While our ability to collect large amounts of experimental data in a timely and cost-effective manner represented, until recently, a top priority, a different challenge is currently taking shape.
Now the goal is to integrate the datasets generated by diverse approaches to allow the meaningful and accurate interpretation of complex biological phenomena. In this context, systems biology, which Hans Westerhoff and Lilia Alberghina, in their book Systems Biology Definitions and Perspectives (Springer 2007), refer to as being “new and old at the same time,” holds the key to understanding biological systems in their true complexity and dynamics.
“Over the next 10 years, a systems approach will dominate the landscape of understanding all simple and complex diseases,” said Leroy E. Hood, M.D., Ph.D., president of the Institute of Systems Biology.
In a recent study that integrated different levels of global information obtained from several inbred mouse strains with subtractive analysis, Dr. Hood and collaborators investigated the cellular perturbations during prion disease progression (see Molecular Systems Biology, published online April 7, 2009).
“The reason why global analysis is important is because it allowed us to demonstrate that we can explain virtually all of the known pathophysiology and it provided fundamental new insights into disease modules that people had no idea were associated with prion disease,” added Dr. Hood.
With the vast amounts of information that omics approaches generate, the challenges are now shifting toward finding the most robust methods for interpreting the data.
“The problem with any large-scale analysis, be it transcriptome, proteome, or genome-wide scans, is that the signal-to-noise challenge is enormous. While these omics approaches identify large lists of genes, I would argue that most of the genes on the list are noise and do not reflect pathological mechanisms. Appreciating the signal-to-noise ratio is absolutely critical,” explained Dr. Hood.
His group developed and implemented new statistical methods for dealing with noise and for integrating the datasets from different animals in new and powerful ways. Along with a thorough understanding of prion biology, this approach identified 333 genes that appear central to prion disease.
One remarkable aspect that emerged from this work is that many changes appear at the molecular level long before symptoms became apparent. Moreover, proteomics techniques identified several presymptomatic blood markers of potential diagnostic value.
These global approaches unveiled dynamic cellular networks that provide an important framework for drug discovery and design. “I think the future of drug target discovery is going to be understanding the dynamics of disease-perturbed networks,” predicted Dr. Hood. “In 10 years we will see a revolution in medicine like nothing that has ever been seen before.”