Selected Publications
- Martin KR, Barua D, Kauffman AL, Westrate LM, Posner RG, Hlavacek WS, MacKeigan JP (2013) Computational model for autophagic vesicle dynamics in single cells. Autophagy 9: 74–92. PMCID: PMC3542220
- Chylek LA, Akimov V, Dengjel J, Rigbolt KTG, Hu B, Hlavacek WS, Blagoev B (2014) Phosphorylation site dynamics of early T-cell receptor signaling. PLOS ONE 9: e104240. PMCID: PMC4141737
- Szymańska P, Martin KR, MacKeigan JP, Hlavacek WS, Lipniacki T (2015) Computational analysis of an autophagy/translation switch based on mutual inhibition of MTORC1 and ULK1. PLOS ONE 10: e0116550. PMCID: PMC4356596
- Stites EC, Aziz M, Creamer MS, Von Hoff DD, Posner RG, Hlavacek WS (2015) Use of mechanistic models to integrate and analyze multiple proteomic datasets. Biophys J 108: 1819–1829. PMCID: PMC4390817
- Chylek LA, Harris LA, Faeder JR, Hlavacek WS (2015) Modeling for (physical) biologists: an introduction to the rule-based approach. Phys Biol 12: 045007
FRIAS Project
Cellular regulatory systems are complex, and we lack a predictive understanding of how system-level properties depend on molecular properties and molecular interactions. These interactions can now be manipulated clinically using molecularly targeted drugs, such as protein kinase inhibitors, but because of the aforementioned complexity, the best therapeutic strategy for intervening to alter the behavior of a (dysfunctional) regulatory system is often unclear. To address this capability gap, we propose to formalize mechanistic knowledge about an important cellular regulatory system, the molecular network controlling autophagy, to obtain computational models capable of providing causal explanations for empirical observations and of making testable non-obvious predictions and thereby guiding experimental investigations. It is expected that modeling will be informed by data obtained via quantitative mass spectrometry (MS)-based proteomics, such as measurements of site-specific post-translational modifications (PTMs). The immediate goal of the proposed project is to understand the effects of feedback and feedforward loops on activation of autophagy. The significance of achieving this goal will be a sounder fundamental understanding of the molecular mechanisms that regulate autophagy. In the long term, this understanding could contribute to more effective clinical use of molecularly targeted drugs for the treatment of aging-associated diseases in which autophagy plays an important role, such as cancer and neurodegeneration.