Medicinal research is the best funded area in biology, with large inputs of money from pharmaceutical companies as well as from research funding bodies. For longer than anyone can remember, "a reductionist focus has been the Holy Grail" in biological research. This has achieved results: "In medicine, tremendous scientific advancements have been made in understanding diseases from a reductionistic viewpoint." However, it appears that a turning point has arrived. "The success formula built on reductionism seems to have reached its limits and the pharmaceutical industry faces enormous challenges with productivity decreasing and development costs rapidly increasing."
Where next? Do we need a bolt-on to reductionism (so that reductionism is part of the solution), or do we conclude that reductionism is part of the problem and that a different methodology is needed?
The authors of the paper abstracted below have come to the view that Systems Biology is that "new strategic tool in Life Sciences". Systems thinking is a move to holism rather than reductionism. It is thus a complete change of direction, not an add-on.
"Understanding biology requires knowledge of connectivity in systems and their self-organization. [. . .] the need to map patterns of relationship is surfacing, as demonstrated in mammalian systems based on de novo measurements across transcriptomics, proteomics, and metabolomics. To achieve this crucial understanding of complex relationships in an intact system, there is a great need for reliable, high-quality experimental data beyond the cellular level."
"The most important step forward however is the paradigm shift needed to become a system thinking organization. [. . .] Such a paradigm shift will occur when a critical mass of global intelligence has embraced this concept of systems thinking."
All this is very interesting reading for Intelligent Design advocates. The philosophy of reductionism has long been identified as a negative influence when it ceases to be a tool and becomes a statement about the nature of reality. ID scholars have championed the cell, rather than DNA, as the most basic unit of living things. The ID approach is to see the importance of systems, whether they be complex specified systems or irreducibly complex systems. This is a real point of contact with systems biology: there is much common ground and this is to be welcomed. The methodologies being developed within systems biology are methodologies perfectly consistent with those emerging via design inferences.
The authors realise that a paradigm shift is urgently needed. Perhaps this is an area where funding agencies and employers will realise that ID biologists can assist with the development of systems thinking and will come to regard ID as an ally and an asset.
The Art and Practice of Systems Biology in Medicine: Mapping Patterns of Relationships
J. van der Greef, S. Martin, P. Juhasz, A. Adourian, T. Plasterer, E. R. Verheij, and R. N. McBurney
Journal of Proteome Research, ASAP Article 10.1021/pr0606530 S1535-3893(06)00653-1
Abstract: Systems biology has developed in recent years from a technology-driven enterprise to a new strategic tool in Life Sciences, particularly for innovative drug discovery and drug development. Combining the ultimate in systems phenotyping with in-depth investigations of biomolecular mechanisms will enable a revolution in our understanding of disease pathology and will advance translational medicine, combination therapies, integrative medicine, and personalized medicine. A prerequisite for deriving the benefits of such a systems approach is a reliable and well-validated bioanalytical platform across complementary measurement modalities, especially transcriptomics, proteomics, and metabolomics, that operates in concert with a megavariate integrative biostatistical/bioinformatics platform. The applicable bioanalytical methodologies must undergo an intense development trajectory to reach an optimal level of reliable performance and quantitative reproducibility in daily practice. Moreover, to generate such enabling systems information, it is essential to design experiments based on an understanding of the complexity and statistical characteristics of the large data sets created. Novel insights into biology and system science can be obtained by evaluating the molecular connectivity within a system through correlation networks, by monitoring the dynamics of a system, or by measuring the system responses to perturbations such as drug administration or challenge tests. In addition, cross-compartment communication and control/feed-back mechanisms can be studied via correlation network analyses. All these data analyses depend critically upon the generation of high-quality bioanalytical platform data sets. The emphasis of this paper is on the characteristics of a bioanalytical platform that we have developed to generate such data sets. The broad applicability of Systems Biology in pharmaceutical research and development is discussed with examples in disease biomarker research, in pharmacology using system response monitoring, and in cross-compartment system toxicology assessment.
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