Denise Kirschner

brief bio:My research for the past 20 years has focused on questions related to host-pathogen interactions in infectious diseases. My main focus has been to study persistent infections (e.g. Helicobacter pylori and Mycobacterium tuberculosis and HIV-1). Such pathogens have evolved strategies to evade or circumvent the host-immune response and my goal isto understand the complex dynamic involved, together with how perturbations to this interaction (via treatment with chemotherapies or immunotherapies) can lead to prolonged or permanent health. For the past 15 years, our research focus has been the host immune response to M. tuberculosis at multiple spatial and time scales. The grants funding our work aim to examine the immune responses in lymph nodes and lung during TB infection. To date we have worked with cell, mouse, non-human primate and humans data. We have spent a considerable effort to study unique structures, granulomas, that are involved in the immune response to M. tuberculosis We apply a range of computational tools from deterministic mathematical models to more discrete stochastic ones such as Agent Based Models and PDEs to examine spatial questions as well. I currently serve (and has for the past decade) as Editor-in-Chief of the Journal of Theoretical Biology the oldest and one of the top theoretical biology journal as well as chair for many study section review panels at the National Institutes of Health. This gives her knowledge and experience of the field and broad expertise in many areas of computational and mathematical biology.

Some of our studies have been what we call "virtual clinical trials" (for both TB and HIV-1) where we were exploring drug regimens. I think that this is a key place where we can influence clinical practice. Improving clinical trial studies.

MY DEEP THOUGHTS (or shallow depending on who else is in the pool):

A) I am strongly against the new taxonomy and nomenclature that comes with it that is proposed in the report. I think there is enough jargon and partitioning in science between disciplines, and I don't think adding to it will help our cause.


B) everyone should read the book: The creative destruction of medicine by Eric Topol CEO of Scripps...

POINT: the digital revolution can spur unprecedented advances in the medical sciences


1) a technology-enhanced future where new tools are integrated into diagnosing and treating patients, transforming the handling of common medical problems

2) healthcare reform as discussed in government is not about delivery of care, but payment of care (BTW--real revolution would reduce cost automatically)

3) "no single innovation will have a more profound effect than the conversion of biological data. With the aid of technology medical progress may well begin to resemble modern computers' own astonishing surge in processing power and data storage.

4)in the book he continuously checks his blood sugar with an implantable meter, he goes to bed wearing a "Zeo clock" that monitors brain function to help analyze sleep patterns. When he tries to fake sleep so that he can disregard his wife's bedtime chatter, he learns that "it's hard to play possum with a sensor displaying your real-time brain waves.

5)focuses much of his attention on the development of "theranostics," or the integrated use of treatments and diagnostics (especially genomic and protein information) to better guide therapy. These tools, he says, will enable treatment systems that combine the constant monitoring of a patient's biological information and the infusing of targeted medicines.

6)The FDA, he says, should allow the testing of drugs on patients who are selected for their prospect of deriving a benefit. Right now, the FDA usually requires drugs to be tested in a scattershot fashion on large populations. With drugs being tested on cancer patients, he notes, the "FDA insists on a body count to be able to quantify how much and how long the new drug improves survival"—even though diagnostic markers can sometimes reveal in advance which patients are unlikely to gain a benefit.

7)Dr. Topol worries that doctors will resist technologies that empower patients because the tools will also diminish the doctors' gatekeeper role. The American Medical Association, for example, battled firms that provide genetic information directly to patients. "This arrangement ultimately appears untenable," the author writes, "and eventually there will need to be full democratization of DNA for medicine to be transformed."

8) innovation that enables real-time diagnosis and personalized treatments is a certainty, though not because reluctant doctors accept it or because Washington wills it into being. A seductive technology that works like a dream and improves lives will set off a consumer clamor, whether the new tool is an iPhone 4S or an implantable blood-sugar meter.

C) Final topic, Machine learning. Basically best we will do is blood data. If we create large large large interconnected databases with blood data from patients yearly (or more often) for their life with 1000s of analytes and track the changes in time and then the patients outcomes (disease)..take 5-10 years to create biomarkers for every known disease and can then track and predict future events based on changes in these analytes..(stats and modeling obviously important)

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