Large scale dynamic model reconstruction for the central carbon metabolism of escherichia coli

The major objective of metabolic engineering is the construction of industrially relevant microbial strains with desired properties. From an engineering perspective, dynamic mathematical modeling to quantitatively assess intracellular metabolism and predict the complex behavior of living cells is one of the most successful tools to achieve that goal. In this work, we present an expansion of the original E. coli dynamic model [1], which links the acetate metabolism and tricarboxylic acid cycle (TCA) with the phosphotransferase systems, the pentose-phosphate pathway and the glycolysis system based on mechanistic enzymatic rate equations. The kinetic information is collected from available database and literature, and is used as an initial guess for the global fitting. The results of the numeric simulations were in good agreement with the experimental results. Thus, the results are sufficiently good to prompt us to seek further experimental data for comparison with the simulations

BacMap: an interactive picture atlas of annotated bacterial genomes

BacMap is an interactive visual database containing fully labeled, zoomable and searchable chromosome maps from more than 170 bacterial (archaebacterial and eubacterial) species. It uses a recently developed visualization tool (CGView) to generate high-resolution circular genome maps from sequence feature information. Each map includes an interface that allows the image to be expanded and rotated. In the default view, identified genes are drawn to scale and colored according to coding directions. When a region of interest is expanded, gene labels are displayed. Each label is hyperlinked to a custom ‘gene card’ which provides several fields of information concerning the corresponding DNA and protein sequences. Each genome map is searchable via a local BLAST search and a gene name/synonym search. BacMap is freely available at http://wishart.biology.ualberta.ca/BacMap/

DrugBank: a comprehensive resource for in silico drug discovery and exploration

DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains >4100 drug entries including >800 FDA approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, >14 000 protein or drug target sequences are linked to these drug entries. Each DrugCard entry contains >80 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data. Many data fields are hyperlinked to other databases (KEGG, PubChem, ChEBI, PDB, Swiss-Prot and GenBank) and a variety of structure viewing applets. The database is fully searchable supporting extensive text, sequence, chemical structure and relational query searches. Potential applications of DrugBank include in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. DrugBank is available at

Nano sized Powder of Jackfruit Seed: Spectroscopic and Anti-microbial Investigative Approach

This work reports aspect related to nano-sized particles of jackfruit seed. FTIR spectrum was recorded for functional groups analysis and EDAX analysis was done to identify the various elements of the sample. Both FTIR and EDAX analysis results indicated the presence of Starch. FTIR analysis confirmed the availability of anti-microbial Sulphur derivatives compounds. Microbiology assay found that jackfruit seed nanoparticles were effective against Escherichia coli and Bacillus megaterium bacteria. This work also investigated about the dual-function of the sample i.e. food ingredients possessing antimicrobial activities. Specific surface area of bacteria analysis revealed that it played a major role while on reactions with jackfruit seed nanoparticles.Comment: 10 Pages, 4 Tables, 4 Figures. Spectroscopic and Anti-microbial analyzes of nano-sized particles of jackfruit see

BASys: a web server for automated bacterial genome annotation

BASys (Bacterial Annotation System) is a web server that supports automated, in-depth annotation of bacterial genomic (chromosomal and plasmid) sequences. It accepts raw DNA sequence data and an optional list of gene identification information and provides extensive textual annotation and hyperlinked image output. BASys uses >30 programs to determine ∼60 annotation subfields for each gene, including gene/protein name, GO function, COG function, possible paralogues and orthologues, molecular weight, isoelectric point, operon structure, subcellular localization, signal peptides, transmembrane regions, secondary structure, 3D structure, reactions and pathways. The depth and detail of a BASys annotation matches or exceeds that found in a standard SwissProt entry. BASys also generates colorful, clickable and fully zoomable maps of each query chromosome to permit rapid navigation and detailed visual analysis of all resulting gene annotations. The textual annotations and images that are provided by BASys can be generated in ∼24 h for an average bacterial chromosome (5 Mb). BASys annotations may be viewed and downloaded anonymously or through a password protected access system. The BASys server and databases can also be downloaded and run locally. BASys is accessible at

Evaluating the integration of proteomic data for the prediction of intracellular fluxes after knockout experiments

So far, few large scale kinetic models of metabolic networks have been successfully constructed. The main reasons for this are not only the associated mathematical complexity, but also the large number of unknown kinetic parameters required in the rate equations to define the system. In contrast to kinetic models, the constraint-based modelling approach bypasses these difficulties by using basically only stoichiometric information with certain physicochemical constraints to delimit the solution space without large fitted parameter sets. Although these constraintbased models are highly relevant to predict feasible steady-state fluxes under a diverse range of genetic and environmental conditions, the steady-state assumption may oversimplify cellular behaviour and cannot predict time-course profiles. To overcome these problems, combining these two approaches appears as a reasonable alternative to modelling large-scale metabolic networks. On the other hand, several of the experimental data required for model construction are often rare and in this way it is usually assumed that the enzyme concentrations are constant. In this work, we used a central carbon metabolic network of E. coli to investigate whether including high throughput enzyme concentration data into a kinetic model allows improved predictions of metabolic flux distributions in response to single knockouts perturbations. For this purpose, an E. coli model, based on results obtained from flux balance analysis (FBA) and approximate lin-log kinetics was constructed. The intracellular fluxes distributions, obtained using this model, were compared with published in vivo measurements.(undefined

Expression Screening of Fusion Partners from an E. coli Genome for Soluble Expression of Recombinant Proteins in a Cell-Free Protein Synthesis System

While access to soluble recombinant proteins is essential for a number of proteome studies, preparation of purified functional proteins is often limited by the protein solubility. In this study, potent solubility-enhancing fusion partners were screened from the repertoire of endogenous E. coli proteins. Based on the presumed correlation between the intracellular abundance and folding efficiency of proteins, PCR-amplified ORFs of a series of highly abundant E. coli proteins were fused with aggregation-prone heterologous proteins and then directly expressed for quantitative estimation of the expression efficiency of soluble translation products. Through two-step screening procedures involving the expression of 552 fusion constructs targeted against a series of cytokine proteins, we were able to discover a number of endogenous E. coli proteins that dramatically enhanced the soluble expression of the target proteins. This strategy of cell-free expression screening can be extended to quantitative, global analysis of genomic resources for various purposes.National Research Foundation of KoreaKorea (South). Ministry of Education, Science and Technology (MEST) (grant 2011K000841)Korea (South). Ministry of Education, Science and Technology (MEST) (grant 2011-0027901

Prediction of Antibacterial Activity from Physicochemical Properties of Antimicrobial Peptides

Consensus is gathering that antimicrobial peptides that exert their antibacterial action at the membrane level must reach a local concentration threshold to become active. Studies of peptide interaction with model membranes do identify such disruptive thresholds but demonstrations of the possible correlation of these with the in vivo onset of activity have only recently been proposed. In addition, such thresholds observed in model membranes occur at local peptide concentrations close to full membrane coverage. In this work we fully develop an interaction model of antimicrobial peptides with biological membranes; by exploring the consequences of the underlying partition formalism we arrive at a relationship that provides antibacterial activity prediction from two biophysical parameters: the affinity of the peptide to the membrane and the critical bound peptide to lipid ratio. A straightforward and robust method to implement this relationship, with potential application to high-throughput screening approaches, is presented and tested. In addition, disruptive thresholds in model membranes and the onset of antibacterial peptide activity are shown to occur over the same range of locally bound peptide concentrations (10 to 100 mM), which conciliates the two types of observations

Promoter engineering to optimise recombinant periplasmic Fab' fragment production in Escherichia coli

Fab' fragments have become an established class of biotherapeutic over the last two decades. Likewise, developments in synthetic biology are providing ever more powerful techniques for designing bacterial genes, gene networks and entire genomes that can be used to the improve industrial performance of cells used for production of biotherapeutics. We have previously observed significant leakage of an exogenous therapeutic Fab' fragment into the growth medium during high cell density cultivation of an Escherichia coli production strain. In this study we sought to apply a promoter engineering strategy to address the issue of Fab' fragment leakage and its consequent bioprocess challenges. We used site directed mutagenesis to convert the Ptac promoter, present in the plasmid, pTTOD-A33 Fab', to a Ptic promoter which has been shown by others to direct expression at a 35% reduced rate compared to Ptac . We characterised the resultant production trains in which either Ptic or Ptac promoters direct Fab' fragment expression. The Ptic promoter strain showed a 25-30% reduction in Fab' expression relative to the original Ptac strain. Reduced Fab' leakage and increased viability over the course of a fed-batch fermentation were also observed for the Ptic promoter strain. We conclude that cell design steps such as the Ptac to Ptic promoter conversion reported here, can yield significant process benefit and understanding with respect to periplasmic Fab' fragment production. It remains an open question as to whether the influence of transgene expression on periplasmic retention is mediated by global metabolic burden effects or periplasm overcapacity

Detailed simulations of cell biology with Smoldyn 2.1.

Most cellular processes depend on intracellular locations and random collisions of individual protein molecules. To model these processes, we developed algorithms to simulate the diffusion, membrane interactions, and reactions of individual molecules, and implemented these in the Smoldyn program. Compared to the popular MCell and ChemCell simulators, we found that Smoldyn was in many cases more accurate, more computationally efficient, and easier to use. Using Smoldyn, we modeled pheromone response system signaling among yeast cells of opposite mating type. This model showed that secreted Bar1 protease might help a cell identify the fittest mating partner by sharpening the pheromone concentration gradient. This model involved about 200,000 protein molecules, about 7000 cubic microns of volume, and about 75 minutes of simulated time; it took about 10 hours to run. Over the next several years, as faster computers become available, Smoldyn will allow researchers to model and explore systems the size of entire bacterial and smaller eukaryotic cells