# Multi-Scale Engineering of Heterogeneity in the Host-Aware Synthetic Gene Circuits > **NIH NIH R35** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2022 · $368,039 ## Abstract 1 Current approaches to designing and constructing synthetic gene circuits have reached a 2 dilemma due to the substantial heterogeneity driven by circuit-host interactions, especially for 3 large-scale gene circuits. The conventional trial-and-error iteration approach on synthetic gene 4 circuit development is regarded as inefficient since the assembled gene circuits often are 5 susceptible to experimental conditions. One fundamental reason is that the heterogeneity driven 6 by circuit-host interactions become significant with the increase of the number of components in 7 gene circuits but are often neglected. Moreover, the lack of quantitative frameworks for quantifying, 8 characterizing, and controlling heterogeneity in the host-aware synthetic gene circuits impedes 9 the progress in the field. My laboratory has been focusing on dissecting the mechanisms of how 10 the circuit-host mutual interactions affect the gene circuit functions and developing control 11 strategies targeting circuit-host interactions to optimize engineered synthetic gene circuits. 12 Recently we found a topology-dependent interference of synthetic gene circuit function by growth 13 feedback, which was published in Nature Chemical Biology. We also found winner-takes-all 14 resource competition that redirected cascading cell fate transitions, which is in revision to Nature 15 Communication. In the proposed projects, we will establish experimental and computational 16 frameworks to quantify, characterize, and control the gene expression heterogeneity in the host- 17 aware synthetic gene circuits. The heterogeneity can result from stochastic cellular resource 18 allocation, stochastic biochemical reactions in gene circuits, and stochastic cell divisions. These 19 heterogeneities are intertwined due to the complex interactions between the gene circuits and the 20 host organisms, creating another layer of challenge and complexity to engineering robust gene 21 circuits. We will integrate a microfluidics system for time-lapse live-cell analysis, a Turbidostat 22 platform with Python-based easy-to-use web interface for accurate growth rate control and 23 automatic yet remotely-controllable in-situ fluorescence measurement, and hybrid agent-based 24 modeling algorithms for stochastic simulation of all the single cells in the bacterial community to 25 characterize the heterogeneity from various noise sources in the host-aware synthetic gene 26 circuits. I have built up my research group with all the necessary expertise and capabilities to 27 complete the proposed projects. This work will provide a systematic in-depth mechanical 28 understanding of the heterogeneity driven by circuit-host interactions, and will greatly help us to 29 rationally design and control the synthetic gene circuits for sophisticated clinical applications in a 30 real-world environment, such as bacterial infection and tumor microenvironments. ## Key facts - **NIH application ID:** 10468898 - **Project number:** 5R35GM142896-02 - **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS - **Principal Investigator:** Xiaojun Tian - **Activity code:** R35 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $368,039 - **Award type:** 5 - **Project period:** 2021-08-15 → 2026-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10468898 ## Citation > US National Institutes of Health, RePORTER application 10468898, Multi-Scale Engineering of Heterogeneity in the Host-Aware Synthetic Gene Circuits (5R35GM142896-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10468898. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*