An Examination of Synchronisation in Artificial Gene Regulatory Networks

Jonathan Byrne, Miguel Nicolau, Anthony Brabazon and Michael O'Neill

Natural Computing Research and Applications Group

University College Dublin



  • The gene regulatory network (GRN) model
  • Synchronisation step
  • Pole balancing experiment
  • Offset experiments
  • Conclusions

Gene Regulatory Network

  • Gene expression generates proteins
  • Proteins enhance or inhibit gene expression
  • DNA segments indirectly interact through protein production

Banzhaf GRN model

  • Evolutionary: Evolved bitstring model
  • Developmental: Fully connected Network

Evo Representation

  • Defines network structure
  • Promoter defines number of nodes
  • Gene sequence defines the weights

Evo Representation

Devo Representation

  • Fully connected network topology
  • Node interactions defined by weights
  • Node concentrations change over time

Devo Representation

GRN Input and Output

  • "Extra" genes used for input
  • "P" genes used for output
  • Extra concentration set by input value
  • Extra limited to a maximum of 0.4
  • Extra and TF genes normalised together
  • P genes normalised separately

GRN Input and Output

The Synchronisation Step

  • Change in input effects dynamics of the network
  • Stabilisation required to react to input
  • How does the size of the sync step effect the GRN?
  • Is there an automatic approach for choosing the step size?

Pole Balancing Experiment

Pole Balancing Experiment

  • Dynamic experiment benchmark
  • 120,000 time steps
  • Cart re-initialised for every generation
  • 4 Extra genes and 2 P genes

Pole Balancing Experiment

  • X ∈ [−2.4, 2.4] m is the position of the cart from the centre
  • θ ∈ [−12, 12] ◦ is the angle of the pole with the vertical
  • x ∈ [−1, 1] m/s is the velocity of the cart on the track
  • Θ ∈ [−1.5, 1.5] ◦ /s is the angular velocity of the pole

Pole Balancing Results

Offset Experiments

  • Same input with different offsets for target output
  • Can it match the target?
  • Does increasing the size of the sync step effect performance?
  • Does a stabilised network retain information about previous states?

Sine Experiment

Offset 10

Offset 20

Sine Results: Offset 0

Sync step 10 offset 0

Sine Results: Offset 10

Sine Results: Offset 20

Random Walk Experiment

Random Walk Results: Offset 0

Random Walk Results: Offset 10

Random Walk Results: Offset 20


  • Synchronisation can improve performance
  • Improvement increased logarithmically
  • No improvement with greater offsets
  • Variable synchronisation performed worse