Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Metabolomics-based development of bioproduction processes toward industrial-scale production

Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review highlights how metabolomics expedites bioproduction development, as demonstrated through case studies of its integration into microbial strain engineering, culture optimization, and model construction. The Design-Build-Test-Learn (DBTL) cycle serves as a standard workflow for strain engineering. Process development, including the optimization of culture conditions and scale-up, is crucial for industrial production. In silico models facilitate the development of strains and processes. Metabolomics is a powerful driver of the DBTL framework, process development, and model construction.

An architecture for collaboration in systems biology at the age of the Metaverse

As the current state of the Metaverse is largely driven by corporate interests, which may not align with scientific goals and values, academia should play a more active role in its development. Here, we present the challenges and solutions for building a Metaverse that supports systems biology research and collaboration. Our solution consists of two components: Kosmogora, a server ensuring biological data access, traceability, and integrity in the context of a highly collaborative environment such as a metaverse; and ECellDive, a virtual reality application to explore, interact, and build upon the data managed by Kosmogora. We illustrate the synergy between the two components by visualizing a metabolic network and its flux balance analysis. We also argue that the Metaverse of systems biology will foster closer communication and cooperation between experimentalists and modelers in the field.

From beer to breadboards: yeast as a force for biological innovation

The history of yeast Saccharomyces cerevisiae, aka brewer's or baker's yeast, is intertwined with our own. Initially domesticated 8,000 years ago to provide sustenance to our ancestors, for the past 150 years, yeast has served as a model research subject and a platform for technology. In this review, we highlight many ways in which yeast has served to catalyze the fields of functional genomics, genome editing, gene-environment interaction investigation, proteomics, and bioinformatics-emphasizing how yeast has served as a catalyst for innovation. Several possible futures for this model organism in synthetic biology, drug personalization, and multi-omics research are also presented.

Self-driving laboratories to autonomously navigate the protein fitness landscape

Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns protein sequence-function relationships, designs new proteins and sends designs to a fully automated robotic system that experimentally tests the designed proteins and provides feedback to improve the agent's understanding of the system. We deploy four SAMPLE agents with the goal of engineering glycoside hydrolase enzymes with enhanced thermal tolerance. Despite showing individual differences in their search behavior, all four agents quickly converge on thermostable enzymes. Self-driving laboratories automate and accelerate the scientific discovery process and hold great potential for the fields of protein engineering and synthetic biology.

Genesis-DB: a database for autonomous laboratory systems

Summary

Artificial intelligence (AI)-driven laboratory automation-combining robotic labware and autonomous software agents-is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond.

Availability and implementation

Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology.

BacterAI maps microbial metabolism without prior knowledge

Training artificial intelligence (AI) systems to perform autonomous experiments would vastly increase the throughput of microbiology; however, few microbes have large enough datasets for training such a system. In the present study, we introduce BacterAI, an automated science platform that maps microbial metabolism but requires no prior knowledge. BacterAI learns by converting scientific questions into simple games that it plays with laboratory robots. The agent then distils its findings into logical rules that can be interpreted by human scientists. We use BacterAI to learn the amino acid requirements for two oral streptococci: Streptococcus gordonii and Streptococcus sanguinis. We then show how transfer learning can accelerate BacterAI when investigating new environments or larger media with up to 39 ingredients. Scientific gameplay and BacterAI enable the unbiased, autonomous study of organisms for which no training data exist.

High-throughput metabolomics for the design and validation of a diauxic shift model

Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.

Machine learning alternative to systems biology should not solely depend on data

In recent years, artificial intelligence (AI)/machine learning has emerged as a plausible alternative to systems biology for the elucidation of biological phenomena and in attaining specified design objective in synthetic biology. Although considered highly disruptive with numerous notable successes so far, we seek to bring attention to both the fundamental and practical pitfalls of their usage, especially in illuminating emergent behaviors from chaotic or stochastic systems in biology. Without deliberating on their suitability and the required data qualities and pre-processing approaches beforehand, the research and development community could experience similar 'AI winters' that had plagued other fields. Instead, we anticipate the integration or combination of the two approaches, where appropriate, moving forward.

Social impact and governance of AI and neurotechnologies

Advances in artificial intelligence (AI) and brain science are going to have a huge impact on society. While technologies based on those advances can provide enormous social benefits, adoption of new technologies poses various risks. This article first reviews the co-evolution of AI and brain science and the benefits of brain-inspired AI in sustainability, healthcare, and scientific discoveries. We then consider possible risks from those technologies, including intentional abuse, autonomous weapons, cognitive enhancement by brain-computer interfaces, insidious effects of social media, inequity, and enfeeblement. We also discuss practical ways to bring ethical principles into practice. One proposal is to stop giving explicit goals to AI agents and to enable them to keep learning human preferences. Another is to learn from democratic mechanisms that evolved in human society to avoid over-consolidation of power. Finally, we emphasize the importance of open discussions not only by experts, but also including a diverse array of lay opinions.

Exploration of Ultralarge Compound Collections for Drug Discovery

Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.

Cell Line-Specific Network Models of ER+ Breast Cancer Identify Potential PI3Kα Inhibitor Resistance Mechanisms and Drug Combinations

Durable control of invasive solid tumors necessitates identifying therapeutic resistance mechanisms and effective drug combinations. In this work, we used a network-based mathematical model to identify sensitivity regulators and drug combinations for the PI3Kα inhibitor alpelisib in estrogen receptor positive (ER⁺) PIK3CA-mutant breast cancer. The model-predicted efficacious combination of alpelisib and BH3 mimetics, for example, MCL1 inhibitors, was experimentally validated in ER⁺ breast cancer cell lines. Consistent with the model, FOXO3 downregulation reduced sensitivity to alpelisib, revealing a novel potential resistance mechanism. Cell line-specific sensitivity to combinations of alpelisib and BH3 mimetics depended on which BCL2 family members were highly expressed. On the basis of these results, newly developed cell line-specific network models were able to recapitulate the observed differential response to alpelisib and BH3 mimetics. This approach illustrates how network-based mathematical models can contribute to overcoming the challenge of cancer drug resistance. SIGNIFICANCE: Network-based mathematical models of oncogenic signaling and experimental validation of its predictions can identify resistance mechanisms for targeted therapies, as this study demonstrates for PI3Kα-specific inhibitors in breast cancer.

Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction

Predicting bioproduction titers from microbial hosts has been challenging due to complex interactions between microbial regulatory networks, stress responses, and suboptimal cultivation conditions. This study integrated knowledge mining, feature extraction, genome-scale modeling (GSM), and machine learning (ML) to develop a model for predicting Yarrowia lipolytica chemical titers (i.e., organic acids, terpenoids, etc.). First, Y. lipolytica production data, including cultivation conditions, genetic engineering strategies, and product information, was manually collected from literature (~100 papers) and stored as either numerical (e.g., substrate concentrations) or categorical (e.g., bioreactor modes) variables. For each case recorded, central pathway fluxes were estimated using GSMs and flux balance analysis (FBA) to provide metabolic features. Second, a ML ensemble learner was trained to predict strain production titers. Accurate predictions on the test data were obtained for instances with production titers >1 g/L (R² = 0.87). However, the model had reduced predictability for low performance strains (0.01-1 g/L, R² = 0.29) potentially due to biosynthesis bottlenecks not captured in the features. Feature ranking indicated that the FBA fluxes, the number of enzyme steps, the substrate inputs, and thermodynamic barriers (i.e., Gibbs free energy of reaction) were the most influential factors. Third, the model was evaluated on other oleaginous yeasts and indicated there were conserved features for some hosts that can be potentially exploited by transfer learning. The platform was also designed to assist computational strain design tools (such as OptKnock) to screen genetic targets for improved microbial production in light of experimental conditions.