The function and organization of molecular networks.
Understanding the organization and function of molecular networks:
Our lab endeavors to understand the organization, function and evolution of molecular networks. The molecular network needs to sense multiple signals from the environment, robustly process an appropriate cellular response and orchestrate the regulation of hundreds of genes and proteins to execute this response. This remarkable functionality occurs through diverse mechanisms including regulation of transcription, epigenetic changes, translation, degradation, post-translational signaling, and localization The advent of high throughput genomic and proteomic technologies is providing biology with an explosion of new experimental data, quantitatively measuring the molecular workings of the cell at a genome-wide scale.
High throughput datasets are rapidly being produced, probing the diverse facets of the cell’s activity on a genome wide scale.Microarrays provide a global snap shot of gene expression and factor binding under different environmental conditions and stimuli.SNP arrays read up to 500,000 nucleotide polymorphisms in an individual’s genome.Flow cytometry combined with florescent antibodes measure the level and activities of proteins in thousands of individual cells.Small interfering RNA and synthetic biology techniques facilitate the perturbation of the molecular network in a variety of sophisticated ways. Our lab combines high-throughput experimentation along with the development of novel algorithms and computational learning methods to integrate diverse high throughput data and unravel from these the workings of the cell. The computational methodology is used to reconstruct models of the molecular network and these models are then used to elucidate properties of molecular networks, the design principles by which they function, and the forces that drove their evolution.
The type of question we ask is “How does a mutation at one point in the network, propagate through the network and influence signal processing at a more global scale?” A population contains many genetic sequence polymorphisms that lead to variability in the complex web of regulatory interactions between individuals.We study how genetic variation perturbs the regulatory network, leading to changes gene expression and manifesting in phenotypic diversity. We use this approach to ask questions such as:How do changes in the molecular network influence fitness under different environmental conditions?How do changes in the network lead to dysfunctional signal processing, causing human disease such as cancer and autoimmunity?
Potential projects include:
- Cancer Genomics -Combining heterogeneous measurements of tumors such as sequence (copy number, SNPs),gene expression,and signaling events to understand how dysfunctional regulation in cancer cells.
- Personalized theraputics - Understanding the genetic determinants of tumor response to drug and how to design better personalized therapies.
- Genetic Genomics – Combining genotype, environment, gene expression and complex phenotypes to understand how the molecular network is altered between individuals and how these differences manifest in phenotype.
- Mammalian Signaling – Understanding cell signal processing using high-dimensional (35 proteins) measurements in single cells.
- Immune development, understanding the functional differences between cells in the immune system and how the same core signaling pathways respond differently between different cell types.
- Evolution and fitness – Understand the connection between regulation and fitness and how this connection drives the evolution of molecular networks.
- See lab webpage for more projects.
- Bendall SC*., Simonds EF*., Qiu P., Amir ED., Krutzik PO., Finck R., Bruggner RV., Melamed R. Ornatsky OI., Balderas RS., Plevritis SK., Sachs K., Pe’er D., Tanner SD., Nolan GP., (May 2011) Single-cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum Science 6;332(6030):687-96.
- Pe’er D., HaCohen N (March 2011) Principles and strategies for developing network models in cancer Cell 18;144(6):864-73.
- Akavia UD*., Litvin O*., Kim J., Sanchez-Garcia F., Causton, HC., Pochanard P., Mozes E., Kotliar D., Garraway LA., Pe'er D (Dec 2010) An Integrated Approach to Uncover Drivers of Cancer Cell 10;143(6):1005-17.
- Chen, BJ., Causton, HC., Goddard, NL., Perlstein, EO. and Pe’er, D (Oct 2009) Harnessing gene expression to identify the genetic basis of drug resistance Molecular Systems Biology 2009;5:310.
- Litvin, O., Chen, BJ., Causton, HC. and Pe’er, D. (April 2009) Modularity and interactions in the genetics of gene expression Proceedings of the National Academy of Science 106(16):6441-6.
- Lee, S., Dudley, A., Drubin, D., Silver, P., Krogan, N., Pe’er, D., and Koller, D. (Jan 2009) Learning a Prior on Regulatory Potential from eQTL Data PLOS Genetics 5(1):e1000358.
- Lee, S*., Pe’er, D*., Dudley, A., Church, G., and Koller, D. (Sep 2006) Identifying Regulatory Mechanisms and their Individual Variation Reveals Key Role of Chromatin Modification. PNAS 103(38): 14062-7.
- Pe’er, D., Regev, A., and Tanay, A. (Feb 2006) Minreg: A Scalable Algorithm for Learning Parsimonious Regulatory networks in Yeast and Mammals. Journal of Machine Learning Research 7: 167-189.
- Sachs, K*., Perez, O*., Pe’er D*., Lauffenburger, D., and Nolan, G., (April 2005 Runner up for Science breakthrough of the year. (288 ISI citations)) Causal protein-signaling networks derived from multiparameter single-cell data Science 308:523-529.
- Sachs, K*., Perez, O*., Pe’er, D*., Lauffenburger, D., and Nolan, G. (April 2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308: 523-529.