It is a great pleasure to report to you the achievements of IMBM in the last year, as well as discuss our plans for the next year. Our research progresses as expected, both in the stem cell and in the immunotherapy projects. We are also initiating a new research direction in IMBM, which, hopefully, will contribute to position biomathema-tics in mainstream science. Below, I briefly present these achievements and plans.

Over the past decade we have investigated stem cell fate decision by developing increasingly complex mathematical models and verifying them experimentally, when possible. By this strategy one, first, constructs an abstract model, which will capture the “universe”, and provide a universal solution to the problem at hand. This solution, expected to be too general to be applicable, serves as a conceptual beacon, showing the way along which increasingly specific models can be developed and verified experimentally.

This project was initiated by our search for the simplest possible stem cell fate decision mechanism, which guarantees the system’s homeostasis. To this end we have developed a minimal Cellular Automata model, encapsulating the proliferation and differentiation properties of individual stem cells. Model analysis proves that long-term homeostasis is guaranteed by negative feedback control of cell density on stem cell proliferation (denoted Quorum Sensing). Essentially, this means that a stem cell “counts” its stem neighbors and switches from proliferation to differentiation when their number is above a certain threshold (Agur et al., 2002; Kirnasovsky et al., 2008). In collaboration with scientists from the Paterson Institute for Cancer Research, University of Manchester, U.K., we have verified the existence of the Quorum Sensing mechanism in vitro in a cancer cell line (Agur et al., 2009).

Equipped with the above understanding, we set upon ourselves to identify the Quorum Sensing molecule(s) controlling fate decision in breast cancer stem cells, and to examine, both theoretically and experimentally, how they can be modulated. We developed a mathematical model for the major signaling pathways governing breast cancer stem cells, whose mathematical analysis points to a specific protein as the Quorum Sensing molecule (Kirnasovsky et al., 2008). Model simulations suggest a critical dose of this protein, below which stem cell proliferation remains unaffected, or slightly increases, and above which it is significantly suppressed. Our theoretical results were confirmed experimentally by checking the effects of increasing protein administration doses on the proliferation capacity of cancer stem cells, both in laboratory cell-lines and in primary culture of invasive breast cancer. These results suggest that one can eliminate cancer stem cells using high doses of the protein (Agur et al., 2009)

In the immunotherapy project we have introduced the concept of immunotherapy personalization by use of a mathematical model, and have retrospectively validated it by phase II clinical trial results of a prostate cancer allogeneic whole-cell vaccine. This work has been carried out in collaboration with Professor S. Vuk-Pavlovic of the Mayo Clinic, Rochester, U.S.A.

ONY-P is a new investigational vaccine treatment designed to stimulate the immune system to attack prostate cancer. In a Phase IIa proof-of-principle study, primary endpoints were met, the treatment was very well tolerated and side effects were minimal. Nevertheless, these results clearly demonstrate that difficulty in designing effective immunotherapy for PCa. To help elucidate immune mechanisms in PCa, and gain insights into how it may be effectively targeted therapeutically, we formed a mathematical model describing the major players in the immune system-PCa interactions. The model, constructed and calibrated using preclinical and clinical data from the literature, was validated by its success in retrieving PCa progression of patients vaccinated by ONY-P. Using the validated model we developed a novel concept and an accompanying algorithm for immunotherapy personalization. Our approach involves an interactive dialogue between modeling experts and clinicians, aimed at carrying out in-treatment model-based personalization and protocol adjustment. Hopefully, our developed computational tool will aid in planning better vaccination regimens and serve as a scaffold for patient-specific immunotherapy. The figure below may help appreciate both the precision of model validation and the developed personalization method.

A significant part of our scientific effort has been dedicated to pushing forward our new concept, termed, SysBioMath, of genotype-to-phenotype dynamic modeling, based on systems biology-processed experimental, pharmacological and clinical data. The integration is mandatory for coherently embedding vast and diverse data, extracted from different levels of biological organization, within their clinical consequences, in terms of the patient’s response to drug treatment.

This year we are celebrating our ten year anniversary. We are proud of our achievements and would like to expose them to our friends and public. Your helping the preparations will be greatly appreciated.

Finally, as always, we thank you for continuously involvement and support.

Yours,

Zvia Agur.