Systems biology focus in Cell 5/19/05
Reposted from AN's sparsely posted-to blog.
Four interesting commentaries have come out in Cell 121:4 about the nascent "systems biology" field.
Marc Kirschner's "The meaning of systems biology" offers historical context for this new branch of biology, and whether it is really that different from molecular biology. He points out how this new field aims to parallel the paradigm of prediction, model and experimentation used by physicists. He suggests that a precise definition of systems biology is difficult to pin down because the field is just emerging, and that a precise definition "should await the words of the first great modern systems biologist." In an interesting comment on biology in general, he says systems biology "differs from physics in that the primary task is to understand how biology generates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name."
Alan Aderem's perspective of systems biology speaks to the ideas of complex systems theory - that the phenotypic behavior exhibited by a biological system is an emergent property of the interactions of its constituent parts. He introduces a new term (to me, at least) "network modeling by iterative refinement", and a nice diagram showing how high-throughput "omics" feeds computational models, analysis and further experimentation. He focuses on the need to translate this information into interpretable visual representations and to allow biologists everywhere to access the information. There are technical challenges to high-throughput data acquisition such as how to measure response concentrations. An interesting prediction: nanotechnology will allow us to measure parametric values, such as the forces and kinetics associated with protein/protein, protein/DNA, and protein/drug interactions. A long-term goal of systems biology is to generate tailored medicine to each individual personal needs.
I know that a lot of people look at systems biology with raised eyebrows because it seems futile to try to model a cell, organism or ecosystem with N differential equations and the accompanying massive set of seemingly arbitrary parameter values. These concerns might stem in part from the fact that many biologists do not have formal traning in the computational approaches used by complex systems analysts. Aderem suggests that current academic achievement and funding practices should loosen up so as to allow increased cooperation between engineers, computer modelers and biologists.
Personally, I would want to be able to play around with a model myself, and at least be able to loosely interpret the computational approaches used in a paper. My sense is that the same feeling is shared by most biologists with weak mathematical backgrounds; to get systems approaches to biology to really catch on, computational biology (software and publications) will have to be easily interpretable and maleable by experimental biologists. For the PI/post-grad/doc sector, this might require e.g. week-long intensive training courses to get people familiar with complex systems analysis, and perhaps training for up-and-coming biologists in differential equations, linear algebra and computer modeling.
Four interesting commentaries have come out in Cell 121:4 about the nascent "systems biology" field.
Marc Kirschner's "The meaning of systems biology" offers historical context for this new branch of biology, and whether it is really that different from molecular biology. He points out how this new field aims to parallel the paradigm of prediction, model and experimentation used by physicists. He suggests that a precise definition of systems biology is difficult to pin down because the field is just emerging, and that a precise definition "should await the words of the first great modern systems biologist." In an interesting comment on biology in general, he says systems biology "differs from physics in that the primary task is to understand how biology generates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name."
Alan Aderem's perspective of systems biology speaks to the ideas of complex systems theory - that the phenotypic behavior exhibited by a biological system is an emergent property of the interactions of its constituent parts. He introduces a new term (to me, at least) "network modeling by iterative refinement", and a nice diagram showing how high-throughput "omics" feeds computational models, analysis and further experimentation. He focuses on the need to translate this information into interpretable visual representations and to allow biologists everywhere to access the information. There are technical challenges to high-throughput data acquisition such as how to measure response concentrations. An interesting prediction: nanotechnology will allow us to measure parametric values, such as the forces and kinetics associated with protein/protein, protein/DNA, and protein/drug interactions. A long-term goal of systems biology is to generate tailored medicine to each individual personal needs.
I know that a lot of people look at systems biology with raised eyebrows because it seems futile to try to model a cell, organism or ecosystem with N differential equations and the accompanying massive set of seemingly arbitrary parameter values. These concerns might stem in part from the fact that many biologists do not have formal traning in the computational approaches used by complex systems analysts. Aderem suggests that current academic achievement and funding practices should loosen up so as to allow increased cooperation between engineers, computer modelers and biologists.
Personally, I would want to be able to play around with a model myself, and at least be able to loosely interpret the computational approaches used in a paper. My sense is that the same feeling is shared by most biologists with weak mathematical backgrounds; to get systems approaches to biology to really catch on, computational biology (software and publications) will have to be easily interpretable and maleable by experimental biologists. For the PI/post-grad/doc sector, this might require e.g. week-long intensive training courses to get people familiar with complex systems analysis, and perhaps training for up-and-coming biologists in differential equations, linear algebra and computer modeling.
posted by KD at 8:03 PM
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