Analysis and control of genetic toggle switches subject to periodic multi-input stimulation

In this letter, we analyze a genetic toggle switch recently studied in the literature where the expression of two repressor proteins can be tuned by controlling two different inputs, namely the concentration of two inducer molecules in the growth medium of the cells. Specifically, we investigate the dynamics of this system when subject to pulse-width modulated (PWM) input. We provide an analytical model that captures qualitatively the experimental observations reported in the literature and approximates its asymptotic behavior. We also discuss the effect that the system parameters have on the prediction accuracy of the model. Moreover, we propose a possible external control strategy to regulate the mean value of the fluorescence of the reporter proteins when the cells are subject to such periodic forcing.Comment: Preprint accepted for publication on L-CSS (First submission 31.05.2018, accepted 21.08.2018

Ratiometric control for differentiation of cell populations endowed with synthetic toggle switches

We consider the problem of regulating by means of external control inputs the ratio of two cell populations. Specifically, we assume that these two cellular populations are composed of cells belonging to the same strain which embeds some bistable memory mechanism, e.g. a genetic toggle switch, allowing them to switch role from one population to another in response to some inputs. We present three control strategies to regulate the populations' ratio to arbitrary desired values which take also into account realistic physical and technological constraints occurring in experimental microfluidic platforms. The designed controllers are then validated in-silico using stochastic agent-based simulations.Comment: Accepted to CDC'201

Analysis and Control of Bacterial Populations in Synthetic Biology

Synthetic Biology is a new field of research that aims at engineering new functionalities in living beings. Analogously to electronic circuits, more advanced functionalities can be realised by putting together smaller functional modules that perform elementary tasks; however, the interaction of these basic pieces is somewhat complex and fragile. Therefore, to increase the robustness and reliability of the whole system, typical tools from Control Theory, such as feedback loops, can be employed. In the first part of this thesis we propose feedback control strategies to balance the gene expression of a bistable genetic circuit, known as genetic toggle switch, in an unstable region far away from its stable equilibria - a problem analogous to the stabilization of the inverted pendulum in mechanics. The effectiveness of the proposed control strategies is validated via realistic agent-based simulations of a bacterial population endowed with the genetic toggle switch. Later in the thesis we move towards the growth control of bacterial cells in bioreactors, introducing a novel open-source and versatile design of a turbidostat to host in vivo control experiments. In the last part, we want to control bioreactors to guarantee the coexistence of multiple species in the same environment. We analyse the dynamics of a simple one-chamber bioreactor, proposing control strategies to achieve the control goal. However, simple bioreactors have several drawback when the concentrations of multiple species are regulated at the same time; for these reason, we propose a novel layout for a bioreactor, with two growth chambers and a mixing one, to be used in multicellular in vivo control experiments

Stochastic analysis of nonlinear dynamics and feedback control for gene regulatory networks with applications to synthetic biology

The focus of the thesis is the investigation of the generalized repressilator model (repressing genes ordered in a ring structure). Using nonlinear bifurcation analysis stable and quasi-stable periodic orbits in this genetic network are characterized and a design for a switchable and controllable genetic oscillator is proposed. The oscillator operates around a quasi-stable periodic orbit using the classical engineering idea of read-out based control. Previous genetic oscillators have been designed around stable periodic orbits, however we explore the possibility of quasi-stable periodic orbit expecting better controllability. The ring topology of the generalized repressilator model has spatio-temporal symmetries that can be understood as propagating perturbations in discrete lattices. Network topology is a universal cross-discipline transferable concept and based on it analytical conditions for the emergence of stable and quasi-stable periodic orbits are derived. Also the length and distribution of quasi-stable oscillations are obtained. The findings suggest that long-lived transient dynamics due to feedback loops can dominate gene network dynamics. Taking the stochastic nature of gene expression into account a master equation for the generalized repressilator is derived. The stochasticity is shown to influence the onset of bifurcations and quality of oscillations. Internal noise is shown to have an overall stabilizing effect on the oscillating transients emerging from the quasi-stable periodic orbits. The insights from the read-out based control scheme for the genetic oscillator lead us to the idea to implement an algorithmic controller, which would direct any genetic circuit to a desired state. The algorithm operates model-free, i.e. in principle it is applicable to any genetic network and the input information is a data matrix of measured time series from the network dynamics. The application areas for readout-based control in genetic networks range from classical tissue engineering to stem cells specification, whenever a quantitatively and temporarily targeted intervention is required

Natural Computing and Beyond

This book contains the joint proceedings of the Winter School of Hakodate (WSH) 2011 held in Hakodate, Japan, March 15–16, 2011, and the 6th International Workshop on Natural Computing (6th IWNC) held in Tokyo, Japan, March 28–30, 2012, organized by the Special Interest Group of Natural Computing (SIG-NAC), the Japanese Society for Artificial Intelligence (JSAI). This volume compiles refereed contributions to various aspects of natural computing, ranging from computing with slime mold, artificial chemistry, eco-physics, and synthetic biology, to computational aesthetics

Analysis and control of biomolecular networks by microfluidics

The process by which the cells respond and adapt to internal and external stimuli, is almost always controlled by a complex network of genes, proteins, small molecules, and their mutual interactions, called signalling network. Over the last years, it has become apparent that quantitative and methodological tools from Biomedical and Control Engineering can be used to understand how these networks work, but also to engineer "synthetic" networks to robustly steer cellular behavior in a prescribed fashion. This possibility will be transformative, enabling myriad applications in biotechnology, chemical industry, health and biomedicine, food, and the environment. Cybergenetics is a new discipline merging the tools of Synthetic Biology with those of Biomedical and Control Engineering, with the aim of building robust synthetic gene networks to engineer biological processes. This Thesis is within this research topic, and comprises two different applications, one in yeast cells and one in human cells: (1) closed-loop feedback control to synchronise the cell cycle across a population of yeast cells (Saccharomyces cerevisiae); (2) quantitative analysis and model of TFEB nuclear translocation dynamics following mTOR inhibition in human cells (HeLa)

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 389)

This bibliography lists 234 reports, articles, and other documents recently introduced into the NASA Scientific and Technical Information System. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

An optimal approach to the design of experiments for the automatic characterisation of biosystems

The Design-Build-Test-Learn cycle is the main approach of synthetic biology to re-design and create new biological parts and systems, targeting the solution for complex and challenging paramount problems. The applications of the novel designs range from biosensing and bioremediation of water pollutants (e.g. heavy metals) to drug discovery and delivery (e.g. cancer treatment) or biofuel production (e.g. butanol and ethanol), amongst others. Standardisation, predictability and automation are crucial elements for synthetic biology to efficiently attain these objectives. Mathematical modelling is a powerful tool that allows us to understand, predict, and control these systems, as shown in many other disciplines such as particle physics, chemical engineering, epidemiology and economics. Yet, the inherent difficulties of using mathematical models substantially slowed their adoption by the synthetic biology community. Researchers might develop different competing model alternatives in absence of in-depth knowledge of a system, consequently being left with the burden of with having to find the best one. Models also come with unknown and difficult to measure parameters that need to be inferred from experimental data. Moreover, the varying informative content of different experiments hampers the solution of these model selection and parameter identification problems, adding to the scarcity and noisiness of laborious-to-obtain data. The difficulty to solve these non-linear optimisation problems limited the widespread use of advantageous mathematical models in synthetic biology, broadening the gap between computational and experimental scientists. In this work, I present the solutions to the problems of parameter identification, model selection and experimental design, validating them with in vivo data. First, I use Bayesian inference to estimate model parameters, relaxing the traditional noise assumptions associated with this problem. I also apply information-theoretic approaches to evaluate the amount of information extracted from experiments (entropy gain). Next, I define methodologies to quantify the informative content of tentative experiments planned for model selection (distance between predictions of competing models) and parameter inference (model prediction uncertainty). Then, I use the two methods to define efficient platforms for optimal experimental design and use a synthetic gene circuit (the genetic toggle switch) to substantiate the results, computationally and experimentally. I also expand strategies to optimally design experiments for parameter identification to update parameter information and input designs during the execution of these (on-line optimal experimental design) using microfluidics. Finally, I developed an open-source and easy-to-use Julia package, BOMBs.jl, automating all the above functionalities to facilitate their dissemination and use amongst the synthetic biology community

Modeling and data analysis of biochemical oscillators using Chemical Master Equation and AI: applications to the NF-kB activity in patient derived xenografts

There are two stages from DNA sequence of a gene to protein: transcription, i.e. the process of making a strand of RNA molecule, and translation, that is the process by which a protein is synthesized from the information contained in a molecule of RNA. In the process of transcription, proteins called “transcription factors” play a central role because they bind to a DNA sequence and help the transcription initiation complex. In this work of thesis, we are particularly interested in modeling the nuclear factor-kappa B (NF-kB) activity which is ubiquitous within cells and its dysfunction leads to chronic diseases, cancers, neurodegenerative diseases and much other. However, we do not start immediately modeling its behavior. In this stochastic context, firstly we aim to deepen into algorithms to solve the Chemical Master Equation (CME) giving an our alternative algorithm called “hybrid” because it combines the Gillespie’s Stochastic Simulation Algorithm (SSA) with the tauleaping algorithm with the aim to improve the algorithm’s speed; secondly we analyse the stochastic simulation results of three basics genetic circuits (the simplest model of gene expression, the autorepressor and the toggle switch); third, we faced the problem of parameters estimation of these simple models using artificial neural networks; finally, aware of what we have learned after all such steps, we provide a very little NF-kB model using the CME. The relevant results are the following: the hybrid algorithm applied to the first genetic model is faster than the SSA in configurations where the number of molecules produced tends to be high; periodicity arises from what we defined as unpredictable (being stochastic processes); neural networks learn to predict the model parameters given the autocorrelation as input but the choice of chemical specie makes the difference; finally, our little NF-kB model shows an oscillating behavior that is similar to the one found by experiments