Systems Biomedicine: Concepts and Perspectives is a new book exploring systems complexity in biomedical research, authored by Edison Liu, a member of the A*STAR editorial board and executive director of the Genome Institute of Singapore, and his colleague, Douglas Lauffenburger, professor of the Massachusetts Institute of Technology.
Foundations for systems biomedicine
Systems Biomedicine: Concepts and Perspectives, an introduction by Edison Liu.
Quantitative biology, mathematical biology and mathematical modeling have all been part of biological investigations in one form or another since the beginnings of investigative biology and medicine. Carl Linnaeus’s creation of binary nomenclature1 in 1735 marks the origin of biologic taxonomy and provides the basis for phylogenic analysis.
Mathematical analysis in biology is also apparent in William Harvey’s 1847 model of blood circulation2. Harvey quantified the amount of blood in the chambers of the heart and calculated the output of the heart by multiplying the volume by the number of heartbeats per day. In doing so, he noted that the output differed wildly from the volume of blood in an individual at any one time. With this information, he developed a model of circulating blood that explains the blood volume discrepancies with supporting evidence from the anatomic presence of valves in veins. The mathematical tradition in biology therefore, runs long and deep.
Systems biology, as we now conceive of it, differs in scale and formalism from these earlier quantitative traditions. As any new field, there are many opinions as to the scope of systems biology. In essence, it can be described as a discipline that seeks to quantify and annotate the complexity of biological systems in order to construct algorithmic models capable of predicting outcomes from component inputs. Systems biomedicine is an extension of these strategies to the study of biomedical problems. This demarcation is relevant given the challenges of the complexity of the human organism and the human impact of the results of these investigations.
The above definition of systems biomedicine highlights the difference between quantitative data acquisition and systems biology. The scale of data acquisition in biology is unparalleled in history. Analog and descriptive data such as cellular images are now digitalized and converted to discrete data points, and genomic and proteomic scale information is registered in gigabyte quantities for each experiment. This reality demands formal mathematical and algorithmic conversion of experimental data in biology in order for the data to be understood by the investigator. The interposition of computers and their algorithms as an essential part of biological research immediately places, at least, a rudimentary mathematical formalism around all experiments conducted in this fashion.
Although measuring outcomes is standard in day-to-day biological experiments, the quantitative approaches to systems biology do not scale. While detailed biochemical kinetics can be calculated for a single biochemical reaction, most commonly, researchers have tended to resort to descriptive generalizations when ascending to physiological scales. With modern technologies capable of acquiring precise, comprehensive and quantitative data, biological complexity can now be analyzed quantitatively. The challenge, however, is to identify the optimal mathematical approaches most suited for this scale and complexity of analysis.
Developing the tools for biological analysis
Our understanding of the cell and molecular biology of human disease has advanced dramatically in the last 25 years. Whereas the pathophysiology of most human diseases was previously limited to the analysis of organ failure, most diseases now have a cellular and molecular explanation. It is precisely this reduction to common units of measure—to the cell and the molecules within the cell—that allows systems analyses to be applied across the entirety of the human condition. The pump dynamics of the heart after myocardial infarction can thus be resolved at the same level as pancreatic beta-cell function in diabetes mellitus. There is convergence.
Modern systems biology includes two important new characteristics that distinguish it from historical physiology and mathematical biology. First, there is a focus on complexity, and second, the fundamental unit of study resides in the DNA (and, by association, protein) sequence. With the nucleotide as the unit of measure, we now have the lingua franca that permits direct translation of experimental results obtained through studies in fields from biochemistry to cell biology, physiology and even population genetics. Ultra-high throughput and multiplex genomic technologies allow for the digitalization of experimental data of such precision and comprehensiveness that the true complexity of a biological system can actually be measured and dissected.
In all aspects—biological and mathematical—the greatest advance has been the availability of computational capabilities that can match the complexity of the systems under study. The reliance on these genomic and computational technologies, and on datasets that can be transmuted across species, has significantly broadened the applicability of systems approaches to very complex problems in human medicine. These advances now allow us to critically analyze the ensemble of emergent properties of much greater function than the component parts.
Early on, geneticists defined emergent properties in the phenotypic interactions among genes or alleles as ‘epistasis’3. In many such interactions, new properties arise: two white flowers that when crossed give a purple flower; or two genes, when mutated individually give no phenotype, but when mutated together show a lethal outcome. Systems biology examines the sum of all epistatic relationships with the intent of uncovering the hierarchy. This indeed has been the direction of this line of genetic research. Tong and co-workers4 crossed mutations in 132 ‘query’ genes into a set of 4,700 viable yeast gene deletion mutants to develop a genetic interaction map containing over 4,000 functional gene interactions. Thus, classical genetics converges with systems biology.
Systems biomedicine is the analysis of medical problems using systems approaches. Pertinence to the human condition is therefore a prerequisite. It has been said5 that biology asks six kinds of questions: How is it built? How does it work? How did it begin? What is it for? What goes wrong? How is it fixed? The last two questions are more in the domain of medicine, and it is to these that systems biomedicine applies, focusing not only on human biology but also on human disease. Exemplifying this approach are efforts to examine perturbations in gene and protein networks for clues to disease etiology.
Of course, in the final analysis, systems biomedicine, by directly benefiting human health, will be a significant endeavor. Any incremental improvement in prediction will help medicine and benefit society. The challenges, however, are logistical, computational and organizational. Logistical because first, for obvious ethical reasons, experimentation in human systems is slower and more ponderous; second, human variation will make initial estimates less generalizable; and third, the further division into organ systems linked by circulation and endocrine factors will increase the number of studies needed to complete the human organism. The computational challenges are most critical to this effort, generating massive amounts of data requiring integration and iterative analysis of high computational complexity.
Daunting as these challenges may be, the stakes are high. Systems approaches in biology will become as common as molecular technologies in biological investigations. Molecular biology, which was itself a new creature in the 1970s and early 1980s, and which spawned biotechnology companies and institutes and departments bearing the title of ‘molecular biology’, is now commonplace and integrated into the fabric of biological teachings. Current medical investigations are all forms of molecular medicine. The same will soon hold true for systems approaches.
Systems biomedicine, indeed, is here to stay.