Biological systems are remarkably complex, and making sense of the huge volumes of data available has meant that methods that can analyse these vast datasets are becoming increasingly important. Eivind Almaas(p. 1548) describes how network theory can be applied to whole biological systems, for example metabolism, and can help researchers understand the complex interactions between different parts of the system, in space and through time. Networks are represented by nodes linked to each other; in a biological system such as metabolism, the proteins in the system are the network nodes while protein–protein interactions form the links between the nodes. By representing and analysing a complex system such as metabolism as a network scientists can learn how its constituent parts interact to contribute to the function of the whole system. The next step is to combine information from different networks, from gene regulation to metabolism, to understand how whole cells function.

Ben Lehner develops the idea of gene and protein networks and how different types of data can be used together to describe networks and how networks in different species have common basic structures. For example, describing networks that regulate gene transcription in worms and yeast can inform researchers about the equivalent networks in humans(p. 1559). Because humans are so complicated, we know little about how genes interact to produce phenotypes, especially when it comes to hereditary diseases, which result from mutations in many genes. One approach to help understand disease is to use mutants in yeast or worms to systematically investigate how genes interact to produce a phenotype, often on a genome-wide scale. One feature of studying networks is the finding that a few so-called `hub' genes can be key, and could influence many unrelated diseases.

While hub genes are an important feature of regulatory networks, Patricia Wittkopp expands on their basic structure, discussing how modulation of networks alters gene expression, which in turn underlies phenotypic plasticity and variation within and between species(p. 1567). By understanding the modulation of networks, researchers can provide `insight into the molecular mechanisms of ecological responses and phenotypic evolution', says Wittkopp. However, to understand changes in gene expression,researchers need to understand how the changes in the regulatory networks alter this expression, such as the interactions between DNA, RNA and proteins,and how they affect transcription. There are common motifs – patterns in how the components in a network are arranged relative to each other –that emerge from networks. These involve interactions between molecules in groups or cascades, feedback loops or hub genes. Many regulatory networks have a hierarchical structure, where genes that control the earliest events occur at the top of the hierarchy and those controlling the final stages of differentiation are found at the bottom.

Nicolas Smith and colleagues use engineering principles to build predictive mathematical models of biological systems, with the aim of understanding how complex systems work based on knowledge of how the individual elements function (p. 1576). Again Smith highlights the need to integrate data from many different sources,from the cellular level to the whole organism, so that researchers can understand how the bits work together to contribute to the functioning of the whole, stating that mathematical models are an ideal way to do this. The model of the heart developed by Smith and his colleagues factors in ion pumps and leaks, muscle contraction, muscle energetics, tissue structure and properties. He cautions that when building such models, it's important that the data which underlies them are relevant and up-to-date. It is also essential that the researchers who build these models have a framework in place, such as Internet forums, to discuss, peerreview and compare their models.