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

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

A protet-based, protonic charge transfer model of energy coupling in oxidative and photosynthetic phosphorylation

Textbooks of biochemistry will explain that the otherwise endergonic reactions of ATP synthesis can be driven by the exergonic reactions of respiratory electron transport, and that these two half-reactions are catalyzed by protein complexes embedded in the same, closed membrane. These views are correct. The textbooks also state that, according to the chemiosmotic coupling hypothesis, a (or the) kinetically and thermodynamically competent intermediate linking the two half-reactions is the electrochemical difference of protons that is in equilibrium with that between the two bulk phases that the coupling membrane serves to separate. This gradient consists of a membrane potential term Δψ and a pH gradient term ΔpH, and is known colloquially as the protonmotive force or pmf. Artificial imposition of a pmf can drive phosphorylation, but only if the pmf exceeds some 150-170mV; to achieve in vivo rates the imposed pmf must reach 200mV. The key question then is 'does the pmf generated by electron transport exceed 200mV, or even 170mV?' The possibly surprising answer, from a great many kinds of experiment and sources of evidence, including direct measurements with microelectrodes, indicates it that it does not. Observable pH changes driven by electron transport are real, and they control various processes; however, compensating ion movements restrict the Δψ component to low values. A protet-based model, that I outline here, can account for all the necessary observations, including all of those inconsistent with chemiosmotic coupling, and provides for a variety of testable hypotheses by which it might be refined.

A computational framework for finding parameter sets associated with chaotic dynamics

Many biological ecosystems exhibit chaotic behavior, demonstrated either analytically using parameter choices in an associated dynamical systems model or empirically through analysis of experimental data. In this paper, we use existing software tools (COPASI, R) to explore dynamical systems and uncover regions with positive Lyapunov exponents where thus chaos exists. We evaluate the ability of the software's optimization algorithms to find these positive values with several dynamical systems used to model biological populations. The algorithms have been able to identify parameter sets which lead to positive Lyapunov exponents, even when those exponents lie in regions with small support. For one of the examined systems, we observed that positive Lyapunov exponents were not uncovered when executing a search over the parameter space with small spacings between values of the independent variables.

A combination of HPLC and automated data analysis for monitoring the efficiency of high-pressure homogenization

Background

Cell disruption is a key unit operation to make valuable, intracellular target products accessible for further downstream unit operations. Independent of the applied cell disruption method, each cell disruption process must be evaluated with respect to disruption efficiency and potential product loss. Current state-of-the-art methods, like measuring the total amount of released protein and plating-out assays, are usually time-delayed and involve manual intervention making them error-prone. An automated method to monitor cell disruption efficiency at-line is not available to date.

Results

In the current study we implemented a methodology, which we had originally developed to monitor E. coli cell integrity during bioreactor cultivations, to automatically monitor and evaluate cell disruption of a recombinant E. coli strain by high-pressure homogenization. We compared our tool with a library of state-of-the-art methods, analyzed the effect of freezing the biomass before high-pressure homogenization and finally investigated this unit operation in more detail by a multivariate approach.

Conclusion

A combination of HPLC and automated data analysis describes a valuable, novel tool to monitor and evaluate cell disruption processes. Our methodology, which can be used both in upstream (USP) and downstream processing (DSP), describes a valuable tool to evaluate cell disruption processes as it can be implemented at-line, gives results within minutes after sampling and does not need manual intervention.

Evolutionary algorithms and synthetic biology for directed evolution: commentary on "on the mapping of genotype to phenotype in evolutionary algorithms" by Peter A. Whigham, Grant Dick, and James Maclaurin

I rehearse two issues around the commentary of Whigham and colleagues. (1) There really are many more reasons than those given as to why natural evolution cannot reasonably find or select the 'optimal' individual. (2) A series of experimental molecular biology programmes, known generically as directed evolution, can use operators and selection schemes that natural evolution cannot. When developed further using the methods of synthetic biology, there are no operators or schemes for in silico evolution that cannot be applied precisely to directed evolution. The issues raised apply only to natural evolution but not to directed evolution.

Individuality, phenotypic differentiation, dormancy and 'persistence' in culturable bacterial systems: commonalities shared by environmental, laboratory, and clinical microbiology

For bacteria, replication mainly involves growth by binary fission. However, in a very great many natural environments there are examples of phenotypically dormant, non-growing cells that do not replicate immediately and that are phenotypically 'nonculturable' on media that normally admit their growth. They thereby evade detection by conventional culture-based methods. Such dormant cells may also be observed in laboratory cultures and in clinical microbiology. They are usually more tolerant to stresses such as antibiotics, and in clinical microbiology they are typically referred to as 'persisters'. Bacterial cultures necessarily share a great deal of relatedness, and inclusive fitness theory implies that there are conceptual evolutionary advantages in trading a variation in growth rate against its mean, equivalent to hedging one's bets. There is much evidence that bacteria exploit this strategy widely. We here bring together data that show the commonality of these phenomena across environmental, laboratory and clinical microbiology. Considerable evidence, using methods similar to those common in environmental microbiology, now suggests that many supposedly non-communicable, chronic and inflammatory diseases are exacerbated (if not indeed largely caused) by the presence of dormant or persistent bacteria (the ability of whose components to cause inflammation is well known). This dormancy (and resuscitation therefrom) often reflects the extent of the availability of free iron. Together, these phenomena can provide a ready explanation for the continuing inflammation common to such chronic diseases and its correlation with iron dysregulation. This implies that measures designed to assess and to inhibit or remove such organisms (or their access to iron) might be of much therapeutic benefit.

Individuality, phenotypic differentiation, dormancy and 'persistence' in culturable bacterial systems: commonalities shared by environmental, laboratory, and clinical microbiology

For bacteria, replication mainly involves growth by binary fission. However, in a very great many natural environments there are examples of phenotypically dormant, non-growing cells that do not replicate immediately and that are phenotypically 'nonculturable' on media that normally admit their growth. They thereby evade detection by conventional culture-based methods. Such dormant cells may also be observed in laboratory cultures and in clinical microbiology. They are usually more tolerant to stresses such as antibiotics, and in clinical microbiology they are typically referred to as 'persisters'. Bacterial cultures necessarily share a great deal of relatedness, and inclusive fitness theory implies that there are conceptual evolutionary advantages in trading a variation in growth rate against its mean, equivalent to hedging one's bets. There is much evidence that bacteria exploit this strategy widely. We here bring together data that show the commonality of these phenomena across environmental, laboratory and clinical microbiology. Considerable evidence, using methods similar to those common in environmental microbiology, now suggests that many supposedly non-communicable, chronic and inflammatory diseases are exacerbated (if not indeed largely caused) by the presence of dormant or persistent bacteria (the ability of whose components to cause inflammation is well known). This dormancy (and resuscitation therefrom) often reflects the extent of the availability of free iron. Together, these phenomena can provide a ready explanation for the continuing inflammation common to such chronic diseases and its correlation with iron dysregulation. This implies that measures designed to assess and to inhibit or remove such organisms (or their access to iron) might be of much therapeutic benefit.

Modelling the rheological properties of sludge during anaerobic digestion in a batch reactor by using electrical measurements

Anaerobic digestion is a significant process leading to biogas production and waste management. Despite this double interest, professionals still face a lack of efficient tools to monitor and manage the whole procedure. This is especially true for rheological properties of the material inside the reactor, which are of major importance for anaerobic digestion management. However, rheological properties can hardly be determined in-situ and it would be very helpful to determine indicators of their evolution. To solve this problem, this paper investigates the evolution of sewage sludge rheological and electrical properties during the anaerobic digestion in a batch reactor. We especially focus on apparent viscosity and complex impedance, measured by electrical impedance spectroscopy. Both of them can be modelled by a linear combination of raw sludge and inoculum properties, weighted by time-dependent coefficients. Thus, by determining digested sludge electrical signature, it is possible to obtain those coefficients and model sludge apparent viscosity. This work offers many theoretical and practical prospects.

A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.

Improving metabolic flux predictions using absolute gene expression data

Background

Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se.

Results

An alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from Saccharomyces cerevisiae cultures under two growth conditions, the method outperforms traditional approaches for predicting experimentally measured exometabolic flux that are reliant upon maximisation of the rate of biomass production.

Conclusion

Due to its improved prediction of experimentally measured metabolic fluxes, and of its lack of a requirement for knowledge of the biomass composition of the organism under the conditions of interest, the approach is likely to be of rather general utility. The method has been shown to predict fluxes reliably in single cellular systems. Subsequent work will investigate the method’s ability to generate condition- and tissue-specific flux predictions in multicellular organisms.

The genetic control of growth rate: a systems biology study in yeast

BACKGROUND

Control of growth rate is mediated by tight regulation mechanisms in all free-living organisms since long-term survival depends on adaptation to diverse environmental conditions. The yeast, Saccharomyces cerevisiae, when growing under nutrient-limited conditions, controls its growth rate via both nutrient-specific and nutrient-independent gene sets. At slow growth rates, at least, it has been found that the expression of the genes that exert significant control over growth rate (high flux control or HFC genes) is not necessarily regulated by growth rate itself. It has not been determined whether the set of HFC genes is the same at all growth rates or whether it is the same in conditions of nutrient limitation or excess.

RESULTS

HFC genes were identified in competition experiments in which a population of hemizygous diploid yeast deletants were grown at, or close to, the maximum specific growth rate in either nutrient-limiting or nutrient-sufficient conditions. A hemizygous mutant is one in which one of any pair of homologous genes is deleted in a diploid, These HFC genes divided into two classes: a haploinsufficient (HI) set, where the hemizygous mutants grow slower than the wild type, and a haploproficient (HP) set, which comprises hemizygotes that grow faster than the wild type. The HI set was found to be enriched for genes involved in the processes of gene expression, while the HP set was enriched for genes concerned with the cell cycle and genome integrity.

CONCLUSION

A subset of growth-regulated genes have HFC characteristics when grown in conditions where there are few, or no, external constraints on the rate of growth that cells may attain. This subset is enriched for genes that participate in the processes of gene expression, itself (i.e. transcription and translation). The fact that haploproficiency is exhibited by mutants grown at the previously determined maximum rate implies that the control of growth rate in this simple eukaryote represents a trade-off between the selective advantages of rapid growth and the need to maintain the integrity of the genome.

THEORETICAL AND EXPERIMENTAL RECOVERY OF OSCILLATIONS DURING BATCH YEAST GROWTH IN A PURE CULTURE SUBJECT TO NUTRITIONAL STRESS

A new model is proposed for the recovery of the complete cell growth dynamics, and showed to compare well with new experiments that we conducted for a pure culture of the T206 strain of Saccharomyces cerevisiae. The proposed model results show that the batch yeast growth in a limited nutrient medium (5% grape juice) is associated with substantial oscillations, which damp out over time. Additional results obtained by using the proposed model show the recovery of effects that are frequently encountered in experiments such as a "Lag Phase" as well as an inflection point in the "ln curve" of the cell concentration. The proposed model recovers also the logistic growth curve as a special case.

Absolute quantification of the glycolytic pathway in yeast: deployment of a complete QconCAT approach

The availability of label-free data derived from yeast cells (based on the summed intensity of the three strongest, isoform-specific peptides) permitted a preliminary assessment of protein abundances for glycolytic proteins. Following this analysis, we demonstrate successful application of the QconCAT technology, which uses recombinant DNA techniques to generate artificial concatamers of large numbers of internal standard peptides, to the quantification of enzymes of the glycolysis pathway in the yeast Saccharomyces cerevisiae. A QconCAT of 88 kDa (59 tryptic peptides) corresponding to 27 isoenzymes was designed and built to encode two or three analyte peptides per protein, and after stable isotope labeling of the standard in vivo, protein levels were determined by LC-MS, using ultra high performance liquid chromatography-coupled mass spectrometry. We were able to determine absolute protein concentrations between 14,000 and 10 million molecules/cell. Issues such as efficiency of extraction and completeness of proteolysis are addressed, as well as generic factors such as optimal quantotypic peptide selection and expression. In addition, the same proteins were quantified by intensity-based label-free analysis, and both sets of data were compared with other quantification methods.

The use of continuous culture in systems biology investigations

When acquiring data for systems biology studies, it is essential to perform the experiments in controlled and reproducible conditions. Advances in the fields of proteomics and metabolomics allow the quantitative analysis of the components of the biological cell. It is essential to include a method in the experimental pipeline to culture the biological system in controlled and reproducible conditions to facilitate the acquisition of high-quality data. The employment of continuous culture methods for the growth of microorganisms is an ideal tool to achieve these objectives. This chapter will review the continuous culture approaches which may be applied in such studies, outline the experimental options which should be considered, and describe the approach applied in the production of steady-state cultures of Saccharomyces cerevisiae.

Systematic integration of experimental data and models in systems biology

Background

The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.

Results

Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis.

Conclusions

Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

Continuous differential impedance spectroscopy of single cells

A device for continuous differential impedance analysis of single cells held by a hydrodynamic cell trapping is presented. Measurements are accomplished by recording the current from two closely-situated electrode pairs, one empty (reference) and one containing a cell. We demonstrate time-dependent measurement of single cell impedance produced in response to dynamic chemical perturbations. First, the system is used to assay the response of HeLa cells to the effects of the surfactant Tween, which reduces the impedance of the trapped cells in a concentration dependent way and is interpreted as gradual lysis of the cell membrane. Second, the effects of the bacterial pore-forming toxin, Streptolysin-O are measured: a transient exponential decay in the impedance is recorded as the cell membrane becomes increasingly permeable. The decay time constant is inversely proportional to toxin concentration (482, 150, and 30 s for 0.1, 1, and 10 kU/ml, respectively). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10404-009-0534-2) contains supplementary material, which is available to authorized users.

Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics

The chemical identification of mass spectrometric signals in metabolomic applications is important to provide conversion of analytical data to biological knowledge about metabolic pathways. The complexity of electrospray mass spectrometric data acquired from a range of samples (serum, urine, yeast intracellular extracts, yeast metabolic footprints, placental tissue metabolic footprints) has been investigated and has defined the frequency of different ion types routinely detected. Although some ion types were expected (protonated and deprotonated peaks, isotope peaks, multiply charged peaks) others were not expected (sodium formate adduct ions). In parallel, the Manchester Metabolomics Database (MMD) has been constructed with data from genome scale metabolic reconstructions, HMDB, KEGG, Lipid Maps, BioCyc and DrugBank to provide knowledge on 42,687 endogenous and exogenous metabolite species. The combination of accurate mass data for a large collection of metabolites, theoretical isotope abundance data and knowledge of the different ion types detected provided a greater number of electrospray mass spectrometric signals which were putatively identified and with greater confidence in the samples studied. To provide definitive identification metabolite-specific mass spectral libraries for UPLC-MS and GC-MS have been constructed for 1,065 commercially available authentic standards. The MMD data are available at http://dbkgroup.org/MMD/.

Super life--how and why 'cell selection' leads to the fastest-growing eukaryote

What is the highest possible replication rate for living organisms? The cellular growth rate is controlled by a variety of processes. Therefore, it is unclear which metabolic process or group of processes should be activated to increase growth rate. An organism that is already growing fast may already have optimized through evolution all processes that could be optimized readily, but may be confronted with a more generic limitation. Here we introduce a method called 'cell selection' to select for highest growth rate, and show how such a cellular site of 'growth control' was identified. By applying pH-auxostat cultivation to the already fast-growing yeast Kluyveromyces marxianus for a sufficiently long time, we selected a strain with a 30% increased growth rate; its cell-cycle time decreased to 52 min, much below that reported to date for any eukaryote. The increase in growth rate was accompanied by a 40% increase in cell surface at a fairly constant cell volume. We show how the increase in growth rate can be explained by a dominant (80%) limitation of growth by the group of membrane processes (a 0.7% increase of specific growth rate to a 1% increase in membrane surface area). Simultaneous activation of membrane processes may be what is required to accelerate growth of the fastest-growing form of eukaryotic life to growth rates that are even faster, and may be of potential interest for single-cell protein production in industrial 'White' biotechnology processes.

Respiratory oscillations in yeasts

Respiratory oscillations in yeasts have been studied in three time domains with periods of (a) about a minute, (b) about 40 min, and (c) about a day. Reactive responses (damped oscillations), rhythms and temperature-compensated clocks have been described for (b) and (c), but a timekeeping clock has not yet been shown for (a). Synchronous populations reveal the time-structure that can only otherwise be studied in single organisms; this is because time-averaging through an asynchronous population conceals its fine structure. Early studies with synchronous cultures made by size selection methods indicated ultradian-clock driven oscillations in respiration, pools of adenylates, total protein, RNA synthesis and many enzyme activities (tau = 40 min in Schizosaccharomyces pombe, 30 min in Candida utilis), and more recently in self-synchronised continuous cultures of Saccharomyces cerevisiae (tau = 48 min). Most detailed understanding comes from the latter system, where continuous, noninvasive real-time monitoring (of 02 uptake, CO2 production, and NAD(P)H redox state) is combined with frequent discrete time samples (for other redox components, including H2S, GSH and cytochromes, metabolites, and mRNA levels). A redox switch lies at the heart of this ultradian clock and a plethora of outputs is optimized to a time-base that is genetically-determined and differs in different organisms. It is suggested that the entire temporal landscape of all eukaryotic organisms and the cells of higher plants and animals is constructed on this basis. A time frame for the coordination and coherence of all intracellular processes and the construction and assembly of cellular structures is provided by the ultradian clock The circadian clock matches these functions to the daily cycle of the external environment.

Redox rhythmicity: clocks at the core of temporal coherence

Ultradian rhythms are those that cycle many times in a day and are therefore measured in hours, minutes, seconds or even fractions of a second. In yeasts and protists, a temperature-compensated clock with a period of about an hour (30-90 minutes) provides the time base upon which all central processes are synchronized. A 40-minute clock in yeast times metabolic, respiratory and transcriptional processes, and controls cell division cycle progression. This system has at its core a redox cycle involving NAD(P)H and dithiol-disulfide interconversions. It provides an archetype for biological time keeping on longer time scales (e.g. the daily cycles driven by circadian clocks) and underpins these rhythms, which cannot be understood in isolation. Ultradian rhythms are the foundation upon which the coherent functioning of the organism depends.

A tuneable attractor underlies yeast respiratory dynamics

Our understanding of the molecular structure and function in the budding yeast, Saccharomyces cerevisiae, surpasses that of all other eukaryotic cells. However, the fundamental properties of the complex processes and their control systems have been difficult to reconstruct from detailed dissection of their molecular components. Spontaneous oscillatory dynamics observed in self-synchronized continuous cultures is pervasive, involves much of the cellular network, and provides unique insights into integrative cell physiology. Here, in non-invasive experiments in vivo, we exploit these oscillatory dynamics to analyse the global timing of the cellular network to show the presence of a low-order chaotic component. Although robust to a wide range of environmental perturbations, the system responds and reacts to the imposition of harsh environmental conditions, in this case low pH, by dynamic re-organization of respiration, and this feeds upwards to affect cell division. These complex dynamics can be represented by a tuneable attractor that orchestrates cellular complexity and coherence to the environment.

Evaluation of a new annular capacitance probe for biomass monitoring in industrial pilot-scale fermentations

The four-pin electrode capacitance probe has already shown to be a valuable tool for on-line monitoring viable biomass concentration in industrial-type fermentations. A new prototype annular probe was developed and its performance in real-time monitoring the concentration of viable cells during industrial pilot-scale fermentation for the production of an Active Pharmaceutical Ingredient (API) was investigated and compared to the four-pin probe. A set of 14 fermentations was monitored on-line: four of them with the four-pin probe, the remaining with the annular probe. The performance of both the annular and the four-pin electrode probe were compared against each other and against off-line measurements (viscosity and packed mycelial volume). The prototype annular probe showed to have higher signal intensity and sensitivity than the standard four-pin probe, with higher signal-to-noise ratio. Furthermore, its new design and construction proved to be easier to handle in an industrial environment.

On-line biomass monitoring of CHO perfusion culture with scanning dielectric spectroscopy

In this work, dielectric spectroscopy was used to monitor two CHO perfusion culture experiments (B14 and B16). The capacitance of the cell suspension was recorded every 20 minutes over an excitation frequency range of 0.2 MHz to 10.0 MHz. A phase plot of the capacitance at a low excitation frequency vs. the value at a higher frequency proved to be an accurate indicator of the major transition points of the culture, i.e., maximum cell viability, end of lactate consumption, point of zero viability. For both experiments, the capacitance signal correlated very well (R(2) >0.98) with viable cell number up to concentrations of 1 x 10(7) cells/mL. Visual observation of the capacitance spectra indicated that changes in the capacitance relative to frequency were related to the cellular morphology. A multivariate model was developed using off-line data that could predict the median cell diameter within a single experiment (B14) with an error of 0.34 microm (2%). Upon extension to a subsequent experiment (B16), the predicted error was 1.18 microm (9%).

Recovery of an oscillatory mode of batch yeast growth in water for a pure culture

New experiments that we conducted show an oscillatory mode of batch yeast growth in water, for a pure culture of the T206 strain of Saccharomyces cerevisiae. The oscillations are damped over time, allowing the cell concentration to stabilize at the stationary equilibrium. A new proposed model that includes the complete cell growth dynamics is introduced and showed to recover the experimental oscillatory results. In addition the proposed model recovers effects that are frequently encountered in experiments such as a "Lag Phase" as well as an inflection point in the "ln curve" of the cell concentration. The proposed model recovers also the Logistic Growth Curve as a special case. For purposes of providing some interesting contrast we present additional experimental as well as computational results for the growth of the VIN7 strain of S. cerevisiae in a 5% grape juice medium. The latter indicates even stronger oscillations during the growth process. In order to capture experimentally the oscillatory growth behavior, very frequent readings are required (every 15-30 min) and the measurement process needs to be extended to longer than usual periods (over 250 h).

Clock control of ultradian respiratory oscillation found during yeast continuous culture

A short-period autonomous respiratory ultradian oscillation (period approximately 40 min) occurs during aerobic Saccharomyces cerevisiae continuous culture and is most conveniently studied by monitoring dissolved O(2) concentrations. The resulting data are high quality and reveal fundamental information regarding cellular dynamics. The phase diagram and discrete fast Fourier transformation of the dissolved O(2) values revealed a square waveform with at least eight harmonic peaks. Stepwise changes in temperature revealed that the oscillation was temperature compensated at temperatures ranging from 27 to 34 degrees C when either glucose (temperature quotient [Q(10)] = 1.02) or ethanol (Q(10) = 0.82) was used as a carbon source. After alteration of the temperature beyond the temperature compensation region, phase coherence events for individual cells were quickly lost. As the cell doubling rate decreased from 15.5 to 9.2 h (a factor of 1.68), the periodicity decreased by a factor of 1.26. This indicated that there was a degree of nutrient compensation. Outside the range of dilution rates at which stable oscillation occurred, the mode of oscillation changed. The oscillation in respiratory output is therefore under clock control.

On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning

At present, the assignment of function to novel genes uncovered by the systematic genome-sequencing programmes is a problem. Many studies anticipate that this can be achieved by analysing patterns of gene expression via the transcriptome, proteome and metabolome. Thus, functional genomics is, in part, an exercise in pattern classification. Because many genes have known functional classes, the problem of predicting their functional class is a supervised learning problem. However, most pattern classification methods that have been applied to the problem have been unsupervised clustering methods. Consequently, the best classification tools have not always been used. Furthermore, the present functional classes are suboptimal and new unsupervised clustering methods are needed to improve them. Better-structured functional classes will facilitate the prediction of biochemically testable functions.

Metabolic modeling of Saccharomyces cerevisiae using the optimal control of homeostasis: a cybernetic model definition

A model is presented to describe the observed behavior of microorganisms that aim at metabolic homeostasis while growing and adapting to their environment in an optimal way. The cellular metabolism is seen as a network with a multiple controller system with both feedback and feedforward control, i.e., a model based on a dynamic optimal metabolic control. The dynamic network consists of aggregated pathways, each having a control setpoint for the metabolic states at a given growth rate. This set of strategies of the cell forms a true cybernetic model with a minimal number of assumptions. The cellular strategies and constraints were derived from metabolic flux analysis using an identified, biochemically relevant, stoichiometry matrix derived from experimental data on the cellular composition of continuous cultures of Saccharomyces cerevisiae. Based on these data a cybernetic model was developed to study its dynamic behavior. The growth rate of the cell is determined by the structural compounds and fluxes of compounds related to central metabolism. In contrast to many other cybernetic models, the minimal model does not consist of any assumed internal kinetic parameters or interactions. This necessitates the use of a stepwise integration with an optimization of the fluxes at every time interval. Some examples of the behavior of this model are given with respect to steady states and pulse responses. This model is very suitable for describing semiquantitatively dynamics of global cellular metabolism and may form a useful framework for including structured and more detailed kinetic models.

Variable selection and multivariate methods for the identification of microorganisms by flow cytometry

BACKGROUND

When exploited fully, flow cytometry can be used to provide multiparametric data for each cell in the sample of interest. While this makes flow cytometry a powerful technique for discriminating between different cell types, the data can be difficult to interpret. Traditionally, dual-parameter plots are used to visualize flow cytometric data, and for a data set consisting of seven parameters, one should examine 21 of these plots. A more efficient method is to reduce the dimensionality of the data (e.g., using unsupervised methods such as principal components analysis) so that fewer graphs need to be examined, or to use supervised multivariate data analysis methods to give a prediction of the identity of the analyzed particles.

MATERIALS AND METHODS

We collected multiparametric data sets for microbiological samples stained with six cocktails of fluorescent stains. Multivariate data analysis methods were explored as a means of microbial detection and identification.

RESULTS

We show that while all cocktails and all methods gave good accuracy of predictions (>94%), careful selection of both the stains and the analysis method could improve this figure (to > 99% accuracy), even in a data set that was not used in the formation of the supervised multivariate calibration model.

CONCLUSIONS

Flow cytometry provides a rapid method of obtaining multiparametric data for distinguishing between microorganisms. Multivariate data analysis methods have an important role to play in extracting the information from the data obtained. Artificial neural networks proved to be the most suitable method of data analysis.

Quantification of microbial productivity via multi-angle light scattering and supervised learning

This article describes the use of chemometric methods for prediction of biological parameters of cell suspensions on the basis of their light scattering profiles. Laser light is directed into a vial or flow cell containing media from the suspension. The intensity of the scattered light is recorded at 18 angles. Supervised learning methods are then used to calibrate a model relating the parameter of interest to the intensity values. Using such models opens up the possibility of estimating the biological properties of fermentor broths extremely rapidly (typically every 4 sec), and, using the flow cell, without user interaction. Our work has demonstrated the usefulness of this approach for estimation of yeast cell counts over a wide range of values (10(5)-10(9) cells mL-1), although it was less successful in predicting cell viability in such suspensions.