Programmed cell death or apoptosis is an important process in development and tissue homeostasis. Dysfunctional apoptosis contributes to autoimmune and neurodegenerative disorders as well as to cancer. Designing targeted therapies for these diseases could perhaps benefit from a molecular understanding of the cell death process, including the activation of a key group of proteolytic enzymes referred to as Caspases (Cysteine Containing Aspartate Specific Proteases). Caspases, once activated, dismantle the cell by seeking out and selectively cleaving specific protein targets after aspartate residues.

A mathematical model of the regulation of receptor and stress mediated caspase activation is presented. The model, which is currently composed of 200 species and more than 350 states, includes a mechanistic description of the expression, translation and activity of pro- and anti-apoptotic factors, including key cell-cycle regulatory elements such as p53. The transcription of 23 genes key to apoptosis and caspase activation is modeled using a mechanistic description of regulation based upon the calculation of the fractional occupancy of the promoter region. The remaining genes in the model are assumed, largely consistent with observation, to be constitutively expressed. Translation is described using the mechanistic framework of Hatzimanikatis and coworkers while the activity of the cascade is mechanistically described using mass-action kinetics. The role of alternative mRNA splicing is treated in the model although to a less rigorous degree. Initial simulation results of stress and receptor mediated activation are compared to the current literature. Evidence for crosstalk between the endoplasmic reticulum, mitochondria and nucleus during apoptosis, including the role played by Ca2+, will be highlighted and our approach for modeling this crosstalk will be discussed. The effect of modulating the signaling strength of key stress pathways such as TNFa and p38MAPK, and the interplay between survival (NFKB pathway) and death signals will be illustrated in the context of the model. Points of structural fragility, that could serve as potential therapeutic targets to retard or promote apoptosis, are identified by computing first-order sensitivity coefficients. Using these same coefficients, D-optimal experimental designs for the identification of missing kinetic parameters based upon the maximization of the determinant of the Fisher Information Matrix (FIM) are presented. It is shown that while a significant fraction of the unknown parameters can be identified by time-resolved measurements of the concentrations of key protein species, perturbations to the network are required to calculate designs to identify the majority of parameters.