Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying ML models. Application-wise, we show how ML surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate ML models may hold the key to making the simulation of complex biological systems possible using a typical desktop computer.

Networks are widely used to describe many natural and technological systems. Understanding how these evolve over time poses a challenge for existing visualization techniques originally developed for fixed network structures. We describe a method of incorporating the concept of aging into evolving networks, where nodes and edges store information related to the amount of local evolutionary change they have experienced. This property is used to generate visualizations that ensure stable substructures maintain relatively fixed spatial positions, allowing them to act as visual markers and providing context for evolutionary change elsewhere. By further supplementing these visualizations with color cues, the resultant animations enable a clearer portrayal of the underlying evolutionary process.