Biomedical Engineering Reference
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established Consortium for Integrative Computational Oncology at the University of
Southern California, where we are developing this approach with a focus on building
community and training the next generation of interdisciplinary cancer scientists.
5.2.2 Broader Implications and Spillover Benefits
The quest for quantitative accuracy in patient-specific modeling drives advances in
mechanistic modeling. Quantitative testing allows us to choose among competing
models, where multiple models may be qualitatively compelling, but fewer are
quantitatively reasonable. To the extent that rigorously-calibrated models can
successfully make quantitative predictions in individual patients, we gain new
confidence in the underlying models. Because the models are built to be universal
(cancer cells are just cells with different phenotypic parameter values), these
advances will be of use across computational biology. Likewise, efficient
numerical simulation of these increasingly sophisticated models is driving
advances in applied parallel computing and hybrid and multiscale modeling. Any
derived algorithms will be of benefit across applied mathematics and engineering.
If we should reach the point where we can integrate in vitro measurements with
clinical data to accurately predict cancer progression and therapy response in
individual patients, the implications are vast: new insights from wetlab biology
could be immediately evaluated for potential impact in individual patients in
combination with current therapies, offering accelerated discovery and clinical
translation. Ultimately, it is our goal that this approach will help bridge the gap
between theoretical modeling, wetlab biology, and clinical practice to develop and
deliver patient-calibrated predictive tools. We believe that such tools will one day
help clinicians and their patients to make optimal, personalized treatment decisions
that incorporate both accepted clinical practice and cutting-edge research results.
Acknowledgments PM and SM thank the National Institutes of Health for the Physical Sciences
Oncology Center grant 5U54CA143907 for Multi-scale Complex Systems Transdisciplinary
Analysis of Response to Therapy-MC-START. PM thanks the USC James H. Zumberge
Research and Innovation Fund (2012 Large Interdisciplinary Award) for support through the new
Consortium for Integrative Computational Oncology (CICO), and the USC Undergraduates
Research Associate Program (URAP) for student support. JL gratefully acknowledges partial
support from the National Institutes of Health, National Cancer Institute, for funding through
grants 1RC2CA148493-01, P50GM76516 for a Center of Excellence in Systems Biology at the
University of California, Irvine, and P30CA062203 for the Chao Comprehensive Cancer Center
at the University of California, Irvine. JL also acknowledges support from the National Science
Foundation, Division of Mathematical Sciences.
PM thanks David Agus (USC Center for Applied Molecular Medicine); Andrew Evans, Jordan
Lee, Colin Purdie, and Alastair Thompson (U. of Dundee/NHS Tayside); and Paul Newton (USC
Department of Aerospace and Mechanical Engineering) for enlightening discussions. PM thanks
Andrew Evans for Fig.
13
. The authors thank Ying Chen (U. California at Irvine) for Fig.
14
.
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