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 number of 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.

Bridging the gap between mechanistic biological models and machine learning surrogates.

Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying ML models. Application-wise, we show how ML surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate ML models may hold the key to making the simulation of complex biological systems possible using a typical desktop computer.

A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches.

Systems biology approaches are extensively used to model and reverse engineer gene regulatory networks from experimental data. Conversely, synthetic biology allows "de novo" construction of a regulatory network to seed new functions in the cell. At present, the usefulness and predictive ability of modeling and reverse engineering cannot be assessed and compared rigorously. We built in the yeast Saccharomyces cerevisiae a synthetic network, IRMA, for in vivo "benchmarking" of reverse-engineering and modeling approaches. The network is composed of five genes regulating each other through a variety of regulatory interactions; it is negligibly affected by endogenous genes, and it is responsive to small molecules. We measured time series and steady-state expression data after multiple perturbations. These data were used to assess state-of-the-art modeling and reverse-engineering techniques. A semiquantitative model was able to capture and predict the behavior of the network. Reverse engineering based on differential equations and Bayesian networks correctly inferred regulatory interactions from the experimental data.

A flow-through microfluidic chip for continuous dielectrophoretic separation of viable and non-viable human T-cells.

Effective methods for rapid sorting of cells according to their viability are critical in T cells based therapies to prevent any risk to patients. In this context, we present a novel microfluidic device that continuously separates viable and non-viable T-cells according to their dielectric properties. A dielectrophoresis (DEP) force is generated by an array of castellated microelectrodes embedded into a microfluidic channel with a single inlet and two outlets; cells subjected to positive DEP forces are drawn toward the electrodes array and leave from the top outlet, those subjected to negative DEP forces are repelled away from the electrodes and leave from the bottom outlet. Computational fluid dynamics is used to predict the device separation efficacy, according to the applied alternative current (AC) frequency, at which the cells move from/to a negative/positive DEP region and the ionic strength of the suspension medium. The model is used to support the design of the operational conditions, confirming a separation efficiency, in terms of purity, of 96% under an applied AC frequency of 1.5 × 106  Hz and a flow rate of 20 µl/h. This work represents the first example of effective continuous sorting of viable and non-viable human T-cells in a single-inlet microfluidic chip, paving the way for lab-on-a-chip applications at the point of need.

A dual druggable genome-wide siRNA and compound library screening approach identifies modulators of parkin recruitment to mitochondria.

Genetic and biochemical evidence points to an association between mitochondrial dysfunction and Parkinson's disease (PD). PD-associated mutations in several genes have been identified and include those encoding PTEN-induced putative kinase 1 (PINK1) and parkin. To identify genes, pathways, and pharmacological targets that modulate the clearance of damaged or old mitochondria (mitophagy), here we developed a high-content imaging-based assay of parkin recruitment to mitochondria and screened both a druggable genome-wide siRNA library and a small neuroactive compound library. We used a multiparameter principal component analysis and an unbiased parameter-agnostic machine-learning approach to analyze the siRNA-based screening data. The hits identified in this analysis included specific genes of the ubiquitin proteasome system, and inhibition of ubiquitin-conjugating enzyme 2 N (UBE2N) with a specific antagonist, Bay 11-7082, indicated that UBE2N modulates parkin recruitment and downstream events in the mitophagy pathway. Screening of the compound library identified kenpaullone, an inhibitor of cyclin-dependent kinases and glycogen synthase kinase 3, as a modulator of parkin recruitment. Validation studies revealed that kenpaullone augments the mitochondrial network and protects against the complex I inhibitor MPP+. Finally, we used a microfluidics platform to assess the timing of parkin recruitment to depolarized mitochondria and its modulation by kenpaullone in real time and with single-cell resolution. We demonstrate that the high-content imaging-based assay presented here is suitable for both genetic and pharmacological screening approaches, and we also provide evidence that pharmacological compounds modulate PINK1-dependent parkin recruitment.

Mathematical modeling reveals differential effects of erythropoietin on proliferation and lineage commitment of human hematopoietic progenitors in early erythroid culture.

Erythropoietin is essential for the production of mature erythroid cells, promoting both proliferation and survival. Whether erythropoietin and other cytokines can influence lineage commitment of hematopoietic stem and progenitor cells is of significant interest. To study lineage restriction of the common myeloid progenitor to the megakaryocyte/erythroid progenitor of peripheral blood CD34(+) cells, we have shown that the cell surface protein CD36 identifies the earliest lineage restricted megakaryocyte/erythroid progenitor. Using this marker and carboxyfluorescein succinimidyl ester to track cell divisions in vitro, we have developed a mathematical model that accurately predicts population dynamics of erythroid culture. Parameters derived from the modeling of cultures without added erythropoietin indicate that the rate of lineage restriction is not affected by erythropoietin. By contrast, megakaryocyte/erythroid progenitor proliferation is sensitive to erythropoietin from the time that CD36 first appears at the cell surface. These results shed new light on the role of erythropoietin in erythropoiesis and provide a powerful tool for further study of hematopoietic progenitor lineage restriction and erythropoiesis.

Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology.

Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.

Data hazards in synthetic biology.

Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.

Construction and modelling of an inducible positive feedback loop stably integrated in a mammalian cell-line.

Understanding the relationship between topology and dynamics of transcriptional regulatory networks in mammalian cells is essential to elucidate the biology of complex regulatory and signaling pathways. Here, we characterised, via a synthetic biology approach, a transcriptional positive feedback loop (PFL) by generating a clonal population of mammalian cells (CHO) carrying a stable integration of the construct. The PFL network consists of the Tetracycline-controlled transactivator (tTA), whose expression is regulated by a tTA responsive promoter (CMV-TET), thus giving rise to a positive feedback. The same CMV-TET promoter drives also the expression of a destabilised yellow fluorescent protein (d2EYFP), thus the dynamic behaviour can be followed by time-lapse microscopy. The PFL network was compared to an engineered version of the network lacking the positive feedback loop (NOPFL), by expressing the tTA mRNA from a constitutive promoter. Doxycycline was used to repress tTA activation (switch off), and the resulting changes in fluorescence intensity for both the PFL and NOPFL networks were followed for up to 43 h. We observed a striking difference in the dynamics of the PFL and NOPFL networks. Using non-linear dynamical models, able to recapitulate experimental observations, we demonstrated a link between network topology and network dynamics. Namely, transcriptional positive autoregulation can significantly slow down the "switch off" times, as compared to the non-autoregulated system. Doxycycline concentration can modulate the response times of the PFL, whereas the NOPFL always switches off with the same dynamics. Moreover, the PFL can exhibit bistability for a range of Doxycycline concentrations. Since the PFL motif is often found in naturally occurring transcriptional and signaling pathways, we believe our work can be instrumental to characterise their behaviour.

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 modeling 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 the 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 an in vivo experiment using an agent-based simulator describing cell 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.

ß-catenin perturbations control differentiation programs in mouse embryonic stem cells.

The Wnt/ß-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 ß-catenin in ß-catenin null mouse ESCs promotes epiblast-like cell (EpiLC) derivation in vitro. Instead, in wild-type cells, moderate chemical pre-activation of the Wnt/ß-catenin pathway promotes EpiLC in vitro derivation. Finally, we suggest that moderate ß-catenin levels in ß-catenin null mouse ESCs favor early stem cell commitment toward 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 ß-catenin in pluripotency and differentiation, while indicating a role for its doses in promoting specific differentiation programs.

Advanced medical micro-robotics for early diagnosis and therapeutic interventions.

Recent technological advances in micro-robotics have demonstrated their immense potential for biomedical applications. Emerging micro-robots have versatile sensing systems, flexible locomotion and dexterous manipulation capabilities that can significantly contribute to the healthcare system. Despite the appreciated and tangible benefits of medical micro-robotics, many challenges still remain. Here, we review the major challenges, current trends and significant achievements for developing versatile and intelligent micro-robotics with a focus on applications in early diagnosis and therapeutic interventions. We also consider some recent emerging micro-robotic technologies that employ synthetic biology to support a new generation of living micro-robots. We expect to inspire future development of micro-robots toward clinical translation by identifying the roadblocks that need to be overcome.

Derivation, identification and validation of a computational model of a novel synthetic regulatory network in yeast.

Systems biology aims at building computational models of biological pathways in order to study in silico their behaviour and to verify biological hypotheses. Modelling can become a new powerful method in molecular biology, if correctly used. Here we present step-by-step the derivation and identification of the dynamical model of a biological pathway using a novel synthetic network recently constructed in the yeast Saccharomyces cerevisiae for In-vivo Reverse-Engineering and Modelling Assessment. This network consists of five genes regulating each other transcription. Moreover, it includes one protein-protein interaction, and its genes can be switched on by addition of galactose to the medium. In order to describe the network dynamics, we adopted a deterministic modelling approach based on non-linear differential equations. We show how, through iteration between experiments and modelling, it is possible to derive a semi-quantitative prediction of network behaviour and to better understand the biology of the pathway of interest.

A Microfluidic/Microscopy-Based Platform for on-Chip Controlled Gene Expression in Mammalian Cells.

Applications of control engineering to mammalian cell biology have been recently implemented for precise regulation of gene expression. In this chapter, we report the main experimental and computational methodologies to implement automatic feedback control of gene expression in mammalian cells using a microfluidics/microscopy platform.

Testing Theoretical Minimal Genomes Using Whole-Cell Models.

The minimal gene set for life has often been theorized, 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 h, 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 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.

Understanding Metabolic Flux 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 and 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.

Minimal Genome Design Algorithms Using Whole-Cell Models.

Synthetic biologists engineer cells and cellular functions using design-build-test cycles; when the task is to extensively engineer entire genomes, the lack of appropriate design tools and biological knowledge about each gene in a cell can lengthen the process, requiring time-consuming and expensive experimental iterations.Whole-cell models represent a new avenue for genome design; the bacteria Mycoplasma genitalium has the first (and currently only published) whole-cell model which combines 28 cellular submodels and represents the integrated functions of every gene and molecule in a cell.We created two minimal genome design algorithms, GAMA and Minesweeper, that produced 1000s of in silico minimal genomes by running simulations on multiple supercomputers. Here we describe the steps to produce in silico cells with reduced genomes, combining minimisation algorithms with whole-cell model simulations.We foresee that the combination of similar algorithms and whole-cell models could later be used for a broad spectrum of genome design applications across cellular species when appropriate models become available.

ChipSeg: An Automatic Tool to Segment Bacterial 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. We describe ChipSeg, a computational tool that segments bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows us to segment individual cells in high cell density microfluidic devices, to quantify fluorescent protein expression over a time-lapse experiment, and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customized for other experimental settings and research aims.

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, characterize, and control cells over time. We demonstrate Cheetah's core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah's 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.