http://user.it.uu.se/~jarst116/slides/week4.pdf

Graph-Based Algorithms for Boolean Function Manipulation

The three Rat Sarcoma (RAS) genes in humans (HRAS, KRAS, and NRAS) are the most common oncogenes in mutated in human cancer. Mutations that permanently activate RAS are found in 20% to 25% of all human tumors and up to 90% in certain types of cancers. For this reason, the RAS pathway is being heavily studied, however, there are still many unknowns in regulation and downstream effects of RAS in cancer. Previous computational work that focused on signaling in the RAS pathway relied mainly on continuous models that use ordinary differential equations or reaction rule-based models. These approaches are most often limited in size due to the lack of the information required to create such models. In this work, we use a discrete modeling approach, in which elements of the intracellular signaling network are modeled as discrete variables, and the elements’ regulatory functions are either implemented as logical functions, weighted sums, or min/max functions. The network that we are modeling includes a number of intertwined feedback and feed-forward loops. We developed several versions of the RAS pathway model, and our results demonstrate the advantages of the discrete logical modeling approach when studying intra-cellular signaling networks. In addition to model design and analysis, we have worked on developing a translator from the biological mechanism representation used by continuous models, into the decision tree representation, namely Binary and Algebraic Decision Diagrams. The goal of this translation effort is to allow for a standardized and comprehensive modeling approach, which can capture all the available system parameters, while at the same time enabling model design and evaluation even in the absence of these parameters.

An Approach to Combine Continuous and Discrete Modeling of Intra-Cellular Networks with Application to RAS Signaling

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An Approach to Combine Continuous and Discrete Modeling of Intra-Cellular Networks with Application to RAS Signaling