Executable cell biology
Fisher et al. (2007): Executable cell biology
The authors "call the approach of constructing computational models of biological systems ‘executable biology’, as it focuses on the design of executable computer algorithms that mimic biological phenomena". They "survey the main modeling efforts in this direction" and "claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research".
[T]he basic entity of computational models is the state machine, which relates different qualitative configurations (states') to each other. A state machine may be specified by simple computer programs that define how, given certain events, one state is transformed into another. Complex computational models are constructed through the composition of state machines, yielding a reactive system. The components of such a system represent biological entities, such as cells, which react to events involving neighboring components by state transformations. This is often useful in cell biology, because it requires the modeler to think in terms of 'cause and effect' rather than rates of change. Such computational models can have a very large number of states, are often highly nonlinear and nondeterministic and are generally not amenable to mathematical analysis. [...] [C]omputational models can be useful even when not every detail about
a system is known.
In the chapter "Models for executable biology", the authors "summarize several research efforts aimed at realizing the executable biology framework" such as Boolean networks, Petri nets, interacting state machine models, process calculi, and hybrid models. Furthermore, in the chapter "Challenges" the authors write:
Executable biology poses new challenges both for computer science and biology. One challenge facing computer science is to adjust formalisms that were originally developed for modeling hardware and software systems to the modeling of biological systems. We must also develop techniques that handle the complexity and magnitude of biological systems and modeling tools that are more accessible to biologists. For biology, some of the key challenges are to develop quantitative techniques to experimentally test dynamic scenarios proposed by executable models, to identify useful building blocks of complex biological networks and perhaps most importantly, to shift biology toward an engineering science, where students learn to use formal approaches.