Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry.

Integration of machine learning and high throughput measurements are essential to drive the next generation of the design-build-test-learn (DBTL) cycle in synthetic biology. Here, we report the use of active learning in combination with metabolomics for optimising production of surfactin, a complex lipopeptide resulting from a non-ribosomal assembly pathway. We designed a media optimisation algorithm that iteratively learns the yield landscape and steers the media composition toward maximal production. The algorithm led to a 160 % yield increase after three DBTL runs as compared to an M9 baseline. Metabolomics data helped to elucidate the underpinning biochemistry for yield improvement and revealed Pareto-like trade-offs in production of other lipopeptides from related pathways. We found positive associations between organic acids and surfactin, suggesting a key role of central carbon metabolism, as well as system-wide anisotropies in how metabolism reacts to shifts in carbon and nitrogen levels. Our framework offers a novel data-driven approach to improve yield of biological products with complex synthesis pathways that are not amenable to traditional yield optimisation strategies.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes.

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Applications of artificial intelligence and machine learning in dynamic pathway engineering.

Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.

Discovery of senolytics using machine learning.

Cellular senescence is a stress response involved in ageing and diverse disease processes including cancer, type-2 diabetes, osteoarthritis and viral infection. Despite growing interest in targeted elimination of senescent cells, only few senolytics are known due to the lack of well-characterised molecular targets. Here, we report the discovery of three senolytics using cost-effective machine learning algorithms trained solely on published data. We computationally screened various chemical libraries and validated the senolytic action of ginkgetin, periplocin and oleandrin in human cell lines under various modalities of senescence. The compounds have potency comparable to known senolytics, and we show that oleandrin has improved potency over its target as compared to best-in-class alternatives. Our approach led to several hundred-fold reduction in drug screening costs and demonstrates that artificial intelligence can take maximum advantage of small and heterogeneous drug screening data, paving the way for new open science approaches to early-stage drug discovery.

Bayesian Optimization for Design of Multiscale Biological Circuits.

Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.

Deep learning for optimization of protein expression.

Advances in high-throughput DNA synthesis and sequencing have fuelled the use of massively parallel reporter assays for strain characterization. These experiments produce large datasets that map DNA sequences to protein expression levels, and have sparked increased interest in data-driven methods for sequence-to-expression modeling. Here, we highlight progress in deep learning models of protein expression and their potential for optimizing strains engineered to produce recombinant proteins. We discuss recent works that built highly accurate models as well as the challenges that hinder wider adoption by end users. There is a need to better align this technology with the requirements and capabilities encountered in strain engineering, particularly the cost of data acquisition and the need for interpretable models that generalize beyond the training data. Overcoming these barriers will help to incentivize academic and industrial laboratories to tap into a new era of data-centric strain engineering.

P1 Bacteriophage-Enabled Delivery of CRISPR-Cas9 Antimicrobial Activity Against Shigella flexneri.

The discovery of clustered, regularly interspaced, short palindromic repeats (CRISPR) and the Cas9 RNA-guided nuclease provides unprecedented opportunities to selectively kill specific populations or species of bacteria. However, the use of CRISPR-Cas9 to clear bacterial infections in vivo is hampered by the inefficient delivery of cas9 genetic constructs into bacterial cells. Here, we use a broad-host-range P1-derived phagemid to deliver the CRISPR-Cas9 chromosomal-targeting system into Escherichia coli and the dysentery-causing Shigella flexneri to achieve DNA sequence-specific killing of targeted bacterial cells. We show that genetic modification of the helper P1 phage DNA packaging site (pac) significantly enhances the purity of packaged phagemid and improves the Cas9-mediated killing of S. flexneri cells. We further demonstrate that P1 phage particles can deliver chromosomal-targeting cas9 phagemids into S. flexneri in vivo using a zebrafish larvae infection model, where they significantly reduce the bacterial load and promote host survival. Our study highlights the potential of combining P1 bacteriophage-based delivery with the CRISPR chromosomal-targeting system to achieve DNA sequence-specific cell lethality and efficient clearance of bacterial infection.

Accuracy and data efficiency in deep learning models of protein expression.

Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain optimization. But such models need large and costly training data that create steep entry barriers for many laboratories. Here we study the relation between accuracy and data efficiency in an atlas of machine learning models trained on datasets of varied size and sequence diversity. We show that deep learning can achieve good prediction accuracy with much smaller datasets than previously thought. We demonstrate that controlled sequence diversity leads to substantial gains in data efficiency and employed Explainable AI to show that convolutional neural networks can finely discriminate between input DNA sequences. Our results provide guidelines for designing genotype-phenotype screens that balance cost and quality of training data, thus helping promote the wider adoption of deep learning in the biotechnology sector.

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health.

Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, we report an integrated DNA methylation and phenotypic study of the circulating proteome in relation to brain health. Methylome-wide association studies of 4058 plasma proteins are performed (N = 774), identifying 2928 CpG-protein associations after adjustment for multiple testing. These are independent of known genetic protein quantitative trait loci (pQTLs) and common lifestyle effects. Phenome-wide association studies of each protein are then performed in relation to 15 neurological traits (N = 1,065), identifying 405 associations between the levels of 191 proteins and cognitive scores, brain imaging measures or APOE e4 status. We uncover 35 previously unreported DNA methylation signatures for 17 protein markers of brain health. The epigenetic and proteomic markers we identify are pertinent to understanding and stratifying brain health.

Design and Study of a Photo-Switchable Polymeric System in the Presence of ZnS Nanoparticles under the Influence of UV Light Irradiation.

Recent progress in the field of photosensitive materials has prompted a need to develop efficient methods to synthesize materials with basic intermolecular architectural designs and novel properties. Accordingly, in this work we design and study a photoactive polymer as a photo-switchable polymeric system in the presence and absence of ZnS nanoparticles (average size < 10 nm) at 5 wt.%. The influence of UV light irradiation on its properties were also studied. The photoactive block copolymer was obtained from styrene (S) and methyl methacrylate (MMA) as monomers and 1-(2-hydroxyethyl)-3,3-dimethylindoline-6-nitrobenzopyran (SP) was grafted to the block copolymer backbone as a photochromic agent. Furthermore, the incorporation of ZnS (NPs) as photo-optical switch component into the system enhances the purple colored photo-emission, with the open form of the spiropyran derivative (merocyanine, MC). The ZnS stabilize the isomeric equilibrium in the MC interconversion of the photochromic agent. The photo-switchable properties of the PS-b-PMMA-SP in the presence of ZnS (NPs) were examined using UV-VIS spectroscopy, Photoluminescence (PL) spectroscopy, optical fluorescence and scanning electronic microscopy (SEM-EDX.). The observed changes in the absorbance, fluorescence and morphology of the system were associated to the reversible interconversion of the two states of the photochromic agent which regulates the radiative deactivation of the luminescent ZnS NPs component. After UV irradiation the photoactive polymer becomes purple in color. Therefore, these basic studies can lead to the development of innovative functional and nanostructured materials with photosensitive character as photosensitive molecular switches.

Stabilization of antithetic control via molecular buffering.

A key goal in synthetic biology is the construction of molecular circuits that robustly adapt to perturbations. Although many natural systems display perfect adaptation, whereby stationary molecular concentrations are insensitive to perturbations, its de novo engineering has proven elusive. The discovery of the antithetic control motif was a significant step towards a universal mechanism for engineering perfect adaptation. Antithetic control provides perfect adaptation in a wide range of systems, but it can lead to oscillatory dynamics due to loss of stability; moreover, it can lose perfect adaptation in fast growing cultures. Here, we introduce an extended antithetic control motif that resolves these limitations. We show that molecular buffering, a widely conserved mechanism for homeostatic control in Nature, stabilizes oscillations and allows for near-perfect adaptation during rapid growth. We study multiple buffering topologies and compare their performance in terms of their stability and adaptation properties. We illustrate the benefits of our proposed strategy in exemplar models for biofuel production and growth rate control in bacterial cultures. Our results provide an improved circuit for robust control of biomolecular systems.

Microbial polysaccharides: An emerging family of natural biomaterials for cancer therapy and diagnostics.

Microbial polysaccharides (MPs) offer immense diversity in structural and functional properties. They are extensively used in advance biomedical science owing to their superior biodegradability, hemocompatibility, and capability to imitate the natural extracellular matrix microenvironment. Ease in tailoring, inherent bio-activity, distinct mucoadhesiveness, ability to absorb hydrophobic drugs, and plentiful availability of MPs make them prolific green biomaterials to overcome the significant constraints of cancer chemotherapeutics. Many studies have demonstrated their application to obstruct tumor development and extend survival through immune activation, apoptosis induction, and cell cycle arrest by MPs. Synoptic investigations of MPs are compulsory to decode applied basics in recent inclinations towards cancer regimens. The current review focuses on the anticancer properties of commercially available and newly explored MPs, and outlines their direct and indirect mode of action. The review also highlights cutting-edge MPs-based drug delivery systems to augment the specificity and efficiency of available chemotherapeutics, as well as their emerging role in theranostics.

Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering.

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.

Exploration of Copper Oxide Nanoneedle Electrosynthesis Applied in the Degradation of Methylene Blue.

In this study, we report a low cost, fast and unexplored electrochemical synthesis strategy of copper oxide nanoneedles films as well as their morphological and chemical characterization. The nanostructured films were prepared using electrochemical anodization in alkaline electrolyte solutions of ethylene glycol, water and fluoride ions. The film morphology shows nanoneedle-shaped structures, with lengths up to 1-2 µm; meanwhile, high-resolution X-ray photoelectron spectroscopy (HRXPS) and spectroscopy Raman analyses indicate that a mixture of Cu(II) and Cu(I) oxides, or only Cu(I) oxide, is obtained as the percentage of water in the electrolyte solution decreases. A preliminary study was also carried out for the photocatalytic degradation of the methylene blue (MB) dye under irradiation with simulated sunlight in the presence of the nanoneedles obtained, presenting a maximum degradation value of 88% of MB and, thus, demonstrating the potential characteristics of the material investigated in the degradation of organic dyes.

Preparation of photoactive ZnS-composite porous polymer films: Fluorescent and morphological properties.

This work describes the use of the breath figure (BF) method for the fabrication of photoactive porous polymer films and the characterization of their responsive to photo stimulus. The films incorporate self-assembled photoactive polymers and ZnS nanoparticles (NPs). The effect of both components on the optical and morphological properties of the films were analyzed. Films with a hexagonally ordered pattern were obtained. The photoactive polymer was prepared by grafting the photochromic component 1-(2-hydroxyethyl)-3,3-dimethylindoline-6-nitrobenzopyran (SP) to polystyrene-block-polymethacrylic acid (PS-b-PMMA). ZnS NPs were incorporated into the polymer solution, and the films were prepared using spin-coating on glass substrates before subjecting them to the BF method. The hollow footprints were obtained before introducing the ZnS NPs in order to maintain the necessary conditions for hexagonal film growth. Accordingly, the SEM micrographs of the films prepared in the presence of ZnS NPs displayed a loss in the pore arrangement as a consequence of the interaction between SP moiety and NPs. The light-emitting properties of films were characterized by blue and violet colors when exposed to UV light under fluorescence. Progress in the field of breath-figure formation and its application, such as exemplified in this work, leads to functional structures with suitable applications in chemistry and materials science. It is expected that such microstructured polymeric films will have interesting applications in photonic and optoelectronic devices.

Unraveling the Ion Adsorption Kinetics in Microporous Carbon Electrodes: A Multiscale Quantum-Continuum Simulation and Experimental Approach.

Understanding sorption in porous carbon electrodes is crucial to many environmental and energy technologies, such as capacitive deionization (CDI), supercapacitor energy storage, and activated carbon filters. In each of these examples, a practical model that can describe ion electrosorption kinetics is highly desirable for accelerating material design. Here, we proposed a multiscale model to study the ion electrosorption kinetics in porous carbon electrodes by combining quantum mechanical simulations with continuum approaches. Our model integrates the Butler-Volmer (BV) equation for sorption kinetics and a continuously stirred tank reactor (CSTR) formulation with atomistic calculations of ion hydration and ion-pore interactions based on density functional theory (DFT). We validated our model experimentally by using ion mixtures in a flow-through electrode CDI device and developed an in-line UV absorption system to provide unprecedented resolution of individual ions in the separation process. We showed that the multiscale model captures unexpected experimental phenomena that cannot be explained by the traditional ion electrosorption theory. The proposed multiscale framework provides a viable approach for modeling separation processes in systems where pore sizes and ion hydration effects strongly influence the sorption kinetics, which can be leveraged to explore possible strategies for improving carbon-based and, more broadly, pore-based technologies.

Systems level profiling of chemotherapy-induced stress resolution in cancer cells reveals druggable trade-offs.

Cancer cells can survive chemotherapy-induced stress, but how they recover from it is not known. Using a temporal multiomics approach, we delineate the global mechanisms of proteotoxic stress resolution in multiple myeloma cells recovering from proteasome inhibition. Our observations define layered and protracted programs for stress resolution that encompass extensive changes across the transcriptome, proteome, and metabolome. Cellular recovery from proteasome inhibition involved protracted and dynamic changes of glucose and lipid metabolism and suppression of mitochondrial function. We demonstrate that recovering cells are more vulnerable to specific insults than acutely stressed cells and identify the general control nonderepressable 2 (GCN2)-driven cellular response to amino acid scarcity as a key recovery-associated vulnerability. Using a transcriptome analysis pipeline, we further show that GCN2 is also a stress-independent bona fide target in transcriptional signature-defined subsets of solid cancers that share molecular characteristics. Thus, identifying cellular trade-offs tied to the resolution of chemotherapy-induced stress in tumor cells may reveal new therapeutic targets and routes for cancer therapy optimization.

Prediction of Cellular Burden with Host-Circuit Models.

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology.