Biology subjects

Martin, H. G.

Publications and source records attributed to Martin, H. G..

6 recordsLinked to original sources

bioRxiv2025

Prediction of metabolic dynamics through deep learning and high-throughput multiomics data

Synthetic biologys remarkable potential to tackle important societal problems is held back by our inability to predictably engineer biological systems. Here, we collected one of the largest public multiomics synthetic biology datasets generated to date, and used it to train a novel deep learning algorithm able to predict product and metabolic dynamics with great accuracy, starting to approach the predictive capabilities found in physics and chemistry. We were able to predict production time series with 90-99% accuracy, and final production with 96% accuracy. Further, we were able to produce good predictions for a majority of extracellular metabolites, and twenty different intracellular metabolites. These predictions were provided for a target of industrial relevance: a non-model yeast (Pichia kudriavzevii) engineered to produce large amounts of malonic acid, a desirable biomanufacturing target. This approach is generally applicable to any host, pathway, and product because all required knowledge is inferred from experimental data.

bioengineering↗

bioRxiv2024

Biosynth Pipeline: An integrated platform for merging the computational design of chimeric type I polyketide synthases with enzymatic pathways for chemical biosynthesis

Synthetic biology offers the promise of manufacturing chemicals more sustainably than petrochemistry. Yet, both the rate at which biomanufacturing can synthesize these molecules and the net chemical accessible space are limited by existing pathway discovery methods which can often rely on arduous literature searches. Here, we present an automated retrobiosynthesis tool, BioPKS Pipeline, that simultaneously tackles both problems by integrating multifunctional type I polyketide synthases (PKSs) with monofunctional enzymes to propose the synthesis of desired target chemicals via two new tools: DORAnet and RetroTide. While monofunctional enzymes are valuable for carefully decorating a substrates carbon backbone, they typically cannot expand the backbone itself. PKSs can, instead, predictably do this through their unique ability to catalyze carbon-carbon bond formation reactions iteratively. We have evaluated the performance of BioPKS Pipeline against a previously published set of 155 molecules of interest for biomanufacturing, and report that BioPKS Pipeline could produce exact designs for 93 of them, as well as pipelines to a chemically similar product for most of the remaining molecules. Furthermore, BioPKS Pipeline successfully proposes biosynthetic routes for complex therapeutic natural products (cryptofolione and basidalin) for which no known biosynthetic pathway currently exists.

bioinformatics↗

bioRxiv2021

Scalable and Automated CRISPR-Based Strain Engineering Using Droplet Microfluidics

We present a droplet-based microfluidic system that enables CRISPR-based gene editing and high-throughput screening on chip. The microfluidic device contains a 10 x 10 element array, each element containing sets of electrodes for two electric field actuated operations-electrowetting for merging droplets to mix reagents and electroporation for transformation. It can perform up to 100 genetic modifications in parallel, providing a scalable platform for generating the large number of engineered strains required for combinatorial optimization of genetic pathways and predictable bioengineering. We demonstrate the systems capabilities through CRISPR-based engineering of two test cases-1) disruption of the function of enzyme galactokinase (galK) in E. coli and 2) targeted engineering of glutamine synthetase gene (glnA) and blue-pigment synthetase (bpsA) enzyme to improve indigoidine production in E. coli.

synthetic biology↗

bioRxiv2020

Multiomics data collection, visualization, and utilization for guiding metabolic engineering

Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we present a set of tools that, combined, provide the ability to store, visualize and leverage these data to predict the outcome of bioengineering efforts.

synthetic biology↗

bioRxiv2019

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can complement each other and be used in a combined approach to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which, once phenotyped, provide the basis for machine learning algorithms to be trained and used for new design recommendations. The approach enables successful forward engineering of aromatic amino acid metabolism in yeast, with the new recommended designs improving tryptophan production by up to 17% compared to the best designs used for algorithm training, and ultimately producing a total increase of 106% in tryptophan accumulation compared to optimized reference designs. Based on a single high-throughput data-generation iteration, this study highlights the power of combining mechanistic and machine learning models to enhance their predictive power and effectively direct metabolic engineering efforts.

bioengineering↗

bioRxiv2019

Identification, characterization, and application of a highly sensitive lactam biosensor from Pseudomonas putida

Caprolactam is an important polymer precursor to nylon traditionally derived from petroleum and produced on a scale of 5 million tons per year. Current biological pathways for the production of caprolactam are inefficient with titers not exceeding 2 mg/L, necessitating novel pathways for its production. As development of novel metabolic routes often require thousands of designs and result in low product titers, a highly sensitive biosensor for the final product has the potential to rapidly speed up development times. Here we report a highly sensitive biosensor for valerolactam and caprolactam from Pseudomonas putida KT2440 which is >1000x more sensitive to exogenous ligand than previously reported sensors. Manipulating the expression of the sensor oplR (PP_3516) substantially altered the sensing parameters, with various vectors showing Kd values ranging from 700 nM (79.1 g/L) to 1.2 mM (135.6 mg/L). Our most sensitive construct was able to detect in vivo production of caprolactam above background at ~6 g/L. The high sensitivity and range of OplR is a powerful tool towards the development of novel routes to the biological synthesis of caprolactam. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/700484v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@ff5b8corg.highwire.dtl.DTLVardef@d365aaorg.highwire.dtl.DTLVardef@b79b18org.highwire.dtl.DTLVardef@27732f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗