Integrating Top-Down and Bottom-up Models in Systems Biology with Applications to Cell Cycle Control in Budding Yeast

Source of Support: NIH

Abstract: Two distinct approaches are being used to study complex cellular systems. The first approach automatically searches large datasets for correlations between genes and proteins and represents these as a graph with nodes and edges. The second approach painstakingly crafts detailed models that can be simulated by computer. These approaches have largely been developed separately until now. This project will meld these two approaches into a single framework, thereby allowing fast database searches to augment models that can be simulated. Specifically, the project will 1. Develop fast algorithms to search databases of molecular data to suggest extensions to models of cellular control systems 2. Develop new principles to test how well these extended models match experimental data and 3. Design experimental tests that can validate the predictions made by the first two steps. The project will validate this system by studying the mechanism of cell division, a system involved in the development of cancer. In the long term, the methods developed by this project can be used to study any complex cellular system, e.g., those implicated in infectious diseases.

Public Health Relevance: This project will meld two distinct approaches for studying complex cellular systems, one top-down and the other bottom-up, into a single framework. The project will combine the power of fast database searches with hand-crafted models. The project will validate this system by studying the mechanism of cell division, a system involved in the development of cancer.

Publications

Bharadwaj A, Singh DP, Ritz A, Tegge AN, Poirel CL, Kraikivski P, Adames NR, Luther K, Kale SD, Peccoud J, Tyson JJ, Murali TM (2017) GraphSpace: Stimulating interdisciplinary collaborations in network biology Bioinformatics 33(19): 3134 DOI: 10.1093/bioinformatics/btx382

Pratapa A, Adames NR, Kraikivski P, Franzese N, Tyson JJ, Peccoud J, Murali TM (2018) CrossPlan: Systematic planning of genetic crosses to validate mathematical models Bioinformatics 34 (13): 2237 DOI: 10.1093/bioinformatics/bty072

Adames NR, Schuck PL, Chen KC, Murali TM, Tyson JJ, Peccoud J (2015) Experimental testing of a new integrated model of the budding yeast Start transition Molecular Biology of the Cell 26(22): 3966. DOI: 10.1091/mbc.E15-06-0358

Peccoud J (2014) If you can’t measure it, you can’t manage it PLoS Computational Biology 10(3): e1003462. DOI: 10.1371/journal.pcbi.1003462

Ball DA, Lux M, Adames NR, Peccoud J (2014) Adaptive imaging cytometry to estimate parameters of gene networks models in systems and synthetic biology. PLOS ONE 9(9): e107087. DOI: 10.1371/journal.pone.0107087

Ball DA, Adames NR, Reischmann N, Barik D, Franck C, Tyson JJ, Peccoud J (2013) Measurement and modeling of transcriptional noise in the cell cycle regulatory network. Cell Cycle 12(19): 3392. DOI: 10.4161/cc.26257

Peccoud J, Isalan M (2012) The PLOS ONE Synthetic Biology Collection: Six Years and Counting, PLOS ONE 7(8): e43231. DOI:10.1371/journal.pone.0043231

Ball DA, Marchand J, Poulet M, Baumann WT, Chen KC, Tyson JJ, Peccoud J (2011) Oscillatory dynamics of cell cycle proteins in single yeast cells analyzed by imaging cytometry, PLOS ONE 6(10): e26272. DOI: 10.1371/journal.pone.0026272