Using CRISPRi to Control Local Supercoiling Density in Transcriptionally Active DNA

Objective: The goal of this project is to develop a rational, quantitative approach for engineering simultaneous control of multiple genes within an operon or multiple operons within the genome of a new organism.

This project explores the integration of ​CRISPRi and remodeling of local supercoiling density as a generalized strategy for engineering the dynamics of the transcriptome in canonical and emerging microbial hosts. Our approach centers on the fundamental biophysics of DNA supercoiling, using a combination of mathematical modeling, ​in situ experimental measurements, and genetic engineering to develop ​CRISPRi circuits that simultaneously control multiple operons or genes within the genome of a target microbe. The biotechnology developed in this project lays the foundation for genome-wide control in an expanding palette of microbial hosts for synthetic biology, with applications in biocomputing, biosensing, and biomaterials.

Support: One PhD student, 1 postdoctoral scholar; funding provided by the Institute of Collaborative Biotechnologies

Engineering Layered Genetic Circuits to Enact Temporal Encoding

Research Question: Can we engineer synthetic analog computing devices in living cells to encode entire temporal sequences of a signal? 

This proposal aims to develop an integrated experimental-computational framework for engineering deep, synthetic logic networks in bacteria for encoding and extracting temporal data for DoD relevant biochemical signals.  Existing synthetic biological sensors often rely on a single protein, riboswitch, or transcription factor to allosterically transduce the arrival of a biochemical cue to a transcriptional response.  Engineered genetic logic circuits typically determine whether the biochemical signal is currently present, and whether it is present in combination with other biochemical cues in the form of OR, XOR, AND, NAND, or NOR logic.  

In a complementary fashion, genetic sensors that implement temporal logic can distinguish between the order of arrival of multiple biochemical analytes, while molecular counters can count the number of times an analyte has appeared.   However, despite the advances in digital logic, snapshotting, quantitation and memory,  there is a lack of synthetic biotechnology for quantitatively recording the entire temporal sequence of a target biochemical analyte’s concentration in situ.  This project seeks to develop a new class of deep synthetic cellular logic networks, inspired from the fields of artificial intelligence, biological computing, and synthetic biology.   

Support: Two PhD students; funding provided by the Institute of Collaborative Biotechnologies

Discovering Fundamental Laws Governing Surface-to-Surface Transition in Prokaryotic Adaptation

Research Question: Can we discover and abstract conceptual models for how bacteria adapt to survive surface-to-surface transition? 

This project seeks to blend experimental techniques of whole-cell measurement and high-dimensional sensor fusion of whole cell computational models that describe the adaptation response and learning mechanisms employed in surface-to-surface transition in hardy prokaryotic systems. What does the learning architecture of a bacteria suddenly transferred from soil to a river system look like? How do we build models and computational simulation engines that mimic the adaptation response?

This project is funded by an Army Research Office Young Investigator Program Award.

Support: Two PhD students; funding provided by the Army Research Office

Genomic Optimization to Sustain Engineered Biological Function in Soil Microbes

Objective: This project seeks to develop methods and technological platforms for rapid high-throughput engineering of genomes of non-model microbes that exhibit robustness (and engineered fragility) to fluctuations in microbiome population dynamics and growth conditions.

The project is motivated by the observation that synthetic biological design is often delicate. To advance the field of synthetic biology to the next era of robust design, we seek to identify genetic and biological control mechanisms that confer increased or decreased fitness in organisms hosting synthetic gene circuits, either on plasmid or in the genome.

This project is led by Dr. Rob Egbert, through a collaborative consortium of institutions, including the Pacific Northwest National Laboratory, the Lawrence Berkeley National Laboratory, the University of Washington, Oak Ridge National Laboratory, the University of California, Santa Barbara, and the University of California, Berkeley.

Support: Two PhD students in the Biological Control Lab; funding provided by a Scientific Focus Area Award from the Department of Energy, Biological Energy Research Office and the Pacific Northwest National Laboratory

Data-Driven Discovery and Design of Synthetic Biological Circuits

This project aimed to develop generalized design platforms, algorithms, and robust methods for reproducible experimental execution within the context of a distributed cloud lab. Our role in this project was to develop algorithms for facilitating discovery and design from biological data collected in a distributed cloud-lab infrastructure and in semi-automated benchtop labs.

Publications:

• Al Maruf, A., Kundu, S., Yeung, E. and Anghel, M., 2018, June. Decomposition of nonlinear dynamical networks via comparison systems. In 2018 European Control Conference (ECC) (pp. 190-196). IEEE.
• Yeung, E., Liu, Z. and Hodas, N.O., 2018, June. A koopman operator approach for computing and balancing gramians for discrete time nonlinear systems. In 2018 Annual American Control Conference (ACC) (pp. 337-344). IEEE.
• Liu, Z., Kundu, S., Chen, L. and Yeung, E., 2018, June. Decomposition of nonlinear dynamical systems using koopman gramians. In 2018 Annual American Control Conference (ACC) (pp. 4811-4818). IEEE.
• Sinha, S., Vaidya, U. and Yeung, E., 2019, July. On computation of koopman operator from sparse data. In 2019 American Control Conference (ACC) (pp. 5519-5524). IEEE.
• Liu, Z., Ding, G., Chen, L. and Yeung, E., 2020, July. Towards scalable Koopman operator learning: Convergence rates and a distributed learning algorithm. In 2020 American Control Conference (ACC) (pp. 3983-3990). IEEE.
• Hasnain, A., Sinha, S., Dorfan, Y., Borujeni, A.E., Park, Y., Maschhoff, P., Saxena, U., Urrutia, J., Gaffney, N., Becker, D. and Siba, A., 2019, October. A data-driven method for quantifying the impact of a genetic circuit on its host. In 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 1-4). IEEE.
• Hasnain, A., Boddupalli, N. and Yeung, E., 2019, December. Optimal reporter placement in sparsely measured genetic networks using the koopman operator. In 2019 IEEE 58th Conference on Decision and Control (CDC) (pp. 19-24). IEEE.
• Hasnain, A., Boddupalli, N., Balakrishnan, S. and Yeung, E., 2020, July. Steady state programming of controlled nonlinear systems via deep dynamic mode decomposition. In 2020 American Control Conference (ACC) (pp. 4245-4251). IEEE.
• Nandanoori, S.P., Sinha, S. and Yeung, E., 2022. Data-driven operator theoretic methods for phase space learning and analysis. Journal of Nonlinear Science, 32(6), pp.1-35.

This project was supported by the DARPA Information Innovation Office’s Synergistic Discovery and Design Program, lead by Dr. Jen Roberts, and is a collaborative multi-institutional program involving members from MIT, the Wyss Institute, Ginkgo Bioworks, Transcriptic, Netrias, Aptima, Two-Six Labs, Haverford College, the University of Washington, Duke University, Montana State University, and the Texas Advanced Computing Center at the University of Texas Austin.

Constructive Learning Algorithms for Microbial Pathogen Detection from Soil Samples

In this project, we aimed to develop an end-to-end pathogen classification platform using a combination of advanced microbial extraction methods, multi-omics host-pathogen interrogation assays, and machine learning decision analytics. Currently, there is no standardized assay for quantifying or classifying the pathogenicity of a novel microbial organism derived from soil samples. This project aimed to examine the morphological and genetic phenotypes that arise in host-pathogen interactions, to develop quantitative and categorical measures of pathogenicity.

Major research thrusts in this project included the development of optimized experimental assays to handle samples with extremely low cell counts, generating targeted mammalian reporter libraries for dynamic profiling of host-pathogen response, and the development of one-shot or few-shot learning algorithms with computational certificates of correctness.

This co-funded project was a collaboration with Drs. Janet Jansson, Becky Hess, Aaron Wright, and Rob Egbert at the Pacific Northwest National Laboratory, supported by the DARPA Biological Technologies Office “Friend or Foe” Program.

Machine-Assisted Design of Robust Biological Circuits with Extended Operational Envelopes

Synthetic biological circuits have, for the most part, been designed to operate in sustained rich media, in domesticated cell lines, at constant temperature and exponential growth phase. This project seeks to examine use a combination of high-throughput DNA assembly and machine learning to identify sequence determinants of synthetic biological parts that confer extended or complementary range of function in non-model growth conditions. How well do genetic circuits perform in the transition from exponential growth phase to stationary phase? Is it possible to engineer circuits that operate across the transition point? What are the fundamental limits of performance for genetic circuits optimized to work in stationary phase?

Publications:

• Boddupalli, N., Hasnain, A., Nandanoori, S.P. and Yeung, E., 2019, December. Koopman operators for generalized persistence of excitation conditions for nonlinear systems. In 2019 IEEE 58th Conference on Decision and Control (CDC)(pp. 8106-8111). IEEE.
• Harrison, J. and Yeung, E., 2021. Stability Analysis of Parameter Varying Genetic Toggle Switches Using Koopman Operators. Mathematics, 9(23), p.3133.
• Nandanoori, S.P., Sinha, S. and Yeung, E., 2022. Data-driven operator theoretic methods for phase space learning and analysis. Journal of Nonlinear Science, 32(6), pp.1-35.