Systems biology promises to transform how biology is done—away from a reductionist focus on a limited number of molecular components to a comprehensive understanding of how large numbers of interrelated components of a system comprise modules or networks whose functional properties emerge as definable phenotypes. The benefits of systems biology can be classified into three broad areas:

1. technology development
2. advances in basic concepts of biology
3. real-world practical applications such as predictive and preventive medicine

Technology development: Cutting edge biology drives the invention and development of new technologies that, in turn, expand the scope of what biologists are able to discover. Systems biology has been made possible by technological advances in large-scale data collection strategies, computational power for managing data, and mathematical/statistical/modeling theories. Given that success breeds more of the same, it is anticipated that systems biology will continue to spur technology development in new and, quite possibly, unanticipated ways.

For example, the need to process minute quantities of large numbers of samples on which thousands of measurements can be made simultaneously is motivating the invention of nanotechnology and microfluidic devices. The ISB is collaborating with scientists at Caltech and UCLA to develop these devices and apply them to disease marker diagnostic applications within the context of a new organization called the NanoSystems Biology Alliance. A technology for successfully measuring a large panel of protein biomarkers has to be able to:

• quantitatively sense over a fairly broad dynamic range
• scale to thousands of measurements or more
• pass tests of sensitivity and specificity (i.e, avoid false negatives and false positives)
• be coupled with a computational platform that can extract meaningful results out of a multiparameter analysis
• be able to deal effectively with small amounts of sample
• be automated, fast, and straightforward to use and manufacture

Figuring out WHAT to measure (i.e., determining what proteins serve as biomarkers for what disease processes) and HOW to perform the measurements (i.e., identify the protein and measure its concentration, sort out signal/noise processing issues) constitute significant challenges which, when they are met, will result in an array of spin-off companies that will supply the devices for diagnosis of cancers, infectious diseases, and neurodegenerative processes, ideally before the diseases manifest as observable symptoms.

DNA sequencing is another area in which technological advances are likely. The National Human Genome Research Institute has requested proposals for developing high-throughput, inexpensive technologies for sequencing entitled “REVOLUTIONARY GENOME SEQUENCING TECHNOLOGIES — THE $1000 GENOME”. De novo sequencing is needed to decipher the “parts lists” for the vast number of infectious microbes, and agriculturally important crops and livestock that impact our daily lives. Rapid resequencing technologies are required for large-scale genetic variation studies. Being able to read one’s own genome will ultimately be required for personalized medicine—family history, though helpful, is not sufficiently specific.

Unless computational approaches to modeling and simulation are improved, we will be overwhelmed with data we are unable to productively interpret. Protein fingerprints obtained off a nanosenser will be reflective of the interrelated behaviors of the gene-protein-metabolic networks of the cell types from which the fingerprints were derived. Multivariate statistical modeling approaches, with appropriate classifiers (e.g., diseased vs. Normal) must be developed. But the process of designing a classifier without explicit knowledge of the data generation models is a challenging task. For example, the training sets that are used to define the classifiers might not be representative of, or generalizable to, larger populations.

Fortunately, the problems of systems biology are seen as exciting to mathematicians and network theorists, so there are grounds for optimism that the required computational breakthroughs will be forthcoming. Such breakthroughs might be applied to drug-testing—that is, eliminating drug candidates through simulation experiments might reduce the need for laboratory animals and improve the efficiency and the effectiveness of clinical trials.

Advances in basic biological concepts: While of extreme interest, Homo sapiens is a poor model system because humans are highly complex and not a proper subject for systematic systems biology experimentation except through cell lines. Model organisms such as yeast, are popular with systems biologists because of the incredible power they have to reveal fundamental molecular mechanisms, and because of the knowledge-base and set of global analysis tools that have already been established (genomic sequence, deletion mutants, microarrays, protein chips). At the cellular level, yeast has the same organelles and basic processes that human cells have, so what is learned about the regulatory circuitry and signal transduction networks that results in cellular differentiation or organelle biogenesis is expected to apply to the cells of higher organisms. The promise is that systems biology research -- iterations of model building, hypothesis formation, experimental perturbations, data collection and analysis, simulations, etc that result in accurate quantitative prediction of the behavior of the genes and proteins in the underlying networks -- will elucidate a catalogue, or “periodic chart” of modules that cells typically use to perform basic biological processes. Though these modules and networks may be complex, involving hundreds of genes and proteins, they are tractable from an experimental standpoint. Insights gained from yeast and other simple model systems as to promising gene or protein targets (e.g., hubs in a molecular interaction network) will assist with the rational design of pharmaceuticals.

Practical applications of systems biology: Systems biology’s goal is to move beyond the description of biological systems into the realm of targeted prediction and control. With a catalogue of, say, gene regulatory network modules in hand, genetically modified bacteria might be engineered to facilitate environmental clean-up of toxic wastes or increase carbon sequestration from carbon dioxide (i.e., reducing the “greenhouse” effect) or produce alternative sources of energy to replace coal and oil which will become scarce, over the coming century.

Perhaps the most excitement about systems biology lies in the area of predictive, preventive, personalized and participatory (P4 Medicine^TM) medicine. The goals of P4 Medicine include:

• Stratification of diseases and patient populations such that diagnosis is more specific and treatment more effective. ‘Cancer’ encompasses a heterogeneous collection of cellular abnormalities, and ‘depression’ a spectrum of neurochemical imbalances. Instead, diseases need to be classified in terms of the cell-type specific biological networks that are perturbed through defective genes and/or environmental stimuli.
• More rational drug design based on computer simulations of a compound’s effect on a complex protein or gene regulatory network, with the goal of improving efficacy and decreasing side effects.
• Use of genetic information to determine a person’s probable health history and blood biomarker diagnostic tests to gauge the state of a person’s health in real time. The blood will become a window into health and disease.
• Restoration of a disease-perturbed network to its normal state by genetic or pharmacological intervention, with reagents that are specifically tailored to the patient and to the diseased networks. The goal is to identify pathogenic changes in cellular networks at the earliest possible stage and, with appropriate therapy, prevent or limit the deleterious effects of a disease.

P4 Medicine has the potential to transform medicine by decreasing morbidity and mortality due to chronic diseases such as cancer, Parkinson’s and diabetes. Other applications will increase personal and communal well-being as the burden of neurological and behavioral abnormalities associated with mental illness are eased through appropriate clinical intervention.  (Find more on predictive, preventive, personalized and participatory medicine here.)