Abstract
Cancer progression is significantly affected by signaling cascades, with the Purinergic receptor
(P2X7R) pathways playing a role in the regulation of the programmed cell death
through Adenosine Triphosphate (ATP)-mediated activation. The heterogeneity of the
P2X7R and downstream activation of proliferation across individuals, makes therapeutic
prediction challenging.
This research focus on modeling the P2X7 receptor pathway to explore how different
kinetic parameters influence system behavior. We develop a computational model that is
capable of simulating individual specific response, This model can be use for personalized
drug response and precision diagnostics.
The system was modeled using the fourth-order Runge-kutta (RK4) method to solve the
ordinary differential equations(ODEs) that governs the P2X7 receptor dynamics. However,
this approach encounter some limitations such as computational inefficiency and numerical
instability which limit the progress of the biological systems. Also, developing a data driven
model was a challenge due to lack of experimental data.
To address this challenges, a physics-Inspired Neural Network (PINN) model was developed.
This model combine the strength of ODE modeling and data-driven learning by
incorporating the physical law of P2X7R signaling into the neural network training process.
As a result of this, PINN enhance model robustness and reduces computational stiffness
which adapt to variation in tumor micro-environmental parameters.
In this form, the Physics Inpired Neural Network (PINN) model developed operates as
pseudo-digital twin, a computational replica that interpret the individual-specific signaling
behavior that based on periodic simulation data instead of a continuous real time data
input. The trained PINN achieved a low mean absolute error (MAE < 0.027) across all
simulated plots which demonstrate that the PINNs approximate the ODEs trajectories
across different individual kinetic parameter and establish a foundation for individualspecific
digital twins in cancer research and offering a pathways toward predictive tumor
modeling