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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?

A High school kid today who needs to do a science fair project for high school, can search “Breast Cancer” in NCBI site, find and download around 1 lakh samples of Breast Cancer. More than 2,000 independent experiments on Breast Cancer. And what kind of questions can they ask? Even a high school kid can say, what is common across all these 2,000 experiments.
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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.

In contrast, a typical engineering approach would involve systematic reconstruction of a component diagram from a normal radio (e.g. regulatory network), and compare the broken radios with the normal reference. Can a similar problem-solving mindset help expedite advances in biomedical research?
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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.

Once a company has acquired an in depth understanding of the pathophysiology of disease, it can develop a probe molecule and biomarkers for early testing of the CIR (Confidence in rationale) in humans.
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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. 

New approaches rely on an accurate global understanding of the mechanisms of diseases. Comprehensive understanding of the network and regulatory circuit for a particular disease process would help to identify network hubs with the potential to be novel drug targets.
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