Systems biology is a rapidly evolving discipline. Even the word itself means different things to different people. In its most basic interpretation, systems biology describes approaches to characterize the effects of drugs, for example, on the activity of large, complex biological networks. At the recent CSB2 “Systems Biology of Human Disease” conference, scientists discussed different ways to model pathways, interpret data, and derive therapeutic targets from biological networks.
It would be a dream come true to organize and structure the explosion of biological data generated from high-throughput screening into a coherent cellular biological portrait. But to do that, researchers must have an accurate blueprint of the physical interactions that underlie all cellular responses. Massive data sets from genetics and all the omics can seem like mounds of bricks, mortar, and steel in which scientists must deduce what the building looks like, room by room. That is the task of biological reverse engineering.
“The complexities of biological systems are orchestrated by vast networks of interacting molecules that include DNA, RNA, proteins, and small molecules,” noted Gustavo Stolovitzky, Ph.D., manager of functional genomics and systems biology at the IBM Computational Biology Center.
“Currently, all we have is a partial understanding of how these systems interact. What we need is an accurate map of such molecular interactions. That’s the goal of the Dialogue for Reverse Engineering Assessments and Methods (DREAM) project established by the IBM Computational Biology Center and the MAGNet National Center for Biomedical Computing at Columbia University.”
According to Dr. Stolovitzky, the joint project hosts DREAM conferences that provide not only a forum for researchers to discuss the field but also a reverse-engineering challenge to those who wish to test their algorithms in order to discover if they can identify a gene-gene interaction, given only high-throughput data sets painstakingly assembled by the organizers with the help of contributing scientific partners.
“The reverse-engineering challenges are designed to verify algorithms. In other words, how can I massage the data given using statistical analyses and existing literature in an intelligent way to evaluate the data at hand? For example, from the 11 teams that participated in one of the DREAM2 (second conference) challenges, eight of them did no better than chance. However, those who achieved more did so with the simple premise that less is better. As you narrow the biological area in a more meaningful way, you get rid of data that is noise, and this provides the best route to understanding the pathways.”
Can we expect human-directed computing to be replaced by artificial intelligence? Dr. Stolovitzky thinks not, at least not yet. “I feel that the first and most important step in seeking to understand data is human insight. Artificial intelligence is a long way off from developing that. There is so much biology that still needs to be discovered that we cannot yet embed all knowledge in our algorithms. But, the trend is definitely there, and we are learning significant lessons from all of our challenges.”
The “DREAM3” conference, to be held later this month at the Broad Institute, will be a joint venture with the “Annual RECOMB Satellite on Regulatory Genomics” and the “Annual RECOMB Satellite on Systems Biology” conferences.