System theory inspired thinking has led to the identification of ideas behind data processing in nature, but also in machines, such as silicon computers.
"This idea based thinking led to three distinct, but inter-related approaches, termed natural computing: computing inspired by nature, computer models of nature, and computing with natural materials " (see the figure)Data processing in nature
Focusing on information flow can help us to understand better how cells and organisms work . Data processing can be found in nature all down to the atomic and molecular level. Examples are DNA information storage, and the histone code . Moreover, cells have the potential to compute, both intra cellular (e.g. transcription networks) and during cell to cell communication . Higher order cell systems such as the immune and the endocrine system, the homeostasis system, and the nerve system can be described as computational systems. The most powerful biological computer we know is the human brain .Computing inspired by nature
General systems theory is an important fundament for computer science . Interesting work has be done, as discussed above, by the Biological Computer Laboratory led by Heinz Foerster  .
In practical terms, nature inspired to programming paradigms such as cellular automata, artificial neural networks, evolutionary algorithms, evolutionary biology, genetic programming, swarm intelligence, artificial immune systems, membrane computing and amorphous computing  . The common aim of all these concepts is solving complex problems.Computer models of nature
The aim of the simulation and emulation of nature in computers is to test biological theories, and provide models that can be used to facilitate biological discovery. Moreover, these models can potentially be used for computer aided design of artificial biological systems.
Systems biology provides theoretical tools to model complex interactions in biological systems . Design principles of biological circuits have been translated into mathematical models. These design models find their practical application in synthetic biology in general, and cellular computer especially. The different areas of natural computing clearly influence each other.
A breakthrough in the modeling and synthesis of natural patterns and structures was the recognition that nature is fractal . A fractal is a group of shapes that describes irregular and fragmented patterns in nature, different from Euclidean geometric forms .
Other mathematical systems, as cellular automata, are both inspired by nature and can be used to modulate nature in silico, as some biological processes occur, or can be simulated, by them such as shell growth and patterns, neurons and fibroblast interaction [ 21] [ 22].
Another computational model of nature is the Lindenmayer-system (or L-system), which is used to model the growth process of plant development . A major step towards the creation of artificial life was recently achieved by Karr et al . This group reports a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. This model provides new insight into the in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. Moreover, model predictions led to experiments which identified previously undetected kinetic parameters and biological functions.Computing with natural materials
Engineering ideas behind silicon computers can be applied to engineering with natural materials in order to gain control over biological systems. This concept started to emerge in the 1960s when Sugita published ground breaking theoretical work where he performed a functional analysis of chemical systems in vivo using a logical circuit equivalent [ 25] [ 26]. He discussed the idea of a molecular automaton, the molecular biological interpretation of the self-reproducing automata theory, and the chemico-physical interpretation of information in biological systems [ 27] [ 28]. Sugita made analogies between an enzymatic cascade and logic, values and concentrations, and interactions and circuit wires.
The emerging field of synthetic biology has contributed with novel engineering concepts for biological systems  . The development of standardized biological parts has been a major task in synthetic biology, which led among other things to the open MIT Registry of Standard Biological Parts, and the BIOFAB DNA tool kit   . Another engineering principle, abstraction hierarchy, deals with the question of how standardized parts build a complex system. Systems (systemics) are another important engineering paradigm dealing with complexity  . A system is a set of interacting or independent components forming an integrated whole. Common characteristics of a system are: components, behaviors and interconnectivity. Systems have a structure defined by components. Systems behavior involves input, processing and output of data. Behavior can be described with terms such as self-organizing, dynamic, static, chaotic, strange attractor, adaptive. Systems have interconnectivity. This means that the parts of the system have functional as well as structural relationships between each other. This kind of thinking represents a move form molecular to modular biology . The challenge is to define the hierarchical abstraction for such a modular system for biocomputers, and finally actually build such a system.
A breakthrough paper was published in 1994 by Leonard Adleman . For the first time a biocomputer, based on DNA, was built. This system was able to solve a complex, combinatorial mathematical problem, the directed Hamiltonian path problem. This problem is in principle similar to the following: Imagine you wish to visit 7 cities connected by a set of roads. How can you do this by stopping in each city only once? The solution of this problem, a directed graph was encoded in molecules of DNA. Standard protocols and enzymes were used to perform the “operations” of the computation. Other papers using DNA computing for solving mathematical problems followed . Adelman's paper basically kick started the field of biological computers (reviewed in     )."http://bit.ly/YI13bF