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NASA NTRS2019

Biological CubeSats: What Have We Learned so Far and What Is Next?

Since Apollo 17 in 1972, NASA has sent no humans or other biological organisms outside of Earth's protective magnetosphere. Recently, NASA has set its sights on human exploration in deep space, with an ambitous plan to put astronauts back on the Moon by 2024 and to eventually land human missions on Mars. Such missions will require significant countermeasures, likely both technological and biomedical, to protect biology from chronic radiation exposure. CubeSats can inform these countermeasures by querying relevant space environments with model organisms.NASA has launched five biological CubeSat missions into low-Earth orbit (LEO). GeneSat-1 was launched in 2006 to study gene expression and increase our knowledge of how spaceflight affects microbes. Similar life-support technologies were then used in PharmaSat and O/OREOS, which launched in 2009 and 2010, respectively. PharmaSat contained optical systems to examine how yeast cells responded to an antifungal treatment. One of O/OREOS payloads, SESLO (Space Environment Survivability of Living Organisms), housed dormant microorganisms, which were rehydrated on orbit to track alterations to growth and metabolism induced by microgravity and radiation. In 2014, NASA launched SporeSat to study the mechanisms of plant cell gravity sensing using lab-on-a-chip devices. Most recently, in 2017, NASA launched EcAMSat (E. coli AntiMicrobial Satellite), which investigated the effects of microgravity on antibiotic resistance of a pathogenic bacterium. Each one of these missions increased our understanding of the biological effects of spaceflight in LEO, while refining technologies and imparting valuable lessons to the next generation of CubeSats.CubeSats housing translational biological models are therefore ideal for defining the hazards of deep space travel, as they can provide critical data over relevant durations. BioSentinel, a next-generation deep-space CubeSat, is planned to launch as a secondary payload on Artemis 1 in 2020. BioSentinel will study the DNA damage response to deep space radiation in yeast.

Santa Maria, Sergio R.↗
NASA NTRS2023

Data Sharing in Radiation Biology: Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally "Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗
NASA NTRSDate not supplied

GL4U: Using Space Biology Omics Data to Provide Bioinformatics Training for Students and Educators

NASA’s GeneLab project provides researchers open access to space-relevant multi-omics data via the Open Science Data Repository (OSDR) that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct (training students) and indirect (training educators) approaches. The GL4U pilot programs were conducted in June 2021 (direct training) and 2022 (indirect training). During the pilots, students and educators at Historically Black Colleges and Universities (HBCUs) and Minority Serving Institutions (MSIs) participated in a week-long (direct training) or two-week-long (indirect training) bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze space biology RNA sequencing data from OSDR. During the educator pilot, participants received materials, training, and the necessary compute resources to enable them to run the bootcamp at their home institutions, thereby extending the reach of this initiative. In July 2023, GeneLab is partnering with JPL to expand GL4U to include amplicon sequencing (Amp-Seq) analysis training. During the GL4U Amp-Seq bootcamp, student and educator participants will receive training on how to analyze and interpret Amp-Seq data using the NASA GeneLab data processing pipeline. All bootcamp material, including instructions for requesting compute resources, will be made publicly available on GitHub for educators to teach the GL4U content in subsequent semesters. GL4U provides undergraduate students from underrepresented groups the opportunity to learn about NASA and Space Biology, and to enhance their career prospects by gaining hands-on experience analyzing omics data, a skillset that is highly applicable and marketable in the life sciences. We present results from pre- and post-training surveys completed by all participants of the Amp-Seq bootcamp.

Amanda M Saravia-Butler↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRSDate not supplied

Developing Autonomous Technologies for Biological Missions to Deep Space

In upcoming biological missions beyond low Earth orbit (LEO), the use of autonomous instrumentation will allow scientists to perform a variety of experiments, including the characterization of the response to different space environments (Moon, Mars, interplanetary space) using biological models like microbes, plants, organoids, and tissue chips. BioSentinel is an ongoing deep space mission, currently at over 50 million kilometers from Earth and the first instrument developed to perform biological experiments beyond LEO. Even though the primary objective of this CubeSat mission was to investigate the effects of the deep space radiation environment on budding yeast, the spacecraft bus (i.e., all the subsystems that support the biological payload like power, thermal, data telemetry, navigation, etc.) can accommodate a variety of biological (and physical) experiments and model organisms. LEIA, an upcoming CLPS mission to the lunar surface, uses a microfluidic and optical instrument based on BioSentinel to study the effects of the lunar environment on different cellular processes and on bioproduction of antioxidants. A new series of science mission concepts are being proposed to be accommodated into platforms like BioSentinel. These missions will investigate the response of a variety of organisms to the deep space environment, including but not limited to single-cell eukaryotes, cyanobacteria, plants (including crops), organoids, and tissue chips. In addition to optical absorbance measurements like the ones performed in BioSentinel (and LEIA), we are investigating the use of fluorescence detection, microscopy, sequencing devices, etc. Thus, instruments like the ones proposed here can be adapted to a variety of platforms like free-flyers, deployable payloads, landers, rovers, and the lunar Gateway. These technologies can be used as steppingstones for establishing a sustained human presence on the Moon and in deep space while providing knowledge for the development of potential countermeasures.

Sergio R Santa Maria↗
DOE OSTI2024

VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images

Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question8 answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images

Maruf, M [Virginia Tech, Blacksburg]↗
DOE OSTI2025

Utilizing plant synthetic biology to accelerate plant-microbe interactions research

Plant-microbe interactions are critical to ecosystem resilience and substantially influence crop production. From the perspective of plant science, two important focus areas concerning plant-microbe interactions include: 1) understanding plant molecular mechanisms involved in plant-microbe interfaces and 2) engineering plants for increasing plant disease resistance or enhancing beneficial interactions with microbes to increase their resilience to biotic and abiotic stress conditions. Molecular biology and genetics approaches have been used to investigate the molecular mechanisms underlying plant responses to various beneficial and pathogenic microbes. While these approaches are valuable for elucidating the functions of individual genes and pathways, they fall short of unraveling the complex cross-talk across pathways or systems that plants employ to respond and adapt to environmental stresses. Also, genetic engineering of plants to increase disease resistance or enhance symbiosis with microbes has mainly been attempted or conducted through targeted manipulation of single genes/pathways of plants. Recent advancements in synthetic biology tool development are paving the way for multi-gene characterization and engineering in plants in relation to plant-microbe interactions. Here, we briefly summarize the current understanding of plant molecular pathways involved in plant interactions with beneficial and pathogenic microorganisms. Then, we highlight the progress in applying plant synthetic biology to elucidate the molecular basis of plant responses to microbes, enhance plant disease resistance, engineer synthetic symbiosis, and conduct in situ microbiome engineering. Lastly, we discuss the challenges, opportunities, and future directions for advancing plant-microbe interactions research using the capabilities of plant synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Synthetic Biology Toolbox for Nitrogen-Fixing Soil Microbes

The soil environment adjacent to plant roots, termed the rhizosphere, is home to a wide variety of microorganisms that can significantly affect the physiology of nearby plants. Microbes in the rhizosphere can provide nutrients, secrete signaling compounds, and inhibit pathogens. These processes could be manipulated with synthetic biology to enhance the agricultural performance of crops grown for food, energy, or environmental remediation, if methods can be implemented in these nonmodel microbes. A common first step for domesticating nonmodel organisms is the development of a set of genetic engineering tools, termed a synthetic biology toolbox. A toolbox comprises transformation protocols, replicating vectors, genome engineering (e.g., CRISPR/Cas9), constitutive and inducible promoter systems, and other gene expression control elements. Furthermore, this work validated synthetic biology toolboxes in three nitrogen-fixing soil bacteria: Azotobacter vinelandii, Stutzerimonas stutzeri (Pseudomonas stutzeri), and a new isolate of Klebsiella variicola. All three organisms were amenable to transformation and reporter protein expression, with several functional inducible systems available for each organism. S. stutzeri and K. variicola showed more reliable plasmid-based expression, resulting in successful Cas9 recombineering to create scarless deletions and insertions. Using these tools, we generated mutants with inducible nitrogenase activity and introduced heterologous genes to produce resorcinol products with relevant biological activity in the rhizosphere.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A machine learning Automated Recommendation Tool for synthetic biology

Abstract Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

Aerospace Medicine and Biology: A Continuing Bibliography With Indexes

This issue of Aerospace Medicine and Biology, A Continuing Bibliography with Indexes NASA SP-7O11 lists reports, articles, and other documents recently announced in the NASA STI Database. In its subject coverage, Aerospace Medicine and Biology concentrates on the biological, physiological, psychological, and environmental effects to which humans are subjected during and following simulated or actual flight in the Earth's atmosphere or in interplanetary space. References describing similar effects on biological organisms of lower order are also included. Such related topics as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, exobiology, and personnel factors receive appropriate attention. Applied research receives the most emphasis, but references to fundamental studies and theoretical principles related to experimental development also qualify for inclusion.

BIBLIOGRAPHIES↗
DOE OSTI2023

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

Abstract Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Characterization of biological materials with soft X-ray scattering

The complex structure of biological assemblies is crucial for function yet challenging to discern given the chemical similarities between constituent components. Hard X-ray techniques, for example, rely on small density differences between domains that lead to modest scattering intensities. Resonant soft X-ray scattering (RSoXS) uses X-rays below 2 keV to access absorption edges of low-Z elements. In this way, RSoXS can enhance scattering contrast between domains of different chemical compositions or bonding motifs, thus providing structural information about specific chemical motifs. RSoXS is emerging as a technique applicable for biological systems, having been used to characterize protein structure in solution and polysaccharide organization in plant cell walls. Sample environment instrumentation, however, is challenging in the current state of the art, particularly with liquid samples. Here, this chapter contains a brief introduction to RSoXS and current beamline capabilities, and provides methods to prepare, store, and mount biological samples for RSoXS characterization. Furthermore, key details during RSoXS and X-ray absorption data acquisition are highlighted and some future opportunities in RSoXS instrumentation for biological systems are discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Phonon-mediated lipid raft formation in biological membranes

Short-wavelength collective molecular motions, also known as phonons, have recently attracted much interest in revealing dynamic properties of biological membranes through the use of neutron and x-ray scattering, infrared and Raman spectroscopies, and molecular dynamics simulations. Experimentally detecting unique vibrational patterns such as, shear phonon excitations, viscoelastic crossovers, transverse acoustic phonon gaps, and continuous and truncated optical phonon modes in cellular membranes, to name a few, has proven non-trivial. Here, we review recent advances in liquid thermodynamics that have resulted in the development of the phonon theory of 1 liquids. The theory has important predictions regarding the shear vibrational spectra of fluids, namely the emergence of viscoelastic crossovers and transverse acoustic phonon gaps. Furthermore, we show that these vibrational patterns are common in soft (non-crystalline) materials, including, but not limited to liquids, colloids, liquid crystals (mesogens), block copolymers, and biological membranes. The existence of viscoelastic crossovers and acoustic phonon gaps define the self-diffusion properties of cellular membranes and provide a molecular picture of the transient nature of lipid rafts.1 Importantly, the timescales (picoseconds) for the formation and dissolution of transient lipid rafts match the lifetime of the formation and breakdown of interfacial water hydrogen bonds. Apart from acoustic propagating phonon modes, biological membranes can also support more energetic non-propagating optical phonon excitations, also known as standing waves or breathing modes. Importantly, optical phonons can be truncated due to the existence of finite size nanodomains made up of strongly correlated lipid-cholesterol molecular pairs. These strongly coupled molecular pairs can serve as nucleation centers for the formation of stable rafts at larger length scales, due to correlations of spontaneous fluctuations (Onsager’s regression hypothesis). Finally and importantly, molecular level viscoelastic crossovers, acoustic phonon gaps, and continuous and truncated optical phonon modes may offer insights as to how lipid-lipid and lipid-protein interactions enable biological function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics

Abstract While many aspects of archaeal cell biology remain relatively unexplored, systems biology approaches like mass spectrometry (MS) based proteomics offer an opportunity for rapid advances. Unfortunately, the enormous amount of MS data generated often remains incompletely analyzed due to a lack of sophisticated bioinformatic tools and field-specific biological expertise for data interpretation. Here we present the initiation of the Archaeal Proteome Project (ArcPP), a community-based effort to comprehensively analyze archaeal proteomes. Starting with the model archaeon Haloferax volcanii , we reanalyze MS datasets from various strains and culture conditions. Optimized peptide spectrum matching, with strict control of false discovery rates, facilitates identifying > 72% of the reference proteome, with a median protein sequence coverage of 51%. These analyses, together with expert knowledge in diverse aspects of cell biology, provide meaningful insights into processes such as N-terminal protein maturation, N -glycosylation, and metabolism. Altogether, ArcPP serves as an invaluable blueprint for comprehensive prokaryotic proteomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Primary biomolecular adsorption energetics of core–shell nanocomplexes: Implications for biological interactions

Molecular organization at the nano-bio interface governing the colloidal stability, reactivity, immune recognition, and drug delivery performance of nanoparticles remains difficult to predict. Quantifying the primary hydration energetics of biomolecule-coated nanomaterials can determine those interactions and provide a basis for engineered nanocarriers with tailored behavior in biological systems. Here, we measured the thermodynamics of water adsorption on patchy dry magnetite (Fe 3 O 4 ) nanoparticles coated with three model biomolecules, bovine serum albumin, potato starch, and lauric acid and compared these properties to the hydration energetics of the corresponding free dry biomolecules. The results demonstrate how the surface functionalization alters the hydrophilicity, the accessible hydrophilic surface, and the interaction potential of the nanocomplex surface with biological media. The protein coating increases the interaction potential of the surface of the nanocomplex. The weaker interaction potential of the polysaccharide coating and the relatively large hydrophilic surface area allow dynamic and reversible binding, while the fatty acid rearranges into a partial bilayer with very strong hydrophilicity. The findings establish the hydration enthalpy as a quantitative basis to determine and interpret nanoparticle interactions with proteins, membranes, and biological fluids, and provide a thermodynamic foundation for designing nanocarriers with predictable biological reactivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A rich and bountiful harvest: Key discoveries in plant cell biology

Abstract The field of plant cell biology has a rich history of discovery, going back to Robert Hooke’s discovery of cells themselves. The development of microscopes and preparation techniques has allowed for the visualization of subcellular structures, and the use of protein biochemistry, genetics, and molecular biology has enabled the identification of proteins and mechanisms that regulate key cellular processes. In this review, seven senior plant cell biologists reflect on the development of this research field in the past decades, including the foundational contributions that their teams have made to our rich, current insights into cell biology. Topics covered include signaling and cell morphogenesis, membrane trafficking, cytokinesis, cytoskeletal regulation, and cell wall biology. In addition, these scientists illustrate the pathways to discovery in this exciting research field.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Exploring Plant Cis –Regulatory Elements at Single–Cell Resolution: Overcoming Biological and Computational Challenges to Advance Plant Research

Cis-regulatory elements (CREs) are important sequences for gene expression and for plant biological processes such as development, evolution, domestication, and stress response. However, studying CREs in plant genomes has been challenging. The totipotent nature of plant cells, coupled with inability to maintain plant cell types in culture and the inherent technical challenges posed by the cell wall have limited our understanding of how plant cell types acquire and maintain their identities and respond to the environment via CRE usage. Furthermore, advances in single cell epigenomics have revolutionized the field identifying cell-type-specific CREs. These new technologies have the potential to significantly advance our understanding of plant CRE biology, and shed light on how the regulatory genome gives rise to diverse plant phenomena. However, there are significant biological and computational challenges associated with analyzing single cell epigenomic datasets. In this review, we discuss the historical and foundational underpinnings of plant single-cell research, challenges and common pitfalls in analysis of plant single-cell epigenomic data, and highlight biological challenges unique to plants. Additionally, we discuss how the application of single-cell epigenomic data in various contexts stands to transform our understanding of the importance of CREs in plant genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Enabling AI in synthetic biology through Construction File specification

The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for interactive parsing and simulating experiments; ConstructionFileSimulator, a field-tested Java project that showcases 'Experiment' objects expressed as flat files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps Script, providing a user-friendly interface for authoring and simulation of CF. The CF specification not only standardizes and modularizes molecular biology operations but also promotes collaboration, automation, and reuse, significantly reducing potential errors. The potential integration of CF with artificial intelligence, particularly GPT-4, suggests innovative automation strategies for synthetic biology. While challenges such as token limits, data storage, and biosecurity remain, proposed solutions promise a way forward in harnessing AI for experiment design. This shift from human-driven design to AI-assisted workflows, steered by high-level objectives, charts a potential future path in synthetic biology, envisioning an environment where complexities are managed more effectively.

59 BASIC BIOLOGICAL SCIENCES↗