Signal transduction pathways play a key role in many cellular functions as well as intercellular communication. However, elucidating the exact mechanisms involved in signal transduction pathways is non-trivial: crosstalk exists between different pathways, the response within a population of cells can vary significantly, and only limited measurement capabilities are available for observing intracellular signals. One specific example highlighting the importance of signal transduction and how it is affected by cell population is stem cell differentiation. The resulting cell type is affected by the cell population and intercellular communication that activates different signal transduction pathways. This project is focused on the development of a new computational framework that enable the PIs to investigate the role of cell populations on signal transduction. In order to do so, they will derive techniques that allow them to distinguish between stochastic components and population effects. Unlike their past work, which dealt with average properties only, they will focus on developing techniques that consider information about individual cells within a population and use this information for investigating population effects on signal transduction activity. Intellectual Merit: This work includes the following portions:(a) Development of problem formulations and algorithms that can solve inverse problems considering cell populations, rather than just bulk averages, subject to the high level of measurement noise commonly found when studying signal transduction pathways. (b) Derivation of a new approach for determining the optimal set of parameters to estimate in a nonlinear signal transduction pathway model given the available data for a distribution of cells and considering uncertainty in the model. (c) Development of a computational technique for large-scale parameter estimation across populations to determine how intercellular communication affects signal transduction in individual cells, leading to a greater understanding of cellular behavior and improved experimental design. This includes determining the number of cells and their spatial location in experiments in order to avoid results that are skewed because cell population effects have not been considered. In summary, this work will develop and integrate mathematical, computational, and experimental approaches to partition stochastic and population effects with the ultimate goal of developing improved models of signal transduction pathways. These techniques will be applied to the Jak/STAT and the Erk-C/EBPâ signaling pathways which play an important role in many cellular responses, such as stem cell differentiation and the inflammatory response of the liver. Broader Impact: Synergies can be created by integrating research and teaching efforts in the area of systems biology as well as by establishing long-term collaborations between research groups involved in modeling and in the experimental life sciences. Two of the PIs coteach a senior-level undergraduate/graduate elective class on systems biology which integrates theoretical and experimental aspects required for modeling and analysis of bio-systems. The class aligns with departmental curriculum reform plans and will include several modules which can also be used in other courses and outreach activities. Interactive and web-based learning aids will be developed along with the modules and incorporated throughout the course. Additionally, significant effort will be devoted to disseminating research results in the form of software, case studies, undergraduate student education and training, and outreach programs to underrepresented groups.
|Effective start/end date||10/1/09 → 9/30/13|
- National Science Foundation (NSF)