My research focuses on the multiscale behavior of the human heart, bridging the cell, tissue and organ scale, where an improved understanding of the tissue's biophysical functioning is crucial for the choice for, and development of, efficient clinical treatment strategies focused on patient-specific pathophysiology. Using finite element analysis and machine learning techniques, I integrate information from various data sources (including a.o. histological characterization, experimental tissue testing and medical imaging techniques) into computer models that simulate the patient-specific biophysical behavior of the heart as accurately as possible. In addition to the diagnostic value of these models, the developed modeling technology also allows us to predict the acute and chronic effect of various treatment techniques, through e.g. drugs, surgery and/or medical equipment. Consequently, this research offers insights that will have an unmistakable impact on the personalized medicine of the future.

Honors & Awards

  • Postdoc Speaker Audience’s Choice winner, Stanford Cardiovascular Institute: 3rd Annual Cardiovascular Postdoctoral Research Conference (Oct 22nd, 2020)

Professional Education

  • Doctor of Science, Universiteit Gent (2019)
  • Master of Science in Engr, Universiteit Gent (2013)
  • Bachelor of Civil Engineering, Universiteit Gent (2011)

Stanford Advisors

All Publications

  • Precision medicine in human heart modeling Perspectives, challenges, and opportunities BIOMECHANICS AND MODELING IN MECHANOBIOLOGY Peirlinck, M., Costabal, F., Yao, J., Guccione, J. M., Tripathy, S., Wang, Y., Ozturk, D., Segars, P., Morrison, T. M., Levine, S., Kuhl, E. 2021; 20 (3): 803-831


    Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

    View details for DOI 10.1007/s10237-021-01421-z

    View details for Web of Science ID 000617490700001

    View details for PubMedID 33580313

    View details for PubMedCentralID PMC8154814

  • Sex Differences in Drug-Induced Arrhythmogenesis. Frontiers in physiology Peirlinck, M., Sahli Costabal, F., Kuhl, E. 2021; 12: 708435


    The electrical activity in the heart varies significantly between men and women and results in a sex-specific response to drugs. Recent evidence suggests that women are more than twice as likely as men to develop drug-induced arrhythmia with potentially fatal consequences. Yet, the sex-specific differences in drug-induced arrhythmogenesis remain poorly understood. Here we integrate multiscale modeling and machine learning to gain mechanistic insight into the sex-specific origin of drug-induced cardiac arrhythmia at differing drug concentrations. To quantify critical drug concentrations in male and female hearts, we identify the most important ion channels that trigger male and female arrhythmogenesis, and create and train a sex-specific multi-fidelity arrhythmogenic risk classifier. Our study reveals that sex differences in ion channel activity, tissue conductivity, and heart dimensions trigger longer QT-intervals in women than in men. We quantify the critical drug concentration for dofetilide, a high risk drug, to be seven times lower for women than for men. Our results emphasize the importance of including sex as an independent biological variable in risk assessment during drug development. Acknowledging and understanding sex differences in drug safety evaluation is critical when developing novel therapeutic treatments on a personalized basis. The general trends of this study have significant implications on the development of safe and efficacious new drugs and the prescription of existing drugs in combination with other drugs.

    View details for DOI 10.3389/fphys.2021.708435

    View details for PubMedID 34489728

  • Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19 COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING Peirlinck, M., Linka, K., Costabal, F., Bhattacharya, J., Bendavid, E., Ioannidis, J. A., Kuhl, E. 2020; 372
  • Effects of B.1.1.7 and B.1.351 on COVID-19 Dynamics: A Campus Reopening Study ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING Linka, K., Peirlinck, M., Schaefer, A., Tikenogullari, O., Goriely, A., Kuhl, E. 2021
  • COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING Bhouri, M., Costabal, F., Wang, H., Linka, K., Peirlinck, M., Kuhl, E., Perdikaris, P. 2021; 382
  • Bayesian Physics-Based Modeling of Tau Propagation in Alzheimer's Disease. Frontiers in physiology Schafer, A., Peirlinck, M., Linka, K., Kuhl, E., Alzheimer's Disease Neuroimaging Initiative (ADNI) 2021; 12: 702975


    Amyloid-beta and hyperphosphorylated tau protein are known drivers of neuropathology in Alzheimer's disease. Tau in particular spreads in the brains of patients following a spatiotemporal pattern that is highly sterotypical and correlated with subsequent neurodegeneration. Novel medical imaging techniques can now visualize the distribution of tau in the brain in vivo, allowing for new insights to the dynamics of this biomarker. Here we personalize a network diffusion model with global spreading and local production terms to longitudinal tau positron emission tomography data of 76 subjects from the Alzheimer's Disease Neuroimaging Initiative. We use Bayesian inference with a hierarchical prior structure to infer means and credible intervals for our model parameters on group and subject levels. Our results show that the group average protein production rate for amyloid positive subjects is significantly higher with 0.019±0.27/yr, than that for amyloid negative subjects with -0.143±0.21/yr (p = 0.0075). These results support the hypothesis that amyloid pathology drives tau pathology. The calibrated model could serve as a valuable clinical tool to identify optimal time points for follow-up scans and predict the timeline of disease progression.

    View details for DOI 10.3389/fphys.2021.702975

    View details for PubMedID 34335308

  • Effects of B.1.1.7 and B.1.351 on COVID-19 Dynamics: A Campus Reopening Study. Archives of computational methods in engineering : state of the art reviews Linka, K., Peirlinck, M., Schäfer, A., Tikenogullari, O. Z., Goriely, A., Kuhl, E. 2021: 1-12


    The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under baseline reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.

    View details for DOI 10.1007/s11831-021-09638-y

    View details for PubMedID 34456557

    View details for PubMedCentralID PMC8381867

  • The reproduction number of COVID-19 and its correlation with public health interventions. Computational mechanics Linka, K. n., Peirlinck, M. n., Kuhl, E. n. 2020: 1–16


    Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22 ± 1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67 ± 0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24 ± 2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.

    View details for DOI 10.1007/s00466-020-01880-8

    View details for PubMedID 32836597

    View details for PubMedCentralID PMC7385940

  • Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions Comp Meth Biomech Biomed Eng Linka, K., Peirlinck, M., Sahli Costabal, F., Kuhl, E. 2020; 23: 710-717
  • Outbreak dynamics of COVID-19 in China and the United States. Biomechanics and modeling in mechanobiology Peirlinck, M. n., Linka, K. n., Sahli Costabal, F. n., Kuhl, E. n. 2020


    On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system and the global economy. However, the precise timeline of the disease, its transmissibility, and the effect of mitigation strategies remain incompletely understood. Here we integrate a global network model with a local epidemic SEIR model to quantify the outbreak dynamics of COVID-19 in China and the United States. For the outbreak in China, in [Formula: see text] provinces, we found a latent period of 2.56 ± 0.72 days, a contact period of 1.47 ± 0.32 days, and an infectious period of 17.82 ± 2.95 days. We postulate that the latent and infectious periods are disease-specific, whereas the contact period is behavior-specific and can vary between different provinces, states, or countries. For the early stages of the outbreak in the United States, in [Formula: see text] states, we adopted the disease-specific values from China and found a contact period of 3.38 ± 0.69 days. Our network model predicts that-without the massive political mitigation strategies that are in place today-the United States would have faced a basic reproduction number of 5.30 ± 0.95 and a nationwide peak of the outbreak on May 10, 2020 with 3 million infections. Our results demonstrate how mathematical modeling can help estimate outbreak dynamics and provide decision guidelines for successful outbreak control. We anticipate that our model will become a valuable tool to estimate the potential of vaccination and quantify the effect of relaxing political measures including total lockdown, shelter in place, and travel restrictions for low-risk subgroups of the population or for the population as a whole.

    View details for DOI 10.1007/s10237-020-01332-5

    View details for PubMedID 32342242