A) Translational Bioinformatics: Transforming 300 Billion Points of Data into Diagnostics, Therapeutics, and New Insights into Disease
We are generating so much data, that Science itself is starting to become obsolete. If we think of Scientific method, going back, say 400 years, the scientific method is, we ask an interesting question or generate a hypothesis and then go get and make measurements to answer that question or address that hypothesis. And what happens in the world when we have so much data already? The new magic is in figuring out what is the cool question I want to ask of this data? And that is really 99% of the work in modern labs. “What is the cool question we want to ask?”. You know, the whole world is waiting for the answer. And nobody even realizes this question is askable today, because we already have the measurements. In that way, the scientific method is becoming backwards, where we have the data already, what do we want to ask of it?
B) An engineering approach to biology:
Using a transistor radio as an analog of a biological system, Yuri Lazebnik described how a biologist would fix a broken radio, assuming no prior knowledge of how the radio components were wired together. A traditional biological approach would involve removing (gene knockout, mutagenesis) each part of a functioning radio and track the changes in performance (phenotype). However, the human “radios” are different and repeating this process on all the components would generate an enormous amount of data, some of which may be redundant or contradictory.
C) Create a clear picture of the pathology of the disease and its physiological responses
Recent research indicates, that there are eight different disease mechanisms underlying Type 2 diabetes.In order to develop a treatment for patients with Type 2 diabetes, it is therefore necessary to understand the “context” of the disease, including:
– The nature and incidence of the various disease subtypes
– Whether all eight mechanisms are amenable to therapeutic intervention
– The relevant targets for therapeutic intervention
– The feasibility of developing biomarkers to identify which patients suffer from which disease subtypes
– The safety characteristics of different potential therapies; and
– The commercial viability of those therapies.
D) Disease network (systems biology) and drug discovery
Some of the long-standing challenges in drug discovery are lack of specificity, high incidence of adverse effects, and unpredicted toxicities of new therapeutic compounds. As a result, modern-day drug discovery employs more targeted approaches, such as virtual screening and structure-based drug design to complement conventional in vitro high-throughput screening.