Design of an embedded inverse-feedforward biomolecular trackingcontroller for enzymatic reaction processes

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 alternative designs, and should have wide applicability throughout the fields of synthetic biology and biological engineering

Robustness analysis of a nucleic acid controller for a dynamic biomolecular process using the structured singular value

In the field of synthetic biology, theoretical frameworks and software tools are now available that allow control systems represented as chemical reaction networks to be translated directly into nucleic acid-based chemistry, and hence implement embedded control circuitry for biomolecular processes. However, the development of tools for analysing the robustness of such controllers is still in its infancy. An interesting feature of such control circuits is that, although the transfer function of a linear system can be easily implemented via a chemical network of catalysis, degradation and annihilation reactions, this introduces additional nonlinear dynamics, due to the annihilation kinetics. We exemplify this problem for a dynamical biomolecular feedback system, and demonstrate how the structured singular value (μ) analysis framework can be extended to rigorously analyse the robustness of this class of system. We show that parametric uncertainty in the system affects the location of its equilibrium, and that this must be taken into account in the analysis. We also show that the parameterisation of the system can be scaled for experimental feasibility without affecting its robustness properties, and that a statistical analysis via Monte Carlo simulation fails to uncover the worst-case uncertainty combination found by μ-analysis.</p

High-speed atomic force microscopy for nano-visualization of dynamic biomolecular processes

金沢大学理工研究域数物科学系The atomic force microscope (AFM) has a unique capability of allowing the high-resolution imaging of biological samples on substratum surfaces in physiological solutions. Recent technological progress of AFM in biological research has resulted in remarkable improvements in both the imaging rate and the tip force acting on the sample. These improvements have enabled the direct visualization of dynamic structural changes and dynamic interactions occurring in individual biological macromolecules, which is currently not possible with other techniques. Therefore, high-speed AFM is expected to have a revolutionary impact on biological sciences. In addition, the recently achieved atomic-resolution in liquids will further expand the usefulness of AFM in biological research. In this article, we first describe the various capabilities required of AFM in biological sciences, which is followed by a detailed description of various devices and techniques developed for high-speed AFM and atomic-resolution in-liquid AFM. We then describe various imaging studies performed using our cutting-edge microscopes and their current capabilities as well as their limitations, and conclude by discussing the future prospects of AFM as an imaging tool in biological research. © 2008 Elsevier Ltd. All rights reserved

Programming dynamic nonlinear biomolecular devices using DNA strand displacement reactions

Recent advances in DNA computing have greatly facilitated the design of biomolecular circuitry based on toehold-mediated DNA strand displacement (DSD) reactions. The synthesis of biomolecular circuits for controlling molecular-scale processes is an important goal of synthetic biology with a wide range of in vitro and in vivo applications. In this thesis, new results are presented on how chemical reaction networks (CRNs) can be used as a programming language to implement commonly used linear and nonlinear system theoretic operators that can be further utilised in combination to form complex biomolecular circuits. Within the same framework, the design of an important class of nonlinear feedback controller, i.e. a quasi sliding mode (QSM) feedback controller, is proposed. The closed loop response of the nonlinear QSM controller is shown to outperform a traditional linear proportional+integrator (PI) controller by facilitating much faster tracking response dynamics without introducing overshoots in the transient response. The resulting controller is highly modular and is less affected by retroactivity effects than standard linear designs. An important issue to consider in this design process for synthetic circuits is the effect of biological and experimental uncertainties on the functionality and reliability of the overall circuit. In the case of biomolecular feedback control circuits, such uncertainties could lead to a range of adverse effects, including achieving wrong concentration levels, sluggish performance and even instability. In this thesis, the robustness properties of two biomolecular feedback controllers; PI and QSM, subject to uncertainties in the experimentally implemented rates of their underlying chemical reactions, and to variations in accumulative time delays in the process to be controlled, are analysed. The simulation results show that the proposed QSM controller is significantly more robust against investigated uncertainties, highlighting its potential as a practically implementable biomolecular feedback controller for future synthetic biology applications. Finally, the thesis presents new results on the design of biomolecular feedback controllers using the set of chemical reactions underlying covalent modification cycles

Design and analysis of DNA controllers

Reliable biochemical implementations of linear controllers can provide a large set of tools for the design and analysis of control in Synthetic Biology. Theoretical frameworks are now available to represent feedback control systems as chemical reaction networks which can be readily translated into equivalent nucleic acid-based chemistry. However, the development of tools for constructing and analysing such controllers is still in its infancy. Nucleic acid-based chemistry is a strong candidate framework for the construction of future synthetic biomolecular control circuits. The capacity of strand displacement reactions with Deoxyribonucleic Acid (DNA) to implement analogue signal processing in vitro and in vivo makes them a promising candidate to embed synthetic feedback control circuitry in biomolecular environments. However, little progress has so far been made in developing the requisite theoretical machinery to inform the systematic design of feedback controllers in this context. Here, the potential complexity of such controllers is extended significantly by showing how time-delays, numerical differentiation (to allow proportional-integral-derivative control), and state feedback may be implemented via chemical reaction network-based designs. This work also provides a number of foundational theoretical results on the equilibria, stability, and dynamics of nucleic acid controllers, and the analysis highlights the many interesting and unique characteristics of this important new class of feedback control systems. In particular, that the implementation of feedback controllers using DNA strand displacement reactions introduces additional nonlinear dynamics, even in the case of purely linear control designs, and a robust design of the linear system does not imply the robustness of its chemical implementation. The robustness of the controllers to experimental uncertainty is analysed with the structured singular value (µ) analysis tool, which is extended with a model of how parametric uncertainty in the system affects the location of its equilibrium. This framework provides more reliable results than sampled based statistical methods, where analysis via Monte Carlo simulation fails to uncover the worst-case uncertainty combination found by µ-analysis. The implementations of the examples and controllers in nucleic acid-based chemistry are simulated and checked using the Visual DSD simulation package, a bespoke software tool for simulating nucleic acid-based circuits

A Meshless Modelling Framework for Simulation and Control of Nonlinear Synthetic Biological Systems

Synthetic biology is a relatively new discipline that incorporates biology and engineering principles. It builds upon the advances in molecular, cell and systems biology and aims to transform these principles to the same effect that synthesis transformed chemistry. What distinguishes synthetic biology from traditional molecular or cellular biology is the focus on design and construction of components (e.g. parts of a cell) that can be modelled, characterised and altered to meet specific performance criteria. Integration of these parts into larger systems is a core principle of synthetic biology. However, unlike some areas of engineering, biology is highly non-linear and less predictable. In this thesis the work that has been conducted to combat some of the complexities associated with dynamic modelling and control of biological systems will be presented. Whilst traditional techniques, such as Orthogonal Collocation on Finite Elements (OCFE) are common place for dynamic modelling they have significant complexity when sampling points are increased and offer discrete solutions or solutions with limited differentiability. To circumvent these issues a meshless modelling framework that incorporates an Artificial Neural Network (ANN) to solve Ordinary Differential Equations (ODEs) and model dynamic processes is utilised. Neural networks can be considered as mesh-free numerical methods as they are likened to approximation schemes where the input data for a design of a network consists of a set of unstructured discrete data points. The use of the ANN provides a solution that is differentiable and is of a closed analytic form, which can be further utilised in subsequent calculations. Whilst there have been advances in modelling biological systems, there has been limited work in controlling their outputs. The benefits of control allow the biological system to alter its state and either upscale production of its primary output, or alter its behaviour within an integrated system. In this thesis a novel meshless Nonlinear Model Predictive Control (NLMPC) framework is presented to address issues related to nonlinearities and complexity. The presented framework is tested on a number of case studies. A significant case study within this work concerns simulation and control of a gene metabolator. The metabolator is a synthetic gene circuit that consists of two metabolite pools which oscillate under the influence of glycolytic flux (a combination of sugars, fatty acids and glycerol). In this work it is demonstrated how glycolytic flux can be used as a control variable for the metabolator. The meshless NLMPC framework allows for both Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) control. The dynamic behaviour of the metabolator allows for both top-down control (using glycolytic flux) and bottom-up control (using acetate). The benefit of using MIMO (by using glycolytic flux and acetate as the control variables) for the metabolator is that it allows the system to reach steady state due to the interactions between the two metabolite pools. Biological systems can also encounter various uncertainties, especially when performing experimental validation. These can have profound effect on the system and can alter the dynamics or overall behaviour. In this work the meshless NLMPC framework addresses uncertainty through the use of Zone Model Predictive Control (Zone MPC), where the control profile is set as a range, rather than a fixed set point. The performance of Zone MPC under the presence of various magnitudes of random disturbances is analysed. The framework is also applied to biological systems architecture, for instance the development of biological circuits from well-characterised and known parts. The framework has shown promise in determining feasible circuits and can be extended in future to incorporate a full list of biological parts. This can give rise to new circuits that could potentially be used in various applications. The meshless NLMPC framework proposed in this work can be extended and applied to other biological systems and heralds a novel method for simulation and control

An E. coli cell-free transcription- translation system: modeling gene expression and characterizing CRISPR elements and gene circuits

University of Minnesota Ph.D. dissertation. September 2019. Major: Physics. Advisor: Vincent Noireaux. 1 computer file (PDF); xi, 125 pages.Cell-free transcription-translation systems are versatile tools for rapid prototyping and characterization of biological systems and processes. Proteins can be expressed and measured in a matter of hours, whereas in vivo experiments often take days to weeks because they require protein purification or live cell transformations and cultures. TXTL systems, however, are still lacking in simple models that quantitatively describe the behavior of reactions. Here, we present an model of the all E. coli TXTL system using ordinary differential equations, encompassing the limited concentrations of transcription and translation machineries, capturing the linear and saturated regime of gene expression. Many biochemical constants are determined through experimental assays. We then show how this TXTL system was used to characterize CRISPR technologies. CRISPR-Cas systems have huge potential to be used as tools for genome engineering, as well as gene silencing and regulation. We characterize a set of sgRNAs, CRISPR nucleases, anti- CRISPR proteins, and determine protospacer-adjacent motifs. Finally, we use the TXTL system to execute gene circuits, including an IFFL and an integral controller

Biosensors

A biosensor is defined as a detecting device that combines a transducer with a biologically sensitive and selective component. When a specific target molecule interacts with the biological component, a signal is produced, at transducer level, proportional to the concentration of the substance. Therefore biosensors can measure compounds present in the environment, chemical processes, food and human body at low cost if compared with traditional analytical techniques. This book covers a wide range of aspects and issues related to biosensor technology, bringing together researchers from 11 different countries. The book consists of 16 chapters written by 53 authors. The first four chapters describe several aspects of nanotechnology applied to biosensors. The subsequent section, including three chapters, is devoted to biosensor applications in the fields of drug discovery, diagnostics and bacteria detection. The principles behind optical biosensors and some of their application are discussed in chapters from 8 to 11. The last five chapters treat of microelectronics, interfacing circuits, signal transmission, biotelemetry and algorithms applied to biosensing

Technology 2001: The Second National Technology Transfer Conference and Exposition, volume 2

Proceedings of the workshop are presented. The mission of the conference was to transfer advanced technologies developed by the Federal government, its contractors, and other high-tech organizations to U.S. industries for their use in developing new or improved products and processes. Volume two presents papers on the following topics: materials science, robotics, test and measurement, advanced manufacturing, artificial intelligence, biotechnology, electronics, and software engineering

Engineering stress resilient plants using gene regulatory network rewiring

In spite of advances in food production brought on by the Green Revolution, the challenge of providing access to nutritious, safe food that has been grown sustainably is considerable. One such barrier to food security is biotic stress - infection with pathogens such as bacteria, fungi and oomycetes impact negatively on plant growth and survival. Synthetic biology, an interdisciplinary field combining biology, engineering and mathematics, is a promising tool for understanding and developing stress tolerant plants. The response of the model plant Arabidopsis thaliana to biotic and abiotic stresses involves the transcriptional reprogramming of thousands of genes. Among these differentially expressed genes are transcription factors, which form complex causal networks specific to the stress in question. This thesis focuses on network rewiring as a tool for enhancing the Arabidopsis response to stress, in particular to Botrytis cinerea infection. This is a model system for studying plant-necrotrophic pathogen interactions and as such, a large amount of data are available, including a high-resolution transcriptomic time series of Arabidopsis during B. cinerea infection. This was used to construct gene regulatory networks with hundreds of transcription factors that are differentially expressed, in order to obtain a systems view of the effects of infection and the relationships between these regulators. Rewiring was applied to subnetworks of the original network using two different methodologies: control engineering, and Gaussian process dynamical systems. The former focuses on eliminating the effects of perturbation on a single node in a small 9-gene network, and requires detailed parameterisation of biological processes such as mRNA degradation and transcription rates. The latter provides a general modelling framework for optimising the overall expression of genes in a larger 70 gene subnetwork that eschews parameterisation or definition of a precise function for modelling relationships between genes. The process of generating stably transformed and rewired Arabidopsis is long and requires growing hundreds of plants for each construct. In order to test the hypotheses generated by such computational tools quickly and on a large scale, Arabidopsis protoplasts treated with chitin were trialled as a model system for studying plant defence responses to B. cinerea. RNAseq analysis of protoplasts was used to determine the similarities and differences between the defence responses triggered in protoplasts and in Arabidopsis plants. Both protoplasts and plants were also rewired, and gene expression measurements used to understand the effects of this genetic engineering on the defence response of each