A Review of Complex Systems Approaches to Cancer Networks

TL;DR

This review explores how complex systems theory and computational tools like machine learning and network science are applied to understand and predict cancer network behaviors and heterogeneity.

Contribution

It synthesizes current approaches using complex systems, machine learning, and computational models for analyzing cancer networks and tumor heterogeneity.

Findings

Complex systems models depict tumors as dynamical systems with multiple attractors.

Machine learning and network science are key tools for cancer network reconstruction.

Deep learning and fluid models improve gene expression forecasting in cancer ecosystems.

Abstract

Cancers remain the lead cause of disease-related, pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system with intractable algorithmic complexity. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple, strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep Learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complex systems and complexity theory.