic GRN model

Possible dynamic representation

Rules for activation and repression

Rules for combinatorial modifier effects

Note on GRN representation

Part 3: Evolving biological clocks

Schematic drawing

Schematic drawing - Phenotype

What does a gene-node consist of?

Encoding of gene-nodes and genome

Encoding of gene-nodes and genome – cont’d

Activation – cont’d

Gene-node activation example

Environmental in-/output

Selection and Variation

Population development example

Individual dynamics analysis

Evolvability - Heterochronicity

Differentiation – schematic

Differentiation: two pathways

Typical example runs for the two settings

Part 4: Netbuilder'

NetBuilder′ (Project Apostrophe)

NetBuilder′  NetBuilder

NetBuilder′ - Petri net

NetBuilder′ - GUI

NetBuilder′ - GUI

NetBuilder′ - GUI

NetBuilder′ - GUI

NetBuilder′ - GUI

Evolution in NetBuilder'

Genotype - Phenotype

Genotype – Phenotype (2)

Summary – NetBuilder'

ICSB 2007 - Posters

Acknowledgement

Resources – Evolutionary Algorithms

References – Evolving biological clocks with aGRNs