tdc-toy-sims/bsimons_okarin_cell_cycle_criticality_eg at main · nadoukkadivin/tdc-toy-sims · GitHub

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"""

Conceptual TDC mapping for “cell cycle criticality” – how measured/estimated biological parameters could be used to compute the Dr index for individual cell fate prediction.

"""

def predict_cell_fate_from_TDC(cell_state, mitogen_signal):

    """

    Predicts G1-S transition outcome using TDC's Dr index.

    Parameters

    ----------

    cell_state : object

        Contains cell-specific parameters (conceptual).

        e.g. chromatin accessibility, receptor levels, division history.

    mitogen_signal : object

        Contains signal properties (amplitude, duration).

    Returns

    -------

    fate : str

        Predicted fate decision.

    Dr : float

        Computed decay-coupling ratio.

    """

    # 1. Estimate persistence time of the 'commitment state' (slow domain)

    #    Proxy: chromatin remodeling timescale or metabolic steady-state.

    tau_decay = estimate_epigenetic_persistence(

        cell_state.histone_modification_halflife,

        cell_state.metabolic_oscillation_period

    )


    # 2. Estimate integration time for mitogenic signals (fast domain)

    #    Proxy: signaling pathway activation kinetics.

    tau_coupling = estimate_signal_integration_window(

        mitogen_signal.amplitude,

        cell_state.egf_receptor_activity,

        cell_state.mapk_activation_rate

    )


    # 3. Compute the critical index

    Dr = tau_decay / tau_coupling


    # 4. Fate prediction based on Dr proximity to criticality (Dr=1)

    fate = None

    if 0.9 <= Dr <= 1.1:  # Critical region - high sensitivity

        # Stochastic routing influenced by history (residues) & noise

        if cell_state.division_history.residues > threshold:

            fate = "bias_toward_self_renewal"

        else:

            fate = "bias_toward_differentiation"

        # Fate is "poised" and highly responsive to niche signals

    elif Dr < 0.9:

        # Fast coupling dominates -> rapid commitment

        fate = "differentiation_primed"

    else:  # Dr > 1.1

        # Slow decay dominates -> state persists

        fate = "self_renewal_primed"

    return fate, Dr

# This framework makes testable predictions:

# 1. Mutants (e.g. cyclin overexpressors) will have altered tau_coupling -> Dr != 1.
# 2. Population feedback adjusts average tau_decay (via residues) to maintain Dr ~1.
# 3. Therapeutic perturbations can widen the Dr gap between wild-type and mutant cells.