Searching for Model Structures on Murine Neonatal Sepsis Data Using Genetic Programming

Mathematical models can help to describe complex biological systems. In the case of sepsis, which is a dysregulated immune response to infection, mathematics can help shed a light on the immune dynamics and highlight the mechanisms that lead to septic results. However, the complexity underpinning biological systems and the difficulty of interpretation of experimental outputs, often make modellers reliant on standard prototype equation systems. We propose to overcome these limitations with a genetic programming informed approach to modelling. We aim to highlight the potential of this approach whilst demonstrating that genetic programming cannot simply replace modellers. Instead, traditional modelling and genetic programming can be combined, to capture the complexity of these systems and work synergistically towards an understanding of the underlying biological processes.