A New Aproach to Decoding Life: Systems Biology
Ideker et al. (2001): A New Aproach to Decoding Life: Systems Biology
This is a rather old and quite basic paper much of the contents of which have already been covered in other papers I reviewed. One of the more interesting passages:
Biological information has several important features: It operates on multiple hierarchical levels of organization. It is processed in complex networks. These information networks are typically robust, such that many single per-turbations will not greatly effect them. There are key nodes in the network where perturbations may have profound effects; these offer powerful targets for the understanding and manipulation of the system.
The authors further describe various types of perturbations of biological systems, including high-throughput genetic manipulation, systematic gene mutations, and gene disruption in trans. A chapter on quantitative high-throughput biological tools follows. Then comes a chapter on biological databases. Regarding computer modelling, the authors write:
A wide variety of cellular models have been proposed, each of differing complexity and abstraction. For example, chemical kinetic models attempt to represent a cellular process as a system of distinct chemical reactions. In this case, the network state is defined by the instantaneous quantity (or concentration) of each molecular species of interest in the cell, and molecular species may interact via one or more reactions. Often, each reaction is represented by a differential equation relating the quantity of reactants to the quantity of postreaction products, according to a reaction rate and other parameters. This system of differential equations is usually too complex to be solved explicitly, but given an initial network state, the quantity of each gene product or other molecular species can be simulated to produce a state transition path or trajectory, i.e., the succession of states adopted by the network over time. A variety of biological systems havebeen modeled in this way, including the networks controlling bacterial chemotaxis, developmental patterning in Drosophila, and infection of E. coli by lambda phage. Recently, it has been pointed out that transcription, translation, and other cellular processes may not behave deterministically but instead are better modeled as random events. Models have been investigated that address this concern by abandoning differential equations in favor of stochastic relationsto describe each chemical reaction. In contrast to models involving systems of chemical reactions, another popular approach has been to model a genetic network as a simplified discrete circuit. [...] Discrete circuit models have been investigated extensively, and simulation software is available. Clearly, such models are greatly simplified compared to a kinetic model. Proponents of discrete circuit models argue that they preserve the essential features of the underlying biology while greatly reducing network complexity and simulation time. A major criticism has been that they require the model to update simultaneously for all nodes, whereas molecular interactions within the cell are not synchronous. Also, a two-level representation of molecular species may not always be sufficient to capture the underlying biological behavior of the network.
Finally, the authors propose a framework for systems biology, consisting of these steps:
Define all of the components of the system. [...] Systematically perturb and monitor components of the system. [...] Reconcile the experimentally observed responses with those predicted by the model. [...] Design and perform new perturbation experiments to distinguish between multiple or competing model hypotheses.
And at the end of the paper a couple of examples of systems biology are provided, such as cis gene regulation in the sea urchin, a network controlling galactose utilization in yeast, and bacterial chemotaxis (a robust signal-transduction network). In conclusion, the most striking challenges arising from systems biology are listed:
The inclusion of nongenetic molecules, small and large, into the systems picture. [...] Further development of theoretical frameworks and tools for integrating thevarious levels of biological information, displaying them graphically, and,finally, mathematical modeling and simulation of biological systems. Systematic and detailed annotations of information in the public databases. [...] Education of cross-disciplinary scientists. [...] The integration of technology, biology, and computation.