Data-driven system identification in cancer systems biology: A multiscale modeling approach to melanoma

Developing systems biology models of cancer is critical for improving understanding of complex biological interactions and advancing appropriate therapeutic strategies. However, constructing complex, multiscale models that are easily parameterized by the available data can be difficult. Advances in computational methods have provided data-driven frameworks utilizing machine learning approaches to build predictive systems biology models. These methods display a strong ability to fit data and predict cellular and molecular trajectories but can be limited by either poor mechanistic interpretations or require a large prior understanding of the system of interest. In many cases, such as cancer, the systems are highly complex, resulting in limited availability of prior knowledge and a strong need for mechanistic insight from the developed models. This work builds upon recent developments in system identification to construct fully mechanistic models directly from the available data, serving as a proof-of-concept study for multi-scale system identification with synthetic data.

We utilize ADAM-SINDy (sparse identification of nonlinear dynamics with ADAM) to accomplish this objective, a recently developed differentiable optimization method for the identification of nonlinear, parameterized dynamical systems, with advancements to facilitate its use in systems biology settings. Specifically, we focus on the usage of prior biological knowledge and the subsequent finetuning through a two-stage identification process. We apply the framework in the context of melanoma (a dangerous skin cancer) and quantitative systems pharmacology, developing a benchmark multiscale model connecting sub-cellular signaling and cell scale dynamics in response to therapy.

The ADAM-SINDy framework is shown to be capable of identifying a ground truth system of equations directly from synthetically generated data. Coupling mechanisms between scales (subcellular and cellular) are also identifiable with this framework. At high temporal resolutions, the method achieves 100% reconstruction accuracy, dropping to 89.9% with a two times coarser resolution.

The framework presented in this work is capable of constructing systems biology networks and mathematical models directly from data, while maintaining clear mechanistic interpretations. This system identification paradigm in systems biology can help remove the need for extensive biological insight into the network of interest a priori, which can be exceedingly difficult in the context of cancer.