Enoch Yeung
University of California, Santa Barbara, Mechanical Engineering, Faculty Member
Modern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised... more
Modern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised from common lab strains of Escherichia coli. It is clear that new applications such as chemical sensing and event logging in complex environments will benefit from new host chassis. This study quantitatively compared how the same chemical event logger performed across four strains and three different microbial species. An integrase-based sensor and memory device was operated by two representative soil Pseudomonads—Pseudomonas fluorescens SBW25 and Pseudomonas putida DSM 291. Quantitative comparisons were made between these two non-traditional hosts and two benchmark E. coli chassis including the probiotic Nissle 1917 and common cloning strain DH5α. The performance of sensor and memory components changed according to each host, such that a clear chass...
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This supplement provides detailed descriptions of the origin and construction of genetic circuits described in the paper, the DNA sequences of all genetic circuit components used to make the transcriptional event detector, as well as... more
This supplement provides detailed descriptions of the origin and construction of genetic circuits described in the paper, the DNA sequences of all genetic circuit components used to make the transcriptional event detector, as well as detailed mathematical analysis relating system crosstalk to the gain of entries in the dynamical structure function.
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In this paper, we develop the concept of information transfer between the Borel-measurable sets for a dynamical system described by a measurable space and a non-singular transformation. The concept is based on how Shannon entropy is... more
In this paper, we develop the concept of information transfer between the Borel-measurable sets for a dynamical system described by a measurable space and a non-singular transformation. The concept is based on how Shannon entropy is transferred between the measurable sets, as the dynamical system evolves. We show that the proposed definition of information transfer satisfies the usual notions of information transfer and causality, namely, zero transfer and transfer asymmetry. Furthermore, we show how the information transfer measure can be used to characterize ergodicity and mixing in dynamical systems. We also develop the computational methods for information transfer computation and apply the framework for optimal placement of actuators and sensors for control of non-equilibrium dynamics.
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In this paper, we provide an algorithm for online computation of Koopman operator in real-time using streaming data. In recent years, there has been an increased interest in data-driven analysis of dynamical systems, with operator... more
In this paper, we provide an algorithm for online computation of Koopman operator in real-time using streaming data. In recent years, there has been an increased interest in data-driven analysis of dynamical systems, with operator theoretic techniques being the most popular. Existing algorithms, like Dynamic Mode Decomposition (DMD) and Extended Dynamic Mode Decomposition (EDMD), use the entire data set for computation of the Koopman operator. However, many real life applications like power system analysis, biological systems, building systems etc. requires the real-time computation and updating of the Koopman operator, as new data streams in. In this paper, we propose an iterative algorithm for online computation of Koopman operator such that at each time step the Koopman operator is updated incrementally. In particular, we propose a Recursive Extended Dynamic Decomposition (rEDMD) algorithm for computation of Koopman operator from streaming data. Further, we test the algorithm in ...
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Dictionary methods for system identification typically rely on one set of measurements to learn governing dynamics of a system. In this paper, we investigate how fusion of output measurements with state measurements affects the dictionary... more
Dictionary methods for system identification typically rely on one set of measurements to learn governing dynamics of a system. In this paper, we investigate how fusion of output measurements with state measurements affects the dictionary selection process in Koopman operator learning problems. While prior methods use dynamical conjugacy to show a direct link between Koopman eigenfunctions in two distinct data spaces (measurement channels), we explore the specific case where output measurements are nonlinear, non-invertible functions of the system state. This setup reflects the measurement constraints of many classes of physical systems, e.g., biological measurement data, where one type of measurement does not directly transform to another. We propose output constrained Koopman operators (OC-KOs) as a new framework to fuse two measurement sets. We show that OC-KOs are effective for sensor fusion by proving that when learning a Koopman operator, output measurement functions serve to ...
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ABSTRACTModern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly... more
ABSTRACTModern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised from common lab strains ofEscherichia coli. It is clear that new applications – such as chemical sensing and event logging in complex environments – will benefit from new host chassis. This study quantitatively compared how a chemical event logger performed across multiple microbial species. An integrase-based sensor and memory device was operated by two representative soil Pseudomonads –Pseudomonas fluorescensSBW25 andPseudomonas putidaDSM 291. Quantitative comparisons were made between these two non-traditional hosts and two bench-markEscherichia colichassis including the probiotic Nissle 1917 and common cloning strain DH5α. The performance of sensor and memory components changed according to each host, such that a clear chassis effect was ...
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Synthetic gene expression is highly sensitive to intragenic compositional context (promoter structure, spacing regions between promoter and coding sequences, and ribosome binding sites). However, much less is known about the effects of... more
Synthetic gene expression is highly sensitive to intragenic compositional context (promoter structure, spacing regions between promoter and coding sequences, and ribosome binding sites). However, much less is known about the effects of intergenic compositional context (spatial arrangement and orientation of entire genes on DNA) on expression levels in synthetic gene networks. We compare expression of induced genes arranged in convergent, divergent, or tandem orientations. Induction of convergent genes yielded up to 400% higher expression, greater ultrasensitivity, and dynamic range than divergent- or tandem-oriented genes. Orientation affects gene expression whether one or both genes are induced. We postulate that transcriptional interference in divergent and tandem genes, mediated by supercoiling, can explain differences in expression and validate this hypothesis through modeling and in vitro supercoiling relaxation experiments. Treatment with gyrase abrogated intergenic context ef...
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Chemical reaction networks model biological interactions that regulate the functional properties of a cell; these networks characterize the chemical pathways that result in a particular phenotype. One goal of systems biology is to... more
Chemical reaction networks model biological interactions that regulate the functional properties of a cell; these networks characterize the chemical pathways that result in a particular phenotype. One goal of systems biology is to understand the structure of these networks given concentration measurements of various species in the system. Previous work has shown that this network reconstruction problem is fundamentally impossible, even for simplified linear models, unless a particular experiment design is followed. Nevertheless, reconstruction algorithms have been developed that attempt to approximate a solution using sparsity or similar heuristics. This work compares, in silico, the results of three of these methods in situations where the necessary experiment design has been followed, and it illustrates the degradation of each method as increasing noise levels are added to the data.