Automatic Design of Synthetic Gene Circuits through Mixed Integer Non-linear Programming

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits

Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch

The formation of spatiotemporal patterns of gene expression is frequently guided by gradients of diffusible signaling molecules. The toggle switch subnetwork, composed of two cross-repressing transcription factors, is a common component of gene regulatory networks in charge of patterning, converting the continuous information provided by the gradient into discrete abutting stripes of gene expression. We present a synthetic biology framework to understand and characterize the spatiotemporal patterning properties of the toggle switch. To this end, we built a synthetic toggle switch controllable by diffusible molecules in Escherichia coli. We analyzed the patterning capabilities of the circuit by combining quantitative measurements with a mathematical reconstruction of the underlying dynamical system. The toggle switch can produce robust patterns with sharp boundaries, governed by bistability and hysteresis. We further demonstrate how the hysteresis, position, timing, and precision of the boundary can be controlled, highlighting the dynamical flexibility of the circuit

On the Interplay between the Evolvability and Network Robustness in an Evolutionary Biological Network: A Systems Biology Approach

In the evolutionary process, the random transmission and mutation of genes provide biological diversities for natural selection. In order to preserve functional phenotypes between generations, gene networks need to evolve robustly under the influence of random perturbations. Therefore, the robustness of the phenotype, in the evolutionary process, exerts a selection force on gene networks to keep network functions. However, gene networks need to adjust, by variations in genetic content, to generate phenotypes for new challenges in the network’s evolution, ie, the evolvability. Hence, there should be some interplay between the evolvability and network robustness in evolutionary gene networks. In this study, the interplay between the evolvability and network robustness of a gene network and a biochemical network is discussed from a nonlinear stochastic system point of view. It was found that if the genetic robustness plus environmental robustness is less than the network robustness, the phenotype of the biological network is robust in evolution. The tradeoff between the genetic robustness and environmental robustness in evolution is discussed from the stochastic stability robustness and sensitivity of the nonlinear stochastic biological network, which may be relevant to the statistical tradeoff between bias and variance, the so-called bias/variance dilemma. Further, the tradeoff could be considered as an antagonistic pleiotropic action of a gene network and discussed from the systems biology perspective

Computational design and characterisation of synthetic genetic switches

Genetic toggle switches consist of two mutually repressing transcription factors. The switch motif forms the basis of epigenetic memory and is found in natural decision making systems, such as cell fate determination in developmental pathways. A synthetic genetic switch can be used for a variety of applications, like recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. In this thesis, the genetic toggle switch was studied computationally and experimentally. Bayesian model selection methods were used to compare competing model designs of the genetic toggle switch. It was found that the addition of positive feedback loops to the genetic toggle switch increases the parametric robustness of the system. A computational tool based on Bayesian statistics was developed, that can identify regions of parameter space capable of producing multistable behaviour while handling parameter and initial conditions uncertainty. A collection of models of genetic switches were examined, ranging from the deterministic simplified toggle switch to stochastic models containing different positive feedback connections. The design principles behind making a bistable switch were uncovered, as well as those necessary to make a tristable or quadristable switch. Flow Cytometry was used to characterise a known toggle switch plasmid. A computational tool was developed which uses Bayesian statistics to infer model parameter values from flow cytometry data. This tool was used to characterise the toggle switch plasmid and fit a stochastic computational model to experimental data. The work presented here suggests ways in which the construction of genetic switches can be enhanced. The algorithms developed were shown to be useful in synthetic system design as well as parameter inference. The tools developed here can enhance our understanding of biological systems and constitute an important addition to the systems approach to synthetic biology engineerin

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

The Development of Synthetic Gene Biomarkers for the Field-Based Forensic Detection of Body Fluids and Sex Determination

Body fluid and sex determination at the crime scene are important forensic questions where information can be obtained using various approaches. The standard techniques involved are well established and often utilise cheap, rapid tests to presumptively detect the presence of characteristic proteins and chemicals. However, the relatively poor accuracies of these tests compared to laboratory-based techniques that analyse nucleic acids limits their effectiveness and can lead to inefficient sample triage. This presents a need for novel field-based biomarker detection techniques that are both sensitive and specific. Toehold switches are de novo designed RNA/DNA sequences that contain the genetic motifs necessary for coupled transcription-translation of a reporter gene. Gene expression is repressed in the absence of a specific complementary target “trigger” sequence due to switch hairpin formation. Hybridisation of the trigger to the switch initiates hairpin unwinding and enables downstream gene expression. This specific and sensitive approach has led to toehold switches becoming an emerging platform for bio-detection, primarily in the field of viral RNA detection. This thesis identifies a lack of such applications to forensic science alongside a need for novel field-based DNA detection tools amongst forensic end-users with a market research study. To address these issues, a set of toehold switches were designed in-house for the detection of mRNA sequences specific to blood, saliva, semen, and the sex marker amelogenin. A contemporary qPCR assay was internally validated to act as a performance benchmark. To facilitate in vitro expression of toehold switches without expensive commercial cell-free protein synthesis systems, an Escherichia coli cell lysate was developed and optimised in-house. Gene expression from control plasmids was comparable to a commercial equivalent at 37°C and exceeded it at 29°C, with a shelf-life of approximately 6 – 8 months at -80°C. Toehold switches circuit function was unsuccessful with either system, which suggested an issue with toehold switch design. A screening framework utilising a combined melting curve and in silico thermodynamic analysis approach was devised to highlight high-performance toehold switches. This framework was able to predict the performances of six novel toehold switch designs and recommended designs to be discarded or studied further, but further characterisation is required to assess prediction accuracy. Results throughout are discussed in relation to the needs of forensic scientists and the capabilities of toehold switches as bio-detection tools compared to existing techniques