A5062241736- Microbial Metabolic Engineering and Bioproduction
- Gene Regulatory Network Analysis
- Bioinformatics and Genomic Networks
- RNA and protein synthesis mechanisms
- Computational Drug Discovery Methods
- Metabolomics and Mass Spectrometry Studies
- Enzyme Catalysis and Immobilization
- Biofuel production and bioconversion
- Mass Spectrometry Techniques and Applications
- Protein Structure and Dynamics
- Scientific Computing and Data Management
- Genomics and Phylogenetic Studies
- Chemistry and Chemical Engineering
- Advanced Proteomics Techniques and Applications
- Genetics, Bioinformatics, and Biomedical Research
- Bacterial Genetics and Biotechnology
- Microbial Natural Products and Biosynthesis
- Natural Language Processing Techniques
- CRISPR and Genetic Engineering
- Innovative Microfluidic and Catalytic Techniques Innovation
- Research Data Management Practices
- Biomedical Text Mining and Ontologies
- Fungal and yeast genetics research
- Click Chemistry and Applications
- Machine Learning in Materials Science
University of Liverpool
2019-2023
University of Manchester
2012-2022
Czech Academy of Sciences, Institute of Biotechnology
2016-2022
Systems Biology Institute
2021
Biotechnology and Biological Sciences Research Council
2016-2020
Digital Research Alliance of Canada
2020
Indian Institute of Technology Bombay
2020
Engineering and Physical Sciences Research Council
2016-2018
Beilstein-Institut
2018
Virginia Tech
2009
ChEBI is a database and ontology containing information about chemical entities of biological interest. It currently includes over 46 000 entries, each which classified within the assigned multiple annotations including (where relevant) structure, cross-references, synonyms literature citations. All content freely available can be accessed online at http://www.ebi.ac.uk/chebi. In this update paper, we describe recent improvements additions to offering. We have substantially extended our...
The human genome-scale metabolic reconstruction details all known reactions occurring in humans, and thereby holds substantial promise for studying complex diseases phenotypes. Capturing the whole is an on-going task since last community effort generated a consensus reconstruction, several updates have been developed. We report new version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing further key objectives...
The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful growing catalog natural products and high-value chemicals. However, development at industrial levels been hindered by large resource investments required. Here we present an integrated Design-Build-Test-Learn (DBTL) pipeline for discovery optimization biosynthetic pathways, which is designed be compound agnostic automated throughout. We initially applied...
Abstract Background Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive at the transcriptional, proteomic metabolic levels under defined controlled conditions are currently lacking. Results Metabolic control analysis is being exploited in systems biology study eukaryotic cell. Using chemostat culture, we measured impact changes flux (growth rate) transcriptome, proteome,...
The chemical identification of mass spectrometric signals in metabolomic applications is important to provide conversion analytical data biological knowledge about metabolic pathways. complexity electrospray acquired from a range samples (serum, urine, yeast intracellular extracts, footprints, placental tissue footprints) has been investigated and defined the frequency different ion types routinely detected. Although some were expected (protonated deprotonated peaks, isotope multiply charged...
Abstract Background Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size complexity such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, methods are unable give insight into cellular substrate concentrations. Instead, long-term goal systems biology is use kinetic modelling characterize fully mechanics each enzymatic...
Abstract Background Systems biology projects and omics technologies have led to a growing number of biochemical pathway models reconstructions. However, the majority these are still created de novo , based on literature mining manual processing data. Results To increase efficiency model creation, Path2Models project has automatically generated mathematical from representations using suite freely available software. Data sources include KEGG, BioCarta, MetaCyc SABIO-RK. Depending source data,...
Abstract Background Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, genomic (BiGG) knowledge-bases for target organisms by capturing currently available information a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is human pathogen, causes various diseases its increasing antibiotic resistance poses public health problem. Results Here, we describe community-driven effort, which more than 20...
Abstract Background Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation a cellular objective function such as the rate or efficiency biomass production. Whilst this assumption may be valid in case microorganisms growing under certain conditions, it is likely invalid general, and especially for multicellular organisms, where objectives differ greatly both between within cell types. Moreover, purposes biotechnological applications, normally flux to...
We present an experimental and computational pipeline for the generation of kinetic models metabolism, demonstrate its application to glycolysis in Saccharomyces cerevisiae . Starting from approximate mathematical model, we employ a “cycle knowledge” strategy, identifying steps with most control over flux. Kinetic parameters individual isoenzymes within these are measured experimentally under standardised set conditions. Experimental strategies applied establish vivo concentrations...
In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such depends on the discipline science humble bricks mortar that make integration possible; identifiers a core component this infrastructure. Drawing our experience work by other groups, we outline 10 lessons have learned about identifier qualities best practices facilitate large-scale integration. Specifically, propose actions...
We exploit the recent availability of a community reconstruction human metabolic network ('Recon2') to study how close in structural terms are marketed drugs nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds chemical fingerprints give greater differences, we find 166 Public MDL Molecular Access (MACCS) keys 90 % have Tanimoto similarity more than 0.5 (structurally) 'nearest' metabolite. This suggests 'rule 0.5' mnemonic for assessing...
Synthetic biology applies the principles of engineering to in order create biological functionalities not seen before nature. One most exciting applications synthetic is design new organisms with ability produce valuable chemicals including pharmaceuticals and biomaterials a greener; sustainable fashion. Selecting right enzymes catalyze each reaction step desired target compound is, however, trivial. Here, we present Selenzyme, free online enzyme selection tool for metabolic pathway design....
The field of synthetic biology aims to make the design biological systems predictable, shrinking huge space practical numbers for testing. When designing microbial cell factories, most optimization efforts have focused on enzyme and strain selection/engineering, pathway regulation, process development. In silico tools predictive bacterial ribosome binding sites (RBSs) RBS libraries now allow translational tuning biochemical pathways; however, methods predicting optimal combinations in...
To date, several genome-scale network reconstructions have been used to describe the metabolism of yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced well annotated, under-represented metabolite transport, lipid other pathways, was not amenable constraint-based analyses because lack pathway connectivity.We expanded reconstruction incorporate many new reactions from literature represented these a...
Abstract Background Genome-scale metabolic reconstructions have been recognised as a valuable tool for variety of applications ranging from engineering to evolutionary studies. However, the reconstruction such networks remains an arduous process requiring high level human intervention. This is further complicated by occurrences missing or conflicting information and absence common annotation standards between different data sources. Results In this article, we report semi-automated...
Summary The generation and use of metabolic network reconstructions has increased over recent years. development such typically involved a time-consuming, manual process. Recent work shown that steps undertaken in reconstructing networks are amenable to automation. SuBliMinaL Toolbox (http://www.mcisb.org/subliminal/) facilitates the reconstruction process by providing number independent modules perform common tasks, as generating draft reconstructions, determining metabolite protonation...
Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and assembly features. These can be used to draw illustrations communication instruction, as image assets computer-aided design. SBOL community standard, freely available personal, academic, commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been in scientific publications...
We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from data using graph convolution networks (GCNs). Since experimentally obtained property scores recognised having potentially gross errors, we adopted robust loss for model. Combinations these terms, including drug likeness and synthetic accessibility, then optimized reinforcement learning based on policy approach. Some...