Molecular networks are subject to many interactions between components, while the components themselves can exist in multiple states. Researchers need sophisticated mathematical methods to understand how such complex networks process information, as Dr Jeremy Gunawardena, Associate Professor at Harvard Medical School explains
Making sense of molecular networks The complexity of molecular networks is a challenge for biology researchers, who need sophisticated new mathematical methods to track how these networks process information and make decisions. This is an area of long-standing interest to Dr Jeremy Gunawardena, coordinator of the Algebraic Geometric Approaches to Biological Complexity project. “This project is trying to exploit some methods from pure mathematics which have not been used in biology at the molecular level before. We think these methods have some very powerful features that allow us to rise above molecular complexity,” he says. Based at Harvard Medical School in the US, Dr Gunawardena and his colleagues are now using these ideas, which have their roots in a discipline called algebraic geometry, to get a sense of the capabilities of very complicated networks. “We’re really trying to get a functional understanding of the network of molecular interactions. The underlying complexity has two forms,” he outlines. “One is that there are a lot of interactions between the components. The other issue is that the components themselves can exist in multiple states. So you have the complexity of the components themselves, as well as the complexity of the interactions through which the components talk to each other.” Chemical signals
The molecular networks within cells can be stimulated by external chemical signals, such as growth factors or hormones that are typically conveyed around the body in blood or within tissues. These signals impinge on molecules in the membrane of the cell, the receptor molecules. “These receptor molecules
16
become activated, and they instigate a process of signal transduction inside the cell. They recruit various molecular components, and they undertake various forms of processing on that signal until the cell decides what to do as a result of seeing that signal. Signal transduction networks are typical of the systems we look at,” explains Dr Gunawardena. The signal might be a cue for the cell to begin to divide and proliferate, or to go down a differentiation pathway and become a particular type of cell. “These signal transduction networks could participate in decision-making in early development,” continues Dr Gunawardena. “Once the organism has actually been constructed and reached adulthood then evolution is very good at re-using these mechanims, so that the same signal transduction networks can implement the organism’s normal physiological responses. They can also become deranged when the organism falls ill, which is why understanding their functionality can help us develop more effective therapies.” There are multiple layers of complexity within these networks, with various signals, mechanisms and events affecting the way they are structured. The project is mainly focusing on the protein level, at which there is a particularly high level of complexity. “Once a gene has been turned on, once a protein has been expressed and is present in the cell, evolution has found ways to actually modify the structure of the protein,” explains Dr Gunawardena. These modifications can occur with different chemical groups. “Pretty much any protein in any cell in your body is continuously being subjected to this post-
EU Research
