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Hood et al. (2004): Systems biology and new technologies enable predictive and preventative medicine
This paper deals with "disease-perturbed protein and gene regulatory networks", which "differ from their normal counterparts" in "multiparameter measurements of the blood".
"A model of a metabolic process (galactose utilization) in yeast" was implemented, which "provided new insights into the control of a metabolic process and its interactions with other cellular processes" and "also suggested several concepts for systems approaches to human disease".
If gene expression in diseased tissues also reveals patterns characteristic of pathologic, genetic, or environmental changes that are, in turn, reflected in the pattern of secreted proteins in the blood, then perhaps blood could serve as a diagnostic window for disease analysis.
The paper also deals with cancer (in particular prostate cancer), about which the authors write:
It is becoming clear from our research that the evolving states of prostate cancer are reflected in dynamically changing expression patterns of the genes and proteins within the diseased cells.
In order to construct a systems biology network model, "a comprehensive expressed-mRNA database on the cell type of interest" was built. For this, the authors "used a technology called multiple parallel signature sequencing (MPSS) to sequence a complementary DNA (cDNA) library at a rate of a million sequences in a single run and to detect mRNA transcripts down to one or a few copies per cell". The authors further write:
By comparing the prostate database with a tissue-wide database of 58 million MPSS signatures from 29 normal tissues from Lynx Therapeutics, about 300 prostate-specific genes were identified, approximately 60 of which possessed signal peptides, suggesting that they may be secreted. [...] Given enough measurements, one can presumably identify distinct patterns for each of the distinct types of a particular cancer, the various stages in the progression of each disease type, the partition of the disease into categories defined by critical therapeutic targets, and the measurement of how drugs alter the disease patterns. The fascinating question is how many parameters need to be measured in order to stratify and follow the progression of various prostate cancers, or to stratify and follow the progression of the most frequent 20 or 30 cancers, or eventually the most common diseases. Finally, changes in the tissue-specific markers might identify critical points within the network. It is the key nodal points within these perturbed networks that may be affected by drugs, either to convert the diseased network back toward normalcy or to permit the specific killing of the diseased cells.
According to the authors, "[t]he systems biology approach toward constructing a predictive network model of a metabolic process in yeast required approximately 10^5 measurements" and the prostate cancer example 10^8 measurements.
However, for constructing a predictive model of human disease, methods that can address the heterogeneity that characterizes biology - from the differences in how individual cells respond to environmental perturbations, to the diversity of cell types and environments within real tissues - will be critical. [...] Various investigators have used cell sorting, manual dissection, or laser capture microdissection (LCM) to obtain relatively homogeneous populations of cells. However, cell sorting and LCM themselves may cause processing-induced changes in gene expression, and manual microdissection rarely provides completely homogeneous cell types.
A "powerful new technology" is multilayer elastomer microfluidics:
[It] allows for the integration of many pumps, valves, and channels within an easily fabricated microchip. This means that multiple operations, such as cell sorting, DNA purification, and single-cell gene expression profiling, can be executed in parallel. [...] It is likely that within the next couple of years, miniaturized and automated microfluidics/nanotech platforms that integrate operations such as cell sorting and serum purification with measurements of 5 to 10 biomarkers from single cells or very small fluid volumes will emerge. New measurement types, such as quantifying the forces associated with protein/protein, protein/DNA, and protein/drug interactions, are possible.
According to the authors, "[t]he challenge is to reduce the large numbers of elements delineated in the network analyses to one of a few targets of molecular imaging biomarkers that can provide critical tests of the network".
Ultimately, medicine will move from being reactive towards being preventive:
Preventive medicine will follow as disease-perturbed networks can be used to identify drug targets - first for therapy and later for prevention. Pharmacological intervention will focus on preventing disease-mediated transitions, as well as reversing or terminating those that have occurred. This will require building a fundamental understanding of the systems biology that underlies normal biological and pathological processes, and the development of new technologies that will be required to achieve this goal. Predictive and preventative medicine will lead naturally to a personalized medicine that will revolutionize health care.
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