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Modularity has been the subject of intense investigation in systems biology for more than two decades. Whether modularity holds in biological networks is a question that has attracted renewed attention in recent years with the advent of synthetic biology, mostly as a convenient property to perform bottom-up design of complex systems. A number of studies have appeared, which fundamentally challenge modularity as a property of engineered biological circuits. Here, we summarize some of these studies and potential engineering solutions that have been proposed to enable modular composition of biological circuits. ©2019 Elsevier Ltd., NSF through CMMI (Award no. 1727189)

Degradation of proteins in cells plays a large role in the dynamics of gene networks. This degradation is enabled by proteases, which are found in limited quantities in the cell. Proteins that compete for protease may therefore become coupled by non-explicit interactions, which are often neglected in mathematical models. In this work, we develop a model for these non-explicit interactions in gene networks. We examine the effects of protease sharing on the number of equilibria of a system and on the steady state protein concentrations. As a consequence of this analysis, we find that protease sharing effects may be used to cancel undesirable effects due to ribosome sharing.

In this paper, we consider the problem of designing in-vehicle driver-assist systems that warn or override the driver to prevent collisions with a guaranteed probability. The probabilistic nature of the problem naturally arises from many sources of uncertainty, among which the behavior of the surrounding vehicles and the response of the driver to on-board warnings. We formulate this problem as a control problem for uncertain systems under probabilistic safety specifications and leverage the structure of the application domain to reach computationally efficient implementations. Simulations using a naturalistic data set show that the empirical probability of safety is always within 5% of the theoretical value in the case of direct driver override. In the case of on-board warnings, the empirical value is more conservative due primarily to drivers decelerating more strongly than requested. However, the empirical value is greater than or equal to the theoretical value, demonstrating a clear safety benefit.

Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies) in need of transportation. The Duckietown platform offers a wide range of functionalities at a low cost. Duckiebots sense the world with only one monocular camera and perform all processing onboard with a Raspberry Pi 2, yet are able to: follow lanes while avoiding obstacles, pedestrians (duckies) and other Duckiebots, localize within a global map, navigate a city, and coordinate with other Duckiebots to avoid collisions. Duckietown is a useful tool since educators and researchers can save money and time by not having to develop all of the necessary supporting infrastructure and capabilities. All materials are available as open source, and the hope is that others in the community will adopt the platform for education and research.

A common approach to design genetic circuits is to compose gene expression cassettes together. While appealing, this modular approach is challenged by the fact that expression of each gene depends on the availability of transcriptional/translational resources, which is in turn determined by the presence of other genes in the circuit. This raises the question of how competition for resources by different genes affects a circuit’s behavior. Here, we create a library of genetic activation cascades in bacteriaE. coli, where we explicitly tune the resource demand by each gene. We develop a general Hill-function-based model that incorporates resource competition effects through resource demand coefficients. These coefficients lead to non-regulatory interactions among genes that reshape circuit’s behavior. For the activation cascade, such interactions result in surprising biphasic or monotonically decreasing responses. Finally, we use resource demand coefficients to guide the choice of ribosome binding site (RBS) and DNA copy number to restore the cascade’s intended monotonically increasing response. Our results demonstrate how unintended circuit’s behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.

A signaling pathway transmits information from an upstream system to downstream systems, ideally in a unidirectional fashion. A key obstacle to unidirectional transmission is retroactivity, the additional reaction flux that affects a system once its species interact with those of downstream systems. This raises the fundamental question of whether signaling pathways have developed specialized architectures that overcome retroactivity and transmit unidirectional signals. Here, we propose a general procedure based on mathematical analysis that provides an answer to this question. Using this procedure, we analyze the ability of a variety of signaling architectures to transmit one-way (from upstream to downstream) signals, as key biological parameters are tuned. We find that single stage phosphorylation and phosphotransfer systems that transmit signals from a kinase show a stringent design tradeoff that hampers their ability to overcome retroactivity. Interestingly, cascades of these architectures, which are highly represented in nature, can overcome this tradeoff and thus enable unidirectional transmission. By contrast, phosphotransfer systems, and single and double phosphorylation cycles that transmit signals from a substrate, are unable to mitigate retroactivity effects, even when cascaded, and hence are not well suited for unidirectional information transmission. These results are largely independent of the specific reaction-rate constant values, and depend on the topology of the architectures. Our results therefore identify signaling architectures that, allowing unidirectional transmission of signals, embody modular processes that conserve their input/output behavior across multiple contexts. These findings can be used to decompose natural signal transduction networks into modules, and at the same time, they establish a library of devices that can be used in synthetic biology to facilitate modular circuit design., National Science Foundation (U.S.) (Expedition award 1521925), National Institute of General Medical Sciences (U.S.) (grant P50 GMO98792)

Motivated by driver-assist systems that warn the driver before taking control action, we study the safety problem for a class of bounded hybrid automata. We show that for this class there exists a least restrictive safe feedback controller that has a simple structure and can be computed efficiently online. The theoretical results are then used to design driver-assist systems for rear-end and merging collision scenarios., National Science Foundation (U.S.). Cyber-Physical Systems (Award number 1239182)

Control theory has been instrumental for the development of a number of engineering systems, including aerospace and transportation systems, robotics and intelligent machines, manufacturing chains, electrical, power, and information networks. In the past decade, the ability of engineering biomolecular networks has become a reality in the rising field of synthetic biology. Biomolecular networks are composed of repression and activation interactions among genes, proteins, and small signaling molecules, and have the potential of implementing arbitrarily complex functions. While modular analysis and design is a promising approach to engineer complex networks, it is still subject of debate whether a modular approach is viable in biomolecular systems. The dynamics of these networks are highly nonlinear and therefore addressing this question requires the use of tools from nonlinear control theory. Here, we review some of the techniques that we have been developing in order to analyze and design biomolecular networks modularly.

Single phosphorylation cycles have been found to have insulation device abilities, that is, they attenuate the effect of retroactivity applied by downstream systems and hence facilitate modular design in synthetic biology. It was recently discovered that this retroactivity attenuation property comes at the expense of an increased retroactivity to the input of the insulation device, wherein the device slows down the signal it receives from its upstream system. In this paper, we demonstrate that insulation devices built of cascaded phosphorylation cycles can break this tradeoff, allowing to attenuate the retroactivity applied by downstream systems while keeping a small retroactivity to the input. In particular, we show that there is an optimal number of cycles that maximally extends the linear operating region of the insulation device while keeping the desired retroactivity properties, when a common phosphatase is used. These findings provide optimal design strategies of insulation devices for synthetic biology applications., National Institutes of Health (U.S.) (NIH P50 GMO98792)

We use a controlled invariance approach to design a semi-autonomous lane departure assist system that is guaranteed to keep the vehicle in the lane. The controlled invariant safe set is the set of system states from which an input exists that can keep the vehicle in the lane. First we provide theoretical conditions under which this set has a simple characterization and can be computed in real-time. We then use this characterization to derive a feedback strategy that keeps the vehicle in the lane and overrides the driver only if he/she could otherwise force a future lane departure.

This paper studies how retroactivity impacts the robustness of gene transcription networks against parameter perturbations. By employing the linearization technique and the real stability radius, we provide comparisons of the robustness between gene transcription networks with retroactivity and ones without retroactivity. Both numerical and analytical results show that retroactivity tends to decrease such robustness. This finding in turn implies that modular genetic networks tend to be more robust against parameter perturbations., National Science Foundation (U.S.) (NSF-CCF-I058127)

This paper applies Buckingham's pi theorem to the problem of building a scaled car whose longitudinal and power-train dynamics are similar to those of a full-size high-mobility multipurpose wheeled vehicle (HMMWV). The scaled vehicle uses hardware-in-the-loop (HIL) simulation to capture some of the scaled HMMWV dynamics physically, and simulates the remaining dynamics onboard in real time. This is performed with the ultimate goal of testing cooperative collision avoidance algorithms on a testbed comprising a number of these scaled vehicles. Both simulation and experimental results demonstrate the validity of this HIL-based scaling approach.

IEEE CSM interviews Domitilla Del Vecchio, assistant professor in the Electrical Engineering and Computer Science Department of the University of Michigan in Ann Arbor and Athanasios (Thanos) C. Antoulas of the Department of Electrical and Computer Engineering at Rice University.

In this paper, we consider the problem of keeping the state of a system outside of an undesired set of states with probability at least P. We focus on a class of order preserving systems with a constant input disturbance that is extracted from a known probability distribution. Leveraging the structure of the system, we construct an explicit supervisor that guarantees the system state to be kept outside the undesired set with at least probability P. We apply this supervisor to a collision avoidance problem, where a semi-autonomous vehicle is engaged in preventing a rear-end collision with a preceding human-driven vehicle, while stopping at a stop sign. We apply the designed supervisor in simulations in which the preceding vehicle trajectories are taken from a test data set. Using this data, we demonstrate experimentally that the probability of preventing a rear-end collision while stopping at the stop sign is at least P, as expected from theory. The simulation results further show that this probability is very close to P, indicating that the supervisor is not conservative., National Science Foundation (U.S.) (award #1161893)

The objective of this research is to develop quantitative techniques for the de novo design and fabrication of biosensing devices in living cells. Such devices will be entirely composed of bio-molecular building blocks extracted from living organisms and re-engineered for enhanced functionality. These biosensing devices will be able to reproduce themselves, will be both far smaller and significantly cheaper than current nanoscale electronic devices, and will have the capacity for complex computations.

This paper addresses the design of a decentralized safety controller for two agents, subject to communication delay and imperfect measurements. The control objective is to ensure safety, meaning that the state of the two-agent system does not enter an undesired set in the state space. Assuming that we know a feedback map designed for the delay free-case, we propose a state estimation strategy which guarantees control agreement between the two agents. We present an estimation technique for bounded communication delays, assuming that the agents share the same internal clock, and extend it for infinitely-distributed communication delays by determining a lower bound for the probability of safety. We also explain how the proposed approach can be extended to a general system of N agents and discuss efficient computation of our estimation strategy. Performance of the controller and relevance of the proposed approach are discussed in light of simulations performed for a collision avoidance problem between two semi-autonomous vehicles at an intersection. Keywords: Multi-agent systems; Communication delay; Estimation/prediction approaches; Safety control

This paper describes the implementation of a multi-vehicle supervisor to prevent collisions at intersections. The experiments are performed on an intersection testbed consisting of three RC cars. Here, we account for uncertainty in car dynamics and state measurement, and the presence of an uncontrolled car, which is human-driven. The supervisor overrides the controlled cars only when necessary to avoid a possible future collision. From the experiments, we demonstrate that intersection collisions are averted, that is, the cars continuously and safely run on the paths without stopping 92.8% of the times.

Modularity is the property according to which the input/output dynamic behavior of a system does not appreciably change after interconnection with other systems. Whether modularity is a natural property of biological systems is one of the most vexing questions in systems biology and crucial for the advancement of synthetic biology. In this paper, we recall design techniques for disturbance attenuation, which are well established in the control theory literature, and illustrate how the underlying principles are also found in biological systems as means to attain modularity. The specific system structure that we consider is the one where an integral action and the system internal dynamics occur at a much faster time scale than the reference input and external disturbances. In this case, the system displays a separation of time scales and can be taken to standard singular perturbation form to show that on the timescale of the reference input the effect of the disturbance is attenuated. We illustrate how this fast integral action structure is found in some interconnected biomolecular systems, where it allows to track time-varying input stimuli while rejecting loading disturbances due to interconnection with other systems., United States. Air Force. Office of Scientific Research (Grant # FA9550-12-1-0129), National Science Foundation (U.S.). Division of Computing and Communication Foundations (Award # 1058127)

Several control strategies are presented and studied for an industrial under-actuated tubular chemical reactor. This work presents a case-study of the performance of a decentralized versus centralized control strategy. The tubular reactor under consideration is characterized by nonlinear kinetic laws, and it has some structural constraints on the location of the heat exchangers and of the sensors. For this system, a set of PI controllers is considered and a multivariable LQR controller is constructed to optimally choose the gains. The performance of these control strategies is studied. Finally, a direct numerical treatment of optimal control of the partial differential equations is presented. Industrial results are given for the linear controllers. Simulations emphasize the possible relevance of a direct numerical treatment of the nonlinear partial differential equations. � 2005 Elsevier Ltd. All rights reserved.

Using tools from dynamical systems and systems identification, we develop a framework for the study of primitives for human motion, which we refer to as movemes. The objective is understanding human motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet of dynamical systems. We develop a segmentation and classification algorithm in order to reduce a complex activity into the sequence of movemes that have generated it. We test our ideas on data sampled from five human subjects who were drawing figures using a computer mouse. Our experiments show that we are able to distinguish between movemes and recognize them even when they take place in activities containing an unspecified number of movemes.

Using tools from dynamical systems theory and systems identification theory we develop the study of primitives for human motion which we refer to as movemes. We introduce basic definitions of dynamical independence of linear time-invariant dynamical systems (LTI) and segmentability of signals and we develop classification and segmentation algorithms for two dimensional motions. We test our ideas on data sampled from four human subjects who were engaged in a simple real-life activity including two movemes. Our experiments show that we are able to distinguish between the two movemes and recognize them even when they take place in an activity containing more than one moveme.

This paper describes the design of a supervisory controller (supervisor) that manages controlled vehicles to avoid intersection collisions in the presence of uncontrolled vehicles. Two main problems are addressed: verification of the safety of all vehicles at an intersection, and management of the inputs of controlled vehicles. For the verification problem, we employ an inserted idle-time scheduling approach, where the “inserted idle-time” is a time interval when the intersection is deliberately held idle for uncontrolled vehicles to safely cross the intersection. For the management problem, we design a supervisor that is least restrictive in the sense that it overrides controlled vehicles only when a safety violation becomes imminent. We analyze computational complexity and propose an efficient version of the supervisor with a quantified approximation bound., National Science Foundation (U.S.) (Award 1239182)

This paper considers the problem of attenuating retroactivity, that is, the effect of loads in biological networks and demonstrates that signal transduction cascades incorporating phosphotransfer modules have remarkable retroactivity attenuation ability. Uncovering the biological mechanisms for retroactivity attenuation is relevant in synthetic biology to enable bottom-up modular composition of complex circuits. It is also important in systems biology for deepening our current understanding of natural principles of modular organization. In this paper, we perform a combined theoretical and computational study of a cascade system comprising two phosphotransfer modules, ubiquitous in eukaryotic signal transduction, when subject to load from downstream targets. Employing singular perturbation on the finite time interval, we demonstrate that this system implements retroactivity attenuation when the input signal is sufficiently slow. Employing trajectory sensitivity analysis about nominal parameters that we have identified from in vivo data, we further demonstrate that the key parameters for retroactivity attenuation are those controlling the timescale of the system., Grant FA9550-12-1-0219, National Science Foundation (U.S.). Division of Computing and Communication Foundations (1058127)

We consider the problem of collision avoidance at road intersections in vehicular networks in the presence of uncontrolled vehicles, a disturbance, and measurement uncertainty. Our goal is to construct a supervisor of the continuous time system that is safe (i.e., avoids collisions), non-blocking (i.e., all vehicles eventually cross the intersection), and maximally permissive with respect to the discretization, despite the presence of a disturbance and of measurement uncertainty. We proceed in four steps: defining a discrete event system (DES) abstraction of the continuous time system, using uncontrollable events to model the uncontrolled vehicles and the disturbance; translating safety and non-blocking requirements to the DES level; solving at the DES level; and translating the resulting supervisor back from the DES level to the continuous level. We give sufficient conditions for this procedure to maintain the safety, non-blocking and maximal permissive properties as the supervisor is translated back from the DES level to the continuous level. Prior work on this problem based on similar abstractions assumes perfect measurement of position. Our method for handling measurement uncertainty is to introduce measurement events into the DES abstraction and then to compute the observer of the DES abstraction and the supremal controllable solution of the DES supervisory control problem., National Science Foundation (U.S.) (grant CNS-0930081)

One of the major challenges in systems and synthetic biology is the lack of modular composition. Modules change their behavior once connected, due to retroactivity. In this paper, we build upon our earlier results and provide a theorem establishing how the dynamics of a master module change once slave modules are present. We quantify the change in the dynamics of the master module due to interconnection as a function of measurable biochemical parameters. Based on this, we provide a bound on the difference between the trajectories of the connected system and those of the isolated system by employing contraction theory. Therefore, we obtain a measure of robustness, which helps evaluating the degree of modularity in a system, while providing guidelines for robust module design. We illustrate the results by considering a recurring motif in gene transcription networks: an autorepressed gene regulating the expression of several downstream targets., Grant FA9550-10-1-0242

Protein overexpression leads to growth inhibition and decreased expression of native proteins. This effect is mostly due to competition for RNAP and ribosomes. This competition affects also synthetic biology circuits, which may not behave as expected. The aims of this project are to: (1) characterize/quantify the effect of competition for RNAPs and ribosomes on synthetic genetic circuits; (2) determine the extent to which RNAPs or ribosomes are limiting; (3) develop models that predict the change of behavior of circuits due to competition; (4) demonstrate the predictions on the design of a synthetic cascade. We have constructed a synthetic circuit in E. coli and characterized through theory and experiments the effect of competition on the expression of a constitutively expressed gene when a different inducible gene is activated. For this system, we have also developed predictive models, both analytical and computational. Our finding quantifies the extent of competition and provides initial tools to predict this effect. Our experimental results and models will enable design methods for next-generation circuits that are robust to competition (to be investigated later). This ability will be critical for constructing large circuits reliably.

Control theory has been instrumental for the analysis and design of a number of engineering systems, including aerospace and transportation systems, robotics and intelligent machines, manufacturing chains, electrical, power, and information networks. In the past several years, the ability of de novo creating biomolecular networks and of measuring key physical quantities has come to a point in which quantitative analysis and design of biological systems is possible. While a modular approach to analyze and design complex systems has proven critical in most control theory applications, it is still subject of debate whether a modular approach is viable in biomolecular networks. In fact, biomolecular networks display context-dependent behavior, that is, the input/output dynamical properties of a module change once this is part of a network. One cause of context dependence, similar to what found in many engineering systems, is retroactivity, that is, the effect of loads applied on a module by downstream systems. In this paper, we focus on retroactivity and review techniques, based on nonlinear control and dynamical systems theory, that we have developed to quantify the extent of modularity of biomolecular systems and to establish modular analysis and design techniques.

In this paper, we leverage vehicle-to-vehicle (V2V) communication technology to implement computationally efficient decentralized algorithms for two-vehicle cooperative collision avoidance at intersections. Our algorithms employ formal control theoretic methods to guarantee a collision-free (safe) system, whereas overrides are only applied when necessary to prevent a crash. Model uncertainty and communication delays are explicitly accounted for by the model and by the state estimation algorithm. The main contribution of this work is to provide an experimental validation of our method on two instrumented vehicles engaged in an intersection collision avoidance scenario in a test track.

The past several years have witnessed an increased presence of control theoretic concepts in synthetic biology. This review presents an organized summary of how these control design concepts have been applied to tackle a variety of problems faced when building synthetic biomolecular circuits in living cells. In particular, we describe success stories that demonstrate how simple or more elaborate control design methods can be used to make the behaviour of synthetic genetic circuits within a single cell or across a cell population more reliable, predictable and robust to perturbations. The description especially highlights technical challenges that uniquely arise from the need to implement control designs within a new hardware setting, along with implemented or proposed solutions. Some engineering solutions employing complex feedback control schemes are also described, which, however, still require a deeper theoretical analysis of stability, performance and robustness properties. Overall, this paper should help synthetic biologists become familiar with feedback control concepts as they can be used in their application area. At the same time, it should provide some domain knowledge to control theorists who wish to enter the rising and exciting field of synthetic biology., United States. Air Force. Office of Scientific Research (grant no. FA9550-14-1- 0060), United States. Office of Naval Research (grant no. N000141310074), National Science Foundation (U.S.). Graduate Research Fellowship Program

This paper is concerned with safety control of systems with imperfect state information and disturbance input. Specifically, we consider the class of systems whose dynamics preserve a partial ordering. We provide necessary and sufficient conditions under which a given set of initial states is steerable away from a specified bad set. Moreover, a control strategy is provided that guarantees that the bad set is avoided. Such characterization is achieved for order preserving systems while for general systems only an approximated solution is achievable. A method for implementation of the control strategy is provided and the effectiveness of the proposed method is illustrated via a numerical example and employed for obstacle avoidance of a ship., National Science Foundation (U.S.) (NSF CAREER AWARD # CNS-0642719)

In this paper, the problem of collision avoidance between two vehicles is considered, in which one vehicle is autonomous and the other one is human-driven. This problem arises in cooperative active safety systems at traffic intersections, mergings, and roundabouts, in which some vehicles are equipped with on-board communication and automatic control, while others are not capable of communicating and are human-driven. We model the human driving behavior through a hybrid automaton, whose current mode is determined by the driver's decisions, and solve the problem as a safety control problem for hybrid systems with imperfect state information. The experimental results demonstrate that our solution is substantially less conservative than solutions employing worst-case design., National Science Foundation (U.S.) (NSF CAREER Award Number CNS-0642719)

A bottom-up approach to designing biomolecular circuits in living cells is based on composing simple working modules to create larger circuits with more complicated functions. For this approach to work, the behavior of the modules should be, to some extent, isolated from that of the surrounding modules. Unfortunately, impedance effects in biomolecular systems, called retroactivity, alter the behavior of a module upon interconnection with other systems. In this paper, we perform a mathematical analysis of a Goldbeter-Koshland covalent modification cycle, a recurrent circuit structure in natural signal transduction networks, and demonstrate that it can be tuned to function as an insulating amplifier, hence buffering systems from retroactivity.

We consider the problem of safety control in Hidden Mode Hybrid Systems (HMHS) that arises in the development of a semi-autonomous cooperative active safety system for collision avoidance at an intersection. We utilize the approach of constructing a new hybrid automaton whose discrete state is an estimate of the HMHS mode. A dynamic feedback map can then be designed that guarantees safety on the basis of the current mode estimate and the concept of the capture set. In this work, we relax the conditions for the termination of the algorithm that computes the capture set by constructing an abstraction of the new hybrid automaton. We present a relation to compute the capture set for the abstraction and show that this capture set is equal to the one for the new hybrid automaton.

In this paper, we show how to design a provably safe robotic roundabout system comprised of three vehicles. This is accomplished by combining two-vehicle collision avoidance primitives, which are each computationally light given the natural partial order structure on which the system evolves. We show how to design the system parameters in order to prevent conflicts among the control primitives, and to thus ensure the safety and liveness of the system as a whole. We implement our design on a multi-vehicle test-bed involving three vehicles continuously running on three intersecting roundabouts, and provide experimental results demonstrating the system is collision free and live.

As in several engineering applications, biomolecular systems present impedance-like effects at interconnections called retroactivity. This phenomenon presents a challenge for modular design in synthetic biology. An insulation device is a biological module that enables attenuation of retroactivity at interconnections. In this paper, we propose the design of an insulation device by exploiting the naturally existing time-scale separation of phosphotranspher systems.1

In prior work, we have shown that just as in many engineering systems, impedance-like effects appear at the interconnection of biomolecular systems. These effects are called retroactivity, to extend the notion of impedance to biological systems. Signaling components, such as covalent modification cycles, play a central role in the transmission of signals within a cell and from outside the cell. They are typically found in highly interconnected architectures in which a component has several downstream clients. In this paper, we show that retroactivity from downstream targets decreases the sensitivity of response to an input stimulus.Copyright © 2010 by ASME

This paper describes a synthetic in vitro genetic circuit programmed to work as an insulating device. This circuit is composed of nucleic acids, which can be designed to interact according to user defined rules, and of few proteins that perform catalytic functions. A model of the circuit is derived from first principle biochemical laws. This model is shown to exhibit time-scale separation that makes its output insensitive to downstream time varying loads. Simulation results show the circuit effectiveness and represent the starting point for future experimental testing of the device.

Modularity plays a fundamental role in the prediction of the behavior of a system from the behavior of its components, guaranteeing that the properties of individual components do not change upon interconnection. Just as electrical, hydraulic, and other physical systems often do not display modularity, nor do many biochemical systems, and specifically, genetic and signaling networks. Here, we study the effect of interconnections on the input/output dynamic characteristics of transcriptional components, focusing on a concept, which we call “retroactivity” that plays a role similar to impedance in electrical circuits. In order to attenuate the effect of retroactivity on a system dynamics, we propose to design insulation devices based on a feedback mechanism inspired by the design of amplifiers in electronics. In particular, we introduce a bio-molecular realization of an insulation device based on phosphorylation.

In this paper, we employ partial order techniques to develop linear complexity algorithms for guaranteed collision avoidance between vehicles at highway and roundabout mergings. These techniques can be employed by virtue of the rich structure offered by such traffic systems, which constrain vehicles to advance unidirectionally along a path. The algorithms are safe by construction while maintaining the liveness of the system. The proposed algorithms are on-line implemented in a decentralized fashion on an experimental testbed composed of two in-scale communicating vehicles continuously running on an autonomous roundabout system.

In standard control systems theory, a system is modeled as an input/output (I/O) module with internal dynamics. This implicitly assumes that the dynamics of a module do not change when the module is connected to other components. However, such an assumption is not realistic in a wide number of electrical, hydraulic, mechanical, and biological systems. We thus propose a new model that incorporates signals that travel from downstream to upstream, which we broadly call retroactivity. We quantify such a retroactivity in transcriptional components and show how to attenuate its effect by the design of insulation devices.

Retroactivity is a phenomenon that changes the desired input/output response of a system when it is connected to ?downstream? systems. Transcriptional networks are not immune to this phenomenon. In this paper, we propose a phosphorylation-based design for a bio-molecular system that acts as an insulator between its upstream systems and its downstream ones in a transcriptional network. Performing singular perturbation analysis, we mathematically show that such a design attenuates retroactivity. Stochastic simulations are run to analyze the robustness of the proposed device to biological noise and to highlight design tradeoffs.

This paper presents the application of Buckingham’s π theorem to scale the powertrain of a High Mobility Multipurpose Wheeled Vehicle (HMMWV) by deriving non dimensional ratios called π parameters. A Hardware In the Loop (HIL) setup is constructed and the resulting longitudinal dynamics of the scaled vehicle are validated against those of a full scale vehicle model. This is performed with the ultimate goal of testing cooperative collision avoidance algorithms on a testbed comprising a number of these scaled vehicles. This paper is based on “Development of a scaled vehicle with Longitudinal dynamics of a HMMWV for ITS testbed”, by Verma, R., Domitilla Del Vecchio, and Hosam K. Fathy which appeared in IEEE/ASME Transactions on Mechatronics, February 2008 and is being reprinted with permission from IEEE.Copyright © 2008 by ASME

Recent experimental results demonstrate that flies possess a robust tendency to orient towards the frontally-centered focus of the visual motion field that typically occurs during upwind flight. We present a closed loop flight model, with a control algorithm based on feedback of the location of the visual focus 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.

Using tools from dynamical systems and systems identification, we develop a framework for the study of primitives for human motion, which we refer to as movemes. The objective is understanding human motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet of dynamical systems. We develop a segmentation and classification algorithm in order to reduce a complex activity into the sequence of movemes that have generated it. We test our ideas on data sampled from five human subjects who were drawing figures using a computer mouse. Our experiments show that we are able to distinguish between movemes and recognize them even when they take place in activities containing an unspecified number of movemes.

The rules that govern decision making in systems controlled by humans are often simple to describe. However, deriving these rules from the actions of a group can be very difficult, making human behavior hard to predict. We develop an algorithm to determine the rules implemented by drivers at a traffic intersection by observing the trajectories of their cars. We apply such algorithm to a traffic intersection scenario reproduced in the Caltech multi-vehicle lab, with human subjects remotely driving kinematic robots. The results obtained on these data suggest that this kind of human behavior is to some extent predictable on our data set, and different subjects implement similar rules.

Modularity plays a fundamental role in the prediction of the behavior of a system from the behavior of its components, guaranteeing that the properties of individual components do not change upon interconnection. Just as electrical, hydraulic, and other physical systems often do not display modularity, nor do many biochemical systems, and specifically, genetic networks. Here, we study the effect of interconnections on the input-output dynamic characteristics of transcriptional components, focusing on a property, which we call 'retroactivity', that plays a role analogous to non-zero output impedance in electrical systems. In transcriptional networks, retroactivity is large when the amount of transcription factor is comparable to, or smaller than, the amount of promoter-binding sites, or when the affinity of such binding sites is high. To attenuate the effect of retroactivity, we propose a feedback mechanism inspired by the design of amplifiers in electronics. We introduce, in particular, a mechanism based on a phosphorylation-dephosphorylation cycle. This mechanism enjoys a remarkable insulation property, due to the fast timescales of the phosphorylation and dephosphorylation reactions.