Cellular functions have evolved over time to better enable organisms to survive major environmental challenges (see Chapter 1). Improvements to basic functions or new functions that more successfully supported survival were preserved and shared between organisms, either by infections or by interbreeding. Because the environment is constantly changing, the complex functions have evolved to be robust so that they will work under many different conditions and the design principles of robust devices (see text box below) can be used to understand them. A complex function involves multiple functional modules coordinated to perform a complex task. In this chapter, we will describe a general approach to understanding the features of complex functions and how the functional modules that underlie complex functions may be linked to yield the desired emergent properties across different scales. An emergent property is the outcome of many steps and many modules in a complex function. We suggest that models of complex functions violating the principles of robust devices are likely to be wrong. Furthermore, we will describe why it is important to quantitatively measure the performance of complex biological functions (outputs) under different circumstances (inputs) to better test models of different complex functions.

Principles of Robust Machines with Standard Functions

We propose that cells are highly engineered, robust machines. They are also very small machines where all actions are dominated by diffusion. The stochastic nature of diffusion processes can account for some of the observed biological variability. However, biological systems have devised mechanisms to use diffusion to drive muscle movements and to direct the shaping of the organism. Evolutionary refinements of the basic mechanisms of biological systems enable them to reproducibly create organisms of diverse shapes using the information encoded in the DNA. Thus, fruit flies with the same DNA will look the same as well as identical twins. In the case of the twins the development took many years and yet they often do look identical. Thus, the robustness of the biological systems extends to making reliable decisions using inherently noisy stochastic processes. Because it seems that the cells that form the basis of biological systems are robust machines, the complex functions that they utilize should follow the design principles of robust devices (Figure 2.1, from Thomas et al., 2004).