Heiko Enderling, PhD

Associate Investigator
Assistant Professor, Tufts University School of Medicine
Center of Cancer Systems Biology
St. Elizabeth's Medical Center
Tufts University School of Medicine
736 Cambridge Street
Boston, MA 02135
 
Tel: 617.779.6537
Mail: heiko.enderling [at] tufts.edu

Heiko maintains a personal webpage at http://www.heikman.de.
 

Education and Training:

•  PhD in Mathematical Biology, University of Dundee (Scotland, UK), May 2003 – Aug 2006
•  Diploma in Computer Visualistics, University of Magdeburg (Germany), Oct 1997 – Apr 2003
 

Awards and Fellowships:

2008 – 2011		American Association for Cancer Research Centennial Postdoctoral Fellowship
2007		British Oncology Association Young Investigator Award
2005		Cancer Research UK Pilot Project Research Award

 

Funding History:

2008 – 2011		American Association for Cancer Research Centennial Postdoctoral Fellowship PI: Heiko Enderling ($180,000)
2005		Cancer Research UK Pilot Project PI: Jayant S Vaidya & Heiko Enderling (£20,000)

 

Research Interests:

Mathematical models of tumor dynamics have become more accurate and accepted in recent years and enable a better prediction of biological pathways that may be involved in the initiation and development of a tumor. The big aim for theoretical and practical oncologists is to find ways to treat the disease or improve the life of patients. Mathematical models help to identify crucial mechanisms to compare different treatments or design new treatment strategies. With the growing acceptance of models of tumor development the subsequent application of treatment planning will play an increasingly important role in the clinic. Using models one can compare different approaches or design new treatment strategies, which then can be tailored to individual patient data. With more information on cancer relevant to modelling becoming available the new well parameterised models have the power to predict responses to various treatment techniques such as drug scheduling in chemotherapy, immunotherapy, and radiotherapy as well as combinations of these.

Cancer stem cells and self-metastases.
Tumors are intrinsically heterogeneous. The majority of tumor cells have limited life span and replicative potential, and only a small minority — so-called cancer stem cells — live forever, divide infinitely and potentially produce more such stem cells. It is these stem cells that determine tumor formation, and their dynamics are counterintutively inhibited by their non-stem progeny. Only a high migration rate can liberate stem cells and enable their migration to seed new clones in the vicinity of the original cluster. In this manner, the tumour continually ‘self-metastasizes’.

We use computer models to define the behavior of single cells, and then let single cells populate a computational domain. As the number of cancer cells increases over time, competition for environmental resources (such as space) defines population dynamics. A result is a cancer cell population — a tumor — growing sub-exponentially. Tumor progession is dictated by the ability of stem cells to form self-metastases that together form a malignant invasive morphology.

Breast cancer is a very common cancer in women throughout the world, with one in eight women facing the disease at some point in her life. The development of cancer is a stepwise process through which normal somatic cells acquire mutations that promote the cell's fitness. With the accumulation of beneficial mutations, these cells may escape their normal function in the tissue and could become self-sufficient in survival. However, due to different genetic programs, normal somatic cells may not live long enough to acquire all necessary mutations and give rise to a fully developed tumor. Therefore, the hypothesis of stem cells as the initiator of tumorigenesis became more likely recently. Furthermore, early acquired mutations in stem cells can be spread throughout the ducto-lobular tree during clonal formation of the breast during puberty. We use a hybrid continuous-discrete mathematical model of a sequential mutation pathway to investigate the role of stem cells and early mutations in stem cells on cancer development.

Numerical analysis and computational simulation of partial differential equation models in mathematical biology is now an integral part of the research in this field. Increasingly, we are seeing the development of partial differential equation models in more than one space dimension, and it is therefore necessary to generate a clear and effective visualization platform between mathematicians and biologists to communicate the results. The mathematical extension of models to three spatial dimensions from one or two is often a trivial task, whereas the visualization of the results is more complicated. We apply the established Marching Cubes volume rendering technique to the study of a mathematical model of malignant solid tumor growth and invasion in an irregular heterogeneous three-dimensional domain, i.e. the female breast. Due to the different variables that interact with each other, more than one data set may have to be displayed simultaneously which can be realized through transparency blending.

 
Recent Publications:
(PDFs are copyrighted and provided for personal use only.)