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Rethinking Scientific Workflows

Developing a disease drug model is an intensely creative and collaborative effort. It requires the ability to assemble available knowledge and data and to gain a collective appreciation of important relationships. As this collaborative synthesis gets underway, pharmacometricians are charged with translating the ideas and hypotheses about diseases biology and drug pharmacology into mathematical equations. The equations are then coded into the control streams that, along with the data, become the basis for investigating the feasibility of various hypotheses.

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14 million invisible Americans.

Chana Joffe-Walt,* a reporter for the National Public Radio (NPR) program Planet Money, recently did a fantastic job of investigating one of the most under-appreciated stories of the economic recovery.

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Why don’t computer systems help me as much as I think they should?

Answer: We don’t really understand the requirements of the process.

Chapter 2 of 3. Need to catch up? Read the previous post in this series about scientific workflows.

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Shanghai: A Cacophony of People

Update: For another perspective on tech employment in the United States and China, see Andy Grove’s recent article in Bloomberg Businessweek.

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Celebrating Globalization

The word “globalization” can be interpreted many ways, both positive and negative. In these uncertain economic times, many people think globalization is equivalent to job loss and trade protectionism.

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Progressive Reporting and Model Based Drug Development

Over the years, our relationships with clients have deepened and Cognigen is often asked to begin working on projects at the earliest stages of development and to continue to refine a model as new data arrives from ongoing clinical development programs. Consequently, if a assets continues to show promise, we have the opportunity to provide modeling and simulation results at decision-making milestones over the lifecycle of clinical development. Typically, these activities culminate in a comprehensive synthesis of exposure-response relationships for efficacy and safety endpoints that are included in the regulatory submission.

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Things to worry about

Here is computer scientist David Gelernter’s [1] answer to the annual question “2013 : What *Should* We Be Worried About?” at the website Edge [2].

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Scientific workflows – the knowledge-generating engines of R&D.

Scientific research requires two kinds of effort. One is the generation and synthesis of original ideas by skilled practitioners. This is a desirable and often lauded talent that can spawn remarkable innovations in science and medical care. The second kind of effort is less visible, but equally important—the hard work required to turn an idea into reality. Executing the experiments, analyzing the data, and developing presentations of results are examples of this work. Although these latter efforts are necessary, and even enjoyable, they nonetheless can be tedious, time-consuming, and expensive.

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Robots

Take a look at BigDog and his amazing robot pals in this link to the New York Times. Or, if you love cute and cuddly, you should check out Paro.

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Ted on Ted

I recently stumbled on a website called TED: Ideas worth spreading, and I apologize in advance for sharing this most addicting site with you.

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