4. Computational Systems Biology

The new methods of —omics biology, combined with more traditional experiments, have the capacity of generating more high-quality data than ever before. So, why isn’t that sufficient? What is missing? The missing aspects arise from subtle, but important differences between data, information, knowledge, and understanding. ‘Computational systems biology’ explains how laboratory experiments generate data, whereas understanding additionally requires significant human intelligence and knowledge. Computational systems biology (CSB) attempts to bridge the gap between data and understanding. It uses a pipeline from data to understanding that consists of two toolsets: machine learning and mathematical models. The most useful of these models in CSB fall into two categories: static networks and dynamic biological systems.