Biology subjects

Poh, C. L.

Publications and source records attributed to Poh, C. L..

9 recordsLinked to original sources

bioRxiv2026

High-throughput in vivo DNA data encoding by multiplexed conjugationand orthogonal base editing

Bacterial conjugation and CRISPR base editing offer complementary routes for programmable DNA transfer and heritable genomic writing, but their combined potential for information encoding in living cells remains unexplored. Here, we introduce CONCORD, a bacterial encoding platform that couples multiplexed bacterial conjugation to genomic base editing for synthesis-free in vivo DNA data writing. A recipient Writer strain expresses the base-editing machinery and is equipped with a writing template, whereas reusable donor Guide strains deliver the encoding sgRNA signals by conjugation. Using this setup, we encode binary information into genomic DNA in a high-throughput manner. Moreover, we systematically evaluated multiple orthogonal base editors for an expanded encoding repertoire and improved data recovery through redundancy encoding. We deploy the system to encode 45,689 bits into DNA including two culturally and historically significant artifacts: a greeting from NASA's Voyager Golden Record and a photograph of Navajo petroglyphs and recovered these datasets with 100% and 99.5% accuracy, respectively, without computational error correction. Connecting and engineering the unique mating and immune system of microbial cells we have established a framework for programmable information writing in bacterial populations, with applications for biological event recording and sustainable DNA data storage.

synthetic biology↗

bioRxiv2026

Comprehensive evaluation of LLM capabilities for interpretation and analysis of genome-scale metabolic models in metabolic engineering

Genome-scale metabolic models (GSMs) underpin pathway and strain engineering by linking genes to metabolic reactions and enabling system-level simulation of cellular fluxes and intervention effects, yet end-to-end analysis workflows remain fragmented, expert-demanding, and slow to adapt. Large language models (LLMs) could transform this landscape, lowering the barrier by explaining concepts, interpreting GSM files, and turning natural-language instructions into valid analysis code, thereby substantially mitigating the time, effort, and expertise required. However, their reliability for domain-specific tasks remains unexplored. Here, we delivered a systematic benchmark of four leading LLMs (GPT-4, Gemini, Claude, DeepSeek-R1) across four task areas central to metabolic engineering: domain knowledge, metabolic flux prediction, pathway construction, and flux optimization. For benchmarking, we introduced a standardized, rubric-based evaluation framework that uses multi-LLM automated scoring (an ensemble of LLM-as-a-judge assessments) and two distinct sets of nine task-tailored metrics (domain vs coding-focused tasks), rated on a 1-5 scale (up to 45 per task), covering scientific validity and code executability where applicable. Across tasks, we reveal consistent strengths (conceptual explanation, code synthesis) and critical failure modes (e.g., context window limitations, incorrect identifier assumptions, strain-dependent reasoning errors, and errors in domain-specific algorithms). In aggregate, DeepSeek-R1 led in domain tasks, narrowly edging GPT-4, Claude, and Gemini, demonstrating that conceptual biological logic remains highly invariant across architectures. In contrast, Gemini achieved the highest score for coding tasks, distinguished by functional execution and excelled in error handling, documentation, and readability, followed by GPT-4, Claude, and DeepSeek. We also evaluated LLM self-inspection capability by injecting subtle, consequential faults: a stoichiometric sign error causing mass imbalance and an omitted pathway reaction. We reveal that conversational "blind search" prompting completely fails to localize these network faults. Instead, robust error localization requires prompts reframed with domain-informed constraints that force the LLM to leverage tool-assisted code procedures, such as COBRApy mass-balance functions. Together, this work establishes an evidence-based baseline for LLM-enabled GSM analysis, providing actionable guidance for building reliable, automation-ready workflows for pathway and strain design. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/730004v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@413e29org.highwire.dtl.DTLVardef@1581de2org.highwire.dtl.DTLVardef@11ef7borg.highwire.dtl.DTLVardef@1817653_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Machine learning-assisted enzyme engineering through ultra-high throughput sorting and large-scale sequence-function data generation

Machine learning (ML) shows great promise in protein engineering but has yet to be integrated with ultra-high throughput sorting (ultra-HTS) and NGS for large-scale sequence-function data generation to harness its capability to explore wider search space and more complex mutation events. Here, we introduce PUSDA, a framework that rapidly sorts mutant libraries into multiple performance groups with good accuracy and generates large-scale sequence-function data to power ML-driven protein design. As a demonstration, PUSDA generated over five million sequence-function data of an enzyme, with data processing revealing over 1.3 million unique enzyme mutants being sorted in a single day. With a trained ML model that achieved 93.52% accuracy, we further analysed combinatorial mutation events and applied a ratio-based selection approach to design novel enzyme sequences. Validation experiment demonstrated a 16.67-fold improvement in efficiency of identifying high-performance enzymes using PUSDA compared to using ultra-HTS alone. The designed novel enzyme achieved 8.23-fold increase in productivity compared to wild type. PUSDA lays a foundation to integrate ultra-HTS, NGS, and ML for future predictive enzyme engineering, offering a data-driven tool for accelerating breakthroughs in biotechnology.

synthetic biology↗

bioRxiv2024

D2Sim: A Computational Simulator for NanoporeSequencing based DNA Data Storage

DNA data storage has gained significant attention because of its high storage density and durability. However, errors during the storage and reading processes compromise data integrity, prompting research into error correction strategies. Researchers have explored physical (data copies) and logical redundancy (added redundancy in error-correcting codes) to mitigate errors. Evaluating these designs and reconstruction methods typically involves time-consuming and costly trials. To streamline this process, we designed a computational channel simulator, namely D2Sim, for Nanopore sequencing-based DNA data storage. This simulator mimics real experiments, generating data with certain redundancy and errors at the receiver. Integrated with DeepSimulator, D2Sim outputs signals closely resembling actual signals of Nanopore-based DNA storage. Comparative analysis reveals that the proposed simulator yields 16.7% to 88.7% lower sample difference deviations than signals from DeepSimulator alone. This cost-effective and time-efficient tool facilitates the assessment of physical and logical redundancy for data reconstruction in DNA data storage without the need for real-time experiments.

bioengineering↗

bioRxiv2023

Automated Microbial Classification System based on Deep Convolutional Neural Networks using Images from Colony Picker

Colony screening in single and multi-species environments is an essential step for microbiome studies. However, it possesses a high possibility of inaccurately classifying the species of interest and demands a high degree of manpower and time. Thus, automating the classification of microbes is beneficial to minimize the time and inaccuracy in the colony screening/picking step. Here, we developed an automated microbial classification system for five target species, based on deep Convolutional Neural Networks (CNN) using images captured by an automated robotic colony picker. Multiple possible scenarios of colony culture and diverse morphologies of colonies were examined in building the training and test datasets to train and validate the model and performance on real-life implementations. The final model trained using 60,000 training images, with 12,000 images per species and 3-fold cross-validation, achieved a test accuracy of 94.2% and a test loss of 0.154. Upon testing using a deployment dataset of 4,500 images (900 images per species) with different methods of applying cells onto the agar plate, high accuracy of up to 96.6% was obtained. Five evaluation metrics were implemented to evaluate diverse scenarios of the test data to justify the validity of the model in real-life applications. This model forms a foundation for the classification of more species through transfer learning in the future.

synthetic biology↗

bioRxiv2021

Highly Reversible Tunable Thermal-repressible Split-T7 RNA polymerases (Thermal-T7RNAPs) for dynamic gene regulation

Temperature is a physical cue that is easy to apply, allowing cellular behaviors to be controlled in a contactless and dynamic manner via heat-inducible/repressible systems. However, existing heat-repressible systems are limited and rely on thermal sensitive mRNA or transcription factors which function at low temperatures, lack tunability, suffer delays or overly-complex. To provide an alternative mode of thermal regulation, we developed a library of compact, reversible and tunable thermal-repressible split-T7 RNA polymerase systems (Thermal-T7RNAPs) which fuses temperature-sensitive domains of Tlpa protein with split-T7RNAP to enable direct thermal control of the T7RNAP activity between 30 - 42 {degrees}C. We generated a large mutant library with varying thermal performances via automated screening framework to extend temperature tunability. Lastly, using the mutants, novel thermal logic circuitry was implemented to regulate cell growth and achieve active thermal control of the cell proportions within co-cultures. Overall, this technology expands avenues for thermal control in biotechnology applications.

synthetic biology↗

bioRxiv2021

Modelling, characterization of data-dependent and process-dependent errors in DNA data storage

MotivationUsing DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process. ResultsWe first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process. Contactpoh.chuehloo@nus.edu.sg

bioinformatics↗

bioRxiv2021

BMSS2: a unified database-driven modelling tool for systematic model selection and identifiability analysis

SummaryModelling in Synthetic Biology constitutes a powerful tool in our continuous search for improved performance with rational Design-Build-Test-Learn approach. In particular, kinetic models unravel system dynamics, enabling system analysis for guiding experimental designs. However, a systematic yet modular pipeline that allows one to identify the "right" model and guide the experimental designs while tracing the entire model development and analysis is still lacking. Here, we introduce a unified python package, BMSS2, which offers the principal tools in model development and analysis--simulation, Bayesian parameter inference, global sensitivity analysis, with an emphasis on model selection, and a priori and a posteriori identifiability analysis. The whole package is database-driven to support interactive retrieving and storing of models for reusability. This allows ease of manipulation and deposition of models for the model selection and analysis process, thus enabling better utilization of models in guiding experimental designs. Availability and ImplementationThe python package and examples are available on https://github.com/EngBioNUS/BMSS2. A web page which allows users to browse and download the models (SBML format) in MBase is also available with the link provided on GitHub. Supplementary InformationSupplementary data is available.

synthetic biology↗

bioRxiv2018

Capturing Multicellular System Designs Using the Synthetic Biology Open Language (SBOL)

Synthetic biology aims to improve the development of biological systems and in-crease their reproducibility through the use of engineering principles, such as stan-dardisation and modularisation. It is important that these systems can be represented and shared in a standard way to ensure they are easily understood, reproduced, and utilised by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterisation. Thus far, this standard has been used to represent designs in homogeneous systems, where the same design is implemented in every cell. In recent years there has been increasing interest in multicellular systems, where biological designs are split across multiple cells to optimise the system behaviour and function. Here we show how the SBOL standard can be used to represent such multicellular systems, and hence how researchers can better share designs with the community.

synthetic biology↗