SIAM News Blog

NIBIB Supports Modeling, Analysis, and Simulation in Clinical Medicine and Biomedical Research

By Margot Kern and Grace C.Y. Peng

When determining whether to open a blocked artery with a stent, cardiologists calculate the fractional flow reserve (FFR), a number that predicts the likelihood that a blockage is significantly impeding blood flow to the heart. Current methods for calculating FFR involve threading a catheter from the arteries in a patient’s groin or wrist all the way into the coronary arteries. It’s an invasive test, and one that costs several thousand dollars.

Recently, a team of surgeons and biomedical engineers has created an alternative method for determining FFR that doesn’t require catheterization. The method—called HeartFlow—uses anatomical data from a CT scan of a patient’s heart to create a realistic 3D multiscale computational model of the patient’s coronary arteries. Virtual blood, obeying the physical laws of fluid dynamics, runs through the arteries; the FFR can be calculated noninvasively at all points along the arteries. Three recent clinical trials indicate that HeartFlow shows promise for impacting current state-of-the-art diagnostic methods [1–3]. In the future, doctors could use the model to try out virtual stents, determining optimal size and location for stents to be placed in a patient.

HeartFlow is just one example of how computational modeling can help solve complex problems in clinical medicine and biomedical research. Biomedical modeling provides a powerful platform for improving the efficiency of scientific experimentation, data acquisition, knowledge transfer, and technology development. Furthermore, models can be used to test the safety and efficacy of medical devices and drugs in silico, reducing the dependency on human and animal studies and accelerating translation from basic science to clinical medicine. 

Support for Biomedical Modeling, Simulation, and Analysis Research

NIBIB—the National Institute of Biomedical Imaging and Bioengineering—is one of 27 institutes and centers that make up the US National Institutes of Health, the federal government’s premier biomedical research agency. The mission of NIBIB is to support the development of novel biomedical technologies to help diagnose, treat, and prevent illness.

Among many other focus areas, NIBIB provides support for quantitative research projects that contribute to its mission. These projects generally fall into three categories: 

  1. Modeling: biomedical models and computational algorithms with potential clinical or biomedical applications. Research in this area includes mathematical, statistical, transport, network, population, mechanical, electrical, and electronic models applied to a broad range of biomedical fields. Particular emphasis is placed on multiscale modeling and the methods used to bridge scales. 
  2. Analysis: mathematical, statistical, and signal processing methods for the analysis of complex biomedical systems, clinical diagnosis, and patient monitoring
  3. Simulation: technologies for training and education in clinical practice and biomedical research, and simulation methods for emulating work flow, and understanding and predicting health and disease

Interagency Modeling and Analysis Group and the Multiscale Modeling Consortium

NIBIB coordinates the Interagency Modeling and Analysis Group, known as IMAG, which is made up of more than 80 program staff from ten government agencies who are involved in managing research programs in biomedical, biological, and behavioral systems; the programs have in common a need for novel modeling and analysis methods. From its inception in 2003, the group has provided an open forum for sharing updates on individual programs from the various IMAG agencies and for planning transagency activities that will have a broad impact on the communities served by IMAG.

Early in its existence, IMAG recognized that the modeling community was at the forefront of efforts to develop computational methods across the biological continuum. In addition, IMAG identified a strong desire among modelers to form multidisciplinary partnerships across varied research communities. This led to a 2004 solicitation: Interagency Opportunities in Multiscale Modeling in Biomedical, Biological, and Behavioral Systems. In 2006, the 24  awardees from the solicitation formed the Multiscale Modeling Consortium, which has grown to include more than 100 projects relevant to multiscale modeling. 

Funding announcements, meetings, workshops, tutorials, and other information relevant to the multiscale modeling community can be found on the IMAG Wiki page.  

How Can the SIAM Community Help Promote Biomedical Modeling, Analysis, and Simulation?

Although computational models are increasingly recognized as an important platform for understanding complex biological systems and facilitating technology development, some in the biomedical community continue to question their usefulness. A 2012 article in Biomedical Computation Review titled “Meet the Skeptics” explored the hesitancy of biologists and physicians to incorporate biomedical models into their work [4]. The article suggested that the issue stems in part from a combination of biologists who are intimidated by complex math and modelers who are dismissive of those not quantitatively trained. In addition, critics argue that modelers often don’t fully understand the biological conditions they are trying to model and, as a result, base their models on inaccurate assumptions. Some biomedical researchers, while acknowledging the promise of computational modeling, either believe that their biological problems are too complex for a model to be helpful or assume that they don’t have enough data to build an accurate model. Finally, a lack of well-known examples of biomedical models that have had a major impact on human health has led some to question their ultimate usefulness.

Members of the SIAM community can help assuage these concerns and become champions of computational modeling, analysis, and simulation in the following ways:

  • Be prepared to describe and explain your models and analytical methods as simply as possible, working to make them accessible to potential users. 
  • Explain that models are platforms for iterative research efforts, and not necessarily the end solution.
  • Be prepared to explain how modeling can efficiently and economically drive scientific experimentation, data acquisition, technology development, and even policy. Use examples to show how modeling has the potential to accelerate translation from basic science to clinical medicine.
  • Share success stories. Do you have or know of a successful biomedical model that has predicted outcomes that are difficult or impossible to obtain through experiments? If so, NIBIB wants to know about your story (which can be sent to
  • Finally, members of the SIAM community are encouraged to apply for NIH funding and to participate in the NIH Peer Review process. Information on serving as a reviewer can be found here.
[1] B. Koo et al., Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms: Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study, J. Am. Coll. Cardiol., 58:19 (2011), 1989–1997.
[2] J.K. Min et al., Diagnostic accuracy of fractional flow reserve from anatomic CT angiography, JAMA, 308:12 (2012), 1237–1245. 
[3] B.L. Nørgaard et al., Diagnostic performance of non-invasive fractional flow reserve derived from coronary CT angiography in suspected coronary artery disease: The NXT trial, J. Am. Coll. Cardiol., January 2014.
[4] K. Sainani, Meet the skeptics: Why some doubt biomedical models—and what it takes to win them over, Biomedical Computation Review, June 5, 2012. 

Funding Opportunities in Biomedicine

NIBIB encourages the SIAM community to apply for funding of work in which quantitative methods are used to further biomedical research. Initiatives (from NIH and other US agencies) that seek novel biomedical modeling, simulation, and analysis techniques include:

  • Multiscale Modeling Initiative. This initiative sponsored by the Interagency Modeling and Analysis Group (IMAG) seeks new predictive computational models that encompass multiple biological and behavioral scales to accelerate biological, biomedical, behavioral, environmental, and clinical research. More info here.
  • BRAIN Initiative. The goals of the NIH Brain Research through Advancing Innovative Neurotechnologies Initiative are to: (1) map the circuits of the brain; (2) measure the fluctuating patterns of electrical and chemical activity flowing within those circuits; and (3) understand how their interplay creates our unique cognitive and behavioral capabilities. More info here.
  • BD2K. The NIH Big Data to Knowledge initiative aims to develop the new approaches, standards, methods, tools, software, and competencies that will enhance the use of biomedical Big Data by supporting research, implementation, and training in data science and other relevant fields. More info here. Proposals for research in the development of software and methods for biomedical big data in targeted, high-need areas are due June 19, 2014.
  • Biomedical Information Science and Technology Initiative. BISTI, a consortium of representatives from each of the NIH institutes and centers, serves as the focus of biomedical computing issues at NIH. The mission of BISTI is to make optimal use of computer science and technology to address problems in biology and medicine by fostering new basic understandings, collaborations, and transdisciplinary initiatives between the computational and biomedical sciences. More info here.
  • National Robotics Initiative. The interagency NRI seeks the development of intelligent robots that can respond to users’ needs and to changing environments, with the goal of achieving functional independence in humans. Future goals include robots that can help reduce human errors in healthcare settings, monitor symptoms, and dispense drugs. More info here and here

Margot Kern is a science writer for the National Institute of Biomedical Imaging and Bioengineering (NIBIB). Grace C.Y. Peng is a program director at the NIBIB, National Institutes of Health, and chair of the Interagency Modeling and Analysis Group.

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