- Series
- Applied and Computational Mathematics Seminar
- Time
- Monday, February 1, 2010 - 1:00pm for 1 hour (actually 50 minutes)
- Location
- Skiles 255
- Speaker
- Manu O. Platt – Biomedical Engineering (BME), Georgia Tech
- Organizer
- Maria Westdickenberg
Tissue remodeling
involves the activation of proteases, enzymes capable of degrading
the structural proteins of tissue and organs. The implications of
the activation of these enzymes span all organ systems and therefore,
many different disease pathologies, including cancer metastasis.
This occurs when local proteolysis of the structural extracellular
matrix allows for malignant cells to break free from the primary
tumor and spread to other tissues. Mathematical models add value to
this experimental system by explaining phenomena difficult to test at
the wet lab bench and to make sense of complex interactions among the
proteases or the intracellular signaling changes leading to their
expression. The papain family of cysteine proteases, the cathepsins,
is an understudied class of powerful collagenases and elastases
implicated in extracellular matrix degradation that are secreted by
macrophages and cancer cells and shown to be active in the slightly
acidic tumor microenvironment. Due to the tight regulatory
mechanisms of cathepsin activity and their instability outside of
those defined spaces, detection of the active enzyme is difficult to
precisely quantify, and therefore challenging to target
therapeutically. Using valid assumptions that consider these complex
interactions we are developing and validating a system of ordinary
differential equations to calculate the concentrations of mature,
active cathepsins in biological spaces. The system of reactions
considers four enzymes (cathepsins B, K, L, and S, the most studied
cathepsins with reaction rates available), three substrates (collagen
IV, collagen I, and elastin) and one inhibitor (cystatin C) and
comprise more than 30 differential equations with over 50 specified
rate constants. Along with the mathematical model development, we
have been developing new ways to quantify proteolytic activity to
provide further inputs. This predictive model will be a useful tool
in identifying the time scale and culprits of proteolytic breakdown
leading to cancer metastasis and angiogenesis in malignant tumors.