At a microscopic level, organisms are ruled by interacting systems of biomolecules. Historically, scientists painstakingly elucidated chains of molecular events using experiments that reveal individual interactions. In recent years, researchers have built richer, interconnected networks to mathematically summarize their knowledge of these interactions. This systems biology enterprise, largely stimulated by high-throughput tools like microarrays that measure mRNA levels as an indicator of gene expression, is a vital and increasingly important activity in both basic biology and in medicine.
A nagging concern, however, is how accurately these networks represent the biology. For complex systems like biological networks, there are practical limits on how well even massive amounts of data can uniquely define the underlying structure and yield useful predictions of measurable events. Indeed, although its advocates call this process "reverse engineering," the topology and the detailed molecular interactions of the "inferred" networks will likely never be known with precision.
To address such concerns it is important to define a formal framework for assessing the quality of biological network prediction algorithms. For an activity of this type to be successful it is important that the research community participate and offer critical input. To enable and foster this participation, the Center is sponsoring the DREAM (Dialogue for Reverse Engineering Assessments and Methods) conference and competition. The main objective of the DREAM initiative is to catalyze the interaction between experiment and theory in the area of cellular network inference. The fundamental question for DREAM is simple: How can researchers assess how well they are describing the networks of interacting molecules that underlie biological systems? From the earliest planning stages of the DREAM project, a key component of the initiative was the development of a competition in which different teams competed in using the same, blinded data to infer the networks that had generated it. Perhaps only in this way can the community know whether the networks that their methods produce can be trusted. The idea was inspired in part by other competitions, notably the CASP assessment of algorithms for protein-structure prediction.
The protein-folding challenge, however, begins with a precise amino-acid sequence and ends with a three-dimensional structure that is experimentally well defined. For reverse engineering of networks, both the specification of the data and the evaluation of the results are much harder.
A major problem for DREAM is identifying gold-standard networks whose structure can be taken as known. The best-understood networks are those created by people, but many researchers have expressed concerns that these networks would hold little interest for the larger biological community. What has emerged as an acceptable compromise is to select a range of "challenges" that span both large and small biological networks as well as mathematical networks, and, in between, a synthetic network implemented in yeast.
The purpose of DREAM is not to produce the best possible network, but to evaluate the best tools for producing networks. What is still needed, and what DREAM aims to achieve, is a fair comparison of the strengths and weaknesses of the methods and a clear sense of the reliability of the network models they produce. To achieve the goal of meaningful comparisons, the DREAM project provides a gold standard against which the competitors’ results are scored. Ultimately, the first and most important step in seeking to understand data is human insight and combining intuition with computational tools to reveal new and powerful strategies.
The following is a list of DREAM-related links: