Keywords: synthetic biology, Drosophila, transcription factors, genetic computation
People involved: Radoslaw Ejsmont
During animal development, the different types of cells acquire their identities in a multistep process known as differentiation. Differentiation is governed by a special class of proteins, called transcription factors. Unlike typical proteins, transcription factors have the ability to regulate the expression of genes, changing the amount of RNA and eventually the amount of protein produced from those genes. To do so, they attach to a DNA binding domain, segment of the DNA in the vicinity of the target gene. The identity of the key transcription factors that determine the outcome of cell differentiation is already known. However, we do not understand how different transcription factors interact to form complex gene regulatory networks in order to achieve a precise developmental program. Several quantitative approaches have been developed to address these questions, including single cell RNA sequencing, in situ RNA sequencing and quantitative imaging of gene expression patterns using fluorescent reporters. Combination of these techniques finally allows researchers to get quantitative view of gene expression changes in every cell as it progresses through differentiation. This information can be in turn used to create accurate computer models of gene regulatory networks. However, the models should be verified, ideally by recreating and studying gene regulatory networks in vivo, during animal development, which is the goal of this project.
Our team will develop a comprehensive set of synthetic biology tools to study and recreate gene regulatory networks in vivo, using Drosophila, a well-established model organism for studying animal development. To do so, we will combine previously known activation and repression binding domains with engineered ones to create synthetic transcription factors capable of precise control of a target gene expression levels. Our synthetic biology toolkit development will be divided in two sub-projects: (1) recreateing gene regulatory motifs that commonly occur in developmental gene regulatory networks and creating genetic computation units that perform basic logical binary operations; (2) development of efficient methods to screen for groups of candidate target genes that are essential for the function of gene regulatory networks. In combination, these tools will enable researchers to bridge the gap between quantitative biology, that provides gene expression data, systems biology that aims to establish accurate models of gene regulatory network and synthetic biology that will be used to test these models. Data from the synthetic gene regulatory networks can also be used as the baseline, a reliable empirical dataset that can improve existing computational methods for network modeling. Finally, building on the long history of fruit fly in science education, we will use our synthetic gene regulatory networks to control visible and quantitative phenotypes, thus creating new tools for teaching the basics of both genetics and synthetic biology.