Opportunities

Postdoc available (posted 4 August, 2017)

We are looking for a postdoc to support a new 5 year NIH U01 "Multiscale modeling of inherited cardiomyopathies and therapeutic interventions" that we have just received in collaboration with Jonathan Wenk (Co-PI), Lik-Chuan Lee (Michigan State), and Chris Yengo (Penn State Hershey). The project abstract is copied below.

The postdoc will work closely with every team member and will perform:
  • cell and tissue-level measurements of contractile function
  • molecular and cell-level modeling using the MyoSim framework
What we are looking for

We seek an engaged motivated postdoc who wants to drive world-class research. We prefer candidates who have experience in one or more of the following areas:
  • contractile measurements using living or chemically permeabilized cells
  • assessments of ventricular function
  • mathematical modeling
  • code development (ideally MATLAB or C++)
  • scientific writing
What we offer

We are a small lab but we are very active and our work spans from molecular function to clinical cardiology. Excellent candidates will have opportunities to:
  • work with human myocardium from our Cardiovascular Biorepository
  • receive mentoring and support for grant development (e.g. AHA Scientist Development Grants, K99s)
  • receive mentoring and support for career development
Our three previous postdocs all advanced successfully
Our two previous graduate students also went to academic postdocs
How to apply

Email your CV to Ken Campbell at k.s.campbell@uky.edu with a cover letter explaining why you want to work in his lab.


Project abstract

The goal of this research is to develop a predictive multiscale model that will improve understanding of familial cardiomyopathies and that can be used to help screen potential new therapies for cardiac disease. Familial cardiomyopathies are the most frequently inherited heart defect and affect about 700,000 Americans. Most of the genetic mutations affect myosin or regulatory proteins that modulate myosin function. The majority of these mutations also induce abnormal cardiac growth termed hypertrophy. This project will develop, calibrate, and validate an innovative multiscale model that uses data quantifying myosin-level function to predict how hearts hypertrophy over time. This is a critical step on the path to developing patient-specific computer models that can be used to optimize treatments for heart failure and to predict the effects of different types of pharmaceutical intervention. In the future, one could envision clinicians testing drug treatments in silico and selecting the intervention that produces the greatest long-term benefit for their patient.

The research team consists of two physiologists/biophysicists (Campbell & Yengo) and two engineers (Wenk & Lee) who share a common interest in cardiac biology. Together, their research skills span from structure-function analysis of myosin molecules to computer simulations of hearts that grow and remodel over time. The research plan integrates state-of-the-art hierarchically-coupled mathematical models with validation experiments that range from stopped-flow molecular kinetic assays to magnetic resonance imaging of myocardial strain patterns. The model will be tested using molecular to organ-level experimental data obtained from wild-type mice and from transgenic animals that develop cardiac hypertrophy because of a K104E mutation in myosin regulatory light chain. Additional tests will be performed using drugs that enhance (omecamtiv mecarbil) and inhibit (MYK-461) myosin-level contractile function.

There are three specific aims.

Aim 1: Integrate a multistate kinetic model of myosin into an organ-level finite framework to predict the effects of genetic and/or pharmaceutical modulation of myosin function.

Aim 2: Develop growth and remodeling algorithms to predict chronic changes in ventricular structure and function resulting from genetic and/or pharmaceutical modulation of myosin function.

Aim 3: Calibrate and validate the model using experimental data quantifying different spatial and temporal scales.


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