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

Model-Based Methods in the Biopharmaceutical Process Lifecycle

Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

MINING GENOME VARIATION TO ASSOCIATE GENETIC DISEASE WITH MUTATION ALTERATIONS AND ORTHO/PARALOGOUS POLIMORPHYSMS IN TRANSCRIPTION FACTOR BINDING SITE

We have developed a system rSNP_Guide, , predicting the transcription factor (TF) binding sites on DNA, which mutation-caused alterations may explain disease penetration. rSNP_Guide uses the detected alterations in the mutant DNA binding to unknown TF caused by diseases and, upon the DNA sequences, calculates the alterations in known TF sites so that to select only the known ones with calculated alterations in the best consistence with those detected. Our system has been control tested on the SNP's with known site-disease relationships. For practical aims, two TF sites associated with diseases were predicted and confirmed by the immune assay with anti-TF antibodies. In the case of tumor susceptibility, the GATA site in the second intron of mouse K-ras gene was truly predicted, whereas mutation damage of this site causes tumor resistance. In the case of alcohol dependencies and others behavioral diseases, the mutation-caused spurious YY1 site in the sixth intron of human tryptophan 2,3-dioxygenase (TDO2) gene was successfully predicted. Finally, sixteen non-documented TF sites localizable at both orthologous and paralogous genes were first characterized by three rates "present", "weakened" or "absent", with significance estimated by rSNP_Guide relatively to six TF sites with known mutation-caused alterations in DNA/TF-binding.

Significance analysis of time-series transcriptomic data: a methodology that enables the identification and further exploration of the differentially expressed genes at each time-point

Time-series transcriptional profiling experiments are becoming increasingly popular, in light of the abundance of information regarding a biological system's regulation that they are expected to reveal. However, identification of differentially expressed genes as a function of time and comparison between physiological states based on the genes' variability in significance level over time remain intriguing tasks, due to certain limitations in the currently available algorithms. Based on the principles of significance analysis of microarrays (SAM) method, we developed an algorithm that allows for the identification of the differentially expressed genes at each time-point of a time sequence, using a common reference distribution and significance threshold for all time-points. These results are further explored in a systematic way to extract information about (a) individual gene and gene class variability in significance level with time, (b) gene and time-point correlation based on (a), and (c) gene class comparison based on (a). All algorithms have been programmed in C language in the form of four executable files for both Windows and Macintosh platforms under the overall name MiTimeS. MiTimeS was validated in the context of real transcriptomic data. It enables the extraction of biologically relevant information from the dynamic transcriptomic profiles currently unnoticed from the available algorithms. The applicability of MiTimeS is not limited to transcriptomic data, but it could be accordingly used for the analysis of dynamic data from other cellular fingerprints.

Pattern identification and classification in gene expression data using an autoassociative neural network model

The application of DNA microarray technology for analysis of gene expression creates enormous opportunities to accelerate the pace in understanding living systems and identification of target genes and pathways for drug development and therapeutic intervention. Parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and the identification of molecular signatures supporting the histological classification schemes of neoplastic specimens. However, the increasing volume of data generated by microarray experiments poses the challenge of developing equally efficient methods and analysis procedures to extract, interpret, and upgrade the information content of these databases. Herein, a computational procedure for pattern identification, feature extraction, and classification of gene expression data through the analysis of an autoassociative neural network model is described. The identified patterns and features contain critical information about gene-phenotype relationships observed during changes in cell physiology. They represent a rational and dimensionally reduced base for understanding the basic biology of the onset of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of pathological states. The proposed method has been tested on two different microarray datasets-Golub's analysis of acute human leukemia [Golub et al. (1999) Science 286:531-537], and the human colon adenocarcinoma study presented by Alon et al. [1999; Proc Natl Acad Sci USA 97:10101-10106]. The analysis of the neural network internal structure allows the identification of specific phenotype markers and the extraction of peculiar associations among genes and physiological states. At the same time, the neural network outputs provide assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.

Contribution of flagella and invasion proteins to pathogenesis of Salmonella enterica serovar enteritidis in chicks

To explore the relative contribution that flagella and Salmonella invasion proteins make to the virulence of Salmonella enteritidis in poultry, 20-day-old chicks were challenged orally and by subcutaneous injection with wild-type strain SE-HCD, two non-flagellated mutants (fliC::Tn10 mutant and flhD::Tn10 mutant) and two Salmonella invasion protein insertion mutants (sipD and iacP). When injected subcutaneously, wild-type SE-HCD was the only strain to cause substantial mortality and morbidity and to grow well in organs. The flhD mutant of SE-HCD was invasive when given orally, whereas wild-type SE-HCD and the fliC mutant were significantly attenuated. Salmonella invasion protein mutants were not invasive by either route. These results suggest that temporary suppression of Class I regulators of flagellin biosynthesis may aid oral infection in poultry.

The virtual pediatric intensive care unit. Practice in the new millennium

Patients and their families meet with health care providers in a complex marketplace. The information revolution is providing access to vast amounts of information and new ways to understand it. More important, perhaps, is that it also is providing new ways of communicating information not only about health but also about the health care delivery process. This occurrence makes it possible for patients not only to diagnosis and treat themselves but also see how well the professionals do it. Like all marketplaces, asymmetries in information define the value of the interaction. Patients see physicians because they have no way of overcoming this knowledge barrier, and health care is a highly regulated market because of these asymmetries in information. New information technologies in general and telemedicine (which, in this broad sense, include distance learning for patients) can address and erode these information asymmetries. This technology threatens to have a profound effect on health care. Telemedicine offers to increase greatly the reach (connectivity) and richness (bandwidth, customization, and interactivity) of the health care information marketplace. This radically will change the way in which physicians practice critical care. Intensivists must ensure that patients continue to receive high-quality critical care. This practice will require embracing these new technologies. Resisting them will be catastrophic. What is the VPICU? It is a committed group of pediatric intensivits who are dedicated to supporting pediatric critical care medicine in the enhancement of knowledge about pediatric critical care. It includes application of information technologies to support the practice of pediatric critical care. It primarily is focused on understanding the health care delivery process and providing the tools for pediatric intensive care practitioners to better understand the care they deliver. It is the desire of the VPICU to create a virtual community in which pediatric critical care practitioners work together to understand the way they practice and to identify and implement better ways to deliver pediatric critical care. This virtual community will be responsible for clinical and economic performance in the practice of pediatric critical care. The VPICU realizes that this requires the tools to make high-quality decisions and that these decisions depend on data and communication. The author invites all pediatric intensivists to participate in the VPICU to achieve the goals of better practice through the application of information technologies in pediatric critical care.

Lipopolysaccharide O-chain microheterogeneity of Salmonella serotypes Enteritidis and Typhimurium

Variability in the lipopolysaccharide (LPS) of the two most prevalent Salmonella serotypes causing food-borne salmonellosis was assessed using gas chromatography analysis of neutral sugars from 43 Salmonella enterica serovar Enteritidis (S. Enteritidis) and 20 Salmonella enterica serovar Typhimurium (S. Typhimurium) isolates. Four substantially different types of O-chain chemotypes were detected using cluster analysis of sugar compositions; these were low-molecular-mass (LMM) LPS, glucosylated LMM LPS, high-molecular-mass (HMM) LPS and glucosylated HMM LPS. Nineteen out of 20 S. Typhimurium isolates yielded glucosylated LMM. In contrast, S. Enteritidis produced a more diverse structure, which varied according to the source and history of the isolate: 45.5% of egg isolates yielded glucosylated HMM LPS; 100% of stored strains lacked glucosylation but retained chain length in some cases; and 83.3% of fresh isolates from the naturally infected house mouse Mus musculus produced glucosylated LMM LPS. A chain length determinant (wzz) mutant of S. Enteritidis produced a structure similar to that of S. Typhimurium and was used to define what constituted significant differences in structure using cluster analysis. Fine mapping of the S. Enteritidis chromosome by means of a two-restriction enzyme-ribotyping technique suggested that mouse isolates producing glucosylated LMM LPS were closely related to orally invasive strains obtained from eggs, and that stored strains were accumulating genetic changes that correlated with suppression of LPS O-chain glucosylation. These results suggest that the determination of LPS chemotype is a useful tool for epidemiological monitoring of S. Enteritidis, which displays an unusual degree of diversity in its LPS O-chain.

Mining of biological data II: assessing data structure and class homogeneity by cluster analysis

An important step in data analysis is class assignment which is usually done on the basis of a macroscopic phenotypic or bioprocess characteristic, such as high vs low growth, healthy vs diseased state, or high vs. low productivity. Unfortunately, such an assignment may lump together samples, which when derived from a more detailed phenotypic or bioprocess description are dissimilar, giving rise to models of lower quality and predictive power. In this paper we present a clustering algorithm for data preprocessing which involves the identification of fundamentally similar lots on the basis of the extent of similarity among the system variables. The algorithm combines aspects of cluster analysis and principal component analysis by applying agglomerative clustering methods to the first principal component of the system data matrix. As part of a rational strategy for developing empirical models, this technique selects lots (samples) which are most appropriate for inclusion in a training set by analyzing multivariate data homogeneity. Samples with similar data structures are identified and grouped together into distinct clusters. This knowledge is used in the formation of potential training sets. Additionally, this technique can identify atypical lots, i.e., samples that are not simply outliers but exhibit the general properties of one class but have been given the assignment of the other. The method is presented along with examples from its application to fermentation data sets.