New pathway modelling approach combines biologist-friendly graphical notation system, flow simulation algorithm and advanced visualisations. Image A new paper published in Nature Protocols demonstrates how biologists can model biological pathway function, providing better understanding of how components of pathways interact and how these models can be used to predict their function. Biological systems are inherently complex and graphical models can be used to record what is known about them and be updated when new information becomes available. These models also support the analysis of how pathway systems might actually work, allowing biologists to run simulations of their activity under different conditions. Combined approach This is the first modelling approach that combines a notation scheme for depicting details of molecular events with a flow simulation algorithm and a powerful visualisation engine. Pathway models constructed using this approach serve as a knowledge management and information exchange system, and once generated, can provide predictions of how a biological system may behave if disturbed. This new approach does not require the construction of a series of equations or rate constants for model parameterisation, unlike many mathematical approaches to the issue. Biological systems are complicated and piecing together the evidence on how they work is challenging. We have tried to make this easier for biologists by devising a graphical language that enables them to draw diagrams of how molecules interact with each other to form pathways and then built tools to allow them to test how they might actually work. Together you can quickly build and edit these models, and find out what might happen if you perturb the system in some way. We now have knowledge management, experimentation and cool visualisations all built into one system. Professor Tom Freeman, The Roslin Institute The research was carried out in collaboration with experts at the MRC Centre for Reproductive Health, University of Southampton and University of Newcastle (Australia). Contact Professor Tom Freeman Personal Chair of Systems Immunology tom.freeman@roslin.ed.ac.uk Related Links Original publication: Nature Protocols, volume 13, pp 705–722 (2018) doi:10.1038/nprot.2017.144 Mathematical biology, analysis and prediction
