On the origin of modular variation.

We study the dynamics of modularization in a minimal substrate. A module is a functional unit relatively separable from its surrounding structure. Although it is known that modularity is useful both for robustness and for evolvability (Wagner 1996), there is no quantitative model describing how such modularity might originally emerge. Here we suggest, using simple computer simulations, that modularity arises spontaneously in evolutionary systems in response to variation, and that the amount of modular separation is logarithmically proportional to the rate of variation. Consequently, we predict that modular architectures would appear in correlation with high environmental change rates. Because this quantitative model does not require any special substrate to occur, it may also shed light on the origin of modular variation in nature. This observed relationship also indicates that modular design is a generic phenomenon that might be applicable to other fields, such as engineering: Engineering design methods based on evolutionary simulation would benefit from evolving to variable, rather than stationary, fitness criteria, as a weak and problem-independent method for inducing modularity.

A function-based framework for understanding biological systems.

Systems biology research is currently dominated by integrative, multidisciplinary approaches. Although important, these strategies lack an overarching systems perspective such as those used in engineering. We describe here the Axiomatic Design approach to system analysis and illustrate its utility in the study of biological systems. Axiomatic Design relates functions at all levels to the behavior of biological molecules and uses a Design Matrix to understand these relationships. Such an analysis reveals that robustness in many biological systems is achieved through the maintenance of functional independence of numerous subsystems. When the interlinking (coupling) of systems is required, biological systems impose a functional period in order to maximize successful operation of the system. Ultimately, the application of Axiomatic Design methods to the study of biological systems will aid in handling cross-scale models, identifying control points, and predicting system-wide effects of pharmacological agents.