Systems Biology’s Relevance To Human Health and Disease

Perceptive of the factors that sway human health and cause diseases are the chief driving forces of biological research. With advancement in quantitative techniques, large-scale measurement methods and with the close combination between experimental and computational approaches, Biology has lately gained new technological and conceptual tools to investigate, model, and understand living organisms at the system level. The young discipline of Systems Biology is devoted to the study of well-characterized model organisms. It is clear since the days of the human genome project that applications of system-wide approaches to human biology would open up great breaks in medicine.

Recent lessons learned from Systems Biology, when used on simple organisms like bacteria or yeast, predict the kind of understanding that will profit both basic medical research and clinical applications giving deeper appreciation of the genotype–phenotype relationship; impact of the interactions between environmental conditions and genotype; new mechanistic and functional understanding based on global unbiased approaches; explanation of potent predictive models capturing the details of physiological states, progress on these various faces clearly depend on different types of research, ranging from investigations on basic aspects of human biology to the more clinically oriented applications. Appreciably, as techniques and concepts are established, a new discipline is budding at the crossing point between Medicine and Systems Biology.

In fields pertinent to medical research, together with cancer biology, deciphering the mechanisms of disease requires a deep knowledge of how signaling the process of shuffling of genes pathways operates. Quantitative large-scale study of proteins has made possible the simultaneous monitoring of the simultaneous activity of multiple signaling molecules, enabling a broader and unbiased view of cellular signaling proceedings. This type of high-throughput screening can be correlated to biological response like proliferation and cell migration to further understanding of the pathways known to be deregulated in cancer. These approaches reveal the unavoidable fact that biological pathways are highly interrelated, which represents one of the major motivations for adopting a system-level approach in biology. The impact of plugging in on biological outcome is analyzed to explain synergies and other non-intuitive interactions observed between concurrently applied drugs, with vital outcomes for drug design and pharmacology. The concept of linear pathway is confronted by network representations, which highlight the significance of interactions between components of a biological system. This network-based conceptual framework transforms current models in disease classification and treatment. The main practical challenge is how to figure out the structure of complex networks that underlie biological processes and how to characterize their state when disturbed by disease. New calculation strategies combined with the now well-established genome-wide expression profiling techniques provide new tools to reverse-engineer network structure and to identify and track mediators associated with a disease.

In view of the fact of completion of the human genome sequence, research in human genetics has been progressing at a rapid pace. With major achievements including realization of the haplotype map project facilitating the analysis of human genetic variability, the recent flurry of genome-wide associated studies providing a host of potential genetic determinants for major common diseases and the arrival of the first personalized human genome sequences. The power of genetics and genomics to explore the human disease scenery does not need to be demonstrated any more. Beyond genetic determinants, diseases are characterized by a disturbed physiology, and methods providing a wider and deeper window into physiological states will be influential to get hold of an integrated view of human disease. By their proximity to physiological output, metabolite measurements provide such a window, and advances in the associated techniques have led to the development of the field of metabonomics (measuring and mathematically modeling changes in the levels of products of metabolism found in biological fluids and tissues), pioneered by Jeremy Nicholson. The study reveals the deep sway exerted by gut bacterial flora on the metabolic equilibrium of the host and, as a consequence, on its health status. This study demonstrates that the genotype–phenotype relationship is far from being the entire story when dealing with disease, and it emphasizes the vital significance of putting together all aspects of physiology, including contributions from the totality of microbes and environment, thus adopting an even wider scope than the genome-wide model.

Great anticipation generated by the application of high-throughput technologies to human samples is that huge information gathered can lead to more powerful models able to predict susceptibility to disease, response to treatment and even more challenging, help in the prognosis of disease outcome. It is the latter question of prognosis that is addressed in the study by MacBeath and co-workers Knickerbocker et al, 2007, this book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic, prognostic systems. It focuses on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasizing applications based on different types of “omic” data. The work also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. It imparts knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. There are also commentaries from guest experts containing detailed discussions of methodologies and applications based on specific types of “omic” data, as well as their integration. It also covers the main range of data sources currently used for biomarker discovery. It deals with the main range of data sources currently used for biomarker discovery. It emphasizes on concepts, design principles and methodologies that can be extended or tailored to more specific applications. It also offers principles and methods for assessing the bioinformatic-biostatistic limitations, strengths and challenges in biomarker discovery studies. The study discusses systems biology approaches and applications. The work includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations allowing integration of clinical parameters with protein microarray measurements of blood samples permitting improved prediction of early mortality of patients initiating a kidney dialysis treatment. Wider application of these technologies is likely to be instrumental in opening the door to the era of personalized medicine with tailored strategies encircling all aspects of clinical practice, including prevention, diagnosis, treatment and prognosis.

Interpreting the Systems Biology framework to the human ‘system’ is a formidable challenge because of the intimidating intricacy of human physiology and also because the human condition involve serious consideration of ethical, legal, safety, individual and epidemiological issues. Revolutionary technologies, fresh insights, immense digitalization of information will entitle clear thinking and innovation in the formulation of governance policies. These excerpts of recent concrete contribution to the field stimulates reflections and debates, extending beyond the Systems Biology community, enabling to realize full potential and promises of Systems Medicine in harmony with societal standards.

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One thought on “Systems Biology’s Relevance To Human Health and Disease

  1. Pingback: Transitioning Systems Biology To Systems Medicine | Behavioral Medicine

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