This monograph addresses the decomposition of biochemical networks into functional modules that preserve their dynamic properties upon interconnection with other modules, which permits the inference of network behavior from the properties of its constituent modules. The modular decomposition method developed here also has the property that any changes in the parameters of a chemical reaction only affect the dynamics of a single module. To illustrate our results, we define and analyze a few key biological modules that arise in gene regulation, enzymatic networks, and signaling pathways. We also provide a collection of examples that demonstrate how the behavior of a biological network can be deduced from the properties of its constituent modules, based on results from control systems theory. We then use this modular decomposition method to analyze the p53 core regulation network, which plays a key role in tumor suppression in many organisms.

By decomposing the network into modules, we study the evolution of the p53 core regulation network and conduct a formal analysis of the different network configurations that emerge in the evolutionary path to complexity from putative primordial organisms to more evolved vertebrates. We develop an algorithm to solve the system of equations that describe the network behavior by interconnecting the network modules systematically, as these equations are typically difficult to solve using standard numerical solvers. In the process, we qualitatively compare the distinct types of switching behaviors that each network can exhibit. We demonstrate how our novel model for the core regulation network matches experimentally observed phenomena, and contrast this with the plausible behaviors that primordial network configurations can admit.

Specifically, we show that the complexity of the p53 network in humans and evolved vertebrates permits a wide range of behaviors that can bring about distinct cell fate decisions, but that this is not the case for primordial organisms.