Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era

High-throughput electronic biology: mining information for drug discovery

The vast range of in silico resources that are available in life sciences research hold much promise towards aiding the drug discovery process. To fully realize this opportunity, computational scientists must consider the practical issues of data integration and identify how best to apply these resources scientifically. In this article we describe in silico approaches that are driven towards the identification of testable laboratory hypotheses; we also address common challenges in the field. We focus on flexible, high-throughput techniques, which may be initiated independently of 'wet-lab' experimentation, and which may be applied to multiple disease areas. The utility of these approaches in drug discovery highlights the contribution that in silico techniques can make and emphasizes the need for collaboration between the areas of disease research and computational science.

Comprehensive analysis of yeast metabolite GC×GC–TOFMS data: combining discovery-mode and deconvolution chemometric software

The first extensive study of yeast metabolite GC x GC-TOFMS data from cells grown under fermenting, R, and respiring, DR, conditions is reported. In this study, recently developed chemometric software for use with three-dimensional instrumentation data was implemented, using a statistically-based Fisher ratio method. The Fisher ratio method is fully automated and will rapidly reduce the data to pinpoint two-dimensional chromatographic peaks differentiating sample types while utilizing all the mass channels. The effect of lowering the Fisher ratio threshold on peak identification was studied. At the lowest threshold (just above the noise level), 73 metabolite peaks were identified, nearly three-fold greater than the number of previously reported metabolite peaks identified (26). In addition to the 73 identified metabolites, 81 unknown metabolites were also located. A Parallel Factor Analysis graphical user interface (PARAFAC GUI) was applied to selected mass channels to obtain a concentration ratio, for each metabolite under the two growth conditions. Of the 73 known metabolites identified by the Fisher ratio method, 54 were statistically changing to the 95% confidence limit between the DR and R conditions according to the rigorous Student's t-test. PARAFAC determined the concentration ratio and provided a fully-deconvoluted (i.e. mathematically resolved) mass spectrum for each of the metabolites. The combination of the Fisher ratio method with the PARAFAC GUI provides high-throughput software for discovery-based metabolomics research, and is novel for GC x GC-TOFMS data due to the use of the entire data set in the analysis (640 MB x 70 runs, double precision floating point).

Quantification of casein phosphorylation with conformational interpretation using Raman spectroscopy

Raman spectroscopy is emerging as a powerful method for obtaining both quantitative and qualitative information from biological samples. One very interesting area of research, for which the technique has rarely been used, is the detection, quantification and structural analysis of post-translational modifications (PTMs) on proteins. Since Raman spectra can be used to address both of these questions simultaneously, we have developed near infrared Raman spectroscopy with appropriate chemometric approaches (partial least squares regression) to quantify low concentration (4 microM) mixtures of phosphorylated and dephosphorylated bovine alpha(s)-casein. In addition, we have used these data in conjunction with Raman optical activity (ROA) spectra and NMR to assess the structural changes that occur upon phosphorylation.

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( approximately 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs.

Text mining and its potential applications in systems biology

With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.

Animal models of behavioral dysfunctions: Basic concepts and classifications, and an evaluation strategy

In behavioral neurosciences, such as neurobiology and biopsychology, animal models make it possible to investigate brain-behavior relations, with the aim of gaining insight into normal and abnormal human behavior and its underlying neuronal and neuroendocrinological processes. Different types of animal models of behavioral dysfunctions are reviewed in this article. In order to determine the precise criteria that an animal model should fulfill, experts from different fields must define the desired characteristics of that model at the neuropathologic and behavioral level. The list of characteristics depends on the purpose of the model. The phenotype-abnormal behavior or behavioral dysfunctions-has to be translated into testable measures in animal experiments. It is essential to standardize rearing, housing, and testing conditions, and to evaluate the reliability, validity (primarily predictive and construct validity), and biological or clinical relevance of putative animal models of human behavioral dysfunctions. This evaluation, guided by a systematic strategy, is central to the development of a model. The necessity of animal models and the responsible use of animals in research are discussed briefly.

Concepts in classification and their relevance to epilepsy

The classification of the epilepsies was advanced in order to formalize efforts to identify coherent clinical entities and to develop a standardized set of diagnostic terminology for facilitating communication among workers in the field world-wide. The classification initially was, and still is today, based primarily on description and expert opinion. In the era of genomics, neuroimaging and tremendous technological and scientific advances in the neurosciences, it is time to introduce scientific principles and standards into the classification of the epilepsies. Phylogenetic systematics provides an initial model worth studying in this context. While the classification of species cannot be directly applied to the classification of epilepsy syndromes, three general points can be appreciated. (1) In evolutionary biology, there is an operationalized definition of the end point (a species). There is no such definition of a syndrome. (2) There are rules and criteria for the type of evidence and how it is evaluated to determine whether an entity does or does not represent a separate species. There are currently no such rules or criteria for epilepsy syndromes. (3) There is an underlying model (evolution) that generates the diversity among species. With the possible and only partial exception of the idiopathic generalized epilepsies, there are no models to explain the diversity among the epilepsies. Previously held beliefs and convictions must, in time, give way to dispassionate examination of testable scientific hypotheses examined with rigorously collected scientific evidence. Although current classification is in need of revision, it has also come into common use and has great practical utility. Any changes to its form and to the terminology of the classification must be made only after careful deliberation and broad consensus is reached. The change(s) should be based on agreed upon scientific criteria and processes. Any changes should represent major improvements and not merely incremental steps.

Miniaturized separation techniques in glycomic investigations

High-sensitivity glycomic analyses are becoming of a great interest in modern biomedical and clinical research, as well as in the development of recombinant protein products. The evolution of separation techniques for glycomic analysis at high sensitivity is highlighted in this review. These methodologies include capillary liquid chromatography, capillary electrophoresis (CE) and capillary electrochromatography (CEC). The potential of such methodologies in glycomic analysis is demonstrated for model glycoproteins as well as total glycomes derived from biological samples.

A classification of tasks for the systematic study of immune response using functional genomics data

A full understanding of the immune system and its responses to infection by different pathogens is important for the development of anti-parasitic vaccines. A growing number of large-scale experimental techniques, such as microarrays, are being used to gain a better understanding of the immune system. To analyse the data generated by these experiments, methods such as clustering are widely used. However, individual applications of these methods tend to analyse the experimental data without taking publicly available biological and immunological knowledge into account systematically and in an unbiased manner. To make best use of the experimental investment, to benefit from existing evidence, and to support the findings in the experimental data, available biological information should be included in the analysis in a systematic manner. In this review we present a classification of tasks that shows how experimental data produced by studies of the immune system can be placed in a broader biological context. Taking into account available evidence, the classification can be used to identify different ways of analysing the experimental data systematically. We have used the classification to identify alternative ways of analysing microarray data, and illustrate its application using studies of immune responses in mice to infection with the intestinal nematode parasites Trichuris muris and Heligmosomoides polygyrus.

From genomes to systems: the path with yeast

Metabolic Control Analysis (MCA) is a conceptual and mathematical formalism that models the relative contributions of individual effectors in a pathway to both the flux through the pathway and the concentrations of individual intermediates within it. To exploit MCA in an initial Systems Biology analysis of the eukaryotic cell, two categories of experiments are required. In category 1 experiments, flux is changed and the impact on the levels of the direct and indirect products of gene action is measured. We have measured the impact of changing the flux on the transcriptome, proteome and metabolome of Saccharomyces cerevisiae. In this whole-cell analysis, flux equates to growth rate. In category 2 experiments, the levels of individual gene products are altered, and the impact on the flux is measured. We have used competition analyses between the complete set of heterozygous yeast deletion mutants to reveal genes encoding proteins with high flux control coefficients. These genes may be exploited, in a top-down analysis, to build a coarse-grained model of the eukaryotic cell, as exemplified by yeast. More detailed modelling requires that 'natural' biological systems be identified. The combination of flux balance analysis with both genetics and metabolomics in the definition of metabolic systems is discussed.

Metabolomic approaches reveal that phosphatidic and phosphatidyl glycerol phospholipids are major discriminatory non-polar metabolites in responses by Brachypodium distachyon to challenge by Magnaporthe grisea

Metabolomic approaches were used to elucidate some key metabolite changes occurring during interactions of Magnaporthe grisea--the cause of rice blast disease--with an alternate host, Brachypodium distachyon. Fourier-transform infrared (FT-IR) spectroscopy provided a high-throughput metabolic fingerprint of M. grisea interacting with the B. distachyon accessions ABR1 (susceptible) and ABR5 (resistant). Principal component-discriminant function analysis (PC-DFA) allowed the differentiation between developing disease symptoms and host resistance. Alignment of projected 'test-set' on to 'training-set' data indicated that our experimental approach produced highly reproducible data. Examination of PC-DFA loading plots indicated that fatty acids were one chemical group that discriminated between responses by ABR1 and ABR5 to M. grisea. To identify these, non-polar extracts of M. grisea-challenged B. distachyon were directly infused into an electrospray ionization mass spectrometer (ESI-MS). PC-DFA indicated that M. grisea-challenged ABR1 and ABR5 were differentially clustered away from healthy material. Subtraction spectra and PC-DFA loadings plots revealed discriminatory analytes (m/z) between each interaction and seven metabolites were subsequently identified as phospholipids (PLs) by ESI-MS-MS. Phosphatidyl glycerol (PG) PLs were suppressed during both resistant and susceptible responses. By contrast, different phosphatidic acid PLs either increased or were reduced during resistance or during disease development. This suggests considerable and differential PL processing of membrane lipids during each interaction which may be associated with the elaboration/suppression of defence mechanisms or developing disease symptoms.

Theodor Bücher Lecture. Metabolomics, modelling and machine learning in systems biology - towards an understanding of the languages of cells. Delivered on 3 July 2005 at the 30th FEBS Congress and the 9th IUBMB conference in Budapest

The newly emerging field of systems biology involves a judicious interplay between high-throughput 'wet' experimentation, computational modelling and technology development, coupled to the world of ideas and theory. This interplay involves iterative cycles, such that systems biology is not at all confined to hypothesis-dependent studies, with intelligent, principled, hypothesis-generating studies being of high importance and consequently very far from aimless fishing expeditions. I seek to illustrate each of these facets. Novel technology development in metabolomics can increase substantially the dynamic range and number of metabolites that one can detect, and these can be exploited as disease markers and in the consequent and principled generation of hypotheses that are consistent with the data and achieve this in a value-free manner. Much of classical biochemistry and signalling pathway analysis has concentrated on the analyses of changes in the concentrations of intermediates, with 'local' equations - such as that of Michaelis and Menten v=(Vmax x S)/(S+K m) - that describe individual steps being based solely on the instantaneous values of these concentrations. Recent work using single cells (that are not subject to the intellectually unsupportable averaging of the variable displayed by heterogeneous cells possessing nonlinear kinetics) has led to the recognition that some protein signalling pathways may encode their signals not (just) as concentrations (AM or amplitude-modulated in a radio analogy) but via changes in the dynamics of those concentrations (the signals are FM or frequency-modulated). This contributes in principle to a straightforward solution of the crosstalk problem, leads to a profound reassessment of how to understand the downstream effects of dynamic changes in the concentrations of elements in these pathways, and stresses the role of signal processing (and not merely the intermediates) in biological signalling. It is this signal processing that lies at the heart of understanding the languages of cells. The resolution of many of the modern and postgenomic problems of biochemistry requires the development of a myriad of new technologies (and maybe a new culture), and thus regular input from the physical sciences, engineering, mathematics and computer science. One solution, that we are adopting in the Manchester Interdisciplinary Biocentre (http://www.mib.ac.uk/) and the Manchester Centre for Integrative Systems Biology (http://www.mcisb.org/), is thus to colocate individuals with the necessary combinations of skills. Novel disciplines that require such an integrative approach continue to emerge. These include fields such as chemical genomics, synthetic biology, distributed computational environments for biological data and modelling, single cell diagnostics/bionanotechnology, and computational linguistics/text mining.

Global dynamics of biological systems from time-resolved omics experiments

The emergent properties of biological systems, organized around complex networks of irregularly connected elements, limit the applications of the direct scientific method to their study. The current lack of knowledge opens new perspectives to the inverse scientific paradigm where observations are accumulated and analysed by advanced data-mining techniques to enable a better understanding and the formulation of testable hypotheses about the structure and functioning of these systems. The current technology allows for the wide application of omics analytical methods in the determination of time-resolved molecular profiles of biological samples. Here it is proposed that the theory of dynamical systems could be the natural framework for the proper analysis and interpretation of such experiments. A new method is described, based on the techniques of non-linear time series analysis, which is providing a global view on the dynamics of biological systems probed with time-resolved omics experiments.

ORIGINAL RESEARCH—EJACULATORY DISORDERS: A Qualitative Pilot Study of Islamic Men with Lifelong Premature (Rapid) Ejaculation

INTRODUCTION

Premature ejaculation is a common sexual problem in men. Although the etiology is unclear, there is emerging evidence that men from different ethnic backgrounds may be more at risk.

AIM AND OBJECTIVE

The aim of this study was to generate themes and hypotheses around the etiology of premature ejaculation with particular reference to men from Islamic backgrounds.

METHODS

This is an explorative qualitative study using semi-structured interviews with 10 male volunteers with a clinical diagnosis of premature ejaculation. Interviews were tape-recorded and transcribed. Transcriptions were then hand-coded and analyzed using grounded theory.

RESULTS

Anxious first sexual experience (with subtheme: fear of being discovered and wanting to finish early); sex before marriage; sex outside of marriage; religion; "stress;" exposure to Western images; living in the United Kingdom; and the subsequent feeling of freedom were themes that emerged from the transcripts.

CONCLUSIONS

We have identified factors associated with premature ejaculation in patients with Islamic backgrounds attending our unit. This may have useful therapeutic implications when consulting Islamic men with premature ejaculation.

Huntington disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles

There has been considerable progress recently towards developing therapeutic strategies for Huntington's disease (HD), with several compounds showing beneficial effects in transgenic mouse models. However, human trials in HD are difficult, costly and time-consuming due to the slow disease course, insidious onset and patient-to-patient variability. Identification of molecular biomarkers associated with disease progression will aid the development of effective therapies by allowing further validation of animal models and by providing hopefully more sensitive measures of disease progression. Here, we apply metabolic profiling by gas chromatography-time-of-flight-mass spectrometry to serum samples from human HD patients and a transgenic mouse model in a hypothesis-generating search for disease biomarkers. We observed clear differences in metabolic profiles between transgenic mice and wild-type littermates, with a trend for similar differences in human patients and control subjects. Thus, the metabolites responsible for distinguishing transgenic mice also comprised a metabolic signature tentatively associated with the human disease. The candidate biomarkers composing this HD-associated metabolic signature in mouse and humans are indicative of a change to a pro-catabolic phenotype in early HD preceding symptom onset, with changes in various markers of fatty acid breakdown (including glycerol and malonate) and also in certain aliphatic amino acids. Our data raise the prospect of a robust molecular definition of progression of HD prior to symptom onset, and if validated in a genuinely prospective fashion these biomarker trajectories could facilitate the development of useful therapies for this disease.

Modelling strategies for the industrial exploitation of lactic acid bacteria

Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications has increased considerably over the years. Traditionally, process optimization for these applications involved both strain selection and trial and error. More recently, metabolic engineering has emerged as a discipline that focuses on the rational improvement of industrially useful strains. In the post-genomic era, metabolic engineering increasingly benefits from systems biology, an approach that combines mathematical modelling techniques with functional-genomics data to build models for biological interpretation and--ultimately--prediction. In this review, the industrial applications of LAB are mapped onto available global, genome-scale metabolic modelling techniques to evaluate the extent to which functional genomics and systems biology can live up to their industrial promise.

Fundamental issues in systems biology

In the context of scientists' reflections on genomics, we examine some fundamental issues in the emerging postgenomic discipline of systems biology. Systems biology is best understood as consisting of two streams. One, which we shall call 'pragmatic systems biology', emphasises large-scale molecular interactions; the other, which we shall refer to as 'systems-theoretic biology', emphasises system principles. Both are committed to mathematical modelling, and both lack a clear account of what biological systems are. We discuss the underlying issues in identifying systems and how causality operates at different levels of organisation. We suggest that resolving such basic problems is a key task for successful systems biology, and that philosophers could contribute to its realisation. We conclude with an argument for more sociologically informed collaboration between scientists and philosophers.

Proteomic resources: integrating biomedical information in humans

Recent improvements in high-throughput proteomic technologies have unleashed the potential for generating vast amounts of data. Managing and sharing proteomic data is not an easy task. In this article, we will discuss some of the high-throughput proteomic techniques that are commonly used today. We will also review the major issues in sharing and dissemination of proteomic data and the recent community initiatives to standardize data formats and ontologies. An overview of the web-based resources and databases for analysis of proteomic data is also provided. Integration of disparate proteomic data sources with genomic and transcriptomic data should make systems biology type of approaches feasible in the near future.

New hyphenated methodologies in high-sensitivity glycoprotein analysis

High-sensitivity glycoprotein analyses are of particular interest in modern biomedical and clinical research, as well as in the development of recombinant protein products. The evolution of new hyphenated methodologies in high-sensitivity glycoprotein analysis is highlighted in this thematic review. These methodologies include, in particular, capillary LC/MALDI/TOF/TOF MS in conjunction with online permethylation platform, and silica-based lectin microcolumns interfaced to MS. The potential of these methodologies in glycomic and glycoproteomic analysis is demonstrated for model glycoproteins as well as total glycomes and glycoproteomes derived from biological samples. Additionally, the applications of CE-MS, CEC, and nanoLC with graphitized carbon in the areas of glycomics and glycoproteomics are described.

Large-scale in silico and microarray-based identification of direct 1,25-dihydroxyvitamin D3 target genes

1alpha,25-Dihydroxyvitamin D3 [1,25(OH)2D3] regulates calcium homeostasis and controls cellular differentiation and proliferation. The vitamin D receptor (VDR) is a ligand-regulated transcription factor that recognizes cognate vitamin D response elements (VDREs) formed by direct or everted repeats of PuG(G/T)TCA motifs separated by 3 or 6 bp (DR3 or ER6). Here, we have identified direct 1,25(OH)2D3 target genes by combining 35,000+ gene microarrays and genome-wide screens for consensus DR3 and ER6 elements, and DR3 elements containing single nucleotide substitutions. We find that the effect of a nucleotide substitution on VDR binding in vitro does not predict VDRE function in vivo, because substitutions that disrupted binding in vitro were found in several functional elements. Hu133A microarray analyses, performed with RNA from human SCC25 cells treated with 1,25(OH)2D3 and protein synthesis inhibitor cycloheximide, identified more than 900 regulated genes. VDREs lying within -10 to +5 kb of 5'-ends were assigned to 65% of these genes, and VDR binding was confirmed to several elements in vivo. A screen of the mouse genome identified more than 3000 conserved VDREs, and 158 human genes containing conserved elements were 1,25(OH2)D3-regulated on Hu133A microarrays. These experiments also revealed 16 VDREs in 11 of 12 genes induced more than 10-fold in our previous microarray study, five elements in the human gene encoding the epithelial calcium channel TRPV6, as well as novel 1,25(OH2)D3 target genes implicated in regulation of cell cycle progression. The combined approaches used here thus provide numerous insights into the direct target genes underlying the broad physiological actions of 1,25(OH)2D3.

Metabolomics, machine learning and modelling: towards an understanding of the language of cells

In answering the question ‘Systems Biology – will it work?’ (which it self-evidently has already), it is appropriate to highlight advances in philosophy, in new technique development and in novel findings. In terms of philosophy, we see that systems biology involves an iterative interplay between linked activities – for instance, between theory and experiment, between induction and deduction and between measurements of parameters and variables – with more emphasis than has perhaps been common now being focused on the first in each of these pairs. In technique development, we highlight closed loop machine learning and its use in the optimization of scientific instrumentation, and the ability to effect high-quality and quasi-continuous optical images of cells. This leads to many important and novel findings. In the first case, these may involve new biomarkers for disease, whereas in the second case, we have determined that many biological signals may be frequency-rather than amplitude-encoded. This leads to a very different view of how signalling ‘works’ (equations such as that of Michaelis and Menten which use only amplitudes, i.e. concentrations, are inadequate descriptors), lays emphasis on the signal processing network elements that lie ‘downstream’ of what are traditionally considered the signals, and allows one simply to understand how cross-talk may be avoided between pathways which nevertheless use common signalling elements. The language of cells is much richer than we had supposed, and we are now well placed to decode it.

Computational cluster validation in post-genomic data analysis

MOTIVATION

The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge--whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics.

RESULTS

This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation.

AVAILABILITY

The software used in the experiments is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.

SUPPLEMENTARY INFORMATION

Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.

Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets

Microbial production strains are currently improved using a combination of random and targeted approaches. In the case of a targeted approach, potential bottlenecks, feed-back inhibition, and side-routes are removed, and other processes of interest are targeted by overexpressing or knocking-out the gene(s) of interest. To date, the selection of these targets has been based at its best on expert knowledge, but to a large extent also on 'educated guesses' and 'gut feeling'. Therefore, time and thus money is wasted on targets that later prove to be irrelevant or only result in a very minor improvement. Moreover, in current approaches, biological processes that are not known to be involved in the formation of a specific product are overlooked and it is impossible to rank the relative importance of the different targets postulated. Metabolomics, a technology that involves the non-targeted, holistic analysis of the changes in the complete set of metabolites in the cell in response to environmental or cellular changes, in combination with multivariate data analysis (MVDA) tools like principal component discriminant analysis and partial least squares, allow the replacement of current empirical approaches by a scientific approach towards the selection and ranking of targets. In this review, we describe the technological challenges in setting up the novel metabolomics technology and the principle of MVDA algorithms in analyzing biomolecular data sets. In addition to strain improvement, the combined metabolomics and MVDA approach can also be applied to growth medium optimization, predicting the effect of quality differences of different batches of complex media on productivity, the identification of bioactives in complex mixtures, the characterization of mutant strains, the exploration of the production potential of strains, the assignment of functions to orphan genes, the identification of metabolite-dependent regulatory interactions, and many more microbiological issues.

Measuring the metabolome: current analytical technologies

The post-genomics era has brought with it ever increasing demands to observe and characterise variation within biological systems. This variation has been studied at the genomic (gene function), proteomic (protein regulation) and the metabolomic (small molecular weight metabolite) levels. Whilst genomics and proteomics are generally studied using microarrays (genomics) and 2D-gels or mass spectrometry (proteomics), the technique of choice is less obvious in the area of metabolomics. Much work has been published employing mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques, amongst others, for the study of variations within the metabolome in many animal, plant and microbial systems. This review discusses the advantages and disadvantages of each technique, putting the current status of the field of metabolomics in context, and providing examples of applications for each technique employed.

Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data

MOTIVATION

The major difficulties relating to mathematical modelling of spectroscopic data are inconsistencies in spectral reproducibility and the black box nature of the modelling techniques. For the analysis of biological samples the first problem is due to biological, experimental and machine variability which can lead to sample size differences and unavoidable baseline shifts. Consequently, there is often a requirement for mathematical correction(s) to be made to the raw data if the best possible model is to be formed. The second problem prevents interpretation of the results since the variables that most contribute to the analysis are not easily revealed; as a result, the opportunity to obtain new knowledge from such data is lost.

METHODS

We used genetic algorithms (GAs) to select spectral pre-processing steps for Fourier transform infrared (FT-IR) spectroscopic data. We demonstrate a novel approach for the selection of important discriminatory variables by GA from FT-IR spectra for multi-class identification by discriminant function analysis (DFA).

RESULTS

The GA selects sensible pre-processing steps from a total of approximately 10(10) possible mathematical transformations. Application of these algorithms results in a 16% reduction in the model error when compared against the raw data model. GA-DFA recovers six variables from the full set of 882 spectral variables against which a satisfactory DFA model can be formed; thus inferences can be made as to the biochemical differences that are reflected by these spectral bands.

Discrimination of modes of action of antifungal substances by use of metabolic footprinting

Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.

Phosphoproteomics of the Arabidopsis plasma membrane and a new phosphorylation site database

Functional genomic technologies are generating vast amounts of data describing the presence of transcripts or proteins in plant cells. Together with classical genetics, these approaches broaden our understanding of the gene products required for specific responses. Looking to the future, the focus of research must shift to the dynamic aspects of biology: molecular mechanisms of function and regulation. Phosphorylation is a key regulatory factor in all aspects of plant biology; but it is difficult, if not impossible, for most researchers to identify in vivo phosphorylation sites within their proteins of interest. We have developed a large-scale strategy for the isolation of phosphopeptides and identification by mass spectrometry (Nühse et al., 2003b). Here, we describe the identification of more than 300 phosphorylation sites from Arabidopsis thaliana plasma membrane proteins. These data will be a valuable resource for many fields of plant biology and overcome a major impediment to the elucidation of signal transduction pathways. We present an analysis of the characteristics of phosphorylation sites, their conservation among orthologs and paralogs, and the existence of putative motifs surrounding the sites. These analyses yield general principles for predicting other phosphorylation sites in plants and provide indications of specificity determinants for responsible kinases. In addition, more than 50 sites were mapped on receptor-like kinases and revealed an unexpected complexity of regulation. Finally, the data also provide empirical evidence on the topology of transmembrane proteins. This information indicates that prediction programs incorrectly identified the cytosolic portion of the protein in 25% of the transmembrane proteins found in this study. All data are deposited in a new searchable database for plant phosphorylation sites maintained by PlantsP (http://plantsp.sdsc.edu) that will be updated as the project expands to encompass additional tissues and organelles.

Neurogenomics: at the intersection of neurobiology and genome sciences

Neurogenomics is the study of how the genome as a whole contributes to the evolution, development, structure and function of the nervous system. It includes investigations of how genome products (transcriptomes and proteomes) vary in time and space. Neurogenomics differs markedly from the application of genome sciences to other systems, particularly in the spatial category, because anatomy and connectivity are paramount to our understanding of function in the nervous system. We focus here on some of the influences of genomics and its associated technologies on neuroscience. We discuss comparative genomics, gene expression atlases of the brain, network genetics and applications to behavioral phenotypes, and consider the culture, organization and funding of genome-scale projects.

High-throughput metabolic fingerprinting of legume silage fermentations via Fourier transform infrared spectroscopy and chemometrics

Silage quality is typically assessed by the measurement of several individual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the sample metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm(-1)) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins.