IdiotypicNetwork

Implements Jerne's idiotypic network theory for antibody repertoire regulation. Replaces crude epsilon-threshold suppression with principled network dynamics based on Varela & Coutinho's (1991) second- generation immune network model.

Details

The idiotypic network models antibody-antibody interactions where each antibody's variable region can be recognized by other antibodies. This creates a regulatory network with emergent properties:

- A bell-shaped (double-threshold) activation function: too little stimulation leads to cell death, moderate stimulation to activation, and excessive stimulation to suppression. - Population dynamics with source, decay, activation, and suppression terms. - Self-organized repertoire structure with memory and tolerance properties.

This is the single most novel contribution of the overhauled bHIVE package. No existing AIS implementation uses idiotypic network dynamics for repertoire regulation.

Public fields

theta_low

Lower activation threshold. Below this, cells die.

theta_high

Upper activation threshold. Above this, cells are suppressed.

source_rate

Rate of new cell generation (basal production).

decay_rate

Natural cell death rate.

dt

Time step for Euler integration.

timeSteps

Number of simulation time steps.

survival_threshold

Minimum population level to survive.

last_dynamics

Result from the last regulation step.

Methods

Public methods

• IdiotypicNetwork$new()

• IdiotypicNetwork$regulate()

• IdiotypicNetwork$get_network()

• IdiotypicNetwork$get_population()

• IdiotypicNetwork$print()

• IdiotypicNetwork$clone()

---

Method new()

Create a new IdiotypicNetwork regulator.

Usage

IdiotypicNetwork$new(
  theta_low = 0.01,
  theta_high = 0.5,
  source_rate = 0.5,
  decay_rate = 0.1,
  dt = 0.1,
  timeSteps = 20,
  survival_threshold = 0.5
)

Arguments

theta_low

Lower activation threshold.

theta_high

Upper activation threshold.

source_rate

Basal cell production rate.

decay_rate

Natural decay rate.

dt

Euler integration time step.

timeSteps

Number of dynamics simulation steps.

survival_threshold

Minimum population to survive.

---

Method regulate()

Run idiotypic network dynamics on an antibody repertoire.

Usage

IdiotypicNetwork$regulate(
  repertoire,
  affinityFunc = "gaussian",
  affinityParams = list(alpha = 1, c = 1, p = 2)
)

Arguments

repertoire

An ImmuneRepertoire object.

affinityFunc

Character. Affinity function for Ab-Ab interactions.

affinityParams

List. Parameters for the affinity function.

Returns

Invisible self. The repertoire is modified in place (dead antibodies removed). Access $last_dynamics for full results.

---

Method get_network()

Get the Ab-Ab affinity matrix from the last regulation.

Usage

IdiotypicNetwork$get_network()

Returns

Numeric matrix (m x m) or NULL if not yet run.

---

Method get_population()

Get population levels from the last regulation.

Usage

IdiotypicNetwork$get_population()

Returns

Numeric vector or NULL if not yet run.

---

Method print()

Print summary.

Usage

IdiotypicNetwork$print(...)

Arguments

...

Not used.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

IdiotypicNetwork$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Create and run idiotypic regulation
idi <- IdiotypicNetwork$new(theta_low = 0.01, theta_high = 0.5)
A <- matrix(rnorm(50), nrow = 10, ncol = 5)
rep <- ImmuneRepertoire$new(A)
idi$regulate(rep, "gaussian", list(alpha = 0.5))
#> Warning: Idiotypic regulation removed all antibodies. Consider adjusting thresholds (theta_low, theta_high).
print(idi)
#> <IdiotypicNetwork>
#>   Activation window: [0.010, 0.500]
#>   Dynamics: 20 steps (dt=0.100)
#>   Source: 0.500  Decay: 0.100  Survival: 0.500
#>   Last run: 0/10 antibodies survived