Medical Applications of Systems Biology

Systems Medicine is the application of systems biology approaches to medical research and medical practice. Its objective is to integrate a variety of biological and medical data at all relevant levels of cellular organization using the power of computational and mathematical modeling, to enable understanding of the pathophysiological mechanisms, prognosis, diagnosis and treatment of disease.

Medical applications of systems biology is now made possible by identification of preliminary circadian biomarkers for more efficient and personalized drug treatments in cancer patients, mechanistic insights into Creutzfeldt-Jakob disease and identification of new therapies for more effective treatment of psychiatric disorders and understanding of energy metabolism in Parkinson’s disease and modeling of risk factors and better understanding the pathology. The major challenge is how systems biology can contribute to change the medical paradigm in order to build the foundation for a prospective medicine that will be predictive, personalized, preventive and participatory.

Currently there exists substantial detailed data on single biological entities associated with or linked to complex diseases. We can understand single components and, possibly for several of them, their function, but these are only parts of a complex system. Systems biology approaches have so far been successful in plants, animal, cellular models. Studies are accumulating on interacting network analysis of basic biological mechanisms, as well as on analysis of cell-cell interactions to understand behavior of cell populations. The applications of systems biology approaches are already delivering computational and mathematical models of basic biological pathways related to diseases in-vivo in animal models. The technology and tools, in particularly high-throughput methods for genome-wide analysis are now available to apply systems biology in patients. Major and chronic diseases are multifactorial in nature and reductionist biology will not provide solutions. Human disease can be perceived as perturbations of complex, integrated genetic, molecular and cellular networks. Such complexity necessitates the power of mathematical modeling in predicting a systems response in health and disease. The applications of systems biology approaches permit a comprehensive evaluation of underlying predisposition to disease, disease diagnosis and progression.

Success stories for medical applications are starting to appear in the literature. The first examples are at the edge of moving into clinical applications; some examples include: EGF-receptor system stratification in breast cancer; heart modeling – approval by U S Food and Drug Administration of the use of a model to test new cardiac drugs; Entelos has created “virtual asthma patients”; a model of Creutzfeldt-Jakob disease explaining the pathogenesis and disease progression, suggesting new and more effective treatments for psychiatric disorders; chronobiological modeling of drug effects that could improve the efficacy of cancer chemotherapy. The high quality and conclusive systems biology studies of human clinical samples are still rare and that a concrete impact of systems biology applications for patients is still to be proven.

Several areas, for example, drug discovery, where systems biology approaches can bring an important contribution as a paradigm for understanding complexity. Despite the extraordinary advances in biomedical research over the past decade, the positive effect of these efforts on drug discovery and the identification of new, more effective therapies have been limited, possibly because of the inability to visualize the complexity of biological systems. There is little or no information on cellular-system interactions and there is a necessity to understand target response within physiological networks and not in isolation. A vast amount of data is being generated from clinical trials and from individual patients and it is becoming increasingly difficult to piece together this data and to maximize its content. Systems medicine should provide a framework within which such disparate data can be integrated, so that medicine will move from a “guess & pray” mentality to “predict & test” strategies.

However, it is necessary that systems biology efforts world-wide deliver solid experimental evidence with clear added value and societal benefit. An important ingredient for developing Systems Medicine is coordination – a coordinated approach, across disciplines, and across academia and industry. Furthermore, the systems biology community should make best efforts to communicate correctly its goals to the general public and to policy makers, remaining ambitious, but pragmatic and tangible and not overpromising what might be achieved, while considering all potential ethical aspects. There is need for coordination of efforts of all relevant actors to take the first steps towards systems medicine and make a paradigm shift in classical medicine. The endeavor should have a scale of ambition and vision similar to that of the Human Genome project. Creation of a strong networking effort among the funded systems biology projects, in order to share resources on successful methodological approaches and tools with the broader systems biology and clinical community; there is a need to build on already existing systems biology initiatives without re-inventing. There is need for establishment of proof-of-concept for medical applications in order to attract industry into the area. Most importantly, the relevant clinical needs should be the driving force to pave the way for systems medicine.

The importance of formulating the right questions in systems biology is important. Systems biology is neither data gathering, nor solely predictive modeling but about a ‘systems’ understanding of biological systems in health and disease. The challenge is integration of complex data, across time, space and different organizational levels, without forgetting interactions with the external environment. Also fundamental is comparison across different experimental systems. Clinicians should have an overview of the new technologies that have enabled systems biology from – omics that detail our genome, transcriptome, metabolome and proteome to advanced imaging techniques in biology and medicine. The challenge lies in transforming the data produced by the new technologies into new knowledge in biological and medical sciences. This is achieved by common questions that need to be formulated together by researchers and medical doctors. In addition, short and long term goals need to be addressed. One has to identify opportunities for the future and the bottlenecks for systems medicine to become a reality and challenges ahead if systems biology would be applied to medicine.


Transitioning Systems Biology To Systems Medicine

Systems medicine is the application of systems biology approaches to medical research and medical practice. Its objective is to integrate a variety of biological and medical data at all relevant levels of cellular organization using the power of computational and mathematical modeling, to enable understanding of the pathophysiological mechanisms, prognosis, diagnosis and treatment of disease.

The clinical needs should be the driver for the applications of systems biology methods in medicine and for the evolution of the essential new technologies. The possible actions required are, systems biology approaches could guide clinical trial design, shortening times and costs. Re-defining clinical phenotypes based on molecular and dynamic parameters, discovering effective biomarkers of multiple nature for disease progression; clinically useful for risk, prognosis, diagnosis. Combinatorial therapy approach would be useful to find out a combination and lower doses of effective drugs, in particular in the case of co-morbidity, where more than one disease is affecting the patient, upgrading of drug development; optimizing drug efficacy, safety and delivery, timing and dosage of therapy. Finally, healthy individual are to be addressed in the long term.

Scientific areas for partnership in Systems Medicine includes understanding the pathophysiology of chronic diseases, multifactorial diseases like cancer, diabetes, obesity, metabolic disorders, aging through network analysis of disease processes, and the recognition of biomarkers for early diagnosis and prognosis and personalized treatment, combinatorial therapies and combinatorial drug screening and mixing of personalized genomics with personalized metabolomics, endocrinomics, proteomics and clinical phenotyping.

The major confrontation is for systems biology to furnish a change in the medical model in order to build the foundation for a prospective medicine that will be predictive, personalized, preventive and participatory. In order for systems medicine to become a reality, one needs coordinated vision of all relevant stakeholders and a field guide at the same level of ambition as the Human Genome project. In addition, the creation of a strong networking effort among funded systems biology projects is essential, in order to share information and resources on successful methodological approaches and tools with the broader systems biology and clinical community.

Recent years have seen the rapid emergence of systems biology as a new discipline. In the biomedical sciences, this trend is very apparent as research moves from a reductionist approach to a systems understanding model that attempts to understand biology and pathophysiology in an integrative manner, making use of the rapidly increasing amounts of novel (-omics) data and other relevant quantitative biological and medical data that are becoming available.

However, despite the spectacular advances in the post-genomic era, there exists a hiatus between experimental data and medical knowledge, and even a greater gap exists when we evaluate new knowledge in terms of clinical utility and benefit to patients. As a result, despite major technological advances, there are still obstacles that separate systems biology from medical applications. Systems medicine, a newly emerging area should aim the bridging of this gap.

Experts in a wide range of relevant disciplines from clinical, diagnostics and pharmaceutical areas, to high throughput –omics technologies, and computational and systems biology, including representatives from academia, industry, and funding agencies should get together to explore opportunities and challenges for the development of systems medicine. The aims are to analyze the state-of-the-art of systems biology for medical applications, identify key opportunities and bottlenecks for the translation of systems biology to medicine and the clinic, and identifying key research and policy areas for joint research in the short, medium and long term in order to make systems medicine a reality.

Medical Genomic Sequencing

Genomic sequencing has huge impact on the field of medicine. Till date, cost and throughput limitations have made general clinical applications infeasible. At present, though, the price of about 5000 United States Dollars for a normal human genome sequence, exclusive of analysis, and fast throughput; several days to a few weeks, is fast making medical sequencing practical. The high-throughput sequencing is used to help diagnose highly genetically heterogeneous disorders, such as X-linked intellectual disability, congenital disorders of glycosylation and congenital muscular dystrophies; to detect carrier status for rare genetic disorders; and to provide less-invasive detection of fetal aneuploidy through the sequencing of free fetal DNA.

While this is a hopeful start for high-throughput sequencing in the clinic, scientists believe these technologies should be used with care as they have non-negligible false-positive and false-negative rates due to sequencing errors and amplification biases, which need to be improved upon with best library construction methods, enhanced sequencing technologies or filtering algorithms. On the other hand, medical sequencing could possibly be useful in a wide range of settings in the near future. The main areas are cancer, hard-to-diagnose diseases and personalized medicine.

Cancer is a genetic disease, both in predisposition and somatic growth. High-throughput sequencing of cancer genomes has been a major factor in the understanding of the genetics of this complex disease. Exome sequencing (targeted exome capture; strategy to selectively sequence the coding regions of the genome as a cheaper but still effective alternative to whole genome sequencing), RNA sequencing, paired-end sequencing and whole-genome sequencing of cancer genomes have led to a dramatic increase in the number of known recurrent somatic alterations, such as mutations, amplifications, deletions and translocations.

Studies have revealed many interesting findings. As a recent example, using paired-end sequencing, Koichiro Inaki, Axel M. Hillmer and group and co-researchers in 2011 discovered that approximately half of all structural rearrangements in breast cancer genomes result in fusion transcripts, where single segmental tandem duplication spanning multiple genes is a major source. They estimated that 44% of these fusion transcripts are potentially translated, and found a novel RPS6KB1–VMP1 fusion gene that is recurrent in a third of breast cancer samples analyzed, with potential association with prognosis. Simultaneously, Axel Hillmer, PhD, and group of The Genome Institute of Singapore in 2011 applied paired-end sequencing on cancer and non-cancer human genomes, and found that non-cancer genomes contain more inversion, deletions and insertions, whereas cancer genomes are dominated by duplications, translocations and complex rearrangements. Recent works from group of Jan Korbel found that cancer genomes lacking p53 often contain genomic regions that undergo extensive rearrangements called ‘chromothripsis’ (tens to hundreds of chromosomal rearrangements occur in a one-off cellular crisis. Cancer is driven by somatically acquired point mutations and chromosomal rearrangements, conventionally thought to accumulate gradually over time. In chromothripsis, rearrangements involving one or a few chromosomes crisscross back and forth across involved regions, generating frequent oscillations between two copy number states. These genomic hallmarks are highly improbable if rearrangements accumulate over time and instead imply that nearly all occur during a single cellular catastrophe) suggestive of complex chromosome shattering and rejoining in a single event. Much work has also been done on matched tumor–normal pairs and revealed that extensive somatic single nucleotide variants and structural variants occur in cancer genomes.

One important medical conclusion that has emerged from this work is that every tumor is genetically different but that common pathways are often activated. Thus, the sequencing of cancer genomes can help reveal the activated pathways and the information used to suggest therapeutic treatments. For example, the detection of novel fusion transcripts in a difficult diagnostic case of acute promyelocytic leukemia that were previously missed in a regular diagnosis was used to influence the medical care of the patient by John S. Welch, MD, PhD; and colleagues in 2011. In addition, sequencing of carefully selected samples could lead to interesting discoveries of cancer evolution and mutational processes.

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.

Human Biology, Health, and Society

Biology is an important factor for health and illness in society because of the fact that some of the diseases humans are afflicted are passed down through our genes. A mother’s mitochondria play a role in genetics and disease; mothers can pass down a disease to their baby. Also, it is known that if one of the parents is a carrier of a particular disease, the child is likely to be affected by the same disease. The likelihood of the child contracting a disease from one or both of his/her parents is significantly increased when both of the child’s parents are carriers of a particular disease. Biology is used to determine the risk of an individual having a disease. When we know the probability of an individual having a disease, we can better assess their well-being because of the fact that we are able to make a diagnosis and, hopefully, to alleviate the condition using medications or other modalities of treatments.

Biology is also used to analyze how bacteria change over time, for example, becoming transmittable to new species, or antibiotic resistant. It can also be used to help track outbreaks of certain diseases. Promoting health and reducing the risk of disease in the United States and other countries require that practitioners, researchers, and policy makers consider not only the biological and physical aspects of health and illness but also the social, psychological, economic, cultural, and political dimensions.

Many health problems are complex in origin and require that experts with different talents and perspectives work together and with the affected individuals and communities to understand the problems, propose solutions, and take steps to reduce health risks. Advances in the understanding of health risks and the dramatic changes in the management of health problems in the U.S. have caused the roles and responsibilities of health professionals to change dramatically. Those wishing to pursue a health-related career must be prepared to work in this new and ever-changing environment.

The Human Biology, Health, and Society program in the Division of Nutritional Sciences helps one to view human health issues from a broad and multidisciplinary perspective. One is required to develop a strong background in human biology so that they can understand the physiological and biochemical aspects of health issues. One is also required to use perspectives from both the biological sciences and the social sciences to examine health issues. We can select the issues we wish to investigate through the wide array of studies related to human health and well-being in the different departments of the College of Human Ecology.

Some of the issues to explore include:

What physiological and biochemical processes are involved in health and necessary for resistance to disease?

What is normal growth of children and what biological, social, cultural and environmental factors are involved?

How do biological processes explain normal and abnormal behavior?

How do diet and other lifestyle factors influence the risk of chronic disease?

What social, political, economic, and cultural factors explain the differential access to health care in the US and how can this situation be changed?

How can communities, organizations, and practitioners work to promote health in the US and other countries?

What can be done to reduce disease and promote quality of life for older Americans?