Property:Abstract
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S
Recent advances in nucleic acids engineering introduced several RNA-based regulatory components for synthetic gene circuits, expanding the toolsets to engineer organisms. In this work, we designed genetic circuits implementing an RNA aptamer previously described to have the capability of binding to the T7 RNA polymerase and inhibiting its activity in vitro. Using in vitro transcription assays, we first demonstrated the utility of the RNA aptamer in combination with programmable synthetic transcription networks. As a step to quickly assess the feasibility of aptamer functions in vivo, a cell-free expression system was used as a breadboard to emulate the in vivo conditions of E. coli. We tested the aptamer and its three sequence variants in the cell-free expression system, verifying the aptamer functionality in the cell-free testbed. In vivo expression of aptamer and its variants demonstrated control over GFP expression driven by T7 RNA polymerase with different response curves, indicating its ability to serve as building blocks for both logic circuits and transcriptional cascades. This work elucidates the potential of RNA-based regulators for cell programming with improved controllability leveraging the fast production and degradation time scales of RNA molecules. +
V
Recent experimental results demonstrate that flies posses a robust tendency to orient towards
the frontally-centered pole of the visual motion field that typically occurs during upwind flight.
In this paper we present a closed loop flight model, with a control algorithm based on feedback
of the location of the visual pole of contraction, which is affected by changes in wind direction.
The feasibility of visually guided upwind orientation is demonstrated with a model derived
from current understanding of the biomechanics and sensorimotor computation of insects. The
matched filter approach used to model the visual system computations compares extremely well
with open-loop experimental data. +
Q
Recent work has introduced biocircuit architectures that exhibit robust oscillatory behavior in organisms ranging from cyanobacteria to mammals. Complementary research in synthetic biology has introduced oscillators in vivo and in vitro suggesting that robust oscillation can be recapitulated using a small number of biochemical components. In this work, we introduce signaling crosstalk in biocircuits as a consequence of enzyme-mediated biochemical reactions. As a motivating example, we consider an in vitro oscillator with two types of crosstalk: crosstalk in production and degradation of RNA signals. We then pose a framework for quantifying crosstalk and use it to derive several dynamical constraints and suggest design techniques for ameliorating crosstalk in in vitro biochemical systems. As an example, we show that the balance between production and degradation crosstalk plays a key role in determining system stability, potentially leading to loss of oscillatory behavior. We demonstrate that the effects of crosstalk can attenuated through the effective tuning of two key parameters in order to recover desired system dynamics. +
E
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks +
Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break be- fore an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) on- line learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework lever- ages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties.
Our novel controller synthesis algorithm, RL-CBF, guaran- tees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous car-following with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process. +
T
RoboFlag is a robotic version of capture the flag that can be played either with autonomous controllers or with up to two humans in the loop. The goal of the game is to capture the flag of the other team, which is located in their defense zone, and bring it back to your home zone. The other team defends their flag by tagging your robots on their side of the field, at the same time as trying to capture your flag. Some of the interesting features of RoboFlag are that the robots have limited sensing capability and distributed processing, requiring that information be communicated between robots across limited bandwidth links. This makes the game much more challenging since you have two determine your actions without a complete view of the playing field.
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This paper gives an overview of the RoboFlag competition and describes some of the research challenges that are represented in the game. +
S
Robot navigation in urban environments requires situational reasoning. Given the complexity of the environment and the behavior specified by traffic rules, it is necessary to recognize the current situation to impose the correct
traffic rules. In an attempt to manage the complexity of the situational reasoning subsystem, this paper describes a finite state machine model to govern the situational reasoning process. The logic state machine and its interaction with the planning system are discussed. The approach was implemented on Alice, Team Caltech's entry into the 2007 DARPA Urban Challenge. Results from the qualifying rounds are discussed. The approach is validated and the shortcomings of the implementation are identified. +
G
Robotic locomotion is based in a variety of instances upon cyclic changes in the shape
of a robot mechanism. Certain variations in shape exploit the constrained nature of a
robot's interaction with its environment to generate net motion. This is true for legged
robots, snakelike robots, and wheeled mobile robots undertaking maneuvers such as parallel
parking. In this paper we explore the use of tools from differential geometry to model and
analyze this class of locomotion mechanisms in a unified way. In particular, we describe
locomotion in terms of the geometric phase associated with a connection on a principal
bundle, and address issues such as controllability and choice of gait. We also provide an
introduction to the basic mathematical concepts which we require and apply the theory to
numerous example systems. +
R
Robots with structural flexibility provide an attractive alternative
to rigid robots for many of the new and evolving applications in
robotics. In certain applications their use is unavoidable. The
increased complexity in modeling and control of such robots is offset
by desirable performance enhancements in some respects. In this thesis
we present a singular perturbation approach for modeling, analysis and
control of robots with flexibility. As our singular perturbation
approach does not treat the flexible manipulator as a perturbation of
the rigid manipulator, it can treat significant flexibility, beyond
the linear range. Analysis based on this approach leads to some
provably stable control laws for the hybrid position and force control
of flexible-link manipulators. The analysis is done in the framework
of a single robot manipulator in a constrained motion
task. Simulations and experimental results are presented for this
case. To show applicability of the results to more general and
complex systems with flexibilities we also present experimental data
from a planar, two-fingered, reconfigurable grasping setup which allows
rigid and flexible configurations. The aim of the experimentation is
to show the applicability of the control laws and analysis developed,
and to determine the performance enhancements resulting from the
introduction of flexibility. Experimental data is analysed to show the
tradeoffs between controller complexity and performance enhancement as
we deal with greater flexibility. Various performance criteria are set
up and experimental results are discussed within their framework. We
conclude that large flexibility can be controlled without too much
additional effort, has performance comparable to that of rigid robots,
and possesses enhancing properties which make it appealing for use in
certain types of applications. +
A
Robustness to temperature variation is an important specification in biomolecular circuit design. While the cancellation of parametric temperature dependencies has been shown to improve the temperature robustness of the period in a synthetic oscillator design, the performance of other biomolecular circuit designs in different temperature conditions is relatively unclear. Using a combination of experimental measurements and mathematical models, we assessed the temperature robustness of two biomolecular circuit motifs—a negative feedback loop and a feedforward loop. We found that the measured responses of both the circuits changed with temperature, both in the amplitude and in the transient response. We also found that, in addition to the cancellation of parametric temperature dependencies, certain parameter regimes could facilitate the temperature robustness of the negative feedback loop, although at a performance cost. We discuss these parameter regimes in the context of the measured data for the negative feedback loop. These results should help develop a framework for assessing and designing temperature robustness in biomolecular circuits. +
N
Rotating stall and surge are aerodynamic instabilities that limit the performance of
aeroengines. A set of magnetic bearings supporting the compressor rotor is a potential
actuator for active control of rotating stall and surge. Based on a first-principles model
we show that using this type of actuation, the first harmonic mode of rotating stall is
linearly controllable, but the second harmonic mode and the surge mode are linearly
uncontrollable. We then give an explicit procedure for designing feedback laws such that
the first mode is linearly stabilized and the criticality of the Hopf bifurcations of the
second mode and the surge mode are supercritical. We also investigate the effects of
magnitude saturation on the regions of attraction. We demonstrate the theoretical results
by numerical simulations of a model for a transonic compressor at the NASA Lewis Research
Center. +
T
Rules or specifications for autonomous vehicles are currently formulated on a case-by-case basis, and put together in a rather ad-hoc fashion. As a step towards eliminating this practice, we propose a systematic procedure for generating a set of supervisory specifications for self-driving cars that are 1) associated with a distributed assume-guarantee structure and 2) characterizable by the notion of consistency and completeness. Besides helping autonomous vehicles make better decisions on the road, the assume-guarantee contract structure also helps address the notion of blame when undesirable events occur. We give several game-theoretic examples to demonstrate applic bility of our framework. +
R
Safe and robust G&C (Guidance and Control) algorithms for onboard implementation are developed by augmenting a model predictive control technique with a safety mode. The application example herein is spacecraft small-body proximity operations where model and constraint uncertainty warrant G&C algorithms with a degree of autonomous, onboard decision capability. The algorithm enforces state and control constraints and merges two operational modes: (I) standard mode guides the spacecraft to the proximity of a target state in a robust and resolvable model-predictive manner; (II) safety mode, if activated, maintains the spacecraft near a safety reference for all time. The algorithm utilizes separate feedforward and feedback components. In standard mode, the feedforward guidance solutions come from a way-point generation algorithm that uses a discrete linear-time-varying dynamics model. This approach provides a convex formulation of the problem (solvable onboard as a second-order cone program) that includes control and state constraints; the safety-mode availability adds a constraint in this standard-mode formulation as well. The feedback guarantees standard-mode resolvability to update the guidance profile in a robust, model-predictive manner. In safety mode, an offline-designed feedforward policy with the added feedback maintains the spacecraft in a hovering state in the proximity of its position at safety-mode activation time; this provides robustness to unexpected state-constraint changes such as unexpected ground proximity during landing operations. A simulation demonstrating both the standard and safety modes is provided for a spacecraft autonomous-descent scenario toward a small asteroid with an uncertain gravity model and errors in the surface altitude constraint. +
A
Safety guarantees are built into a robust MPC (Model Predictive Control) algorithm for uncertain nonlinear systems. The algorithm is designed to obey all state and control constraints and blend two operational modes: (I) standard mode guarantees resolvability and asymptotic convergence to the origin in a robust receding-horizon manner; (II) safety mode, if activated, guarantees containment within an invariant set about a safety reference for all time. This research is motivated by physical vehicle control-algorithm design (e.g. spacecraft and hovercraft) in which operation mode changes must be considered. Incorporating safety mode provides robustness to unexpected state-constraint changes; e.g., other vehicles crossing/stopping in the feasible path, or unexpected ground proximity in landing scenarios. The safety-mode control is provided by an offline designed control policy that can be activated at any arbitrary time during standard mode. The standard-mode control consists of separate feedforward and feedback components; feedforward comes from online solution of a FHC (Finite-Horizon optimal Control problem), while feedback is designed offline to generate an invariant tube about the feedforward tra jectory. The tube provides robustness (to uncertainties and disturbances in the dynamics) and guarantees FHC resolvability. The algorithm design is demonstrated for a class of systems with uncertain nonlinear terms that have norm-bounded Jacobians. +
R
Serine integrases are bacteriophage proteins responsible for integrating the phage genome into that of the host. Synthetic biologists have co-opted these proteins into useful tools for permanent DNA logic, utilizing their specific DNA recombination abilities to build synthetic cell differentiation and genetic memory systems. Each integrase has a specific pair of DNA sequences (attP/attB sites) that it recombines, but multiple identical sites can result in unpredictable recombination. We have developed a way to control integrase activity on identical attP/attB sites by using catalytically dead Cas9 (dCas9) as a programmable binding protein that can compete with integrase for binding to specific attachment sites. Utilizing a plasmid that contains two identical Bxb1 attP sites, integration can be repressed up to 8 fold at either one of the two attP sites when guide RNA and dCas9 are present. Guide RNA sequences that bind specifically to attB, or either of two attP sites, have been developed. Future goals are to utilize this technology to construct larger and more complex integrase logic circuits. +
U
Single-cell bacterial sensors have numerous applications in human health monitoring, environmental chemical detection, and materials biosynthesis. Many previous efforts for synthetic bacteria strains seek to optimize homogenous single cell behavior. Rather than attempt to reduce noise in circuit behavior, we take advantage of heterogenous single cell responses to record sequences of chemical events within a population. Using an engineered E. coli strain with a 4-state temporal logic gate, we show, both in silico and in vivo, that stochastic digital switching within single cells results in an analog population fractionation that can be used to resolve inducer pulse duration within 30 minutes. Furthermore, these results are preserved in the genome and can be read out at a time that is much later than the time of the event. +
L
Specifications for complex engineering systems are typically decomposed into specifications for individual subsystems in a manner that ensures they are implementable and simpler to develop further. We describe a method to algorithmically construct component specifications that implement a given specification when assembled. By eliminating variables that are irrelevant to realizability of each component, we simplify the specifications and reduce the amount of information necessary for operation. We parametrize the information flow between components by introducing parameters that select whether each variable is visible to a component or not. The decomposition algorithm identifies which variables can be hidden while pre- serving realizability and ensuring correct composition, and these are eliminated from component specifications by quantification and conversion of binary decision diagrams to formulas. The resulting specifications describe component viewpoints with full information with respect to the remaining variables, which is essential for tractable algorithmic synthesis of implementations. The specifications are written in TLA+, with liveness properties restricted to an implication of conjoined recurrence properties, known as GR(1). We define an operator for forming open systems from closed systems, based on a variant of the “while-plus” operator. This operator simplifies the writing of specifications that are realizable without being vacuous. To convert the generated specifications from binary decision diagrams to readable formulas over integer variables, we symbolically solve a minimal covering problem. We show with examples how the method can be applied to obtain contracts that formalize the hierarchical structure of system design. +
C
Standard schemes in system identification and adaptive control rely on persistence of excitation to guaran- tee parameter convergence. Inspired by networked systems, we extend parameter adaptation to the multi-agent setting by combining a gradient law with consensus dynamics. The gradient law introduces a learning signal, while consensus dynamics preferentially push each agentâs parameter estimates toward those of its neighbors. We show that the resulting online, decentralized parameter estimator combines local and neighboring information to identify the true parameters even if no single agent employs a persistently exciting input. We also elaborate upon collective persistence of excitation in networked adaptive algorithms. +
Station keeping and reorientation control of a cluster of fully-actuated
low-thrust micro-satellites is considered in this paper. We address the control
problem by taking advantage of the fully-actuated structure of the micro-satellite.
We propose a very general open-loop solution by solving in real-time constrained
trajectory generation problems for stationkeeping and reorientation. Performance
of this methodology is reported for a typical micro-satellite format ion flying
space mission using the Nonlinear Trajectory Generation software package. +
A
A Bayesian approach to inferring chemical signal timing and amplitude in a temporal logic gate using the cell population distributional response +
Stochastic gene expression poses an important challenge for engineering robust behaviors in a heterogeneous cell population. Cells address this challenge by operating on distributions of cellular responses generated by noisy processes. Similarly, a previously published temporal logic gate considers the distribution of responses across a cell population under chemical inducer pulsing events. The design uses a system of two integrases to engineer an E. coli strain with four DNA states that records the temporal order of two chemical signal events. The heterogeneous cell population response was used to infer the timing and duration of the two chemical signals for a small set of events. Here we use the temporal logic gate system to address the problem of extracting information about chemical signal events. We use the heterogeneous cell population response to infer whether any event has occurred or not and also to infer its properties such as timing and amplitude. Bayesian inference provides a natural framework to answer our questions about chemical signal occurrence, timing, and amplitude. We develop a probabilistic model that incorporates uncertainty in the how well our model captures the cell population and in how well a sample of measured cells represents the entire population. Using our probabilistic model and cell population measurements taken every five minutes on generated data, we ask how likely it was to observe the data for parameter values that describe square-shaped inducer pulses. We compare the likelihood functions associated with the probabilistic models for the event with the chemical signal pulses turned on versus turned off. Hence, we can determine whether an event of chemical induction of integrase expression has occurred or not. Using Markov Chain Monte Carlo, we sample the posterior distribution of chemical pulse parameters to identify likely pulses that produce the data measurements. We implement this method and obtain accurate results for detecting chemical inducer pulse timing, length, and amplitude. We can detect and identify chemical inducer pulses as short as half an hour, as well as all pulse amplitudes that fall under biologically relevant conditions.
A stochastic framework for the design of transient and steady state behavior of biochemical reaction networks +
Stochasticity plays an essential role in biochemical systems. Stochastic behaviors of bimodality, excitability, and fluctuations have been observed in biochemical reaction networks at low molecular numbers. Stochastic dynamics can be captured by modeling the system using a forward Kolmogorov equation known in the biochemical literature as the chemical master equation. The chemical master equation describes the time evolution of the probability distributions of the molecule species. We develop a stochastic framework for the design of these time evolving probability distributions that includes specifying their uni-/multi-modality, their first moments, and their rate of convergence to the stationary distribution. By solving the corresponding optimizations programs, we determine the reaction rates of the biochemical systems that satisfy our design specifications. We then apply the design framework to examples of biochemical reaction networks to illustrate its strengths and limitations. +
