As a field, synthetic biology strives to engineer increasingly complex artificial systems in living cells. Active feedback in closed loop systems offers a dynamic and adaptive way to ensure constant relative activity independent of intrinsic and extrinsic noise. In this work, we use synthetic protein scaffolds as a modular and tunable mechanism for concentration tracking through negative feedback. Input to the circuit initiates scaffold production, leading to colocalization of a two-component system and resulting in the production of an inhibitory antiscaffold protein. Using a combination of modeling and experimental work, we show that the biomolecular concentration tracker circuit achieves dynamic protein concentration tracking in Escherichia coli and that steady state outputs can be tuned.

Living organisms are differentiated by their genetic material -millions to billions of DNA bases encoding thousands of genes. These genes are translated into a vast array of proteins, many of which have functions that are still unknown. Previously, it was believed that simply knowing the genetic sequence of an organism would be the key to unlocking all understanding.However, as DNA sequencing technology has become affordable, even cheap, it has become clear that living cells are governed by complex, multilayered networks of gene regulation that cannot be deduced from sequence alone. Synthetic biology as a field might best be characterized as a learn-by-building approach, in which scientists attempt to engineer molecular pathways that do not exist in nature, and in doing so, test the limits of both natural and engineered organisms. Synthetic biology broadly encompasses the genetic engineering of organisms in order to implement and test new biological functions. A relatively young field, synthetic biology relies on biological discoveries in gene function as well as improvements in molecular biology tools for manipulation of DNA [1]. Current applications of synthetic biology include production of biofuels and other valuable chemicals [2], [3], molecular computation and logic [4], [5], medical diagnostics [6], and artificial microbial communities [7], [8]. These engineered biological circuits are often not robust because of sensitivity to environmental conditions, context effects within the host organism, and stochastic noise due to inherently low molecular counts. Applying feedback control would potentially allow biological circuits to perform their intended function more robustly across a variety of operating conditions, and ease the transition from very controlled lab conditions to practical real world applications. This survey aims to provide a general overview of relevant terms and resources for understanding the intersection of synthetic biology and control theory. A reader with a background in control theory should come away with a reasonable understanding of the current state-of-the-art of biological system identification, controller design and implementation, and To appear, Control Systems Magazine http://www.cds.caltech.edu/~murray/preprints/hsm17-ieeecsm.pdfthe open challenges facing the field. Additionally, this review updates and builds upon previous publications on this subject [9], [10], [11]. As this particular work is limited to a selected number of topics, additional reviews are suggested throughout the text for deeper reading.In the following sections, each of the challenges is addressed within the typical workflow for control implementation of more traditionally engineered systems (Figure 1). Engineered biological systems present a number of challenges to all stages of this workflow for reasons such as limitations in real-time measurement, resource competition with the host organism, and incomplete knowledge of underlying biological processes. First, strategies for framing a biological organism as...

Single-cell bacterial sensors have numerous applications in human health monitoring, environmental chemical detection, and materials biosynthesis. Such bacterial devices need not only the capability to differentiate between combinations of inputs, but also the ability to process signal timing and duration. In this work, we present a two-input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. The temporal logic gate design relies on unidirectional DNA recombination with bacteriophage integrases to detect and encode sequences of input events. When implemented in a chromosomally-modified E. coli strain, we can utilize stochastic single cell responses to predict overall heterogeneous population behavior. We show that a stochastic model can be used to predict final population distributions of this E. coli strain, and thus that final differentiated sub populations can be used to deduce the timing and duration of transient chemical events.

Engineered bacterial sensors have potential applications in human health monitoring, environmental chemical detection, and materials biosynthesis. While such bacterial devices have long been engineered to differentiate between combinations of inputs, their potential to process signal timing and duration has been overlooked. In this work, we present a two‐input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. Our temporal logic gate design relies on unidirectional DNA recombination mediated by bacteriophage integrases to detect and encode sequences of input events. For an E. coli strain engineered to contain our temporal logic gate, we compare predictions of Markov model simulations with laboratory measurements of final population distributions for both step and pulse inputs. Although single cells were engineered to have digital outputs, stochastic noise created heterogeneous single‐cell responses that translated into analog population responses. Furthermore, when single‐cell genetic states were aggregated into population‐level distributions, these distributions contained unique information not encoded in individual cells. Thus, final differentiated sub‐populations could be used to deduce order, timing, and duration of transient chemical events.

The recent abundance of high-throughput data for biological circuits enables data-driven quantitative modeling and parameter estimation. Common modeling issues include long computational times during parameter estimation, and the need for many iterations of this cycle to match data. Here, we present BioSCRAPE (Bio-circuit Stochastic Single-cell Reaction Analysis and Parameter Estimation) -a Python package for fast and flexible modeling and simulation for biological circuits. The BioSCRAPE package can be used for deterministic or stochastic simulations and can incorporate delayed reactions, cell growth, and cell division. Simulation run times obtained with the package are comparable to those obtained using C code -this is particularly advantageous for computationally expensive applications such as Bayesian inference or simulation of cell lineages. We first show the package's simulation capabilities on a variety of example simulations of stochastic gene expression. We then further demonstrate the package by using it to do parameter inference for a model of integrase dynamics using experimental data. The BioSCRAPE package is publicly available online along with more detailed documentation and examples.