Vítor A. P. Martins dos Santos

Rhodo-Box: a Synthetic Biology Toolbox to Facilitate Metabolic Engineering of Rhodobacter sphaeroides

The paper introduces Rhodo-Box, a toolkit for R. sphaeroides that provides standardized genetic parts and cloning workflows to enhance metabolic engineering.

From Light to Lipids: Constraint-Based Metabolic Modeling of Nannochloropsis oceanica Under Light Acclimation Conditions

This paper develops a genome-scale metabolic model for Nannochloropsis oceanica to study lipid production under light acclimation conditions.

Steering fatty acid composition of yeast microbial oil via genetic modification and bioprocess adjustment

This study improves microbial oil fatty acid composition in yeast through genetic modification and cultivation parameter optimization.

Model-driven engineering of Yarrowia lipolytica for improved microbial oil production

This study improves lipid production in Yarrowia lipolytica using a DBTL approach by combining metabolic model predictions with genetic and medium modifications.

Integrative Omics reveals changes in the cellular landscape of yeast without peroxisomes

The paper compares transcriptomics and proteomics data from wild-type and peroxisome-deficient yeast to identify changes in cellular processes due to peroxisome loss.

Computer assisted multi-level optimization of malonyl-CoA availability in Pseudomonas putida

This study enhances malonyl-CoA availability in Pseudomonas putida through multi-level optimization, achieving a 5.8-fold increase in phloroglucinol production.

Combining metabolic engineering and fermentation optimization to achieve cost-effective oil production by Cutaneotrichosporon oleaginosus

This study improves lipid production in Cutaneotrichosporon oleaginosus through genetic modifications and fermentation optimization, achieving high lipid content and productivity.

Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping

The paper introduces four VAE-based deconfounding frameworks for multi-omics clustering, showing they reduce confounding effects and improve biological clustering.

Shikimate pathway-Dependent Catabolism:enabling near-to-maximum production yield of aromatics

This study redesigns bacterial catabolism by repurposing the shikimate pathway for aromatic compound production, achieving over 89% of theoretical yield.

Combinatorial engineering reveals shikimate pathway bottlenecks in para-aminobenzoic acid production in Pseudomonas putida

The paper identifies aroB as a bottleneck in pABA production in Pseudomonas putida by using combinatorial engineering and statistical modeling to optimize the shikimate pathway.

Model-driven engineering of Cutaneotrichosporon oleaginosus ATCC 20509 for improved microbial oil production

This paper improves microbial oil production in C. oleaginosus by identifying genetic targets and medium supplements through a DBTL approach, achieving up to 56% lipid content.

Model-guided metabolic engineering of curcuminoid Production in Pseudomonas putida

This paper optimizes curcuminoid production in Pseudomonas putida using dynamic modeling and gene expression tuning, achieving a 4.1-fold increase in efficiency.

PortPred: exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates

PortPred uses deep learning embeddings to identify transporter proteins and their substrates from amino acid sequences, outperforming existing methods.

mEthAE: an Explainable AutoEncoder for methylation data

The paper introduces mEthAE, an autoencoder for methylation data that reduces dimensions 400-fold while maintaining accuracy and identifies biologically significant CpG sites through perturbation-based interpretation.

P-PPI: accurate prediction of peroxisomal protein-protein interactions (P-PPI) using deep learning-based protein sequence embeddings

This paper benchmarks deep learning-based embeddings for predicting peroxisomal protein-protein interactions using sequence data and integrates AlphaFold2-Multimer predictions for improved accuracy.

Regulating microbiome metabolic stability for stable indigenous liquor fermentation

The paper investigates factors affecting microbiome metabolic stability in traditional Chinese liquor fermentation and identifies strategies to enhance stability through initial conditions and microbial inoculation ratios.

Identification of Aspergillus niger aquaporins involved in hydrogen peroxide signaling

The paper identifies AQPF, an aquaporin in Aspergillus niger, as facilitating hydrogen peroxide transport across the cell membrane, supporting its role as a signaling molecule in glucose oxidase induction.

Paving the way for synthetic C1- metabolism in Pseudomonas putida through the reductive glycine pathway

This paper engineers Pseudomonas putida to assimilate C1 compounds like formate, methanol, and CO2 through the reductive glycine pathway, enabling growth on these substrates as sole feedstocks.

Tailoring and optimizing fatty acid production by oleaginous yeasts through the systematic exploration of their physiological fitness

This study optimizes fatty acid production in oleaginous yeasts by identifying optimal C/N ratios and temperatures using RSM and analyzing fatty acid composition changes.

Predicting Mycoplasma tissue and host specificity from genome sequences

The paper predicts Mycoplasma tissue and host specificity using genome sequences by identifying key proteins and functional groups associated with specific infections.

Exploring the adaptability and robustness of the central carbon metabolism of Mycoplasma pneumoniae

This study analyzes the adaptability and robustness of Mycoplasma pneumoniae's central carbon metabolism using metabolomics data and a dynamic model, identifying key control hubs.

Comparative genome-scale constraint-based metabolic modeling reveals key lifestyle features of plant-associated Pseudomonas spp.

The paper uses metabolic modeling to compare plant-associated Pseudomonas strains and identifies metabolic differences between beneficial and pathogenic species.

OrganelX Web Server for Sub-Peroxisomal and Sub-Mitochondrial protein localisation

The paper introduces a web server with three algorithms for predicting sub-peroxisomal and sub-mitochondrial protein localisation and PTS1 signals, aiming to accelerate research in peroxisomal biology.

Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum

This study develops a genome-scale metabolic model for Anaerotignum neopropionicum to understand ethanol metabolism via the acrylate pathway and validate it with experimental data.

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

SALARECON links the Atlantic salmon genome to metabolic fluxes and growth, showing that marine feed efficiency can be achieved by supplementing plant- and insect-based feeds with specific amino acids.

In-Pero: Exploiting deep learning embeddings of protein sequences to predict the localisation of peroxisomal proteins

In-Pero predicts peroxisomal protein sub-localisation using deep learning embeddings, achieving over 0.9 classification accuracy.

A chromosome-level assembly of the black tiger shrimp (Penaeus monodon) genome facilitates the identification of novel growth-associated genes

This paper presents a chromosome-level genome assembly for the black tiger shrimp, enabling the identification of novel growth-associated genes through transcriptome comparison.

Model-driven design allows growth of Mycoplasma pneumoniae on serum-free media

This study uses a genome-scale metabolic model to design serum-free media for Mycoplasma pneumoniae, enabling growth and supporting vaccine production.

Identification of a novel therapeutic agent for Inflammatory Bowel Disease guided by systems medicine

This study identifies clarithromycin as a potential therapeutic for inflammatory bowel disease through in-silico drug repositioning and experimental validation of its effects on NF-κB signaling.

Modeling a co-culture of Clostridium autoethanogenum and Clostridium kluyveri to increase syngas conversion to medium-chain fatty-acids

The paper develops a genome-scale metabolic model of a co-culture of Clostridium autoethanogenum and Clostridium kluyveri to optimize syngas conversion to medium-chain fatty acids.