Reverse Engineering of Metabolic and Signaltransduction Pathways

Metabolic and gene networks have been likened to electrical circuits, and this analogy contains interesting insights. Biological systems and advanced technologies are similar at the level of systems organization. They both have many levels of robustness and are designed/evolved to cope with uncertain external conditions.

Consider the bacterium Escherichia coli. The minimal number of genes needed for a cell to survive and replicate is estimated at around 300. E. coli has around 4000 genes, much more than the minimum. This large difference is needed to make the cell robust, not just in the face of fluctuating environmental conditions but also of failures in the reaction networks. This robustness comes with a price: complexity. For the cell to respond to many different external conditions and be resistant to failure, the internal environment develops a fragile homeostasis, as this stable internal environment enables quicker and more sophisticated responses. But the loss of internal homeostasis in usually catastrophic to the organism. Consider our obligatory use of oxygen as an electron acceptor. Oxygen is a very useful electron acceptor, but its use requires precision in the uptake, transport, and removal of CO2. When this delicate system breaks down, the consequences are fatal. A comparison to E. coli is a Boeing 777 jumbo jet. The aircraft has 150 000 different subsystems that communicate through an elaborate system of protocols automated by about 1000 computers. This complexity is designed to be robust, i. e., to tolerate and respond to failures appropriately.

An important concept in both these systems is modularity. Modules are subsystems that have some independence with respect to modification or evolution. They maintain some form of identity when rearranged and use protocols to communicate with other modules. An example of a module in biology is the segmentation network in the fruit fly Drosophila. This module is robust to internal changes, in that most changes in the reaction rate of a protein or transcription factor do not disturb the module's function [37], i.e., setting up the segments in the fly embryo. Modules communicate through protocols, which are signals that have some agreed-upon meaning. In biological systems this could mean, e.g., that when the amount of some protein X increases, some module changes state. The protocols for feedback control in such systems are some of the more complex, because amplification systems are useful. However, without feedback mechanisms, it is relatively easy to build either uncertain high-gain amplifiers or precise low-gain ones, but prohibitively difficult to build precise high-gain amplifiers [38]. These protocols between modules must be fine-tuned, resulting in robust, sophisticated responses to external stimuli, but catastrophic failures in rare cases.

The concepts of robustness and modularity increase our understanding of complex systems. By regarding gene regulation networks and cell communication networks as many modules with switches, oscillators, and feedback mechanisms between them, the hope is to remove a subsystem and study its behaviour in isolation, which then may enable the construction of networks of systems [39].

Some genetic modules have been removed and analyzed. These form subsystems in gene regulation networks, and have analogies in, for example, electrical circuits:

1. Feedback loops, for example, where a protein modifies its own expression level. These systems have been shown to be more stable than genes without this regulation by both experimental and theoretical studies.

2. Switches, for which, after switching to a stable state after a stimulus, the system 'remembers' the stimulus.

3. Logic gates. Multiple repressors and activators enable 'IF a AND b THEN c' type switches. These can be stacked and joined to form complex decision-making subnetworks (Figure 13.9).

4. Oscillators: these are used by organisms to coordinate different systems and include circadian rhythms. However, the exact mechanism of biological clocks is not yet completely understood.

These basic components partly make up the gene regulatory networks that share many analogies to complex technology. However, the manner in which living systems respond to noise is often very different than that of constructed systems and

Fig. 13.9 A simple Boolean network.

may provide insight into how human technologies can be improved. Understanding noise is crucial to understanding many biological systems, due in part to the inherent stochasicity in many systems, e.g., often the very small numbers of transcription factors, promoter sites, and tRNA molecules lead to high levels of random noise. Not only do living systems seem resistant to high levels of noise, for example, by using mechanisms like cascades of genes as attenuators, and positive and negative feedback loops, but some actually exploit noise. Haematopoietic stem cells, epigenetic inheritance, infection of a bacteria by a lambda phage - all these systems exploit noise. Some cellular processes can actually use noise to attenuate noise, in other words, noise can be used to enhance a signal when certain nonlinear effects are present [40].

By comparing complex biological systems and complex technologies, analogies are found. Electrical circuits give us metaphors for describing biological systems, nature in return rewards us with the need for more and better metaphors.

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