Periodic Reporting for period 2 - MRG-GRammar (Massive Reverse Genomics to Decipher Gene Regulatory Grammar)
Reporting period: 2017-02-01 to 2018-07-31
MRG-GRammar aims to devise an entirely new strategy for deciphering the regulatory rules of gene regulation. We will leverage Synthetic Biology with cutting-edge DNA synthesis technologies and high-throughput analysis to generate new types of biological datasets that systematically explore all possible regulatory landscapes rather than just the naturally occurring regulatory sequences.
The extensive and unbiased nature of these unique datasets will allow us to build new models explaining different aspects of regulatory activity, which will be tested in second-generation libraries, designed based on model predictions. Consequently, through such an iterative process, we expect to make a significant breakthrough in deciphering, and evolving, the regulatory code. Our strategy synergizes four orthogonal objectives that will form a new knowledge base from which the regulatory algorithm can be derived. We will employ our strategy on diverse model organisms from the tree of life, from single cell to whole organism: bacteria, yeast, mouse ex-vivo cells, human cell-lines and finally, whole D. melanogaster and mouse embryos.
We expect this multidisciplinary synthetic biology approach to generate a major technological advance, which will provide the community with algorithms that will not only decipher extant natural regulatory code, but also interpret variations leading to a profoundly deeper understanding of the origins of many diseases. We expect our models to also serve as a reference in designing and implementing accurate and more controllable synthetic biology devices, with applications in fuel production, healthcare and other industrial fields. Thus, our ultimate goal is to substantially accelerate the advance of technologies and knowledge related to generating systematic and personal therapeutic solutions based on the analysis of each individual's natural genomic variations.
The extensive and unbiased nature of these unique datasets will allow us to build new models explaining different aspects of regulatory activity, which will be tested in second-generation libraries, designed based on model predictions. Consequently, through such an iterative process, we expect to make a significant breakthrough in deciphering, and evolving, the regulatory code. Our strategy synergizes four orthogonal objectives that will form a new knowledge base from which the regulatory algorithm can be derived. We will employ our strategy on diverse model organisms from the tree of life, from single cell to whole organism: bacteria, yeast, mouse ex-vivo cells, human cell-lines and finally, whole D. melanogaster and mouse embryos.
We expect this multidisciplinary synthetic biology approach to generate a major technological advance, which will provide the community with algorithms that will not only decipher extant natural regulatory code, but also interpret variations leading to a profoundly deeper understanding of the origins of many diseases. We expect our models to also serve as a reference in designing and implementing accurate and more controllable synthetic biology devices, with applications in fuel production, healthcare and other industrial fields. Thus, our ultimate goal is to substantially accelerate the advance of technologies and knowledge related to generating systematic and personal therapeutic solutions based on the analysis of each individual's natural genomic variations.
The MRG-GRammar - Massive Reverse Genomics to Decipher Gene Regulatory Grammar has four main objectives: Bottom up construction of synthetic enhancers ; Top-down perturbation of known enhancers; Building a predictive nucleotide-level model utilizing two orthogonal modelling approaches; Testing the model’s predictions of enhancer activity using three approaches.
We have now finished phase I and are into phase II and III of the project. End of phase I is the end of – the experimental design phase. In this stage we developed the experimental strategy and designed the synthetic regulatory sequences. We identified and tested candidate minimal enhancers in various organisms - bacteria, yeast, mammalian cells, etc. We also identified the regulatory sequences that we wish to decipher as part of the goals of this project for later use. We also intend to constructs drafts of our regulatory models and use those as a guide for constructing the synthetic regulatory sequences. In parallel, conditions for generating gain- and loss-of-function of transcription factors will be optimized in mammalian cells, while a new system to inactivate transcription factor activity at the protein level will be developed in Drosophila embryos.
Phase II and phase III are on their way – In phase II we are building synthetic enhancers in several organisms using top down and bottom up approaches. Data collected used to test our regulatory models, which will lead to a second round of experimentation. Data and design rules will be shared across platforms to check for common and emerging global rules that will point to physical regulatory mechanisms, rather than particular organism-specific ones. MRG-Grammar project will construct strain libraries for testing effects of gain- and loss-of-function of TFs, and assess the roles of specific TF function in both mammalian cells and Drosophila. Data will be integrated in models of enhancer regulatory rules. In phase III we are using modelling framework to predict different regulatory principles of cis-regulatory sequences that will be tested in all four species (bacteria, yeast, Drosophila, and mammalian cells).
We have now finished phase I and are into phase II and III of the project. End of phase I is the end of – the experimental design phase. In this stage we developed the experimental strategy and designed the synthetic regulatory sequences. We identified and tested candidate minimal enhancers in various organisms - bacteria, yeast, mammalian cells, etc. We also identified the regulatory sequences that we wish to decipher as part of the goals of this project for later use. We also intend to constructs drafts of our regulatory models and use those as a guide for constructing the synthetic regulatory sequences. In parallel, conditions for generating gain- and loss-of-function of transcription factors will be optimized in mammalian cells, while a new system to inactivate transcription factor activity at the protein level will be developed in Drosophila embryos.
Phase II and phase III are on their way – In phase II we are building synthetic enhancers in several organisms using top down and bottom up approaches. Data collected used to test our regulatory models, which will lead to a second round of experimentation. Data and design rules will be shared across platforms to check for common and emerging global rules that will point to physical regulatory mechanisms, rather than particular organism-specific ones. MRG-Grammar project will construct strain libraries for testing effects of gain- and loss-of-function of TFs, and assess the roles of specific TF function in both mammalian cells and Drosophila. Data will be integrated in models of enhancer regulatory rules. In phase III we are using modelling framework to predict different regulatory principles of cis-regulatory sequences that will be tested in all four species (bacteria, yeast, Drosophila, and mammalian cells).
MRG-GRammar aims to devise an entirely new strategy for deciphering the regulatory rules of gene regulation. We will leverage Synthetic Biology with cutting-edge DNA synthesis technologies and high-throughput analysis to generate new types of biological datasets that systematically explore all possible regulatory landscapes rather than just the naturally occurring regulatory sequences. The extensive and unbiased nature of these unique datasets will allow us to build new models explaining different aspects of regulatory activity, which will be tested in second-generation libraries, designed based on model predictions. Consequently, through such an iterative process, we expect to make a significant breakthrough in deciphering, and evolving, the regulatory code. Our strategy synergizes four orthogonal objectives that will form a new knowledge base from which the regulatory algorithm can be derived.
We employ our strategy on diverse model organisms from the tree of life, from single cell to whole organism: bacteria, yeast, mouse ex-vivo cells, human cell-lines and finally, whole D. melanogaster and mouse embryos. We expect this multidisciplinary synthetic biology approach to generate a major technological advance, which will provide the community with algorithms that will not only decipher extant natural regulatory code, but also interpret variations leading to a profoundly deeper understanding of the origins of many diseases. We expect our models to also serve as a reference in designing and implementing accurate and more controllable synthetic biology devices, with applications in fuel production, healthcare and other industrial fields. Thus, our ultimate goal is to substantially accelerate the advance of technologies and knowledge related to generating systematic and personal therapeutic solutions based on the analysis of each individual's natural genomic variations.
We employ our strategy on diverse model organisms from the tree of life, from single cell to whole organism: bacteria, yeast, mouse ex-vivo cells, human cell-lines and finally, whole D. melanogaster and mouse embryos. We expect this multidisciplinary synthetic biology approach to generate a major technological advance, which will provide the community with algorithms that will not only decipher extant natural regulatory code, but also interpret variations leading to a profoundly deeper understanding of the origins of many diseases. We expect our models to also serve as a reference in designing and implementing accurate and more controllable synthetic biology devices, with applications in fuel production, healthcare and other industrial fields. Thus, our ultimate goal is to substantially accelerate the advance of technologies and knowledge related to generating systematic and personal therapeutic solutions based on the analysis of each individual's natural genomic variations.