Biological signaling networks such as those associated with RTKs, WNT, TGbeta, and ER are of critical importance for the survival and proliferation of many human carcinomas. We will create a logical model of cellular networks that will be used to understand and explore these complex signaling networks at a systems biology level in human breast cancer. Our model will be constructed using the Pathway Logic system, which is founded on the computational science of formal methods. Pathway Logic allows a rich set of logical operators to define relationships between and among the components of a complex biological system. A Pathway Logic model has compelling advantages for understanding complex biological processes such as signal transduction and metabolism at a global level. In particular, it provides a mechanism for organizing diverse information concerning biological and biochemical states and processes; its structure naturally represents biological and biochemical change; it is automatically created on demand depending on user specified conditions; and it can be easily modified to accommodate new information, respond to logical queries, and formulate hypotheses about the function of complex networks. Our high throughput experimental approach to the construction and validation of a Pathway Logic model for breast cancer (described in Projects 2-3) involves using a large panel of characterized human breast cancer cell lines [o capture molecular and phenotypic responses to targeted therapeutics. Thus, our model must represent unique states of each cell line, rather than an overall state arbitrarily designated as representative of human breast cancer. Further, it must provide a computational mechanism that enable users to interpret experimental results in a format that can be used to build as well as modify the model in response to new findings. Finally, the model must be capable of generating statements or hypotheses that can be experimentally verified in both automatic and interactive ways. The central goal of this project is to construct a comprehensive Pathway Logic model of oncogemc signaling pathways in human breast cancer with emphasis on the Raf-MEK-ERK module using Pathway Logic, and to provide the necessary infrastructure for interacting with it. Achieving this goal will allow Projects 2-3 of the Program to integrate their findings with one another, and allow us to test and define relationships between specific signaling pathways and their sensitivity to therapeutic intervention. To achieve this goal we will:
Aim 1. Develop an expanded Pathway Logic rnodel of cellular signaling networks related to breast cancer. We will systematically develop a large Pathway Logic model of RTKs, WNT, TGFbeta, and ER signaling networks suitable for studying signaling processes in human breast cancer, initially, all established biological interactions and outputs implicated in normal and oncogenic signaling through this network will be curated from public knowledge bases, including both qualitative (logical) and quantitative behaviors. As the model is developed, it will be verified to ensure that correct or expected behaviors are predicted or maintained. The model will be systematically refined by incorporating the results from Projects 2-3. The mature model will inctude each cell line as a distinct part, allowing the specification of unique values and properties for each cell line.
Aim 2. Extend Pathway Logic to increase its functionality and utility. The current Pathway Logic system must be adapted and linked to the experimental plans that will be performed in Project 2-3. To accomplish this, we will (a) build tools too ease model construction by facilitating input; (b) create a comprehensive database of experimental facts relevant for defined experimental states of each cell line (Projects 2-3); (c) develop a system to allow users to investigate effects of perturbing or changing the mode] or network at points of potential therapeutic intervention, providing a foundation for translationally relevant hypotheses; (d) build an automated system to create prototypical model queries that can indicate undefined values or parameters within the model (experimentally determined in Projects 2-3); (e) develop computational strategies to estimate unknown cell state values; and (f) include computational mechanisms for including dynamic processes within the Pathway Logic approach.
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