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Biomedical subjects

D B Kell

Publications and source records attributed to D B Kell.

At least 55 records · Page 3Linked to original sources
PubMed1996

What bio technologists knew all along...?

Qualitative, trial-and-error methods designed to increase the flux to desirable biotechnological products have led to new technologies and vast improvements in existing ones. However, these methods now appear in many cases to have approached their limit. In addition, there is a strong feeling in industry that much of the recent boom in academic knowledge of biochemistry and molecular biology passes biotechnology by, simply because one cannot evaluate the implications of molecular kinetics for the functioning of the producer organisms as a whole. New methods, or more rational methods, are called for. One, aimed at increasing only the concentration of a single metabolite by site-directed mutagenesis is developed here.

Animals↗
PubMed1996

Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks.

Diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR) was used to analyse 19 hospital isolates which had been identified by conventional means to one Enterococcus faecalis, E. faecium, Streptococcus bovis, S. mitis, S. pneumoniae, or S. pyogenes. Principal components analysis of the FT-IR spectra showed that this 'unsupervised' learning method failed to form six separable clusters (one of each species) and thus could not be used to identify these bacteria base on their FT-IR spectra. By contrast, artificial neural networks (ANNs) could be trained by 'supervised' learning (using the back-propagation algorithm) with the principal components scores of derivatised spectra to recognise the strains from their FT-IR spectra. These results demonstrate that the combination of FT-IR and ANNs provides a rapid, novel and accurate bacterial identification technique.

Algorithms↗
PubMed1996

Why and when channelling can decrease pool size at constant net flux in a simple dynamic channel.

Cornish-Bowden and Cárdenas (Cornish-Bowden, A. and Cárdenas M.L. (1993) Eur. J. Biochem. 213, 87-92) have suggested that simulation results peviously published by us (Mendes, P., Kell, D.B. and Westerhoff, H.V. (1992) Eur. J. Biochem. 204, 255-266) which had demonstrated that large reductions of intermediate pool sizes could be accompanied by increasing channel flux in a model metabolic pathway, were an artefact of changes in the pathway's overall flux of the order of 0.0075%, or of inappropriate alterations of enzyme activities. They also asserted to prove that the "channelling of an intermediate cannot affect its free concentration at constant net flux". We consider the co-response of the intermediate metabolite concentration ('pool') and the channel flux to changes in kinetic (or thermodynamic) parameters. Both by analytical proofs and by numerical examples we show that this co-response can be positive, negative or null, depending on the parameter change. In particular, we prove that there is always a number of ways of changing parameters such that the intermediate metabolite concentration decreases with increasing channel flux, whether the total flux varies or is constant. We also show that increased stability of the (dynamic) enzyme-intermediate-enzyme complex, as well as a single parameter change that similarly displays no cross-over effects, can lead to decreased intermediate metabolite concentration and increased channel flux at constant total flux. In general, a non-zero co-response of the intermediate metabolite concentration ('pool') and the channel flux to changes in kinetic (or other) parameters is the rule rather than the exception. More specifically: (i) The algebraic analysis ('general proof') given in Cornish-Bowden and Cárdenas (1993) contains the constraint that the elasticities of various steps to the modulation parameters which were used to vary the channel flux at constant net flux were unity. This is an unfortunate and unnecessary constraint which, when lifted, means that the concentration of the pool in the general case can indeed change at constant net flux. A 'simplified proof' given in Cornish-Bowden and Cárdenas (1993) also fails, due in addition to the consequent failure to include mass conservation relations for some of the enzymes. (ii) In the systems studied by Cornish-Bowden and Cárdenas (1993), flux is properly to be considered as a variable (since it varies during the transition to the steady state), and not a parameter, and as such cannot per se affect the magnitude of other variables in the steady state. (iii) By relaxing the constraint referred to in (i), above, and by making dual modulations (i.e., of more than one parameter at once) which are different from those carried out in Cornish-Bowden and Cárdenas (1993) we find many instances in which channelling (described by a parameter p) does significantly affect the concentration of the pool intermediate C at constant total flux. (iv) In the same pathways, but in which the flux is held constant by setting it via a zero-order flux-generating reaction, the addition of a channel is also able to significantly to modulate the size of the pool at constant total flux. Our results show that the effectiveness of channelling in decreasing a pool, even at constant flux, is very much a reality.

Animals↗
PubMed1996

Pyrolysis mass spectrometry and its applications in biotechnology.

Pyrolysis mass spectrometry is a rapid and high-resolution method for the analysis of otherwise non-volatile material and has been widely applied for discriminating between closely related microbial strains. Recent advances in statistical and neural network methods based on supervised learning have now permitted exploitation of pyrolysis mass spectrometry in the quantitative analysis of many diverse samples of biotechnological interest; the technique may thus be regarded as an 'anything-sensor'.

Journal Article↗
PubMed1996

Correction of mass spectral drift using artificial neural networks.

For pyrolysis mass spectrometry (PyMS) to be used for the routine identification of microorganisms, for quantifying determinands in biological and biotechnological systems, and in the production of useful mass spectral libraries, it is paramount that newly acquired spectra be compared to those previously collected. Neural network and other multivariate calibration models have been used to relate mass spectra to the biological features of interest. As commonly observed, however, mass spectral fingerprints showed a lack of long-term reproducibility, due to instrumental drift in the mass spectrometer; when identical materials were analyzed by PyMS at dates from 4 to 20 months apart, neural network models produced at earlier times could not be used to give accurate estimates of determinand concentrations or bacterial identities. Neural networks, however, can be used to correct for pyrolysis mass spectrometer instrumental drift itself, so that neural network or other multivariate calibration models created using previously collected data can be used to give accurate estimates of determinand concentration or the nature of bacteria (or, indeed, other materials) from newly acquired pyrolysis mass spectra. This approach is not limited solely to pyrolysis mass spectrometry but is generally applicable to any analytical tool which is prone to instrumental drift, such as IR, ESR, NMR and other spectroscopies, and gas and liquid chromatography, as well as other types of mass spectrometry.

Ampicillin↗
PubMed1996

On the analysis of the inverse problem of metabolic pathways using artificial neural networks.

Here we develop the use of artificial neural networks for solving the inverse metabolic problem, in other words, given a set of steady-state metabolite levels and fluxes in a pathway of known structure to obtain the parameters of the system, in this case the enzymatic limiting rate and Michaelis constants. This requires two main procedures: first the development of a computer program with which one can model metabolism in the forward direction (i.e. given the internal and parameters to determine the steady-state fluxes and metabolite concentrations), and second, given arrays of associated parameters and variables thereby obtained, to exploit artificial neural networks to form a model capable of obtaining the parameters from the variables. We studied 2-step pathways exhibiting first-order kinetics, 2-step pathways exhibiting reversible Michaelis-Menten kinetics and then 3-step pathways (again exhibiting reversible Michaelis-Menten kinetics), modelled using the program Gepasi. Whilst it was fairly easy for the networks to learn most of the parameters in the 2-step pathway, it was found helpful for the Michaelis-Menten case to vary the concentration of the starting pathway substrate for each set of internal parameters, and to train separate networks for each parameter. Some parameters were much easier to learn than others, reverse K(m) and V(max) values normally being the most difficult. For the 3-step pathway learning sometimes required as much as 3 days, and occasionally convergence was not obtained. Overall, neural networks of the present type, with fully interconnected feedforward architectures and trained according to the backpropagation algorithm, scaled poorly as the problem size was increased.

Algorithms↗
PubMed1996

Oscillatory, stochastic and chaotic growth rate fluctuations in permittistatically controlled yeast cultures.

We describe a continuous culture system related to the turbidostat, but using a feedback system based on biomass estimation from the dielectric permittivity of the cell suspension rather than its optical density. It is shown that this system provides an excellent method of maintaining a constant biomass level within a fermentor. The computer-controlled system was able to effect the essentially continuous registration of growth rate by monitoring the rate of medium addition via the time-dependent activity of the pump. At some biomass setpoints for aerobically grown cultures of baker's yeast substantial time-dependent fluctuations in the growth rate of the culture were thereby observed. At some biomass setpoints, however, or under anaerobic conditions, or when using a non-Crabtree yeast, the growth rate was constant, indicating that the fluctuations were inherent to the biological system and not simply a property of the fermentor and control system. A variety of time series analyses (Fourier transformations, Hurst and Lyapunov exponents, the determination of embedding dimension, and non-linear time series predictions based on the methodology of Sugihara and May) were used to demonstrate, for the first time, that as well as stochastic and periodic components these fluctuations exhibited deterministic chaos. 'Trivial predictors' were unable to give accurate predictions of the growth rate in these cultures. The growth rate fluctuations were studied further by means of offline measurements of changes in percentage viability, bud count, and in the external ethanol and glucose concentrations; these data and other evidence suggested that the growth rate fluctuations were closely linked to the primary respiro-fermentative metabolism of this organism. The identification of chaotic growth rates in cell cultures suggests that there may be novel methods for controlling the growth of such cultures.

Biomass↗
PubMed1996

Do bacteria need to communicate with each other for growth?

It is usually assumed that most prokaryotes, when given appropriate nutrients, can grow and divide in the absence of other cells of the same species. However, recent studies have suggested that, for growth, prokaryotes need to communicate with each other using signalling molecules, and a variety of 'eukaryotic' hormones have been shown to stimulate bacterial growth. These observations have important implications for our understanding of bacterial pathogenicity.

Animals↗
PubMed1996

Quantitative analysis of multivariate data using artificial neural networks: a tutorial review and applications to the deconvolution of pyrolysis mass spectra.

The implementation of artificial neural networks (ANNs) to the analysis of multivariate data is reviewed, with particular reference to the analysis of pyrolysis mass spectra. The need for and benefits of multivariate data analysis are explained followed by a discussion of ANNs and their optimisation. Finally, an example of the use of ANNs for the quantitative deconvolution of the pyrolysis mass spectra of Staphylococcus aureus mixed with Escherichia coli is demonstrated.

Escherichia coli↗
PubMed1996

Quantitative Analysis of the Physiological Heterogeneity within Starved Cultures of Micrococcus luteus by Flow Cytometry and Cell Sorting.

A high proportion of Micrococcus luteus cells in cultures which had been starved for 3 to 6 months lost the ability to grow and form colonies on agar plates but could be resuscitated from their dormancy by incubation in an appropriate liquid medium (A. S. Kaprelyants and D. B. Kell, Appl. Environ. Microbiol. 59:3187-3196, 1993). We used flow cytometry and cell sorting to study populations of bacteria that had been starved for 5 months. These cells could be stained by the fluorescent lipophilic cation rhodamine 123, but such staining was almost independent of metabolically generated energy in that it was not affected by uncouplers. Two populations could be distinguished, one with a lower degree of rhodamine fluorescence (a degree of fluorescence referred to as region A and containing approximately 80% of the cells) and one with a more elevated degree of fluorescence (region B, approximately 20% of the cells). Subsequent incubation of starved cells in fresh medium in the presence of the antibiotic chloramphenicol (to which M. luteus is sensitive) resulted in the transient appearance of cells actively accumulating rhodamine 123 (and fluorescing in region B) and of larger cells exhibiting a yet-greater degree of fluorescence (region C). These more fluorescent cells accounted for as much as 50% of the total population, under conditions in which the viable and total counts were constant. Thus, metabolic resuscitation of at least one-half of the cells takes place under conditions in which cryptic growth cannot play any role. Sorting experiments revealed that the great majority of the viable cells in the starved population are concentrated in regions B and C and that the extent of rhodamine staining under conditions of starvation therefore reflects the physiological state of the cells. Physical separation of these cells from cells in region A resulted in an increase (of approximately 25-fold) in the viability of cells in regions B and C and of the population as a whole. Resuscitation of dormant cells in a most-probable-number assay in the presence of supernatant taken from growing M. luteus revealed the resuscitation of cells from regions B and C but not from region A. It is suggested that initially dormant (resuscitable) cells are concentrated in regions B and C.

Journal Article↗
PubMed1996

Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analyses.

The most fundamental questions such as whether a cell is alive, in the sense of being able to divide or to form a colony, may sometimes be very hard to answer, since even axenic microbial cultures are extremely heterogeneous. Analyses that seek to correlate such things as viability, which is a property of an individual cell, with macroscopic measurements of culture variables such as ATP content, respiratory activity, and so on, must inevitably fail. It is therefore necessary to make physiological measurements on individual cells. Flow cytometry is such a technique, which allows one to analyze cells rapidly and individually and permits the quantitative analysis of microbial heterogeneity. It therefore offers many advantages over conventional measurements for both routine and more exploratory analyses of microbial properties. While the technique has been widely applied to the study of mammalian cells, is use in microbiology has until recently been much more limited, largely because of the smaller size of microbes and the consequently smaller optical signals obtainable from them. Since these technical barriers no longer hold, flow cytometry with appropriate stains has been used for the rapid discrimination and identification of microbial cells, for the rapid assessment of viability and of the heterogeneous distributions of a wealth of other more detailed physiological properties, for the analysis of antimicrobial drug-cell interactions, and for the isolation of high-yielding strains of biotechnological interest. Flow cytometric analyses provide an abundance of multivariate data, and special methods have been devised to exploit these. Ongoing advances mean that modern flow cytometers may now be used by nonspecialists to effect a renaissance in our understanding of microbial heterogeneity.

Bacteria↗
PubMed1995

Secretion of an antibacterial factor during resuscitation of dormant cells in Micrococcus luteus cultures held in an extended stationary phase.

A high proportion of Micrococcus luteus cells in cultures starved for 3-6 months in spent medium following growth to stationary phase in batch culture lost the ability to grow and form colonies on agar plates, but could be resuscitated from dormancy by incubation in liquid medium containing supernatant taken from the late log phase of viable cultures of the same organism (Kaprelyants et al. 1994). In the present work, we found that during the first 50-70 h of such resuscitation the dormant cells actually divide for 10-17 generations in lactate minimal medium containing yeast extract whilst remaining nonculturable on agar plates. Further incubation results in a decrease in the total cell number in liquid medium. The addition of viable (culturable) Micrococcus luteus cells in concentrations of up to 10(4) ml-1 to test tubes containing either resuscitating cells or supernatant from these cultures revealed the excretion of a factor or factors which inhibited the proliferation of otherwise viable cells. The maximum production of this factor took place after some 96 h of incubation of starved cells in resuscitation medium. Supernatant from late logarithmic phase batch cultures of M. luteus abolished the antibacterial effect of starved cultures incubated in resuscitation medium. It is concluded that the stimulating effect of viable cells, and of supernatant taken from batch cultures, on the resuscitation of dormant cells might be connected in part with overcoming the activity of an antibacterial factor causing self-poisoning of dormant cells during their resuscitation.

Anti-Bacterial Agents↗
PubMed1995

Solvent selection for whole cell biotransformations in organic media.

Although they were used historically as antimicrobial agents, there is a modern requirement to devise organic solvent systems for exploitation in the biotransformation by intact cells of substrates that are poorly soluble in water. Water-immiscible solvents are normally less cytotoxic than are water-miscible ones. While a unitary mechanism is excluded, damage to the membrane remains the likeliest major mechanism of cytotoxicity, and may be conveniently assessed using an electronic biomass probe. Studies designed to account for the mechanisms of action of general anesthetics and of uncouplers parallel those designed to account for the cytotoxicity of organic solvents. Although there are hundreds of potential physical descriptors of solvent properties, many are broadly similar to each other, such that the intrinsic dimensionality of solvent space is relatively small (< 10). This opens up the possibility of providing a rational biophysical basis for the optimization of the solvents used for biotransformations. The widely used descriptor of solvent behavior, log P (the octanol:water partition coefficient), is a composite of more fundamental molecular descriptors; this explains why there are rarely good correlations between cytotoxicity and log P when a wide variety of solvents is studied. Although the intrinsic dimensionality of solvent space is relatively small, pure solvents still populate it rather sparsely. Thus, mixtures of solvents can and do provide the opportunity of obtaining a solvent optimal for a biotransformation of interest.

Alcohols↗
PubMed1994

Rapid and quantitative analysis of recombinant protein expression using pyrolysis mass spectrometry and artificial neural networks: application to mammalian cytochrome b5 in Escherichia coli.

Recombinant Escherichia coli clones encoding between 0 and 6 copies of the mammalian cytochrome b5 gene were subjected to pyrolysis mass spectrometry (PyMS). To deconvolute the pyrolysis mass spectra so as to obtain quantitative information on the amount of cytochrome b5 produced fully-interconnected feedforward artificial neural networks (ANNs) were studied. It was found that the combination of PyMS and ANNs could be used to predict the amount of cytochrome b5 expressed in E. coli. PyMS is a novel, convenient and rapid method for the screening and analysis of microbial and other cultures producing recombinant proteins.

Animals↗