Biology subjects

Marucci, L.

Publications and source records attributed to Marucci, L..

At least 19 recordsLinked to original sources

bioRxiv2025

Harnessing Dynamic Cell Responses to Optimise Pluripotency Protocols

Pluripotent stem cells have a high potential for research and development applications in biomedicine. They are undifferentiated cells capable of unlimited self-renewal and maintenance of pluripotency, yet can be induced to differentiate into specific cell types through defined cues. Various protocols have been proposed to maintain mouse embryonic stem cells (mESCs) in the pluripotent state; state of the art cell culture protocols are based on serum-free media, and continuous provision of drugs inhibiting pathways associated to differentiation. We explore here the possibility to avoid continuous drug exposure. First, we employ an external feedback control microfluidics/microscopy platform to steer the expression of a pluripotency reporter. Control experiments show a significant delay in cell response to drugs; these dynamics are confirmed by flow-cytometry open-loop experiments. We then harness such delays to design novel culture protocols with limited cell exposure to small-molecule inhibitors; in so doing, we reduce cell exposure to drug, but we maintain pluripotency. Our results suggest new approaches to design cell culture protocols based on the quantitative analysis and control of cellular dynamics.

synthetic biology↗

bioRxiv2024

Embedded Extremum Seeking: A Bio-synthetic Optimiser

In cybergentics, internal control uses synthetic genetic circuits to bypass the sensing and actuation limits of external control. Currently, embedded controllers are limited to basic controllers, like PID. Here, we introduce the Embedded Extremum Seeking Controller, the first example of a bio-synthetic optimiser. Extremum Seeking combines an integral and oscillator circuit to adjust system parameters and achieve a desired optimal steady state. This system uses an integral circuit and oscillator that already exist in synthetic biology. The Extremum Seeker is demonstrated in silico on a Labour Division system, where the controller tunes a kinematic parameter, optimising the ratio of the culture subpopulations to maximise production. This controller paves the way for embedded controllers that precisely direct cellular behaviour without needing external actuation and sensing.

synthetic biology↗

bioRxiv2024

Kernel Filter-Based Adaptive Controllers For Cybergenetics Applications

Cybergenetics is an advancing field that seeks to implement control theory within biological systems. When applying feedback control for the regulation of gene expression or cell proliferation, model-based control strategies can be applied; in this context, online adaptive mathematical models can be used to keep models in tune with the current behaviour of the biological system. Controllers are often constrained by their sampling rate, which is usually relatively low when using microfluidics/microscopy platforms. Current adaptive filters can lead to an inaccurate predictive model when operating with a low sampling rate, leading to sub-optimal control. Here, we propose a kernel filter that can fit model parameters online to produce a more accurate predictive model that can be included within an adaptive model predictive control scheme. The use of the kernel filter is demonstrated in in silico and in vitro experiments, where we control a synthetic gene oscillator and a P53 oscillator, and observe a synthetic toggle switch. Our results show that the kernel filter outperforms a particle filter when used for parameter estimation in both the predictive model accuracy and when included within an adaptive model-based controller.

systems biology↗

bioRxiv2023

In-vivo multicellular control of gene expression in microbial consortia

This paper presents an innovative approach in synthetic biology, focusing on the engineering of biomolecular feedback control systems within microbial consortia to achieve robust and precise regulation of desired phenotypes. Traditional biomolecular control strategies, while effective, are predominantly confined to single-cell applications, limiting their complexity and adaptability due to metabolic constraints. We propose a novel methodology that distributes control functionalities across different cell populations within a microbial consortium. This approach leverages the division of labor and cooperative interactions among microbial populations, significantly enhancing flexibility and robustness. We build on the foundation of synthetically engineered microbial consortia, known for their applications in complex compound production and advanced computational processes. However, the challenge of implementing a distributed feedback control loop for precise phenotype regulation in synthetic communities has remained unaddressed. Our work closes this gap by developing and validating an in-vivo feedback control loop distributed across a bacterial consortium, specifically designed using two Escherichia coli strains. These strains are engineered to perform the three essential control functions: computation, sensing, and actuation. We employ orthogonal quorum sensing signaling molecules for communication between the controller and target cell populations. Through in-vivo experiments, we compare closed-loop and open-loop configurations to demonstrate the effectiveness of our approach. The results show that our distributed control system can regulate gene expression levels in target populations with high precision and robustness, even in the presence of consortium composition variations. This feature is particularly vital for practical applications in synthetic biology where stability and adaptability are crucial. Our study not only addresses a significant gap in synthetic biology literature but also opens avenues for more complex and reliable synthetic biology applications.

synthetic biology↗

bioRxiv2023

Accelerated design of Escherichia coli genomes with reduced size using a whole-cell model and machine learning surrogate

Whole-cell models (WCMs) are multi-scale computational models that aim to simulate the function of all genes and processes within a cell. This approach is promising for designing genomes tailored for specific tasks. However, a limitation of WCMs is their long runtime. Here, we show how machine learning (ML) surrogates can be used to address this limitation by training them on WCM data to accurately predict cell division. Our ML surrogate achieves a 95% reduction in computational time compared to the original WCM. We then show that the surrogate and a genome-design algorithm can generate an in silico reduced E. coli cell, where 40% of the genes included in the WCM were removed. The reduced genome is validated using the WCM and interpreted biologically using gene ontology analysis. This approach illustrates how the holistic understanding gained from a WCM can be leveraged for synthetic biology tasks while reducing its runtime.

bioengineering↗

bioRxiv2022

In-silico and in-vitro morphometric analysis of intestinal organoids

Organoids offer a powerful model to study cellular self-organisation, the growth of specific tissue morphologies in-vitro, and to assess potential medical therapies. However, the intrinsic mechanisms of these systems are not entirely understood yet, which can result in variability of organoids due to differences in culture conditions and basement membrane extracts used. Improving the standardisation of organoid cultures is essential for their implementation in clinical protocols. Developing tools to assess and predict the behaviour of these systems may produce a more robust and standardised biological model to perform accurate clinical studies. Here we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. In addition, we modified an existing two-dimensional agent-based mathematical model of intestinal organoids to better describe the system physiology, and evaluated its ability to replicate budding structures compared to new experimental data we generated. The crypt-counting algorithm proved useful in approximating the average budding structures found in our in-vitro intestinal organoid culture images on days 3 and 7 after seeding. Our changes to the in-silico model maintain the potential to produce simulations that replicate the number of budding structures found on days 5 and 7 of in-vitro data. The present study aims to aid in quantifying key morphological structures and provide a method to compare both in-vitro and in-silico experiments. Our results could be extended later to 3D in-silico models.

systems biology↗

bioRxiv2022

Development of a single layer microfluidic device for dynamic stimulation, culture and imaging of mammalian cells

The possibility to tightly control the cellular microenvironment within microfluidic devices represents an important step forward precision analysis of cellular phenotypes in vitro. In particular, microfluidic platforms that allow both long-term mammalian cell culture and dynamic modulation of the culture environment can support quantitative studies of cells responses to drugs. Here, we report the design and testing of a novel microfluidic device of easy production (single Polydimethylsiloxane layer), which integrates a micromixer with vacuum-assisted cell loading for long-term mammalian cell culture and dynamic mixing of 4 different culture media. Finite element modelling was used to predict the flow rates and device dimensions to achieve diffusion-based fluid mixing. The device showed efficient mixing and dynamic exchange of media in the cell trapping chambers. This work represents the first attempt to integrate single layer microfluidic mixing devices with vacuum-assisted cell loading systems for mammalian cell culture and dynamic stimulation.

bioengineering↗

bioRxiv2022

EXTERNAL CONTROL OF CANCER CELLULAR DYNAMICS USING ADAPTIVE MODEL PREDICTIVE CONTROL

Recent advancements in Cybergenetics have led to the development of new computational and experimental platforms that enable to robustly steer cellular dynamics by applying external feedback control. Such technologies have never been applied to regulate intracellular dynamics of cancer cells. Here, we show in silico that adaptive model predictive control (MPC) can effectively be used to steer signalling dynamics in Non-Small Cell Lung Cancer (NSCLC) cells to resemble those of wild-type cells, and to support the design of combination therapies. Our optimisation-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure, minimising both drug-induced toxicity and resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells for drug combination therapy design.

systems biology↗

bioRxiv2021

Control-based continuation: a new approach to prototype synthetic gene networks

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modelling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch which includes additive process noise and exhibits bistability. We compare results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of a real experiment using an agent-based simulator describing cells growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

synthetic biology↗

bioRxiv2020

Understanding metabolic behaviour in whole-cell model output

Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional, thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts, and, as whole-cell models become more widely built and used, aid genome design.

systems biology↗

bioRxiv2020

ChipSeg: an automatic tool to segment bacteria and mammalian cells cultured in microfluidic devices

Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. While such image segmentation applications have been previously reported, there is in the literature a lack of open-source and documented code for the community. We describe ChipSeg, a computational tool to segment bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy. The method is based on thresholding and uses the same core functions for both cell types. It allows to segment individual cells in high cell-density microfluidic devices, to quantify fluorescence protein expression over a time-lapse experiment and to track individual cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customised for other experimental settings and research aims.

synthetic biology↗

bioRxiv2020

Cheetah: a computational toolkit for cybergenetic control

Advances in microscopy, microfluidics and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah - a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterise and control cells over time. We demonstrate Cheetahs core capabilities by analysing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetahs segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells.

synthetic biology↗

bioRxiv2020

β-catenin perturbations control differentiation programs in mouse embryonic stem cells

The Wnt/{beta}-catenin pathway is involved in development, cancer and embryonic stem cell (ESC) maintenance; its dual role in stem cell self-renewal and differentiation is still controversial. Here, by applying an in vitro system enabling inducible gene expression control, we report that moderate induction of transcriptionally active exogenous {beta}- catenin in {beta}-catenin null mouse ESCs promotes epiblast-like cell (EpiLC) derivation in vitro. Instead, in wild type cells moderate chemical pre-activation of the Wnt/{beta}-catenin pathway promotes EpiLC in vitro derivation. Finally, we suggest that moderate {beta}- catenin levels in {beta}-catenin null mouse ESCs favour early stem cell commitment towards mesoderm if the exogenous protein is induced only in the ground state of pluripotency condition, or endoderm if the induction is maintained during the differentiation. Overall, our results confirm previous findings about the role of {beta}-catenin in pluripotency and differentiation, while indicating a role for its doses in promoting specific differentiation programs.

cell biology↗

bioRxiv2020

Testing Theoretical Minimal Genomes Using whole-cell Models

The minimal gene set for life has often been theorised, with at least ten produced for Mycoplasma genitalium (M. genitalium). Due to the difficulty of using M. genitalium in the lab, combined with its long replication time of 12 - 15 hours, none of these theoretical minimal genomes have been tested, even with modern techniques. The publication of the M. genitalium whole-cell model provided the first opportunity to test them, simulating the genome edits in-silico. We simulated eight minimal gene sets from the literature, finding that they produced in-silico cells that did not divide. Using knowledge from previous research, we reintroduced specific essential and low essential genes in-silico; enabling cellular division. This reinforces the need to identify species-specific low essential genes and their interactions. Any genome designs created using the currently incomplete and fragmented gene essentiality information, will very likely require in-vivo reintroductions to correct issues and produce dividing cells.

synthetic biology↗

bioRxiv2020

In vivo Feedback Control of an Antithetic Molecular-Titration Motif in Escherichia coli using Microfluidics

We study both in silico and in vivo the real-time feedback control of a molecular titration motif that has been earmarked as a fundamental component of antithetic and multicellular feedback control schemes in E. coli. We show that an external feedback control strategy can successfully regulate the average fluorescence output of a bacterial cell population to a desired constant level in real-time. We also provide in silico evidence that the same strategy can be used to track a time-varying reference signal where the set-point is switched to a different value halfway through the experiment. We use the experimental data to refine and parameterize an in silico model of the motif that can be used as an error computation module in future embedded or multicellular control experiments.

synthetic biology↗

bioRxiv2019

Exploring the Dynamics of Nonlinear Biochemical Systems using Control-Based Continuation

AO_SCPLOWBSTRACTC_SCPLOWMathematical modelling is routinely used in Systems Biology to understand the mechanisms causing nonlinear phenomena in gene expression, such as switch-like behaviours and temporal oscillations. The reliability of model predictions and bifurcation analysis depend on modelling assumptions and specific choices of model parameters; however, the identification of models is highly challenging due to the complexity of biochemical interactions and noise in experimental data. This paper numerically investigates the use of control-based continuation (CBC) for tracking dynamical features of biochemical systems and, in particular, the bistable dynamics of a gene regulating pluripotency in embryonic stem cells. CBC is a method that exploits feedback control and path following algorithms to explore the dynamic features of a nonlinear physical system directly during experimental tests. CBC applications have so far been limited to non-living (i.e. electro-mechanical) systems. Our numerical simulations show that, in principle, CBC could also be applied to biological experiments to characterise the switch-like dynamics of genes that are important for cell decision making.

systems biology↗

bioRxiv2019

Towards automated control of embryonic stem cell pluripotency

Mouse embryonic stem cells (mESCs) have been shown to exist in three distinct pluripotent states (ground, naive and primed pluripotent states), depending on culture conditions. External feedback control strategies have been, so far, mainly used to automatically regulate gene expression in bacteria and yeast. Here, we exploit a microfluidics/microscopy platform and segmentation and external feedback control algorithms for the automatic regulation of pluripotency phenotypes in mESCs. We show feasibility of automatically controlling, in living mESCs, levels of an endogenous pluripotency gene, Rex1, through a fluorescent reporter, used as control output, and drugs commonly used to modulate pluripotency (i.e. MEK kinase and Gsk3{beta} inhibitors) as control inputs. Our results will ultimately aid in the derivation of superior protocols for pluripotency maintenance and differentiation of mouse and human stem cells.

developmental biology↗

bioRxiv2019

The genome design suite: enabling massive in-silico experiments to design genomes.

MotivationComputational biology is a rapidly developing field, and in-silico methods are being developed to aid the design of genomes to create cells with optimised phenotypes. Two barriers to progress are that in-silico methods are often only developed on a particular implementation of a specific model (e.g. COBRA metabolic models) and models with longer simulation time inhibit the large-scale in-silico experiments required to search the vast solution space of genome combinations.\n\nResultsHere we present the genome design suite (PyGDS) which is a suite of Python tools to aid the development of in-silico genome design methods. PyGDS provides a framework with which to implement phenotype optimisation algorithms on computational models across computer clusters. The framework is abstract allowing it to be adapted to utilise different computer clusters, optimisation algorithms, or design goals. It implements an abstract multi-generation algorithm structure allowing algorithms to avoid maximum simulation times on clusters and enabling iterative learning in the algorithm. The initial case study will be genome reduction algorithms on a whole-cell model of Mycoplasma genitalium for a PBS/Torque cluster and a Slurm cluster.\n\nAvailabilityThe genome design suite is written in Python for Linux operating systems and is available from GitHub on a GPL open-source licence.\n\nContacto.chalkley@bristol.ac.uk, lacsg@bristol.ac.uk, and lucia.marucci@bristol.ac.uk.

systems biology↗