GMP — good modelling practice: an essential component of good manufacturing practice

Experimental kS estimation: A comparison of methods for Corynebacterium glutamicum from lab to microfluidic scale

Knowledge about the specific affinity of whole cells toward a substrate, commonly referred to as k_S , is a crucial parameter for characterizing growth within bioreactors. State-of-the-art methodologies measure either uptake or consumption rates at different initial substrate concentrations. Alternatively, cell dry weight or respiratory data like online oxygen and carbon dioxide transfer rates can be used to estimate k_S . In this work, a recently developed substrate-limited microfluidic single-cell cultivation (sl-MSCC) method is applied for the estimation of k_S values under defined environmental conditions. This method is benchmarked with two alternative microtiter plate methods, namely high-frequency biomass measurement (HFB) and substrate-limited respiratory activity monitoring (sl-RA). As a model system, the substrate affinity k_S of Corynebacterium glutamicum ATCC 13032 regarding glucose was investigated assuming a Monod-type growth response. A k_S of <70.7 mg/L (with 95% probability) with HFB, 8.55 ± 1.38 mg/L with sl-RA, and 2.66 ± 0.99 mg/L with sl-MSCC was obtained. Whereas HFB and sl-RA are suitable for a fast initial k_S estimation, sl-MSCC allows an affinity estimation by determining t_D at concentrations less or equal to the k_S value. Thus, sl-MSCC lays the foundation for strain-specific k_S estimations under defined environmental conditions with additional insights into cell-to-cell heterogeneity.

NyctiDB: A non-relational bioprocesses modeling database supported by an ontology

Strategies to exploit and enable the digitalization of industrial processes are on course to become game-changers in optimizing (bio)chemical facilities. To achieve this, these industries face an increasing need for process models and, as importantly, an efficient way to store the models and data/information. Therefore, this work proposes developing an online information storage system that can facilitate the reuse and expansion of process models and make them available to the digitalization cycle. This system is named NyctiDB, and it is a novel non-relational database coupled with a bioprocess ontology. The ontology supports the selection and classification of bioprocess models focused information, while the database is in charge of the online storage of said information. Through a series of online collections, NyctiDB contains essential knowledge for the design, monitoring, control, and optimization of a bioprocess based on its mathematical model. Once NyctiDB has been implemented, its applicability and usefulness are demonstrated through two applications. Application A shows how NyctiDB is integrated inside the software architecture of an online educational bioprocess simulator. This implies that NyctiDB provides the information for the visualization of different bioprocess behaviours and the modifications of the models in the software. Moreover, the information related to the parameters and conditions of each model is used to support the users’ understanding of the process. Additionally, application B illustrates that NyctiDB can be used as AI enabler to further the research in this field through open-source and reliable data. This can, in fact, be used as the information source for the AI frameworks when developing, for example, hybrid models or smart expert systems for bioprocesses. Henceforth, this work aims to provide a blueprint on how to collect bioprocess modeling information and connect it to facilitate and empower the Internet-of-Things paradigm and the digitalization of the biomanufacturing industries.

Towards the Development of Digital Twins for the Bio-manufacturing Industry

The bio-manufacturing industry, along with other process industries, now has the opportunity to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully accomplish this, a physical-to-digital-to-physical information loop should be carefully developed. One way to achieve this is, for example, through the implementation of digital twins (DTs), which are virtual copies of the processes. Therefore, in this paper, the focus is on understanding the needs and challenges faced by the bio-manufacturing industry when dealing with this digitalized paradigm. To do so, two major building blocks of a DT, data and models, are highlighted and discussed. Hence, firstly, data and their characteristics and collection strategies are examined as well as new methods and tools for data processing. Secondly, modelling approaches and their potential of being used in DTs are reviewed. Finally, we share our vision with regard to the use of DTs in the bio-manufacturing industry aiming at bringing the DT a step closer to its full potential and realization.

Theoretical design of novel antimalarial agents against P. falciparum strain, Dd2 through the QSAR modeling of synthesized 2'-substituted triclosan derivatives

In an attempt to design compounds with higher antimalarial activities, quantitative structure-activity relationship (QSAR) technique was utilized in the development of a molecular model for some synthesized 2′-substituted triclosan derivatives through a hybrid of the GA-MLR method. The model was found to have excellent statistical parameters (R² = 0.8919, R²_Adj = 0.8728, LOF = 0.2563). The descriptors mean effect (MF) revealed BCUTw-1l, which increases with an increase in molecular weight, to be the most contributive to the antimalarial activity. Consequently, compound 3, with the highest activities (pEC₅₀ = 6.9586) was deployed as the design template. The molecular weight of the template was increasing through substitutions of its atoms at several positions with heavier atoms/groups to increases the descriptor (BCUTw-1l) value. Twelves (12) theoretical derivatives of the template were designed where six of the designed derivatives have better activity than the design template. The most active designed compound, 3L was found to have the highest antimalarial activity (pEC₅₀ = 7.930) than that of the design template.

A Dormant Microbial Component in the Development of Preeclampsia

Preeclampsia (PE) is a complex, multisystem disorder that remains a leading cause of morbidity and mortality in pregnancy. Four main classes of dysregulation accompany PE and are widely considered to contribute to its severity. These are abnormal trophoblast invasion of the placenta, anti-angiogenic responses, oxidative stress, and inflammation. What is lacking, however, is an explanation of how these themselves are caused. We here develop the unifying idea, and the considerable evidence for it, that the originating cause of PE (and of the four classes of dysregulation) is, in fact, microbial infection, that most such microbes are dormant and hence resist detection by conventional (replication-dependent) microbiology, and that by occasional resuscitation and growth it is they that are responsible for all the observable sequelae, including the continuing, chronic inflammation. In particular, bacterial products such as lipopolysaccharide (LPS), also known as endotoxin, are well known as highly inflammagenic and stimulate an innate (and possibly trained) immune response that exacerbates the inflammation further. The known need of microbes for free iron can explain the iron dysregulation that accompanies PE. We describe the main routes of infection (gut, oral, and urinary tract infection) and the regularly observed presence of microbes in placental and other tissues in PE. Every known proteomic biomarker of "preeclampsia" that we assessed has, in fact, also been shown to be raised in response to infection. An infectious component to PE fulfills the Bradford Hill criteria for ascribing a disease to an environmental cause and suggests a number of treatments, some of which have, in fact, been shown to be successful. PE was classically referred to as endotoxemia or toxemia of pregnancy, and it is ironic that it seems that LPS and other microbial endotoxins really are involved. Overall, the recognition of an infectious component in the etiology of PE mirrors that for ulcers and other diseases that were previously considered to lack one.

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.

How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion

One approach to experimental science involves creating hypotheses, then testing them by varying one or more independent variables, and assessing the effects of this variation on the processes of interest. We use this strategy to compare the intellectual status and available evidence for two models or views of mechanisms of transmembrane drug transport into intact biological cells. One (BDII) asserts that lipoidal phospholipid Bilayer Diffusion Is Important, while a second (PBIN) proposes that in normal intact cells Phospholipid Bilayer diffusion Is Negligible (i.e., may be neglected quantitatively), because evolution selected against it, and with transmembrane drug transport being effected by genetically encoded proteinaceous carriers or pores, whose “natural” biological roles, and substrates are based in intermediary metabolism. Despite a recent review elsewhere, we can find no evidence able to support BDII as we can find no experiments in intact cells in which phospholipid bilayer diffusion was either varied independently or measured directly (although there are many papers where it was inferred by seeing a covariation of other dependent variables). By contrast, we find an abundance of evidence showing cases in which changes in the activities of named and genetically identified transporters led to measurable changes in the rate or extent of drug uptake. PBIN also has considerable predictive power, and accounts readily for the large differences in drug uptake between tissues, cells and species, in accounting for the metabolite-likeness of marketed drugs, in pharmacogenomics, and in providing a straightforward explanation for the late-stage appearance of toxicity and of lack of efficacy during drug discovery programmes despite macroscopically adequate pharmacokinetics. Consequently, the view that Phospholipid Bilayer diffusion Is Negligible (PBIN) provides a starting hypothesis for assessing cellular drug uptake that is much better supported by the available evidence, and is both more productive and more predictive.

Automated measurement and monitoring of bioprocesses: key elements of the M(3)C strategy

The state-of-routine monitoring items established in the bioprocess industry as well as some important state-of-the-art methods are briefly described and the potential pitfalls discussed. Among those are physical and chemical variables such as temperature, pressure, weight, volume, mass and volumetric flow rates, pH, redox potential, gas partial pressures in the liquid and molar fractions in the gas phase, infrared spectral analysis of the liquid phase, and calorimetry over an entire reactor. Classical as well as new optical versions are addressed. Biomass and bio-activity monitoring (as opposed to "measurement") via turbidity, permittivity, in situ microscopy, and fluorescence are critically analyzed. Some new(er) instrumental analytical tools, interfaced to bioprocesses, are explained. Among those are chromatographic methods, mass spectrometry, flow and sequential injection analyses, field flow fractionation, capillary electrophoresis, and flow cytometry. This chapter surveys the principles of monitoring rather than compiling instruments.

Multivariate data analysis methods for the interpretation of microbial flow cytometric data

Flow cytometry is an important technique in cell biology and immunology and has been applied by many groups to the analysis of microorganisms. This has been made possible by developments in hardware that is now sensitive enough to be used routinely for analysis of microbes. However, in contrast to advances in the technology that underpin flow cytometry, there has not been concomitant progress in the software tools required to analyse, display and disseminate the data and manual analysis, of individual samples remains a limiting aspect of the technology. We present two new data sets that illustrate common applications of flow cytometry in microbiology and demonstrate the application of manual data analysis, automated visualisation (including the first description of a new piece of software we are developing to facilitate this), genetic programming, principal components analysis and artificial neural nets to these data. The data analysis methods described here are equally applicable to flow cytometric applications with other cell types.

Metabolomics of a superorganism

The human can be thought of as a human-microbe hybrid, and the health of this superorganism will be affected by intrinsic properties such as human genetics, diurnal cycles, and age and by extrinsic factors such as lifestyle choices (food and drink, drug intake) and the acquisition of a stable "healthy" gut microflora (the so-called microbiome). Alterations in this superorganism will be manifest in the metabolite complement within its serum and urine samples. The unraveling of this metabolic compartmentalization in this complex ecosystem will certainly be a challenge for systems biology and necessary for defining human health at the molecular level. Within the systems biology framework, functional analyses at the level of gene expression (transcriptomics), protein translation (proteomics), and, more recently, the metabolite network (metabolomics) have become increasingly popular. Metabolomics experiments aim to quantify all metabolites in a cellular system (cell or tissue) under defined states and at different time points so that the dynamics of any biotic, abiotic, or genetic perturbation can be accurately assessed. This article provides an overview of metabolomics and discusses how data are generated and analyzed within a systems biology framework. The role of metabolomics in nutrigenomics is also discussed, as are the concepts of the human being a superorganism and the complexities required to be overcome to understand human health and disease.

Metabolomics: current technologies and future trends

The ability to sequence whole genomes has taught us that our knowledge with respect to gene function is rather limited with typically 30-40% of open reading frames having no known function. Thus, within the life sciences there is a need for determination of the biological function of these so-called orphan genes, some of which may be molecular targets for therapeutic intervention. The search for specific mRNA, proteins, or metabolites that can serve as diagnostic markers has also increased, as has the fact that these biomarkers may be useful in following and predicting disease progression or response to therapy. Functional analyses have become increasingly popular. They include investigations at the level of gene expression (transcriptomics), protein translation (proteomics) and more recently the metabolite network (metabolomics). This article provides an overview of metabolomics and discusses its complementary role with transcriptomics and proteomics, and within system biology. It highlights how metabolome analyses are conducted and how the highly complex data that are generated are analysed. Non-invasive footprinting analysis is also discussed as this has many applications to in vitro cell systems. Finally, for studying biotic or abiotic stresses on animals, plants or microbes, we believe that metabolomics could very easily be applied to large populations, because this approach tends to be of higher throughput and generally lower cost than transcriptomics and proteomics, whilst also providing indications of which area of metabolism may be affected by external perturbation.

Large area protein nanopatterning for biological applications

Large area nanopatterns of functional proteins are demonstrated. A new approach to analyze atomic force microscopy height histograms is used to quantify protein and antibody binding to nanoscale patches. Arrays of nanopatches, each containing less than 40 laminin molecules, are shown to be highly functional binding close to 1 monoclonal anti-laminin IgG (site by IKVAV sequence) or 3-4 polyclonal anti-laminin IgG's per surface bound laminin. Complementary quartz crystal microbalance measurements indicate higher functionality at nanopatches than on homogeneous surfaces.

Sensor combination and chemometric modelling for improved process monitoring in recombinant E. coli fed-batch cultivations

The key objective for the optimisation of recombinant protein production in bacteria is to optimize the exploitation of the host cell's synthesis potential. Recent studies show that the novel concept of transcription rate control allows the tuning of recombinant gene expression in relation to the metabolic capacity of the host cell. To adjust the inducer-biomass ratio to a tolerable level, real-time knowledge about key process variables is paramount. Since there are no reliable online-sensors for key variables such as biomass or recombinant product, it is necessary to relate available online signals to process variables by mathematical models. To improve chemometric modelling of process variables, dielectric spectroscopy and a multi-wavelength online fluorescence sensor for two-dimensional fluorescence spectroscopy were applied in a series of recombinant Escherichia coli fed-batch cultivations applying two different process operation states. Dielectric spectroscopy signals were closely correlated to biomass, while two-dimensional fluorescence spectroscopy allowed the monitoring of fluorescent biogenic components. Chemometric modelling of key process variables with two different modelling techniques showed that this sensor combination greatly improved the estimation (i.e. reduce error magnitude) of process variables in recombinant E. coli cultivations, thereby enhancing process monitoring capabilities.

Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: application to gibberellic acid production

Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as "black box" methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation-based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis.

Rapid and quantitative detection of the microbial spoilage of meat by fourier transform infrared spectroscopy and machine learning

Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable "fingerprints." Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10(7) bacteria.g(-1) the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.

On-line determination of animal cell concentration in two industrial high-density culture processes by dielectric spectroscopy

Dielectric spectroscopy was applied to two industrial high cell density culture processes and used to determine on-line the concentration of CHO cells immobilized on macroporous microcarriers in a stirred bioreactor and in a packed-bed of disk carriers. The cell concentration predicted from the spectroscopic data was in excellent agreement with off-line cell counting data for both processes. Deviations between the two counting methods only occurred in the case of a significant decrease of the cell viability, from 93% to 64%, which induced a change of the average cell size in the culture. Results for the packed-bed process were further confirmed by the application of indirect yield models based on the measurement of glucose, lactate, and the protein of interest. Moreover, dielectric spectroscopy was used as a tool to characterize the packed-bed process. It was possible to determine both the maximum cell concentration that could be reached in the culture system, 2.0 x 10(11) cell per kg of disk carrier, and to quantify the increase of specific protein productivity induced by the production phase, from 5.14 x 10(-8) microg x cell(-1) x h(-1) to 4.24 x 10(-7) microg x cell(-1) x h(-1).

Differentiation of Phytophthora infestans sporangia from other airborne biological particles by flow cytometry

The ability of two different flow cytometers, the Microcyte (Optoflow) and the PAS-III (Partec), to differentiate sporangia of the late-blight pathogen Phytophthora infestans from other potential airborne particles was compared. With the PAS-III, light scatter and intrinsic fluorescence parameters could be used to differentiate sporangia from conidia of Alternaria or Botrytis spp., rust urediniospores, and pollen of grasses and plantain. Differentiation between P. infestans sporangia and powdery mildew conidia was not possible by these two methods but, when combined with analytical rules evolved by genetic programming methods, could be achieved after staining with the fluorescent brightener Calcofluor white M2R. The potential application of these techniques to the prediction of late-blight epiphytotics in the field is discussed.

Mining of biological data I: identifying discriminating features via mean hypothesis testing

Large volumes of data are routinely collected during bioprocess operations and, more recently, in basic biological research using genomics-based technologies. While these data often lack sufficient detail to be used for mechanism identification, it is possible that the underlying mechanisms affecting cell phenotype or process outcome are reflected as specific patterns in the overall or temporal sensor logs. This raises the possibility of identifying outcome-specific fingerprints that can be used for process or phenotype classification and the identification of discriminating characteristics, such as specific genes or process variables. The aim of this work is to provide a systematic approach to identifying and modeling patterns in historical records and using this information for process classification. This approach differs from others in that emphasis is placed on analyzing the data structure first and thereby extracting potentially relevant features prior to model creation. The initial step in this overall approach is to first identify the discriminating features of the relevant measurements and time windows, which can then be subsequently used to discriminate among different classes of process behavior. This is achieved via a mean hypothesis testing algorithm. Next, the homogeneity of the multivariate data in each class is explored via a novel cluster analysis technique called PC1 Time Series Clustering to ensure that the data subsets used accurately reflect the variability displayed in the historical records. This will be the topic of the second paper in this series. We present here the method for identifying discriminating features in data via mean hypothesis testing along with results from the analysis of case studies from industrial fermentations

Application of model-predictive control based on artificial neural networks to optimize the fed-batch process for riboflavin production

The fed-batch process for commercial production of riboflavin (vitamin B2) was optimized on-line using model-predictive control based on artificial neural networks (ANNs). The information required for process models was extracted from both historical data and heuristic rules. After each cultivation the process model was readapted off-line to include the most recent process data. The control signal (feed rate), however, was optimized on-line at each sampling interval. An optimizer simulated variations in the control signal and assessed the forecasted model outputs according to an objective function. The optimum feed profile for increasing the product yield (YB2/S) and the amount of riboflavin at the time of harvesting was adjusted continuously and applied to the process. In contrast to the control by set-point profiles, the novel ANN-control is able to react on-line to variations in the process and also to incorporate the new process information continuously. As a result, both the total amount of riboflavin produced and the product yield increased systematically by more than 10% and the reproducibility of seven subsequently optimized batches was enhanced.

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.

Engineering a living cell to desired metabolite concentrations and fluxes: pathways with multifunctional enzymes

With molecular genetics enabling modulation of the concentrations of cellular enzymes, metabolic engineering becomes limited by the question of which modulations of the enzyme concentrations are required to bring about a desired pattern of cellular metabolism. In an earlier paper (Kholodenko et al. (1998). Biotechnol. Bioeng. 59, 239-247) we derived a method to determine the required modulations. This method, however, cannot be immediately applied to cellular pathways with enzymes catalyzing more than one step in metabolism (multifunctional enzymes). In the present paper we show to which extent the presence of multifunctional enzymes limits biotechological ambitions, which one might otherwise pursue in vain. In particular, it is impossible to change the concentration of a single intermediate and leave the rest of metabolism unperturbed if that intermediate interacts directly with a multifunctional enzyme. The analytical machinery of Metabolic Control Analysis is used to relate the desired and ensuing changes in the metabolic pattern. An explicit solution to this problem of engineering metabolism is then given in the form of a single matrix equation.

Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics

There are an increasing number of instrumental methods for obtaining data from biochemical processes, many of which now provide information on many (indeed many hundreds) of variables simultaneously. The wealth of data that these methods provide, however, is useless without the means to extract the required information. As instruments advance, and the quantity of data produced increases, the fields of bioinformatics and chemometrics have consequently grown greatly in importance. The chemometric methods nowadays available are both powerful and dangerous, and there are many issues to be considered when using statistical analyses on data for which there are numerous measurements (which often exceed the number of samples). It is not difficult to carry out statistical analysis on multivariate data in such a way that the results appear much more impressive than they really are. The authors present some of the methods that we have developed and exploited in Aberystwyth for gathering highly multivariate data from bioprocesses, and some techniques of sound multivariate statistical analyses (and of related methods based on neural and evolutionary computing) which can ensure that the results will stand up to the most rigorous scrutiny.

Instrumentation of biotechnological processes

Modern bioprocesses are monitored by on-line sensing devices mounted either in situ or externally. In addition to sensor probes, more and more analytical subsystems are being exploited to monitor the state of a bioprocess on-line and in real time. Some of these subsystems deliver signals that are useful for documentation only, other, less delayed systems generate signals useful for closed loop process control. Various conventional and non-conventional monitoring instruments are evaluated; their usefulness, benefits and associated pitfalls are discussed.

An efficient model development strategy for bioprocesses based on neural networks in macroscopic balances: Part II

There is a need for efficient modeling strategies which quickly lead to reliable mathematical models that can be applied for design and optimization of (bio)-chemical processes. The serial gray box modeling strategy is potentially very efficient because no detailed knowledge is needed to construct the white box part of the model and because covenient black box modeling techniques like neural networks can be used for the black box part of the model. This paper shows for a typical biochemical conversion how the serial gray box modeling strategy can be applied efficiently to obtain a model with good frequency extrapolation properties. Models with good frequency extrapolation properties can be applied under dynamic conditions that were not present during the identification experiments. For a given application domain of a model, this property can be used to considerably reduce the number of identification experiments. The serial gray box modeling strategy is demonstrated to be successful for the modeling of the enzymatic conversion of penicillin G In the concentration range of 10-100 mM and temperature range of 298-335 K. Frequency extrapolation is shown by using only constant temperatures in the (batch) identification experiments, while the model can be used reliable with varying temperatures during the (batch) validation experiments. No reliable frequency extrapolation properties could be obtained for a black box model, and for a more knowledge-driven white box model reliable frequency extrapolation properties could only be obtained by incorporating more knowledge in the model. Copyright 1999 John Wiley & Sons, Inc.

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.

Use of flow cytometry to measure the interaction between Escherichia coli heat-stable enterotoxin and its intestinal receptor in mice

Binding of Escherichia coli heat-stable enterotoxin (STa) to its putative receptor on the brush border membrane of enterocytes is a prerequisite for the induction of diarrhea in infected humans and animals. Humans and animals of different ages vary in their susceptibility to the effect STa, perhaps due to the difference in STa interaction with its intestinal receptor. Flow cytometry was compared to indirect immunofluorescence and 125I-STa binding assays to measure the STa-enterocytes receptor interaction in different age groups of Swiss Webster mice (2-, 7-, 14-day-old). Flow cytometry indicated stronger interaction between STa and its putative receptor on enterocytes from the 2-day-old mice than enterocytes from older mice. 125I-STa-binding assay suggested that the stronger fluorescence intensity on enterocytes from younger mice is due to higher STa receptor density and higher receptor affinity to STa. Flow cytometry is more sensitive quantitative assay to measure the interaction between STa and its intestinal receptor than indirect immunofluorescence microscopy.

Analysis and simulation of complex interactions during dynamic microfiltration of Escherichia coli suspensions

Microfiltration is an important unit operation in downstream processing. However, due to the influence of membrane fouling, prediction of the filtration performance for biological suspensions is difficult. This paper describes a modeling approach that allows a comprehensive description of filtration performance. On the basis of experimental data and linguistic information, a specific artificial neural network was developed that predicts the process behavior within a certain range of parameters. This approach allows us to analyze influences of fermentation on filtration. By using extensive simulations, the interactions of 17 parameters were examined and the fouling causes determined. The model was developed for cell harvesting of Escherichia coli through a shear-enhanced module. The method can be applied to any cross-flow filtration process.

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.

Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks

Three rapid spectroscopic approaches for whole-organism fingerprinting-pyrolysis mass spectrometry (PyMS), Fourier transform infra-red spectroscopy (FT-IR) and dispersive Raman microscopy--were used to analyse a group of 59 clinical bacterial isolates associated with urinary tract infection. Direct visual analysis of these spectra was not possible, highlighting the need to use methods to reduce the dimensionality of these hyperspectral data. The unsupervised methods of discriminant function and hierarchical cluster analyses were employed to group these organisms based on their spectral fingerprints, but none produced wholly satisfactory groupings which were characteristic for each of the five bacterial types. In contrast, for PyMS and FT-IR, the artificial neural network (ANN) approaches exploiting multi-layer perceptrons or radial basis functions could be trained with representative spectra of the five bacterial groups so that isolates from clinical bacteriuria in an independent unseen test set could be correctly identified. Comparable ANNs trained with Raman spectra correctly identified some 80% of the same test set. PyMS and FT-IR have often been exploited within microbial systematics, but these are believed to be the first published data showing the ability of dispersive Raman microscopy to discriminate clinically significant intact bacterial species. These results demonstrate that modern analytical spectroscopies of high intrinsic dimensionality can provide rapid accurate microbial characterization techniques, but only when combined with appropriate chemometrics.