Complex combinations of molecular signals in cells are an excellent indicator of multi-gene disorders, including cancers and hereditary diseases. A system capable to detect these conditions may be used as a highly selective tool for diagnosis, prevention, treatment, and monitoring at a single-cell level in ways that achieve optimal and highly specific health-care. Towards this direction, scientists have developed first generation genetic circuits that operate as information-processing systems. However, the utility and scalability of these prototypes is hampered by fluctuations in stoichiometry between different components of the circuit in individual cells. Therefore, it is critical to construct sophisticated expression units whose gene product will depend only weakly on the number of unit copies in a cell and on the global transcription efficiency. Such stand-alone units could then be combined into networks that could be expected to function reliably in the face of large internal fluctuations. We argue that particular network architectures (or topologies) may provide the solution towards this goal. The number of possible topologies for a given set of pathway elements is large and it grows exponentially with the number of elements, making their exhaustive investigation intangible. Fortunately, recent research has uncovered that certain topologies appear more frequently than others. Those topologies, named """"""""network motifs"""""""", are composed of relatively few elements and are embedded as """"""""modules"""""""" or """"""""nodes"""""""" in larger networks and pathways. Based on preliminary experiments, we form the hypothesis that specific families of biological network motifs can be used to reduce noise and fluctuations in intracellular activity and most importantly, the copy number variability. Further investigation of these results with the proposed experiments can radically change the field and lead to several health-related diagnostic and therapeutic applications. More generally, as many human diseases are essentially network-level phenomena, unraveling properties of biological motifs is central to understanding human biology. Our long-term objective is to construct functional and scalable synthetic gene circuits able to perform predetermined functions in the face of large internal fluctuations. The proposed aims will bring us considerably closer to this objective. More specifically, we aim to construct and integrate in mammalian cells a range of feedback and feedforward motif circuits, utilizing a library of building blocks and using both viral delivery and recombinase systems. In order to test our hypothesis, we will characterize the noise and copy number dependence of the genetic circuits using microscopy and flow cytometry measurements. Finally, we propose to implement a first generation of genetic circuits for detection and monitoring of endogenous miRNA signals.
We aim to show that the use of the aforementioned topologies in the circuits renders them suitable for high- throughput monitoring and yields increased accuracy in the miRNA sensing.
We propose a comprehensive characterization of specific circuit architectures and their implementation in first generation sensors for endogenous signal detection and monitoring. The results will spark a wide range of applications relevant to public health and specific to monitoring and processing intracellular signals in a reliable manner.
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