Recent advances in synthetic biology have enabled the design of genetic feedback control circuits that could be implemented to build resilient plants against pathogen attacks. To facilitate the proper design of these genetic feedback... more
Recent advances in synthetic biology have enabled the design of genetic feedback control circuits that could be implemented to build resilient plants against pathogen attacks. To facilitate the proper design of these genetic feedback control circuits, an accurate model that is able to capture the vital dynamical behaviour of the pathogen-infected plant is required. In this study, using a data-driven modelling approach, we develop and compare four dynamical models (i.e. linear, Michaelis-Menten, standard S-System and extended S-System) of a pathogen-infected plant gene regulatory network (GRN). These models are then assessed across several criteria, i.e. ease of identifying the type of gene regulation, the predictive capability, Akaike Information Criterion (AIC) and the robustness to parameter uncertainty to determine its viability of modelling the pathogen-infected plant GRN. Using our defined ranking score, our analyses show that while the extended S-System model ranks highest in ...
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Research Interests: Computer Science, Artificial Intelligence, Computer Vision, Robot Vision, Calibration, and 5 moreROBOT, IEEE, Ta, TS, and TJ
Farming consumes a large amount of water usage and it is reported that large portion of this water is wasted through inefficient water distribution from river to farms. More efficient water distribution and preservation of environmental... more
Farming consumes a large amount of water usage and it is reported that large portion of this water is wasted through inefficient water distribution from river to farms. More efficient water distribution and preservation of environmental demands can be achieved through better control and decision support systems. In order to design better control and decision support systems, a river model is required. This model needs to be able to capture the relevant river dynamics and easy to be used for control design. Traditionally, the Saint Venant equations have been used to model river systems. These equations are nonlinear hyperbolic partial differential equation (PDE) and are solved numerically using Preissmann scheme. The simulated Saint Venant equations are compared against operational data from the Broken River, and it is found that the Saint Venant equations are accurate in representing the river systems. Through further study, it is found that a single segmentation, i.e. treating the ...
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RNA-based regulators are promising tools for building synthetic biological systems that provide a powerful platform for achieving a complex regulation of transcription and translation. Recently, de novo-designed synthetic RNA regulators,... more
RNA-based regulators are promising tools for building synthetic biological systems that provide a powerful platform for achieving a complex regulation of transcription and translation. Recently, de novo-designed synthetic RNA regulators, such as the small transcriptional activating RNA (STAR), toehold switch (THS), and three-way junction (3WJ) repressor, have been utilized to construct RNA-based synthetic gene circuits in living cells. In this work, we utilized these regulators to construct type 1 incoherent feed-forward loop (IFFL) circuits in vivo and explored their dynamic behaviors. A combination of a STAR and 3WJ repressor was used to construct an RNA-only IFFL circuit. However, due to the fast kinetics of RNA–RNA interactions, there was no significant timescale difference between the direct activation and the indirect inhibition, that no pulse was observed in the experiments. These findings were confirmed with mechanistic modeling and simulation results for a wider range of co...
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This paper proposes a mode switching supervisory controller for autonomous vehicles. The supervisory controller selects the most appropriate controller based on safety constraints and on the vehicle location with respect to junctions.... more
This paper proposes a mode switching supervisory controller for autonomous vehicles. The supervisory controller selects the most appropriate controller based on safety constraints and on the vehicle location with respect to junctions. Autonomous steering, throttle and deceleration control inputs are used to perform variable speed lane keeping assist, standard or emergency braking and to manage junctions, including roundabouts. Adaptive model predictive control with lane keeping assist is performed on the main roads and a linear pure pursuit inspired controller is applied using waypoints at road junctions where lane keeping assist sensors present a safety risk. A multi-stage rule based autonomous braking algorithm performs stop, restart and emergency braking maneuvers. The controllers are implemented in MATLAB® and Simulink™ and are demonstrated using the Automatic Driving Toolbox™ environment. Numerical simulations of autonomous driving scenarios demonstrate the efficiency of the la...
Research Interests: Civil Engineering, Model Predictive Control (MPC), Urban And Regional Planning, Matlab, Switched Systems, and 10 moreAutonomous Vehicles, Autonomous driving, TL, Cruise Control, markovian jump systems, Pure pursuit, Switched Control, signalized roundabout, Adaptive Model Predictive Control, and Path following
Abstract This paper proposes the use of the built-in self-scaling (BS) method for ISO classification of road roughness. The technique employs the transfer function between the vehicle body acceleration as input and the suspension travel... more
Abstract This paper proposes the use of the built-in self-scaling (BS) method for ISO classification of road roughness. The technique employs the transfer function between the vehicle body acceleration as input and the suspension travel as output. This transfer function has a nonzero dc gain, which is important for application of the BS method. Frequency response magnitude patterns corresponding to this transfer function are estimated via Bayesian regression, capitalizing on the inherent properties of the BS method where the prior dc gain is incorporated into the formulation. This strategy leads to high classification accuracy. The proposed approach requires only low-cost sensors. It possesses a short detection time of 0.5s and a short training time of 5s for each road class. The method is model-free and does not require recalibration when the load carried by the vehicle changes. Additionally, it is capable of handling varying vehicle velocity and is effective for both passive and active suspensions. A laboratory-scale experiment shows that the proposed technique increases the percentage of correct classification by an average of 34% in the case of constant road profiles, compared with a state-of-the-art method using augmented Kalman filtering. A corresponding value of 24% is achieved for a varying road profile. The significant improvement in the accuracy of road classification is impactful as it will enable controller design for suspension systems to be enhanced resulting in more comfortable ride and higher vehicle stability.
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Chemical reaction networks based on catalysis, degradation, and annihilation may be used as building blocks to construct a variety of dynamical and feedback control systems in Synthetic Biology. DNA strand-displacement, which is based on... more
Chemical reaction networks based on catalysis, degradation, and annihilation may be used as building blocks to construct a variety of dynamical and feedback control systems in Synthetic Biology. DNA strand-displacement, which is based on DNA hybridisation programmed using Watson-Crick base pairing, is an effective primitive to implement such reactions experimentally. However, experimental construction, validation and scale-up of nucleic acid control systems is still significantly lagging theoretical developments, due to several technical challenges, such as leakage, crosstalk, and toehold sequence design. To help the progress towards experimental implementation, we provide here designs representing two fundamental classes of reference tracking control circuits (integral and state-feedback control), for which the complexity of the chemical reactions required for implementation has been minimised. The supplied ‘minimally complex’ control circuits should be ideal candidates for first e...
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Research Interests: Chemical Engineering, Computer Science, Synthetic Biology, Process Control, Modelling and simulation, and 8 moreNonlinear Systems, Robustness (evolution), Industrial and Manufacturing Engineering, Robustness, Parametric Statistics, Chemical Reaction Networks, Nucleic Acid, and Automatic Control and Systems Engineering
Measurement techniques in biology are now able to provide data on the trajectories of multiple individual molecules simultaneously, motivating the development of techniques for the stochastic spatio-temporal modelling of biomolecular... more
Measurement techniques in biology are now able to provide data on the trajectories of multiple individual molecules simultaneously, motivating the development of techniques for the stochastic spatio-temporal modelling of biomolecular networks. However, standard approaches based on solving stochastic reaction-diffusion equations are computationally intractable for large-scale networks. We present a novel method for modeling stochastic and spatial dynamics in biomolecular networks using a simple form of the Langevin equation with noisy kinetic constants. Spatial heterogeneity in molecular interactions is decoupled into a set of compartments, where the distribution of molecules in each compartment is idealised as being uniform. The reactions in the network are then modelled by Langevin equations with correcting terms, that account for differences between spatially uniform and spatially non-uniform distributions, and that can be readily estimated from available experimental data. The ac...
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Cycles of covalent modification are ubiquitous motifs in cellular signalling. Although such signalling cycles are implemented via a highly concise set of chemical reactions, they have been shown to be capable of producing multiple... more
Cycles of covalent modification are ubiquitous motifs in cellular signalling. Although such signalling cycles are implemented via a highly concise set of chemical reactions, they have been shown to be capable of producing multiple distinct input-output mapping behaviours - ultrasensitive, hyperbolic, signal-transducing and threshold-hyperbolic. In this paper, we show how the set of chemical reactions underlying covalent modification cycles can be exploited for the design of synthetic analog biomolecular circuitry. We show that biomolecular circuits based on the dynamics of covalent modification cycles allow (a) the computation of nonlinear operators using far fewer chemical reactions than purely abstract designs based on chemical reaction network theory, and (b) the design of nonlinear feedback controllers with strong performance and robustness properties. Our designs provide a more efficient route for translation of complex circuits and systems from chemical reactions to DNA strand...
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The use of abstract chemical reaction networks (CRNs) as a modelling and design framework for the implementation of computing and control circuits using enzyme-free, entropy driven DNA strand displacement (DSD) reactions is starting to... more
The use of abstract chemical reaction networks (CRNs) as a modelling and design framework for the implementation of computing and control circuits using enzyme-free, entropy driven DNA strand displacement (DSD) reactions is starting to garner widespread attention in the area of synthetic biology. Previous work in this area has demonstrated the theoretical plausibility of using this approach to design biomolecular feedback control systems based on classical proportional-integral (PI) controllers, which may be constructed from CRNs implementing gain, summation and integrator operators. Here, we propose an alternative design approach that utilises the abstract chemical reactions involved in cellular signalling cycles to implement a biomolecular controller - termed a signalling-cycle (SC) controller. We compare the performance of the PI and SC controllers in closed-loop with a nonlinear second-order chemical process. Our results show that the SC controller outperforms the PI controller ...
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Feedback control is widely used in chemical engineering to improve the performance and robustness of chemical processes. Feedback controllers require a 'subtractor' that is able to compute the error between the process output and... more
Feedback control is widely used in chemical engineering to improve the performance and robustness of chemical processes. Feedback controllers require a 'subtractor' that is able to compute the error between the process output and the reference signal. In the case of embedded biomolecular control circuits, subtractors designed using standard chemical reaction network theory can only realise one-sided subtraction, rendering standard controller design approaches inadequate. Here, we show how a biomolecular controller that allows tracking of required changes in the outputs of enzymatic reaction processes can be designed and implemented within the framework of chemical reaction network theory. The controller architecture employs an inversion-based feedforward controller that compensates for the limitations of the one-sided subtractor that generates the error signals for a feedback controller. The proposed approach requires significantly fewer chemical reactions to implement than ...
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The ability to take constraints explicitly into account in the optimisation problem formulation has made Model Predictive Control (MPC) a popular control strategy. However, finding the weights in the optimisation criterion is often... more
The ability to take constraints explicitly into account in the optimisation problem formulation has made Model Predictive Control (MPC) a popular control strategy. However, finding the weights in the optimisation criterion is often non-trivial and requires a fair bit of trial-and-error. Conventional controllers such as PID, on the other hand, are relatively easy to tune, and they often achieve satisfactory performance when there are no constraints. In this paper, we present three approaches for designing an MPC such that it reproduces the conventional controller when there are no constraints. The first approach is from the existing literature where a full order observer is used in the reverse engineering. The second and third approach used a reduced order observer and state augmentation respectively are proposed. The obtained controller can also serve as an initial MPC which can be further fine tuned, and constraints can later be added to evaluate any potential benefit of using MPC compared to the original controller.
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von Willebrand factor (VWF) is a key load bearing domain for mamalian cell adhesion by binding various macromolecular ligands in extracellular matrix such as, collagens, elastin, and glycosaminoglycans. Interestingly, vWF like domains are... more
von Willebrand factor (VWF) is a key load bearing domain for mamalian cell adhesion by binding various macromolecular ligands in extracellular matrix such as, collagens, elastin, and glycosaminoglycans. Interestingly, vWF like domains are also commonly found in load bearing systems of marine organisms such as in underwater adhesive of mussel and sea star, and nacre of marine abalone, and play a critical load bearing function. Recently, Proximal Thread Matrix Protein1 (PTMP1) in mussel composed of two vWF type A like domains has characterized and it is known to bind both mussel collagens and mammalian collagens. Here, we cloned and mass produced a recombinant PTMP1 from E. coli system after switching all the minor codons to the major codons of E. coli. Recombinant PTMP1 has an ability to enhance mouse osteoblast cell adhesion, spreading, and cell proliferation. In addition, PTMP1 showed vWF-like properties as promoting collagen expression as well as binding to collagen type I, subseq...
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A wide range of organisms features molecular machines, circadian clocks, which generate endogenous oscillations with ~24 h periodicity and thereby synchronize biological processes to diurnal environmental fluctuations. Recently, it has... more
A wide range of organisms features molecular machines, circadian clocks, which generate endogenous oscillations with ~24 h periodicity and thereby synchronize biological processes to diurnal environmental fluctuations. Recently, it has become clear that plants harbor more complex gene regulatory circuits within the core circadian clocks than other organisms, inspiring a fundamental question: are all these regulatory interactions between clock genes equally crucial for the establishment and maintenance of circadian rhythms? Our mechanistic simulation for Arabidopsis thaliana demonstrates that at least half of the total regulatory interactions must be present to express the circadian molecular profiles observed in wild-type plants. A set of those essential interactions is called herein a kernel of the circadian system. The kernel structure unbiasedly reveals four interlocked negative feedback loops contributing to circadian rhythms, and three feedback loops among them drive the autono...
Research Interests: Genetics, Computer Science, Algorithms, Mental Health, Computational Biology, and 15 moreCircadian Rhythms, Biology, Interaction, Cell Biology, Medicine, Biological Sciences, Feedback loops, Mathematical Sciences, Circadian Rhythm, Gene, Arabidopsis, Gene Regulatory Networks, Circadian Clock, Circadian Clocks, and Clockwork
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The circadian system generates an endogenous oscillatory rhythm that governs the daily activities of organisms in nature. It offers adaptive advantages to organisms through a coordination of their biological functions with the optimal... more
The circadian system generates an endogenous oscillatory rhythm that governs the daily activities of organisms in nature. It offers adaptive advantages to organisms through a coordination of their biological functions with the optimal time of day. In this paper, a model of the circadian system in the plant Arabidopsis (species thaliana) is built by using system identification techniques. Prior knowledge about the physical interactions of the genes and the proteins in the plant circadian system is incorporated in the model building exercise. The model is built by using primarily experimentally-verified direct interactions between the genes and the proteins with the available data on mRNA and protein abundances from the circadian system. Our analysis reveals a great performance of the model in predicting the dynamics of the plant circadian system through the effect of diverse internal and external perturbations (gene knockouts and day-length changes). Furthermore, we found that the circadian oscillatory rhythm is robust and does not vary much with the biochemical parameters except those of a light-sensitive protein P and a transcription factor TOC1. In other words, the circadian rhythmic profile is largely a consequence of the network’s architecture rather than its particular parameters. Our work suggests that the current experimental knowledge of the gene-to-protein interactions in the plant Arabidopsis, without considering any additional hypothetical interactions, seems to suffice for system-level modeling of the circadian system of this plant and to present an exemplary platform for the control of network dynamics in complex living organisms.
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Farming consumes a large amount of water and a large portion of that water is wasted through inefficient distribution from rivers to farms. More efficient water distribution can be achieved with better control and decision support... more
Farming consumes a large amount of water and a large portion of that water is wasted through inefficient distribution from rivers to farms. More efficient water distribution can be achieved with better control and decision support systems. In order to design such systems, river models are required. Traditionally, the Saint Venant equations which are partial differential equations, have been used
Research Interests: Mathematics, Partial Differential Equations, Computer Science, System Identification, Controller Design, and 11 moreDecision support system, Identification, Control Design, Time Delay, Farming, PARTIAL DIFFERENTIAL EQUATION, Delays, Saint-Venant Equations, Experimental Validation, Water Distribution, and Shallow water equations
The Saint Venant equations are two nonlinear partial differential equations (PDE) which are used to describe the dynamics of one-dimensional flow in open water channels. Despite being nonlinear PDEs, the Saint Venant equations seem to... more
The Saint Venant equations are two nonlinear partial differential equations (PDE) which are used to describe the dynamics of one-dimensional flow in open water channels. Despite being nonlinear PDEs, the Saint Venant equations seem to exhibit linear behaviour in response to sinusoidal input signals. It is therefore of interest to determine “how nonlinear” the Saint Venant equations are. In this paper, we investigate the nonlinearity in the Saint Venant equations using several commonly used nonlinearity tests suggested in the literature. Five different open water channels are considered, and the results from the nonlinearity tests show that the Saint Venant equations are nearly linear in an operating region from at least half the nominal flow to twice the nominal flows, and many of the channels display linear behaviour in a larger operating region. This finding is useful as it further justifies the use of linear control design methodologies for open water channels.
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Research Interests: Engineering, Applied Mathematics, Computer Science, System Identification, Control Engineering, and 15 moreModel Predictive Control, Water resources, Modeling, Model Predictive Control (MPC), Control Systems, Identification, Environmental Systems, Flexibility in engineering design, Water Conservation, Decentralized Control, Control Management, River Systems, Electrical And Electronic Engineering, Predictive Control, and Service Level
Abstract - The effect of input and output noise towards the identification of the best linear approximation of a system is investigated. This leads to theproblem oferrors-in-variables (EIV). The effectiveness of one particular EIV method,... more
Abstract - The effect of input and output noise towards the identification of the best linear approximation of a system is investigated. This leads to theproblem oferrors-in-variables (EIV). The effectiveness of one particular EIV method, namely the bias compensation least squares estimation ...