1

Lotka-Volterra Oscillator

Predator-prey dynamics with sustained oscillations

A classic two-species oscillator: prey (P) grows exponentially, predators (W) consume prey, and predators die off without food. The coupled ODEs produce stable closed orbits in the (P, W) plane. A canonical test for ODE integrators and phase-space visualization.

Species2
Reactions3
Integratorcvode
Total Time30
Snapshots301
Max Level11.99
Runtime0.43s

Species Trajectories

Antimony Model Source

model lotka
  // Predator-prey oscillator
  P = 10; W = 5
  J1:    -> P;  kg*P
  J2: P  -> W;  kc*P*W
  J3: W  -> ;   kd*W
  kg = 1.0; kc = 0.1; kd = 1.0
end

Bigraph Architecture

Bigraph architecture diagram

Composite Document

2

Repressilator Gene Circuit

Three-gene ring oscillator (Elowitz & Leibler 2000)

A synthetic gene network of three mutually repressing genes arranged in a ring. Each protein represses the next gene via Hill kinetics, producing limit-cycle oscillations in all three protein species with 120° phase shifts. Demonstrates bigraph wiring of a multi-species biochemical network.

Species6
Reactions12
Integratorcvode
Total Time400
Snapshots401
Max Level160.48
Runtime0.03s

Species Trajectories

Antimony Model Source

model repressilator
  // Elowitz & Leibler 2000 ring oscillator: three mutually repressing genes
  m1 = 0; m2 = 0; m3 = 0
  p1 = 5; p2 = 0; p3 = 15

  // mRNA production with Hill-type repression + basal rate
  Rm1: -> m1; alpha * (K^n / (K^n + p3^n)) + alpha0
  Rm2: -> m2; alpha * (K^n / (K^n + p1^n)) + alpha0
  Rm3: -> m3; alpha * (K^n / (K^n + p2^n)) + alpha0

  // mRNA degradation
  Dm1: m1 -> ; beta_m * m1
  Dm2: m2 -> ; beta_m * m2
  Dm3: m3 -> ; beta_m * m3

  // Protein production and degradation
  Rp1: -> p1; beta_p * m1
  Rp2: -> p2; beta_p * m2
  Rp3: -> p3; beta_p * m3
  Dp1: p1 -> ; beta_p * p1
  Dp2: p2 -> ; beta_p * p2
  Dp3: p3 -> ; beta_p * p3

  // Oscillatory regime: n=3 cooperativity pushes past the Hopf
  // bifurcation for alpha=216, K=40 (Elowitz-Leibler parameters).
  alpha = 216; alpha0 = 0.2; K = 40; n = 3
  beta_m = 1.0; beta_p = 0.2
end

Bigraph Architecture

Bigraph architecture diagram

Composite Document

3

Stochastic Dimerization

Gillespie SSA trajectory of M + M ⇌ D

Reversible dimerization of a small monomer pool simulated with the Gillespie stochastic algorithm. The noisy trajectory reveals fluctuations around the equilibrium that the deterministic ODE would smooth over. Shows integrator selection and stochastic simulation support in the wrapper.

Species2
Reactions2
Integratorgillespie
Total Time50
Snapshots501
Max Level30.00
Runtime0.02s

Species Trajectories

Antimony Model Source

model dimer
  // Reversible dimerization — stochastic-friendly small system
  M = 80; D = 0
  Jf: 2 M -> D; kf*M*(M-1)/2
  Jr:   D -> 2 M; kr*D
  kf = 0.01; kr = 0.1
end

Bigraph Architecture

Bigraph architecture diagram

Composite Document