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Lactococcus lactis IL1403 was used as an experimental strain to develop a chemically defined medium for study of the physiology and metabolic pathways of lactococci. An experimental leave-one-out technique was employed to determine the necessity of each of the 57 chemical components used in medium development. A statistical experimental design approach including three fractional factorial designs and a central composite design was used to optimize the fermentation process with 21 variables composed of 19 nutritional factors grouped from the 57 components and two environmental factors (initial pH and temperature). For L. lactis IL1403, the maximum biomass concentrations obtained with the two optimal chemically defined media developed in this study (ZMB1 and ZMB2) were generally 3.5- to 4-fold higher than the maximum biomass concentrations obtained with the previously described best synthetic media (SA) and 50% to 68% higher than the maximum biomass concentrations obtained with M17, a complex medium commonly used for lactococci. The new chemically defined media support high-cell-density growth of numerous strains of L. lactis, Enterococcus faecalis, and Streptococcus thermophilus.

A physical and mathematical model for wine fermentation kinetics was adapted to include the influence of temperature, perhaps the most critical factor influencing fermentation kinetics. The model was based on flask-scale white wine fermentations at different temperatures (11 to 35 degrees C) and different initial concentrations of sugar (265 to 300 g/liter) and nitrogen (70 to 350 mg N/liter). The results show that fermentation temperature and inadequate levels of nitrogen will cause stuck or sluggish fermentations. Model parameters representing cell growth rate, sugar utilization rate, and the inactivation rate of cells in the presence of ethanol are highly temperature dependent. All other variables (yield coefficient of cell mass to utilized nitrogen, yield coefficient of ethanol to utilized sugar, Monod constant for nitrogen-limited growth, and Michaelis-Menten-type constant for sugar transport) were determined to vary insignificantly with temperature. The resulting mathematical model accurately predicts the observed wine fermentation kinetics with respect to different temperatures and different initial conditions, including data from fermentations not used for model development. This is the first wine fermentation model that accurately predicts a transition from sluggish to normal to stuck fermentations as temperature increases from 11 to 35 degrees C. Furthermore, this comprehensive model provides insight into combined effects of time, temperature, and ethanol concentration on yeast (Saccharomyces cerevisiae) activity and physiology.

Optimization of recombinant protein production using lactic acid bacteria (LAB) remains an important obstacle on the road to realizing LAB as oral vaccine delivery vehicles. Despite this, there have been few published investigations to explore the higher limits of LAB recombinant protein expression in fed-batch fermentations. In this study, results from response surface experiments suggested an optimal set of conditions for expression of green fluorescent protein (GFP), a model recombinant protein, in bench-scale, fed-batch Lactococcus lactis IL1403 fermentations. The 48 4-L fed-batch fermentations in this set of experiments, along with preliminary studies, investigated the effects of pH, temperature, hemin concentration, concentration of the nisin inducer per cell, and time of induction. Cell densities in this data set ranged from 2.9 to 7.4 g/L and maximum GFP expression per cell ranged from 0.1 to 4.4 relative fluorescence units (RFU)/g. The optimal 4-L, fed-batch fermentation process found here yields growth and protein expression values that dramatically improve upon results from traditional test tube and flask processes. Relative to the traditional process, the experimental optimum conditions yield 4.9 times the cell density, 1.6 times the protein per cell mass, and 8 times the total protein concentration. Unexpectedly, experiments also revealed that the compound hemin, known previously to improve growth and survival of Lactococcus lactis (L. lactis), negatively impacted recombinant protein production when added in concentrations from 5 to 20 microg/mL with this strain. The improvement in protein expression over traditional processes demonstrated here is an important step toward commercial development of LAB for oral delivery of recombinant vaccines and therapeutic proteins., ((c) 2006 Wiley Periodicals, Inc.)

The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metabolism data in the world. It contains records for more than 2180 endogenous metabolites with information gathered from thousands of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the HMDB also contains an extensive collection of experimental metabolite concentration data compiled from hundreds of mass spectra (MS) and Nuclear Magnetic resonance (NMR) metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. This is further supplemented with thousands of NMR and MS spectra collected on purified, reference metabolites. Each metabolite entry in the HMDB contains an average of 90 separate data fields including a comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, biofluid concentrations, disease associations, pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided. The HMDB is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. The HMDB is available at: www.hmdb.ca.

The study of batch kinetics of Lactococcus lactis cell growth and product formation reveals three distinct metabolic behaviors depending upon the availability of oxygen to the culture and the presence of hemin in the medium. These three cultivation modes, anerobic homolactic fermentation, aerobic heterolactic fermentation, and hemin-stimulated respiration have been studied at pH 6.0 and 30 degrees C with a medium containing a high concentration of glucose (60 g/L). A maximum cell density of 5.78 g/L was obtained in the batch culture under hemin-stimulated respiration conditions, about three times as much as that achieved with anerobic homolactic fermentation (1.87 g/L) and aerobic heterolactic fermentation (1.80 g/L). The maximum specific growth rate was 0.60/h in hemin-stimulated respiration, slightly higher than that achieved in homolactic fermentation (0.56/h) and substantially higher than that in heterolactic fermentation (0.40/h). Alteration of metabolism caused by the supplementation of oxygen and hemin is evidenced by changes in both cell growth kinetics and metabolite formation kinetics, which are characterized by a unique pseudo-diauxic growth of L. lactis. We hypothesise that Lactococcus lactis generates bioenergy (ATP) through simultaneous lactate formation and hemin-stimulated respiration in the primary exponential phase, when glucose is abundant, and utilizes lactate for cell growth and cell maintenance in the stationary phase, after glucose is exhausted. We also examined the applicability of a modified logistic model and the Luedeking-Piret model for cell growth kinetics and metabolite formation kinetics, respectively., (Copyright 2006 Wiley Periodicals, Inc.)

We have previously shown the usefulness of historical data for fermentation process optimization. The methodology developed includes identification of important process inputs, training of an artificial neural network (ANN) process model, and ultimately use of the ANN model with a genetic algorithm to find the optimal values of each critical process input. However, this approach ignores the time-dependent nature of the system, and therefore, does not fully utilize the available information within a database. In this work, we propose a method for incorporating time-dependent optimization into our previously developed three-step optimization routine. This is achieved by an additional step that uses a fermentation model (consisting of coupled ordinary differential equations (ODE)) to interpret important time-course features of the collected data through adjustments in model parameters. Important process variables not explicitly included in the model were then identified for each model parameter using automatic relevance determination (ARD) with Gaussian process (GP) models. The developed GP models were then combined with the fermentation model to form a hybrid neural network model that predicted the time-course activity of the cell and protein concentrations of novel fermentation conditions. A hybrid-genetic algorithm was then used in conjunction with the hybrid model to suggest optimal time-dependent control strategies. The presented method was implemented upon an E. coli fermentation database generated in our laboratory. Optimization of two different criteria (final protein yield and a simplified economic criteria) was attempted. While the overall protein yield was not increased using this methodology, we were successful in increasing a simplified economic criterion by 15% compared to what had been previously observed. These process conditions included using 35% less arabinose (the inducer) and 33% less typtone in the media and reducing the time required to reach the maximum protein concentration by 10% while producing approximately the same level of protein as the previous optimum.

Giant vesicles formed of 1,2-dipalmitoylphosphatidylcholine (DPPC) and sterols (cholesterol or ergosterol) in water and water/ethanol solutions have been used to examine the effect of sterol composition and ethanol concentration on the area compressibility modulus (K(a)), overall mechanical behavior, vesicle morphology, and induction of lipid alkyl chain interdigitation. Our results from micropipette aspiration suggest that cholesterol and ergosterol impact the order and microstructure of the gel (L(beta)') phase DPPC membrane. At low concentration (10-15 mol%) these sterols disrupt the long-range lateral order and fluidize the membrane (K(a) approximately 300 mN/m). Then at 18 mol%, these sterols participate in the formation of a continuous cohesive liquid-ordered (L(o)) phase with a sterol-dependent membrane density (K(a) approximately 750 for DPPC/ergosterol and K(a) approximately 1100 mN/m for DPPC/cholesterol). Finally at approximately 40 mol% both cholesterol and ergosterol impart similar condensation to the membrane (K(a) approximately 1200 mN/m). Introduction of ethanol (5-25 vol%) results in drops in the magnitude of K(a), which can be substantial, and sometimes individual vesicles with lowered K(a) reveal two slopes of tension versus apparent area strain. We postulate that this behavior represents disruption of lipid-sterol intermolecular interactions and therefore the membrane becomes interdigitation prone. We find that for DPPC vesicles with sterol concentrations of 20-25 mol%, significantly more ethanol is required to induce interdigitation compared to pure DPPC vesicles; approximately 7 vol% more for ergosterol and approximately 10 vol% more for cholesterol. For lower sterol concentrations (10-15 mol%), interdigitation is offset, but by <5 vol%. These data support the idea that ergosterol and cholesterol do enhance survivability for cells exposed to high concentrations of ethanol and provide evidence that the appearance of the interdigitated (L(beta)I) phase bilayer is a major factor in the disruption of cellular activity, which typically occurs between approximately 12 and approximately 16 vol% ethanol in yeast fermentations. We summarize our findings by producing, for the first time, "elasticity/phase diagrams" over a wide range of sterol (cholesterol and ergosterol) and ethanol concentrations.

The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.

Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input-output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%., (Copyright 2003 Wiley Periodicals, Inc.)

Endocrine function in arthropods has principally been characterized in insects and malacostracan crustaceans. However, meiofauna represent the most abundant metazoan marine taxa, with harpacticoid copepods comprising the second most abundant taxon. In addition, their diminutive biomass has made characterization of endocrine components difficult, so little is known about endocrine control of reproduction, molting, and growth in meiofauna. In this study, a sensitive fluorometric enzyme immunoassay (EIA) was utilized to quantify and compare the arthropod molting hormone, 20-hydroxyecdysone (20E), in various life-cycle and developmental stages of a laboratory reared meiobenthic copepod, Amphiascus tenuiremis, and in an amphipod, Leptocheirus plumulosus. In copepods, gravid females carrying late stage pre-hatch embryos contained significantly more 20E (390+/-252 fmol/female) than gravids carrying early (Stage-I) embryos (172+/-83 fmol/female). In contrast, ecdysteroid levels in Stage-I L. plumulosus gravid females (277+/-83 fmol/female) was greater than pre-hatch gravid females (146+/-42). Stage-I embryos of both copepods (19+/-10) and amphipods (11+/-5 fmol/embryo) possessed lower ecdysteroid content than copepod (35+/-15) and amphipod (43+/-33 fmol/embryo) pre-hatch embryos. Ecdysteroid levels were also assessed in naupliar, juvenile, adult male and non-gravid female copepod life-stages. In addition, ecdysteroids measured in field collected copepod species indicated gravid females possessed ecdysteroid levels similar to gravid A. tenuiremis. However, upon normalization of egg sac 20E content by brood size, embryos from larger broods contained lower levels of ecdysteroids when compared to embryos from smaller clutch sizes-indicating an inverse embryo/ecdysteroid relationship may exist across species.

To develop a useful fermentation process model, it is first necessary to identify which batch operating parameters are critical in determining the process outcome. To identify critical processing inputs in large databases, we have explored the use of Decision Tree Analysis with the decision metrics of Gain (i.e., Shannon Entropy changes), Gain Ratio, and a multiple hypergeometric distribution. The usefulness of this approach lies in its ability to treat "categorical" variables, which are typical of archived fermentation databases, as well as "continuous" variables. In this work, we demonstrate the use of Decision Tree Analysis for the problem of optimizing recombinant green fluorescent protein production in E. coli. A database of 85 fermentations was generated to examine the effect of 15 process input parameters on final biomass yield, maximum recombinant protein concentration, and productivity. The use of Decision Tree Analysis led to a considerable reduction in the fermentation database through the identification of the significant as well as insignificant inputs. However, different decision metrics selected different inputs and different numbers of inputs to classify the data for each output.

The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort.

A physical and mathematical model for wine fermentation kinetics has been developed to predict sugar utilization curves based on experimental data from wine fermentations with various initial nitrogen and sugar concentrations in the juice. The model is based on: (1) yeast cell growth limited by nitrogen; (2) sugar utilization rates and ethanol production rates proportional solely to the number of viable cells; and (3) a death rate for cells proportional to alcohol content. All but one parameter in the model can be estimated from existing data. However, experiments to find this final parameter, a constant describing cell death, indicate that cell death may not be the critical factor in determining fermentation kinetics as cell viability remains significant until sugar utilization has ceased. The model, nevertheless, predicts a transition from normal to sluggish to stuck fermentations as initial nitrogen levels decrease. It also predicts that fermentations with high initial Brix levels may go to completion when supplemented with nitrogen in the form of ammonia. Therefore, we hypothesize that the model is valid but that ethanol causes the yeast cells to become inactive while remaining viable. Experimental verification of the model has been performed using flask-scale experiments. The model has also been used to evaluate the possibility of using nitrogen or viable cell additions to avoid or correct problem (i.e., sluggish or stuck) fermentations., (Copyright 2002 John Wiley & Sons, Inc.)