02.03 / Scientific foundations
After individual analysis, profile structuring and evidence mapping, contextual modelling explores the consequences of explicit assumptions within the person-specific context.
Ordinary differential equations describe rates of change in a well-mixed system. Logical models represent qualitative state transitions; constraint-based models restrict feasible fluxes. The appropriate representation depends on the question and data. SBML supports exchange of several biological model types. [1]
The demonstration uses dx/dt = a·u·(1−x) − b·x, with constant input u, activation rate a and deactivation rate b. The state x is a normalized active fraction, initially zero. The exact solution is x(t) = x∞·(1−exp(−λt)), where λ = a·u+b and x∞ = a·u/λ. This is a generic two-state model, not a cancer response model.
Changing parameters changes the trajectory. Different combinations of parameters can produce similar observations, so fitting a curve does not necessarily identify a unique mechanism. The shaded region varies b by ±25%; it is a scenario envelope, not a confidence interval or patient uncertainty estimate.
Specify initial conditions, parameter units, model version and numerical method. Test against withheld experimental conditions and compare with simpler baselines. Tissue context, feedback, spatial gradients and changing exposures are omitted here. A plausible trajectory does not establish clinical benefit. [1, 2]
dx/dt = a·u·(1−x) − b·xx(0) = 0; a and b use inverse arbitrary time units; u is dimensionless. Exact analytical solution, sampled from t = 0 to 10. The envelope is not a confidence interval.
All inputs are dimensionless or use arbitrary time units. No doses, patient data or clinical predictions are generated.
References support the methods described, not the validation or performance of Curetanics. Source titles are shown in their original language.