A long-standing goal of molecular engineering is to create components that can control genetic processes. There are commonly used predictive models that can design short genetic parts to control gene expression processes both within cells and in cell-free in vitro systems. These parts are then combined into larger genetic systems (operons, regulons) to create desired cellular functions, including sensors, genetic circuits, transporters, multi-enzyme metabolic pathways, organelle compartments, and orthogonal expression systems. These multi-part genetic systems are particularly useful, as they are used by natural organisms as a central information processing component to sense and respond to changing internal and external conditions. Thus, being able to de novo design or engineer these systems offers many points of control of fundamental cellular processes. However, there are several challenges that have been encountered in trying to develop multi-part genetic systems. For example, there are many coupled interactions, between adjacent parts or between distant genetic modules, that alter system function in unpredictable and undesired ways. Therefore, new approaches are needed to correctly design large genetic systems, taking into account these poorly understood mechanisms, within an even larger genomic background.
Breakthrough Capabilities & Milestones
Design of highly-stable, large genetic systems (genomes) with targeted expression levels in a host organism or cell type, incorporating system-wide effects.
Incorporate gene expression interactions into predictable design of prokaryotic genetic systems.
Incorporate gene expression interactions into predictable design of eukaryotic genetic systems.
Discovery and characterization of mechanistic interactions at the systems-level affecting protein activities inside cells.
Whole-tissue or whole-cell, nucleotide-resolution simulations encompassing several layers of models predicting gene regulatory, metabolic, and system-level behaviors.
Ability to rationally engineer sensor suites, genetic circuits, metabolic pathways, signaling cascades, and cell differentiation pathways.
Reliable engineering of genetic circuits with more than ten regulators for sophisticated computations.
Reliable engineering of novel, many-enzyme pathways utilizing combinations of bioprospected enzymes with well-characterized kinetics.
Five-time improvement and expansion of inducers/promoters for model organisms that respond to environmental inputs and any intracellular metabolite.
Utilize machine-learning approaches to use the vast amount of uncurated literature results within pathway design.
Creation of optogenetic tools for in vivo RNA post-transcriptional control to allow for easy control of any gene expression process through mRNA.
Reliable expression of redesigned synthases to produce secondary metabolites, including polyketides and non-ribosomal peptides.
Computational design of protein-ligand and RNA-ligand interfaces suitable for engineering protein-based or RNA-based sensors.
Simultaneous, tunable, timed expression of many transcription factors controlling mammalian cell state.
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