The perception of mathematical modelling in cell biology is changing from a theoretical exercise of limited interest to a tool facilitating mechanistic understanding of complex phenomena and better experimental design. As a result, there has been an enhanced incorporation of mathematical modelling in the study of many complex biological processes. A major goal of modelling biological systems is to be able to provide a mechanistic explanation of the underlying processes. More often, models are built to predict behaviours that may be hard to understand because of the multiple variables involved. These predictions can then be tested by experiments and the model can be refined. Modelling is thus an ongoing exercise of analysis and experimental validation, incorporating new biological data as they become available.
Modelling is particularly useful in analysing information flow through cell signalling networks. Cellular signalling pathways regulate the programmes for various cellular functions that change in response to a stimulus. These functions arise from the underlying biochemical reactions and, therefore, lend themselves to quantitative modelling. Modelling of these networks and pathways allows us to organize existing knowledge and explore the signalling pathways for emergent properties.
One of the key features of cell signalling networks is the non-linearity arising from the presence of regulatory loops and branches in the signalling network. This non-linearity makes it challenging to understand and predict responses to a single stimulus; predicting responses is harder still for multiple stimuli, which is the norm in real biological systems. Developing mathematical models for these complex networks allows us to express these non-linearities in the equations that constitute the computational models and explore the models systematically.
Computational models of signalling networks serve two purposes: organization and validation of known experimental data, and prediction of system behaviour that is not intuitive from prior experiments. Predictions made from the numerical simulations can be experimentally verified or nullified and the model can be improved by an iterative process of models→experiments→models. Thus modelling serves as a tool for obtaining systems level understanding of the underlying biological processes.