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Bacteroidesspecies are prominent members of the human gut microbiota. The prevalence and stability ofBacteroidesin humans make them ideal candidates to engineer as programmable living therapeutics. Here we report a biotic decision-making technology in a community ofBacteroides(consortium transcriptional programming) with genetic circuit compression. Circuit compression requires systematic pairing of engineered transcription factors with cognate regulatable promoters. In turn, we demonstrate the compression workflow by designing, building, and testing all fundamental two-input logic gates dependent on the inputs isopropyl-β-D-1-thiogalactopyranoside and D-ribose. We then deploy complete sets of logical operations in five human donorBacteroides, with which we demonstrate sequential gain-of-function control in co-culture. Finally, we couple transcriptional programs with CRISPR interference to achieve loss-of-function regulation of endogenous genes—demonstrating complex control over community composition in co-culture. This work provides a powerful toolkit to program gene expression inBacteroidesfor the development of bespoke therapeutic bacteria.

Traditionally engineered genetic circuits have almost exclusively used naturally occurring transcriptional repressors. Recently, non-natural transcription factors (repressors) have been engineered and employed in synthetic biology with great success. However, transcriptional anti-repressors have largely been absent with regard to the regulation of genes in engineered genetic circuits. Here, we present a workflow for engineering systems of non-natural anti-repressors. In this study, we create 41 inducible anti-repressors. This collection of transcription factors respond to two distinct ligands, fructose (anti-FruR) or D-ribose (anti-RbsR); and were complemented by 14 additional engineered anti-repressors that respond to the ligand isopropyl β-d-1-thiogalactopyranoside (anti-LacI). In turn, we use this collection of anti-repressors and complementary genetic architectures to confer logical control over gene expression. Here, we achieved all NOT oriented logical controls (i.e., NOT, NOR, NAND, and XNOR). The engineered transcription factors and corresponding series, parallel, and series-parallel genetic architectures represent a nascent anti-repressor based transcriptional programming structure.

We propose an algorithm using method of evolving junctions to solve the optimal path planning problems with piece-wise constant flow fields. In such flow fields, we prove that the optimal trajectories, with respect to a convex Lagrangian in the objective function, must be formed by piece-wise constant velocity motions. Taking advantage of this property, we transform the infinite dimensional optimal control problem into a finite dimensional optimization and use intermittent diffusion to solve the problems. The algorithm is proven to be complete. At last, we demonstrate the performance of the algorithm with various simulation examples.

The performance of a model predictive controller depends on the accuracy of the objective and prediction model of the system. Although significant efforts have been dedicated to improving the robustness of model predictive control (MPC), they typically do not take a risk-averse perspective. In this paper, we propose a risk-aware MPC framework, which estimates the underlying parameter distribution using online Bayesian learning and derives a risk-aware control policy by reformulating classical MPC problems as Bayesian Risk Optimization (BRO) problems. The consistency of the Bayesian estimator and the convergence of the control policy are rigorously proved. Furthermore, we investigate the consistency requirement and propose a risk monitoring mechanism to guarantee the satisfaction of the consistency requirement. Simulation results demonstrate the effectiveness of the proposed approach.

We propose a new concept named subschedulability to relax schedulability conditions on task sets in the context of scheduling and control co-design. Subschedulability is less conservative compared to schedulablity requirement with respect to network utilization. But it can still guarantee that all tasks can be executed before or within a bounded time interval after their deadlines. Based on the subschedulability concept, we derive an analytical timing model to check the sub-schedulability and perform online prediction of time-delays caused by real-time scheduling. A modified event-triggered contention-resolving MPC is presented to co-design the scheduling and control for the sub-schedulable control tasks. Simulation results are demonstrated to show the effectiveness of the proposed method.

In this paper, a constrained cooperative Kalman filter is developed to estimate field values and gradients along trajectories of mobile robots collecting measurements. We assume the underlying field is generated by a polynomial partial differential equation with unknown time-varying parameters. A long short-term memory (LSTM) based Kalman filter, is applied for the parameter estimation leveraging the updated state estimates from the constrained cooperative Kalman filter. Convergence for the constrained cooperative Kalman filter has been justified. Simulation results in a 2-dimensional field are provided to validate the proposed method.