A versatile active learning workflow for optimization of genetic and metabolic networks

Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges

The lignocellulosic biorefinery involves pretreatment, enzymatic hydrolysis, mixed sugar fermentation, and optional anaerobic digestion. This pipeline could be effectively implemented through machine learning (ML)-guided process optimization and strain modification rather than experimental or experience-based ones. This review takes a holistic perspective on the entire pipeline, discussing how ML could aid lignocellulosic, while other published work has focused on individual modules within the pipeline. This review also explores the model construction and evaluation strategies and highlights the emerging potential of transfer learning and hybrid ML models to address data insufficiency and improve model interpretability. Furthermore, challenges and future prospects of ML in lignocellulosic biorefinery will be elaborated in this review. Integrating ML into lignocellulosic biorefinery offers a promising pathway towards sustainable and competitive biorefinery systems.

Designer artificial environments for membrane protein synthesis

Protein synthesis in natural cells involves intricate interactions between chemical environments, protein-protein interactions, and protein machinery. Replicating such interactions in artificial and cell-free environments can control the precision of protein synthesis, elucidate complex cellular mechanisms, create synthetic cells, and discover new therapeutics. Yet, creating artificial synthesis environments, particularly for membrane proteins, is challenging due to the poorly defined chemical-protein-lipid interactions. Here, we introduce MEMPLEX (Membrane Protein Learning and Expression), which utilizes machine learning and a fluorescent reporter to rapidly design artificial synthesis environments of membrane proteins. MEMPLEX generates over 20,000 different artificial chemical-protein environments spanning 28 membrane proteins. It captures the interdependent impact of lipid types, chemical environments, chaperone proteins, and protein structures on membrane protein synthesis. As a result, MEMPLEX creates new artificial environments that successfully synthesize membrane proteins of broad interest but previously intractable. In addition, we identify a quantitative metric, based on the hydrophobicity of the membrane-contacting amino acids, that predicts membrane protein synthesis in artificial environments. Our work allows others to rapidly study and resolve the "dark" proteome using predictive generation of artificial chemical-protein environments. Furthermore, the results represent a new frontier in artificial intelligence-guided approaches to creating synthetic environments for protein synthesis.

ML-enhanced peroxisome capacity enables compartmentalization of multienzyme pathway

Repurposing an organelle for specialized metabolism provides an avenue for fermentable, unicellular organisms such as Saccharomyces cerevisiae to mimic compartmentalization of metabolic pathways within different plant tissues. Peroxisomes are attractive organelles for repurposing as they are not required for yeast viability when grown on glucose and can efficiently compartmentalize heterologous enzymes to enable physical separation of cytosolic native metabolism and peroxisomal engineered metabolism. However, when not required, peroxisomes are repressed, leading to low functional capacities for heterologous proteins. Here we engineer peroxisomes with enhanced functional capacities, with the goal of compartmentalizing up to eight metabolic enzymes to enhance titers. We implement a machine learning pipeline that allows the identification of factors to overexpress, culminating in a 137% increase in peroxisome functional capacity compared to a wild-type strain. Improved pathway compartmentalization enables an 80% increase in the biosynthesis titers of the monoterpene geraniol, up to 9.5 g L-1.

Cascade Catalytic Systems for Converting CO2 into C2+ Products

The excessive emission and continuous accumulation of CO2 have precipitated serious social and environmental issues. However, CO2 can also serve as an abundant, inexpensive, and non-toxic renewable C1 carbon source for synthetic reactions. To achieve carbon neutrality and recycling, it is crucial to convert CO2 into value-added products through chemical pathways. Multi-carbon (C2+) products, compared to C1 products, offer a broader range of applications and higher economic returns. Despite this, converting CO2 into C2+ products is difficult due to its stability and the high energy required for C-C coupling. Cascade catalytic reactions offer a solution by coordinating active components, promoting intermediate transfers, and facilitating further transformations. This method lowers energy consumption. Recent advancements in cascade catalytic systems have allowed for significant progress in synthesizing C2+ products from CO2. This review highlights the features and advantages of cascade catalysis strategies, explores the synergistic effects among active sites, and examines the mechanisms within these systems. It also outlines future prospects for CO2 cascade catalytic synthesis, offering a framework for efficient CO2 utilization and the development of next-generation catalytic systems.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

Molecular insights fast-tracked: AI in biosynthetic pathway research

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

Cell-Free Systems to Mimic and Expand Metabolism

Cell-free synthetic biology incorporates purified components and/or crude cell extracts to carry out metabolic and genetic programs. While protein synthesis has historically been the primary focus, more metabolism researchers are now turning toward cell-free systems either to prototype pathways for cellular implementation or to design new-to-nature reaction networks that incorporate environmentally relevant substrates or new energy sources. The ability to design, build, and test enzyme combinations in vitro has accelerated efforts to understand metabolic bottlenecks and engineer high-yielding pathways. However, only a small fraction of metabolic possibilities has been explored in cell-free systems, and extracts from model organisms remain the most common starting points. Expanding the scope of cell-free metabolism to include extracts from new organisms, alternative metabolic pathways, and non-natural chemistries will enhance our ability to understand and engineer bio-based chemical conversions.

Cell-free protein synthesis for biocatalysis

Cell-free protein synthesis (CFPS) serves as an innovation booster in the field of biocatalysis. By integrating CFPS into the design and development of biocatalysts, the discovery, synthesis, and screening of previously untapped enzymes and their engineered variants can be improved. The high-throughput capability of CFPS accelerates the identification of optimal synthesis conditions, including expression hosts, chaperone sets, temperature, and codon optimization. Moreover, the availability of various CFPS systems facilitates the incorporation of non-canonical amino acids and enables native post-translational modifications. Using CFPS in combination with enzymatic activity assays also helps to determine the best conditions for biocatalytic reactions, such as temperature, pH, substrate, and choice of cofactor. The compatibility of CFPS with robotic and microfluidic systems, along with artificial intelligence, further enhances its high-throughput capabilities. However, challenges remain regarding scalability, the low concentration of the target protein, and the applicability of a generalized CFPS system for the synthesis of any protein. While these challenges impede the incorporation of CFPS in industrial scale biocatalytic processes, its applicability as screening tool is validated to improve biocatalytic reactions. This knowledge can then be transferred to in vivo synthesis systems to improve the overall production outcomes.

A guide for active learning in synergistic drug discovery

Synergistic drug combination screening is a promising strategy in drug discovery, but it involves navigating a costly and complex search space. While AI, particularly deep learning, has advanced synergy predictions, its effectiveness is limited by the low occurrence of synergistic drug pairs. Active learning, which integrates experimental testing into the learning process, has been proposed to address this challenge. In this work, we explore the key components of active learning to provide recommendations for its implementation. We find that molecular encoding has a limited impact on performance, while the cellular environment features significantly enhance predictions. Additionally, active learning can discover 60% of synergistic drug pairs with only exploring 10% of combinatorial space. The synergy yield ratio is observed to be even higher with smaller batch sizes, where dynamic tuning of the exploration-exploitation strategy can further enhance performance. The code can be found at https://github.com/LBiophyEvo/DrugSynergy.

AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks

Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these 'behavioral catalogs' for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

The Theranostic Genome

Theranostic drugs represent an emerging path to deliver on the promise of precision medicine. However, bottlenecks remain in characterizing theranostic targets, identifying theranostic lead compounds, and tailoring theranostic drugs. To overcome these bottlenecks, we present the Theranostic Genome, the part of the human genome whose expression can be utilized to combine therapeutic and diagnostic applications. Using a deep learning-based hybrid human-AI pipeline that cross-references PubMed, the Gene Expression Omnibus, DisGeNET, The Cancer Genome Atlas and the NIH Molecular Imaging and Contrast Agent Database, we bridge individual genes in human cancers with respective theranostic compounds. Cross-referencing the Theranostic Genome with RNAseq data from over 17'000 human tissues identifies theranostic targets and lead compounds for various human cancers, and allows tailoring targeted theranostics to relevant cancer subpopulations. We expect the Theranostic Genome to facilitate the development of new targeted theranostics to better diagnose, understand, treat, and monitor a variety of human cancers.

Synthetic biology of metabolic cycles for Enhanced CO2 capture and Sequestration

In most organisms, the tri-carboxylic acid cycle (TCA cycle) is an essential metabolic system that is involved in both energy generation and carbon metabolism. Its uni-directionality, however, restricts its use in synthetic biology and carbon fixation. Here, it is describing the use of the modified TCA cycle, called the Tri-carboxylic acid Hooked to Ethylene by Enzyme Reactions and Amino acid Synthesis, the reductive tricarboxylic acid branch/4-hydroxybutyryl-CoA/ethylmalonyl-CoA/acetyl-CoA (THETA) cycle, in Escherichia coli for the purposes of carbon fixation and amino acid synthesis. Three modules make up the THETA cycle: (1) pyruvate to succinate transformation, (2) succinate to crotonyl-CoA change, and (3) crotonyl-CoA to acetyl-CoA and pyruvate change. It is presenting each module's viability in vivo and showing how it integrates into the E. coli metabolic network to support growth on minimal medium without the need for outside supplementation. Enzyme optimization, route redesign, and heterologous expression were used to get over metabolic roadblocks and produce functional modules. Furthermore, the THETA cycle may be improved by including components of the Carbon-Efficient Tri-Carboxylic Acid Cycle (CETCH cycle) to improve carbon fixation. THETA cycle's promise as a platform for applications in synthetic biology and carbon fixation.

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.

New-to-nature CO2-dependent acetyl-CoA assimilation enabled by an engineered B12-dependent acyl-CoA mutase

Acetyl-CoA is a key metabolic intermediate and the product of various natural and synthetic one-carbon (C1) assimilation pathways. While an efficient conversion of acetyl-CoA into other central metabolites, such as pyruvate, is imperative for high biomass yields, available aerobic pathways typically release previously fixed carbon in the form of CO2. To overcome this loss of carbon, we develop a new-to-nature pathway, the Lcm module, in this study. The Lcm module provides a direct link between acetyl-CoA and pyruvate, is shorter than any other oxygen-tolerant route and notably fixes CO2, instead of releasing it. The Lcm module relies on the new-to-nature activity of a coenzyme B12-dependent mutase for the conversion of 3-hydroxypropionyl-CoA into lactyl-CoA. We demonstrate Lcm activity of the scaffold enzyme 2-hydroxyisobutyryl-CoA mutase from Bacillus massiliosenegalensis, and further improve catalytic efficiency 10-fold by combining in vivo targeted hypermutation and adaptive evolution in an engineered Escherichia coli selection strain. Finally, in a proof-of-principle, we demonstrate the complete Lcm module in vitro. Overall, our work demonstrates a synthetic CO2-incorporating acetyl-CoA assimilation route that expands the metabolic solution space of central carbon metabolism, providing options for synthetic biology and metabolic engineering.

An enhanced genetic algorithm solution for itinerary recommendation considering various constraints

This paper addresses the challenging task of itinerary recommendation for tourists and proposes an approach for suggesting efficient optimal itineraries in Istanbul, based on constraints. The paper presents an enhanced version of the genetic algorithm (GA), which aims to optimize the itineraries considering various constraints and preferences of the tourists. The improvement of the GA involved suggesting a customized fitness function tailored to address the complexities of the tourism problem, considering factors such as distance, time, cost, tourists' budget, and their desired activities and attractions. Additionally, we proposed a new crossover method, named "Copy Order Crossover" and we modified the tournament selection method beside enhancing the implementation of the swap mutation method for greater efficiency and adaptability. The enhanced GA is evaluated on the Burma dataset taken from TSPLIB, and our constructed Istanbul dataset, achieving significant enhancement rates in GA (43.89% for Istanbul, and 56.60% for Burma). This paper provides a detailed account of the proposed approach, its implementation, and the evaluation conducted. The experimental results conclusively demonstrated the superiority of the proposed approach over alternative methods in terms of time, efficiency, and accuracy. This paper finishes with an outlook with a detailed potential approach to overcome itinerary recommendation problem limitations.

In vitro transcription-based biosensing of glycolate for prototyping of a complex enzyme cascade

In vitro metabolic systems allow the reconstitution of natural and new-to-nature pathways outside of their cellular context and are of increasing interest in bottom-up synthetic biology, cell-free manufacturing, and metabolic engineering. Yet, the analysis of the activity of such in vitro networks is very often restricted by time- and cost-intensive methods. To overcome these limitations, we sought to develop an in vitro transcription (IVT)-based biosensing workflow that is compatible with the complex conditions of in vitro metabolism, such as the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle, a 27-component in vitro metabolic system that converts CO2 into glycolate. As proof of concept, we constructed a novel glycolate sensor module that is based on the transcriptional repressor GlcR from Paracoccus denitrificans and established an IVT biosensing workflow that allows us to quantify glycolate from CETCH samples in the micromolar to millimolar range. We investigate the influence of 13 (shared) cofactors between the two in vitro systems to show that Mg2+, adenosine triphosphate , and other phosphorylated metabolites are critical for robust signal output. Our optimized IVT biosensor correlates well with liquid chromatography-mass spectrometry-based glycolate quantification of CETCH samples, with one or multiple components varying (linear correlation 0.94-0.98), but notably at ∼10-fold lowered cost and ∼10 times faster turnover time. Our results demonstrate the potential and challenges of IVT-based systems to quantify and prototype the activity of complex reaction cascades and in vitro metabolic networks.

Cell-free protein synthesis with technical additives - expanding the parameter space of in vitro gene expression

Biocatalysis has established itself as a successful tool in organic synthesis. A particularly fast technique for screening enzymes is the in vitro expression or cell-free protein synthesis (CFPS). The system is based on the transcription and translation machinery of an extract-donating organism to which substrates such as nucleotides and amino acids, as well as energy molecules, salts, buffer, etc., are added. After successful protein synthesis, further substrates can be added for an enzyme activity assay. Although mimicking of cell-like conditions is an approach for optimization, the physical and chemical properties of CFPS are not well described yet. To date, standard conditions have mainly been used for CFPS, with little systematic testing of whether conditions closer to intracellular conditions in terms of viscosity, macromolecules, inorganic ions, osmolarity, or water content are advantageous. Also, very few non-physiological conditions have been tested to date that would expand the parameter space in which CFPS can be performed. In this study, the properties of an Escherichia coli extract-based CFPS system are evaluated, and the parameter space is extended to high viscosities, concentrations of inorganic ion and osmolarity using ten different technical additives including organic solvents, polymers, and salts. It is shown that the synthesis of two model proteins, namely superfolder GFP (sfGFP) and the enzyme truncated human cyclic GMP-AMP synthase fused to sfGFP (thscGAS-sfGFP), is very robust against most of the tested additives.

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Integrated translation and metabolism in a partially self-synthesizing biochemical network

One of the hallmarks of living organisms is their capacity for self-organization and regeneration, which requires a tight integration of metabolic and genetic networks. We sought to construct a linked metabolic and genetic network in vitro that shows such lifelike behavior outside of a cellular context and generates its own building blocks from nonliving matter. We integrated the metabolism of the crotonyl-CoA/ethyl-malonyl-CoA/hydroxybutyryl-CoA cycle with cell-free protein synthesis using recombinant elements. Our network produces the amino acid glycine from CO2 and incorporates it into target proteins following DNA-encoded instructions. By orchestrating ~50 enzymes we established a basic cell-free operating system in which genetically encoded inputs into a metabolic network are programmed to activate feedback loops allowing for self-integration and (partial) self-regeneration of the complete system.

Mechanism-based and data-driven modeling in cell-free synthetic biology

Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.

Leveraging Active Learning to Establish Efficient In Vitro Transcription and Translation from Bacterial Chromosomal DNA

Gene expression is a fundamental aspect in the construction of a minimal synthetic cell, and the use of chromosomes will be crucial for the integration and regulation of complex modules. Expression from chromosomes in vitro transcription and translation (IVTT) systems presents limitations, as their large size and low concentration make them far less suitable for standard IVTT reactions. Here, we addressed these challenges by optimizing lysate-based IVTT systems at low template concentrations. We then applied an active learning tool to adapt IVTT to chromosomes as template DNA. Further insights into the dynamic data set led us to adjust the previous protocol for chromosome isolation and revealed unforeseen trends pointing at limiting transcription kinetics in our system. The resulting IVTT conditions allowed a high template DNA efficiency for the chromosomes. In conclusion, our system shows a protein-to-chromosome ratio that moves closer to in vivo biology and represents an advancement toward chromosome-based synthetic cells.

Engineering is evolution: a perspective on design processes to engineer biology

Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

ATP-free in vitro biotransformation of starch-derived maltodextrin into poly-3-hydroxybutyrate via acetyl-CoA

In vitro biotransformation (ivBT) facilitated by in vitro synthetic enzymatic biosystems (ivSEBs) has emerged as a highly promising biosynthetic platform. Several ivSEBs have been constructed to produce poly-3-hydroxybutyrate (PHB) via acetyl-coenzyme A (acetyl-CoA). However, some systems are hindered by their reliance on costly ATP, limiting their practicality. This study presents the design of an ATP-free ivSEB for one-pot PHB biosynthesis via acetyl-CoA utilizing starch-derived maltodextrin as the sole substrate. Stoichiometric analysis indicates this ivSEB can self-maintain NADP+/NADPH balance and achieve a theoretical molar yield of 133.3%. Leveraging simple one-pot reactions, our ivSEBs achieved a near-theoretical molar yield of 125.5%, the highest PHB titer (208.3 mM, approximately 17.9 g/L) and the fastest PHB production rate (9.4 mM/h, approximately 0.8 g/L/h) among all the reported ivSEBs to date, and demonstrated easy scalability. This study unveils the promising potential of ivBT for the industrial-scale production of PHB and other acetyl-CoA-derived chemicals from starch.

Jensen M. teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

Iterative design of training data to control intricate enzymatic reaction networks

Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.

Pushing the boundaries of phosphorylase cascade reaction for cellobiose production II: Model-based multiobjective optimization

The inherent complexity of coupled biocatalytic reactions presents a major challenge for process development with one-pot multienzyme cascade transformations. Kinetic models are powerful engineering tools to guide the optimization of cascade reactions towards a performance suitable for scale up to an actual production. Here, we report kinetic model-based window of operation analysis for cellobiose production (≥100 g/L) from sucrose and glucose by indirect transglycosylation via glucose 1-phosphate as intermediate. The two-step cascade transformation is catalyzed by sucrose and cellobiose phosphorylase in the presence of substoichiometric amounts of phosphate (≤27 mol% of substrate). Kinetic modeling was instrumental to uncover the hidden effect of bulk microviscosity due to high sugar concentrations on decreasing the rate of cellobiose phosphorylase specifically. The mechanistic-empirical hybrid model thus developed gives a comprehensive description of the cascade reaction at industrially relevant substrate conditions. Model simulations serve to unravel opposed relationships between efficient utilization of the enzymes and maximized concentration (or yield) of the product within a given process time, in dependence of the initial concentrations of substrate and phosphate used. Optimum balance of these competing key metrics of process performance is suggested from the model-calculated window of operation and is verified experimentally. The evidence shown highlights the important use of kinetic modeling for the characterization and optimization of cascade reactions in ways that appear to be inaccessible to purely data-driven approaches.

Challenges in developing cell culture media using machine learning

Microbial and mammalian cells are widely used in the food, pharmaceutical, and medical industries. Developing or optimizing culture media is essential to improve cell culture performance as a critical technology in cell culture engineering. Methodologies for media optimization have been developed to a great extent, such as the approaches of one-factor-at-a-time (OFAT) and response surface methodology (RSM). The present review introduces the emerging machine learning (ML) technology in cell culture engineering by combining high-throughput experimental technologies to develop highly efficient and effective culture media. The commonly used ML algorithms and the successful applications of employing ML in medium optimization are summarized. This review highlights the benefits of ML-assisted medium development and guides the selection of the media optimization method appropriate for various cell culture purposes.

Enabling pathway design by multiplex experimentation and machine learning

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.

Customizing cellular signal processing by synthetic multi-level regulatory circuits

As synthetic biology permeates society, the signal processing circuits in engineered living systems must be customized to meet practical demands. Towards this mission, novel regulatory mechanisms and genetic circuits with unprecedented complexity have been implemented over the past decade. These regulatory mechanisms, such as transcription and translation control, could be integrated into hybrid circuits termed "multi-level circuits". The multi-level circuit design will tremendously benefit the current genetic circuit design paradigm, from modifying basic circuit dynamics to facilitating real-world applications, unleashing our capabilities to customize cellular signal processing and address global challenges through synthetic biology.

Machine Learning-Supported Enzyme Engineering toward Improved CO2-Fixation of Glycolyl-CoA Carboxylase

Glycolyl-CoA carboxylase (GCC) is a new-to-nature enzyme that catalyzes the key reaction in the tartronyl-CoA (TaCo) pathway, a synthetic photorespiration bypass that was recently designed to improve photosynthetic CO2 fixation. GCC was created from propionyl-CoA carboxylase (PCC) through five mutations. However, despite reaching activities of naturally evolved biotin-dependent carboxylases, the quintuple substitution variant GCC M5 still lags behind 4-fold in catalytic efficiency compared to its template PCC and suffers from futile ATP hydrolysis during CO2 fixation. To further improve upon GCC M5, we developed a machine learning-supported workflow that reduces screening efforts for identifying improved enzymes. Using this workflow, we present two novel GCC variants with 2-fold increased carboxylation rate and 60% reduced energy demand, respectively, which are able to address kinetic and thermodynamic limitations of the TaCo pathway. Our work highlights the potential of combining machine learning and directed evolution strategies to reduce screening efforts in enzyme engineering.

Modular bioengineering of whole-cell catalysis for sialo-oligosaccharide production: coordinated co-expression of CMP-sialic acid synthetase and sialyltransferase

BACKGROUND

In whole-cell bio-catalysis, the biosystems engineering paradigm shifts from the global reconfiguration of cellular metabolism as in fermentation to a more focused, and more easily modularized, optimization of comparably short cascade reactions. Human milk oligosaccharides (HMO) constitute an important field for the synthetic application of cascade bio-catalysis in resting or non-living cells. Here, we analyzed the central catalytic module for synthesis of HMO-type sialo-oligosaccharides, comprised of CMP-sialic acid synthetase (CSS) and sialyltransferase (SiaT), with the specific aim of coordinated enzyme co-expression in E. coli for reaction flux optimization in whole cell conversions producing 3'-sialyllactose (3SL).

RESULTS

Difference in enzyme specific activity (CSS from Neisseria meningitidis: 36 U/mg; α2,3-SiaT from Pasteurella dagmatis: 5.7 U/mg) was compensated by differential protein co-expression from tailored plasmid constructs, giving balance between the individual activities at a high level of both (α2,3-SiaT: 9.4 × 102 U/g cell dry mass; CSS: 3.4 × 102 U/g cell dry mass). Finally, plasmid selection was guided by kinetic modeling of the coupled CSS-SiaT reactions in combination with comprehensive analytical tracking of the multistep conversion (lactose, N-acetyl neuraminic acid (Neu5Ac), cytidine 5'-triphosphate; each up to 100 mM). The half-life of SiaT in permeabilized cells (≤ 4 h) determined the efficiency of 3SL production at 37 °C. Reaction at 25 °C gave 3SL (40 ± 4 g/L) in ∼ 70% yield within 3 h, reaching a cell dry mass-specific productivity of ∼ 3 g/(g h) and avoiding intermediary CMP-Neu5Ac accumulation.

CONCLUSIONS

Collectively, balanced co-expression of CSS and SiaT yields an efficient (high-flux) sialylation module to support flexible development of E. coli whole-cell catalysts for sialo-oligosaccharide production.

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.

De novo artificial synthesis of hexoses from carbon dioxide

Developing artificial "CO2-sugar" platforms is meaningful for addressing challenges posed by land scarcity and climate change to the supply of dietary sugar. However, upcycling CO2 into complex polyoxygenated carbohydrates involves several major challenges, including achieving enantioselective and thermodynamically driven transformation and expanding product repertoires while reducing energy consumption. We present a versatile chemoenzymatic roadmap based on aldol condensation, iso/epimerization, and dephosphorylation reactions for asymmetric CO2 and H2 assembly into sugars with perfect stereocontrol. In particular, we developed a minimum ATP consumption and the shortest pathway for bottom-up biosynthesis of the fundamental precursor, fructose-6-phosphate, which is valuable for synthesizing structure-diverse sugars and derivatives. Engineering bottleneck-associated enzyme catalysts aided in the thermodynamically driven synthesis of several energy-dense and functional hexoses, such as glucose and D-allulose, featuring higher titer (63 mmol L-1) and CO2-product conversion rates (25 mmol C L-1 h-1) compared to established in vitro CO2-fixing pathways. This chemical-biological platform demonstrated greater carbon conversion yield than the conventional "CO2-bioresource-sugar" process and could be easily extended to precisely synthesize other high-order sugars from CO2.

Improving photosynthetic efficiency toward food security: Strategies, advances, and perspectives

Photosynthesis in crops and natural vegetation allows light energy to be converted into chemical energy and thus forms the foundation for almost all terrestrial trophic networks on Earth. The efficiency of photosynthetic energy conversion plays a crucial role in determining the portion of incident solar radiation that can be used to generate plant biomass throughout a growth season. Consequently, alongside the factors such as resource availability, crop management, crop selection, maintenance costs, and intrinsic yield potential, photosynthetic energy use efficiency significantly influences crop yield. Photosynthetic efficiency is relevant to sustainability and food security because it affects water use efficiency, nutrient use efficiency, and land use efficiency. This review focuses specifically on the potential for improvements in photosynthetic efficiency to drive a sustainable increase in crop yields. We discuss bypassing photorespiration, enhancing light use efficiency, harnessing natural variation in photosynthetic parameters for breeding purposes, and adopting new-to-nature approaches that show promise for achieving unprecedented gains in photosynthetic efficiency.

Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.

A Customized Bayesian Algorithm to Optimize Enzyme-Catalyzed Reactions

Design of experiments (DoE) plays an important role in optimizing the catalytic performance of chemical reactions. The most commonly used DoE relies on the response surface methodology (RSM) to model the variable space of experimental conditions with the fewest number of experiments. However, the RSM leads to an exponential increase in the number of required experiments as the number of variables increases. Herein we describe a Bayesian optimization algorithm (BOA) to optimize the continuous parameters (e.g., temperature, reaction time, reactant and enzyme concentrations, etc.) of enzyme-catalyzed reactions with the aim of maximizing performance. Compared to existing Bayesian optimization methods, we propose an improved algorithm that leads to better results under limited resources and time for experiments. To validate the versatility of the BOA, we benchmarked its performance with biocatalytic C-C bond formation and amination for the optimization of the turnover number. Gratifyingly, up to 80% improvement compared to RSM and up to 360% improvement vs previous Bayesian optimization algorithms were obtained. Importantly, this strategy enabled simultaneous optimization of both the enzyme's activity and selectivity for cross-benzoin condensation.

Cell-Free Expression System Derived from a Near-Minimal Synthetic Bacterium

Cell-free expression (CFE) systems are fundamental to reconstituting metabolic pathways in vitro toward the construction of a synthetic cell. Although an Escherichia coli-based CFE system is well-established, simpler model organisms are necessary to understand the principles behind life-like behavior. Here, we report the successful creation of a CFE system derived from JCVI-syn3A (Syn3A), the minimal synthetic bacterium. Previously, high ribonuclease activity in Syn3A lysates impeded the establishment of functional CFE systems. Now, we describe how an unusual cell lysis method (nitrogen decompression) yielded Syn3A lysates with reduced ribonuclease activity that supported in vitro expression. To improve the protein yields in the Syn3A CFE system, we optimized the Syn3A CFE reaction mixture using an active machine learning tool. The optimized reaction mixture improved the CFE 3.2-fold compared to the preoptimized condition. This is the first report of a functional CFE system derived from a minimal synthetic bacterium, enabling further advances in bottom-up synthetic biology.

Exploring alternative pathways for the in vitro establishment of the HOPAC cycle for synthetic CO2 fixation

Nature has evolved eight different pathways for the capture and conversion of CO₂, including the Calvin-Benson-Bassham cycle of photosynthesis. Yet, these pathways underlie constrains and only represent a fraction of the thousands of theoretically possible solutions. To overcome the limitations of natural evolution, we introduce the HydrOxyPropionyl-CoA/Acrylyl-CoA (HOPAC) cycle, a new-to-nature CO₂-fixation pathway that was designed through metabolic retrosynthesis around the reductive carboxylation of acrylyl-CoA, a highly efficient principle of CO₂ fixation. We realized the HOPAC cycle in a step-wise fashion and used rational engineering approaches and machine learning-guided workflows to further optimize its output by more than one order of magnitude. Version 4.0 of the HOPAC cycle encompasses 11 enzymes from six different organisms, converting ~3.0 mM CO₂ into glycolate within 2 hours. Our work moves the hypothetical HOPAC cycle from a theoretical design into an established in vitro system that forms the basis for different potential applications.

Translation initiation consistency between in vivo and in vitro bacterial protein expression systems

Strict on-demand control of protein synthesis is a crucial aspect of synthetic biology. The 5'-terminal untranslated region (5'-UTR) is an essential bacterial genetic element that can be designed for the regulation of translation initiation. However, there is insufficient systematical data on the consistency of 5'-UTR function among various bacterial cells and in vitro protein synthesis systems, which is crucial for the standardization and modularization of genetic elements in synthetic biology. Here, more than 400 expression cassettes comprising the GFP gene under the regulation of various 5'-UTRs were systematically characterized to evaluate the protein translation consistency in the two popular Escherichia coli strains of JM109 and BL21, as well as an in vitro protein expression system based on cell lysate. In contrast to the very strong correlation between the two cellular systems, the consistency between in vivo and in vitro protein translation was lost, whereby both in vivo and in vitro translation evidently deviated from the estimation of the standard statistical thermodynamic model. Finally, we found that the absence of nucleotide C and complex secondary structure in the 5'-UTR significantly improve the efficiency of protein translation, both in vitro and in vivo.

Enhancing the Substrate Specificity of Clostridium Succinyl-CoA Reductase for Synthetic Biology and Biocatalysis

Succinyl-CoA reductase (SucD) is an acylating aldehyde reductase that catalyzes the NADPH-dependent reduction of succinyl-CoA to succinic semialdehyde. The reaction sequence from succinate to crotonyl-CoA is of particular interest for several new-to-nature CO₂-fixation pathways, such as the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle, in which SucD plays a key role. However, pathways like the CETCH cycle feature several CoA-ester intermediates, which could be potentially side substrates for this enzyme. Here, we show that the side reaction for most CETCH cycle metabolites is relatively small (<2%) with the exception of mesaconyl-C1-CoA (16%), which represents a competing substrate in this pathway. We addressed this promiscuity by solving the crystal structure of a SucD of Clostridium kluyveri in complex with NADP⁺ and mesaconyl-C1-CoA. We further identified two residues (Lys70 and Ser243) that coordinate mesaconyl-C1-CoA at the active site. We targeted those residues with site-directed mutagenesis to improve succinyl-CoA over mesaconyl-C1-CoA reduction. The best resulting SucD variant, K70R, showed a strongly reduced side activity for mesaconyl-C1-CoA, but the substitution also reduced the specific activity for succinyl-CoA by a factor of 10. Transferring the same mutations into a SucD homologue from Clostridium difficile similarly decreases the side reaction of this enzyme for mesaconyl-C1-CoA from 12 to 2%, notably without changing the catalytic efficiency for succinyl-CoA. Overall, our structure-based engineering efforts provided a highly specific enzyme of interest for several applications in biocatalysis and synthetic biology.

Enzymatic Conversion of CO2: From Natural to Artificial Utilization

Enzymatic carbon dioxide fixation is one of the most important metabolic reactions as it allows the capture of inorganic carbon from the atmosphere and its conversion into organic biomass. However, due to the often unfavorable thermodynamics and the difficulties associated with the utilization of CO2, a gaseous substrate that is found in comparatively low concentrations in the atmosphere, such reactions remain challenging for biotechnological applications. Nature has tackled these problems by evolution of dedicated CO2-fixing enzymes, i.e., carboxylases, and embedding them in complex metabolic pathways. Biotechnology employs such carboxylating and decarboxylating enzymes for the carboxylation of aromatic and aliphatic substrates either by embedding them into more complex reaction cascades or by shifting the reaction equilibrium via reaction engineering. This review aims to provide an overview of natural CO2-fixing enzymes and their mechanistic similarities. We also discuss biocatalytic applications of carboxylases and decarboxylases for the synthesis of valuable products and provide a separate summary of strategies to improve the efficiency of such processes. We briefly summarize natural CO2 fixation pathways, provide a roadmap for the design and implementation of artificial carbon fixation pathways, and highlight examples of biocatalytic cascades involving carboxylases. Additionally, we suggest that biochemical utilization of reduced CO2 derivates, such as formate or methanol, represents a suitable alternative to direct use of CO2 and provide several examples. Our discussion closes with a techno-economic perspective on enzymatic CO2 fixation and its potential to reduce CO2 emissions.

Cell Extracts from Bacteria and Yeast Retain Metabolic Activity after Extended Storage and Repeated Thawing

Cell-free synthetic biology enables rapid prototyping of biological parts and synthesis of proteins or metabolites in the absence of cell growth constraints. Cell-free systems are frequently made from crude cell extracts, where composition and activity can vary significantly based on source strain, preparation and processing, reagents, and other considerations. This variability can cause extracts to be treated as black boxes for which empirical observations guide practical laboratory practices, including a hesitance to use dated or previously thawed extracts. To better understand the robustness of cell extracts over time, we assessed the activity of cell-free metabolism during storage. As a model, we studied conversion of glucose to 2,3-butanediol. We found that cell extracts from Escherichia coli and Saccharomyces cerevisiae subjected to an 18-month storage period and repeated freeze-thaw cycles retain consistent metabolic activity. This work gives users of cell-free systems a better understanding of the impacts of storage on extract behavior.

Engineering the Substrate Specificity of Toluene Degrading Enzyme XylM Using Biosensor XylS and Machine Learning

Enzyme engineering using machine learning has been developed in recent years. However, to obtain a large amount of data on enzyme activities for training data, it is necessary to develop a high-throughput and accurate method for evaluating enzyme activities. Here, we examined whether a biosensor-based enzyme engineering method can be applied to machine learning. As a model experiment, we aimed to modify the substrate specificity of XylM, a rate-determining enzyme in a multistep oxidation reaction catalyzed by XylMABC in Pseudomonas putida. XylMABC naturally converts toluene and xylene to benzoic acid and toluic acid, respectively. We aimed to engineer XylM to improve its conversion efficiency to a non-native substrate, 2,6-xylenol. Wild-type XylMABC slightly converted 2,6-xylenol to 3-methylsalicylic acid, which is the ligand of the transcriptional regulator XylS in P. putida. By locating a fluorescent protein gene under the control of the Pm promoter to which XylS binds, a XylS-producing Escherichia coli strain showed higher fluorescence intensity in a 3-methylsalicylic acid concentration-dependent manner. We evaluated the 3-methylsalicylic acid productivity of XylM variants using the fluorescence intensity of the sensor strain as an indicator. The obtained data provided the training data for machine learning for the directed evolution of XylM. Two cycles of machine learning-assisted directed evolution resulted in the acquisition of XylM-D140E-V144K-F243L-N244S with 15 times higher productivity than wild-type XylM. These results demonstrate that an indirect enzyme activity evaluation method using biosensors is sufficiently quantitative and high-throughput to be used as training data for machine learning. The findings expand the versatility of machine learning in enzyme engineering.

Cell-free enzyme cascades - application and transition from development to industrial implementation

In the vision to realize a circular economy aiming for net carbon neutrality or even negativity, cell-free bioconversion of sustainable and renewable resources emerged as a promising strategy. The potential of in vitro systems is enormous, delivering technological, ecological, and ethical added values. Innovative concepts arose in cell-free enzymatic conversions to reduce process waste production and preserve fossil resources, as well as to redirect and assimilate released industrial pollutions back into the production cycle again. However, the great challenge in the near future will be the jump from a concept to an industrial application. The transition process in industrial implementation also requires economic aspects such as productivity, scalability, and cost-effectiveness. Here, we briefly review the latest proof-of-concept cascades using carbon dioxide and other C1 or lignocellulose-derived chemicals as blueprints to efficiently recycle greenhouse gases, as well as cutting-edge technologies to maturate these concepts to industrial pilot plants.