Julie AudetPhD
Associate Professor

Contact Info

T. (416) 946-0209
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Room 1104

Research Interests

Stem Cells, Computational Biology


  • University of California, Irvine, CA, U.S., NSERC Research Fellow in Single-cell Kinase Assays, 2002-2003.
  • University of British Columbia, PhD in Chemical and Biological Engineering, 2001.
  • Laval University, Quebec, MSc in Chemical Engineering, 1996.
  • Laval University, Quebec, BSc in Chemical Engineering and Biochemical Engineering, 1995.
  • Laval University, Quebec, BSc in Biochemistry and Microbiology, 1991.


  • Institute of Biomaterials and Biomedical Engineering, University of Toronto.
  • Chemical Engineering and Applied Chemistry, University of Toronto.


Stem cell culture engineering and combinatorial factor search


In the Donnelly Centre, there is open-mindedness and excitement about new research technologies. Our model systems encompass the most primitive (bacteria, yeasts) to the most complex (mammals) and researchers are exposed on a daily basis to a variety of research approaches that constitute the toolbox of each respective scientific field. But the toolbox is no longer restricted to the study of a particular cell type or organism; its utility is extended to address new biological questions. One of the most exciting, but also challenging, aspects of this kind of cross-disciplinary research environment is that you must keep a global view on the biomedical field in general and, at the same time, constantly learn the “language spoken” in new research areas.

red blood cell progenitors in culturered blood cell progenitors in culture Stem and progenitor cells are influenced by multiple interacting components in their environment. My lab studies these interactions at the cellular level; specifically, I wish to understand how cells respond to combinations of environmental cues, such as growth factors and physico-chemical parameters and how these factors work together to influence the survival, differentiation and proliferation of these cells. My goal is to use this understanding to develop new methods to search and identify culture conditions to propagate precursor cells for tissue engineering applications. To reach this goal, my lab combines cutting-edge biology, computation, statistical design of experiments and process engineering. Improved culture conditions for different stem and progenitor cell types will facilitate the study of development, healing and disease processes; improve conventional drug-based treatment for cancer and degenerative diseases; and enable new cellular therapies to be developed.


Development and Application of Biological Search Algorithms for the Modulation of  Stem and Progenitor Cell Fate

Highly efficient combinations of growth factors, inhibitors, extracellular matrix molecules and biomaterials must be identified to enable the bio-manufacturing of tissue engineering products derived from precursor (stem or progenitor) cell culture. Our recent work has focused on understanding the combinatorial control of somatic precursor cells by cytokines during their proliferation and differentiation. However, it is often difficult to directly improve culture conditions from a better understanding of the operation of specific signaling pathways in cells because of the inherent complexity of the underlying biological network. Several groups, including mine, have used statistical experimental design strategies to investigate the response of precursor cells to combination of cytokines and/or matrix proteins in vitro or in vivo. This approach usually relies on exhaustive testing of all (or fractions of all) possible combinations. However, the experimental study of multiple combinations of variables presents a major challenge since the number of experiments grows exponentially with the number of factors and doses/levels tested. In practice, since only a fraction of the high dimensional parameter space can be explored, this results in the inability to solve factor interactions and non-linear dose-response effects and, ultimately, only suboptimal yields in the desired cell types are obtained. Our current work addresses these limitations by developing and using biological search algorithms to optimize large factor combinations (e.g. at least 15 factors at 5 doses/levels). These algorithms have a stochastic component and operate iteratively with experimental measurements. They also take into account the inherent variability of biological systems in their operations and provide a framework in which insights gained from systems biology investigations are integrated. We are working on demonstrating that these search algorithms can significantly facilitate the discovery of completely defined culture conditions for refractory precursor cells and that they can be instrumental for the development of precursor-based cell manufacturing processes that will generate larger quantities and higher quality of cells while requiring less reagents and labor and, therefore, that will be less costly.

My group is interested in developing biological search algorithms for different areas of application, including:

1. The optimization of stem and progenitor cell culture processes

2. The development of serum-free (chemically-defined) cell culture media

3. Combinatorial drug therapy for hematological malignancies



View PubMed search of Dr. Audet's full list of publications.



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