The role of analytical sciences in medical systems biology
Introduction
Interest in the application of systems biology to the life sciences has become widespread over the past five years, although systems perspectives have been applied to many sciences, ranging from biology to cosmology, since at least the beginning of the 20th century. There is a striking commonality between various scientific domains, which each describe the interconnectivity and interdependence of the components of the system and which each recognize that new properties of systems are revealed as increasing levels of complexity are studied. The traditional approach in biology of relating limited observational data to a holistic model is now being complemented with a new wave of life science technologies that are beginning to yield detailed molecular and mechanistic information on an unprecedented scale 1., 2., 3..
The definition of systems biology is highly variable in the literature. Our working definition for medical systems biology is ‘studying biology as an integrated system of genetic, protein, metabolite, cellular and pathway events that are in flux and interdependent’. This does not imply that all elements need to be measured for systems studies but that systems thinking and systems-based analytical strategies are key. For medical systems biology, human samples are often restricted to body fluids and consequently pathway relationships between the components may not be revealed without further study, in contrast to the study of animal models or single cell types where informative pathway information can be gained at an early stage of analysis.
Systems biology requires the integration of biology, medicine, mathematics and chemistry with biostatistics and bioinformatics to transform complex and diverse datasets into useful knowledge 4., 5., 6., 7., 8., 9.••, and systems biology approaches are being increasingly applied in the fields of microbiology, and plant and medical sciences. We focus here on analytical sciences in medical systems biology; in particular, the status and challenges for metabolomics and proteomics. Data pre-processing before the important steps of biostatistics, bioinformatics and modeling is briefly considered and notable references for data evaluation and integration steps are given.
An important principal for the discovery of biomarkers for drug discovery, drug development and disease diagnosis 10.••, 11.••, 12.••, 13., 14. is that multi-factorial disease (Figure 1) involves studying complex and dynamic biomarker patterns rather than a single biomarker such as cholesterol, prostate-specific antigen or glucose. Although this evolution from single biomarker strategies towards those employing biomarker patterns involves managing new levels of complexity in data generation and analysis, such a change will be essential to adequately characterize and ultimately understand the etiology and progression of disease states. It is also important to recognize that different biomarker profiles are found at the onset of a disease versus the late stage where symptoms and indirect effects are prominent. Therefore understanding transitional biomarker profiles in terms of mechanism and validation is crucial.
Implicit in this assumption is that the analytical capabilities to reliably establish biomarker patterns are capable of identifying different levels of complexity that impact the practice of medicine. However, despite recently renewed interest in biomarkers, there are still critical gaps in the analytical capabilities at the levels of primary data acquisition and analysis, especially for proteomics [15••]. This review addresses critical issues confronting medical systems biology. In particular, we focus on analytical issues, describing the status of metabolomics, which has a longer history on profiling and pattern recognition [11••], followed by proteomics and data evaluation.
Section snippets
Analytical tools for metabolome analysis
Recent developments in both NMR and hyphenated MS technology have extended the crucial roles of these tools, although alternative approaches with great potential are also now emerging. Major improvements in NMR include the introduction of extremely high field magnets (currently up to 900 MHz), cryoprobe technology and the measurement of microsamples. When using the cryoprobe in a high field NMR (800 MHz) signal-to-noise ratios are improved by a factor of 15–20, as compared with a standard probe
Challenges in developing analytical tools for proteome analysis
Despite the crucial role that proteomics [38] will play in medical systems biology, the field currently faces huge technical challenges that are a consequence of the complex, dynamic, idiosyncratic and largely uncharacterized proteome present in most samples of human fluids, cells or tissues. Authoritative reviews 15.••, 39.••, 40. summarizing the situation for the analysis of human plasma and serum illustrate the magnitude of the problems. After extensive recent efforts to evolve better
From data to information and knowledge
The avalanche of data resulting from applying ‘-omics’ technologies to complex biological systems requires several crucial steps before integration to a systems level of understanding. Data pre-processing is critical to success in this area, the first issue being the peak (variable) alignment. A significant problem in MS and NMR measurements comes from the fact that the position of a peak has a measurement error and/or a shift in position as seen in the NMR dependency on the chemical
Conclusions and future perspectives
The rapid ongoing technological improvements in the fields of MS and NMR, in which instruments with higher resolution and sensitivity become available, will continue to improve the coverage of the metabolome and proteome from a chemical diversity, coverage and concentration perspective. The optimization of sample preparation and separation methods in hyphenated strategies as well as miniaturization to allow smaller sample volumes or to cover single cell (nano) systems biology studies [63], will
References and recommended reading
Papers of particular interest, published within the annual period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgement
This study was supported by the Centre for Medical Systems Biology (CMSB), a centre of excellence approved by the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research (NWO).
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