[Population Modeling] Examples for review paper

[Feilim Mac Gabhann]

Hi all,

Feilim Mac Gabhann here from Johns Hopkins University's Institute for
Computational Medicine.

Here's my contribution for the current review paper.

*Part 1. Our population modeling work.*

*Example 1. VEGF/Sema network in cancer.*

Research into population-level differences in cancer that have an impact in
drug treatment have largely focused on two components: pharmacokinetics
(the disposition of the drug being acted upon by metabolic enzymes, renal
clearance, and other processes); and genetics (where mutations may render
drugs ineffectual, or may even be required for function). Less well studied
is the impact of the expression of the drug's target protein, as well as
the expression of proteins that interact with that target. Using the gene
and protein expression levels for multiple VEGF-family ligands and their
receptor tyrosine kinases, as well as for the Sema/Plexin family that also
interacts with the VEGF co-receptor Neuropilin, we were able to identify
the relative levels of: the 'accelerator' of blood vessel growth - VEGFR2
signaling; and a 'brake' on blood vessel growth - Plexin signaling. We
could identify both group-wide differences (for example, primary prostate
tumors had both the accelerator and brake on, while prostate metastases had
the accelerator and no brake), and individual differences within those
subpopulations, which enable us to identify optimal treatments for each
case.



*Example 2. Personalized HIV time courses for stem cell transplant.*

Developing a model of the disease course of HIV has enabled us to simulate
complex therapeutic interventions, including a potentially curative bone
marrow transplant - the introduction of donor stem cells that have been
genetically modified to be HIV-resistant. By using longitudinal data from
hundreds of HIV patients, we were able to create a virtual patient
population that could each be tested with these different interventions.
The result is a 'virtual clinical trial', and an estimate of the likelihood
of cure across the population for a given treatment. In addition, we obtain
insight into the most potent levers or indicators of treatment success, in
order to more clearly identify who would be ideal recipients of the
treatment.


REFS:

1. https://www.ncbi.nlm.nih.gov/pubmed/26341082

2. https://www.ncbi.nlm.nih.gov/pubmed/26933519



*Part 2. Categorizing our modeling approaches*

Virtual clinical trials
Population variability in gene and protein expression
Predicting drug effects
Identifying biomarkers


*Part 3. Keywords*

Large coupled sets of ordinary differential equations.
Multiparameter nonlinear optimization.
Virtual patient generation.


Best to all,
Feilim
-- 
=========================
Feilim Mac Gabhann, Ph.D.
Associate Professor
Johns Hopkins University

Institute for Computational Medicine (ICM <http://icm.jhu.edu>)
Institute for NanoBioTechnology (INBT <http://inbt.jhu.edu>)
Department of Biomedical Engineering (BME <http://bme.jhu.edu>)
and Department of Materials Science & Engineering (MSE
<http://engineering.jhu.edu/materials/>)

Director, Hopkins Office for Undergraduate Research (HOUR
<http://research.jhu.edu/hour> | email <hour at jhu.edu>)

ph: (410) 516-4723  |  em: feilim at jhu.edu  |  fx: (410) 516-5294
ws: http://www.icm.jhu.edu/faculty/macgabhann.php
ad: Hackerman Hall 316a, 3400 N. Charles St., Baltimore MD 21218
MWF: Shaffer 204D  ||   TTh: Hackerman Hall 316a

www.researcherid.com/rid/A-3436-2010
scholar.google.com/citations?user=D29eCdcAAAAJ
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