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

The roles of integration in molecular systems biology

A common way to think about scientific practice involves classifying it as hypothesis- or data-driven. We argue that although such distinctions might illuminate scientific practice very generally, they are not sufficient to understand the day-to-day dynamics of scientific activity and the development of programmes of research. One aspect of everyday scientific practice that is beginning to gain more attention is integration. This paper outlines what is meant by this term and how it has been discussed from scientific and philosophical points of view. We focus on methodological, data and explanatory integration, and show how they are connected. Then, using some examples from molecular systems biology, we will show how integration works in a range of inquiries to generate surprising insights and even new fields of research. From these examples we try to gain a broader perspective on integration in relation to the contexts of inquiry in which it is implemented. In today's environment of data-intensive large-scale science, integration has become both a practical and normative requirement with corresponding implications for meta-methodological accounts of scientific practice. We conclude with a discussion of why an understanding of integration and its dynamics is useful for philosophy of science and scientific practice in general.

Pathways and fluxes: exploring the plant metabolic network

The transition from a pathway-centred view of plant metabolism to a network-wide perspective is still incomplete. Further progress in this direction requires tools to facilitate the structural description of the network on the basis of fully annotated genomes, techniques for modelling the properties of the network, and experimental methods for constraining the models and verifying their outputs. It also requires a focus on metabolic flux as the key to understanding the regulation of metabolic activity and the relationship between the inputs and outputs of the network. Progress is being made on several fronts and this Special Issue on 'Pathways and fluxes: exploring the plant metabolic network' describes current developments in the genomic reconstruction of metabolic networks, the application of flux-balance analysis to such networks, kinetic modelling, and both steady-state-and non-steady state isotope-based measurements of multiple fluxes in the network of central carbon metabolism. The papers also highlight insights that can be obtained from pathway analysis, particularly in relation to the thermodynamic and kinetic efficiency of the predicted and observed flux distributions.

Convenience experimentation

Systems biology aims at explaining life processes by means of detailed models of molecular networks, mainly on the whole-cell scale. The whole cell perspective distinguishes the new field of systems biology from earlier approaches within molecular cell biology. The shift was made possible by the high throughput methods that were developed for gathering 'omic' (genomic, proteomic, etc.) data. These new techniques are made commercially available as semi-automatic analytic equipment, ready-made analytic kits and probe arrays. There is a whole industry of supplies for what may be called convenience experimentation. My paper inquires some epistemic consequences of strong reliance on convenience experimentation in systems biology. In times when experimentation was automated to a lesser degree, modeling and in part even experimentation could be understood fairly well as either being driven by hypotheses, and thus proceed by the testing of hypothesis, or as being performed in an exploratory mode, intended to sharpen concepts or initially vague phenomena. In systems biology, the situation is dramatically different. Data collection became so easy (though not cheap) that experimentation is, to a high degree, driven by convenience equipment, and model building is driven by the vast amount of data that is produced by convenience experimentation. This results in a shift in the mode of science. The paper shows that convenience driven science is not primarily hypothesis-testing, nor is it in an exploratory mode. It rather proceeds in a gathering mode. This shift demands another shift in the mode of evaluation, which now becomes an exploratory endeavor, in response to the superabundance of gathered data.

Proof-of-principle study to detect metabolic changes in peritoneal dialysis effluent in patients who develop encapsulating peritoneal sclerosis

BACKGROUND

Prolonged peritoneal dialysis (PD) therapy can result in the development of encapsulating peritoneal sclerosis (EPS), characterized by extensive sclerosis of the peritoneum with bowel adhesions often causing obstruction.

METHODS

As a proof-of-principle study, holistic profiling of endogenous metabolites has been applied in a prospective collection of PD effluent collected in multiple UK renal centres over 6 years in order to investigate metabolic differences in PD effluent between PD therapy patients who later developed clinically defined EPS (n = 11) and controls, who were matched for PD vintage, age and gender (n = 11).

RESULTS

'Fit-for-purpose' analytical methods employing gas chromatography-mass spectrometry (MS), direct injection MS and quality control samples were developed and validated. These methods were applied in a proof-of-principle study to define metabolic differences in PD effluent related to subsequent development of EPS. Changes in amino acids, amines and derivatives, short-chain fatty acids and derivatives and sugars were observed prior to EPS developing, and changes in the metabolomic profiles could be detected.

CONCLUSION

There is potential for applying metabolic profiles to identify patients at risk of developing EPS although long-term prospective studies with larger patient cohorts are required.

Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes.

"Fuzziness" in the celular interactome: a historical perspective

Some historical background is given for appreciating the impact of the empirical construct known as the cellular protein-protein interactome, which is a seemingly de novo entity that has arisen of late within the context of postgenomic systems biology. The approach here builds on a generalized principle of "fuzziness" in protein behavior, proposed by Tompa and Fuxreiter.(1) Recent controversies in the analysis and interpretation of the interactome studies are rationalized historically under the auspices of this concept. There is an extensive literature on protein-protein interactions, dating to the mid-1900s, which may help clarify the "fuzziness" in the interactome picture and, also, provide a basis for understanding the physiological importance of protein-protein interactions in vivo.

Evolutionary systems biology: historical and philosophical perspectives on an emerging synthesis

Systems biology (SB) is at least a decade old now and maturing rapidly. A more recent field, evolutionary systems biology (ESB), is in the process of further developing system-level approaches through the expansion of their explanatory and potentially predictive scope. This chapter will outline the varieties of ESB existing today by tracing the diverse roots and fusions that make up this integrative project. My approach is philosophical and historical. As well as examining the recent origins of ESB, I will reflect on its central features and the different clusters of research it comprises. In its broadest interpretation, ESB consists of five overlapping approaches: comparative and correlational ESB; network architecture ESB; network property ESB; population genetics ESB; and finally, standard evolutionary questions answered with SB methods. After outlining each approach with examples, I will examine some strong general claims about ESB, particularly that it can be viewed as the next step toward a fuller modern synthesis of evolutionary biology (EB), and that it is also the way forward for evolutionary and systems medicine. I will conclude with a discussion of whether the emerging field of ESB has the capacity to combine an even broader scope of research aims and efforts than it presently does.

A strategy for selecting data mining techniques in metabolomics

There is a general agreement that the development of metabolomics depends not only on advances in chemical analysis techniques but also on advances in computing and data analysis methods. Metabolomics data usually requires intensive pre-processing, analysis, and mining procedures. Selecting and applying such procedures requires attention to issues including justification, traceability, and reproducibility. We describe a strategy for selecting data mining techniques which takes into consideration the goals of data mining techniques on the one hand, and the goals of metabolomics investigations and the nature of the data on the other. The strategy aims to ensure the validity and soundness of results and promote the achievement of the investigation goals.

Identification of intra- and inter-individual metabolite variation in plasma metabolite profiles of cats and dogs

The purpose of the present study was first to identify drivers of variance in plasma metabolite profiles of cats and dogs that may affect the interpretation of nutritional metabolomic studies. A total of fourteen cats and fourteen dogs housed in environmentally enriched accommodation were fed a single batch of diet to maintain body weight. Fasting blood samples were taken on days 14, 16 and 18 of the study. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography (LC)-MS/MS and solid-phase extraction-LC-MS/MS analyses were used for metabolite profiling. Principal component (PC) analysis that indicated 31 and 27 % of the variance was explained in PC1 and PC2 for cats and dogs, respectively, with most individuals occupying a unique space. As the individual was a major driver of variance in the plasma metabolome, the second objective was to identify metabolites associated with the individual variation observed. The proportion of intra- and inter-individual variance was calculated for 109 cat and 101 dog metabolites with a low intra-individual variance (SD < 0.05). Of these, fifteen cat and six dog metabolites had inter-individual variance accounting for at least 90 % of the total variance. There were four metabolites common to both species (campesterol, DHA, a cholestenol and a sphingosine moiety). Many of the metabolites with >75 % inter-individual variance were common to both species and to similar areas of metabolism. In summary, the individual is an important driver of variance in the fasted plasma metabolome, and specific metabolites and areas of metabolism may be differentially regulated by individuals in two companion animal species.

Advances in human proteomics at high scale with the SOMAscan proteomics platform

In 1997, while still working at NeXstar Pharmaceuticals, several of us made a proteomic bet. We thought then, and continue to think, that proteomics offers a chance to identify disease-specific biomarkers and improve healthcare. However, interrogating proteins turned out to be a much harder problem than interrogating nucleic acids. Consequently, the 'omics' revolution has been fueled largely by genomics. High-scale proteomics promises to transform medicine with personalized diagnostics, prevention, and treatment. We have now reached into the human proteome to quantify more than 1000 proteins in any human matrix - serum, plasma, CSF, BAL, and also tissue extracts - with our new SOMAmer-based proteomics platform. The surprising and pleasant news is that we have made unbiased protein biomarker discovery a routine and fast exercise. The downstream implications of the platform are substantial.

Visualizing the drug target landscape

Generating new therapeutic hypotheses for human disease requires the analysis and interpretation of many different experimental datasets. Assembling a holistic picture of the current landscape of drug discovery activity remains a challenge, however, because of the lack of integration between biological, chemical and clinical resources. Although tools designed to tackle the interpretation of individual data types are abundant, systems that bring together multiple elements to directly enable decision making within drug discovery programmes are rare. In this article, we review the path that led to the development of a knowledge system to tackle this problem within our organization and highlight the influences of existing technologies on its development. Central to our approach is the use of visualization to better convey the overall meaning of an integrated set of data including disease association, druggability, competitor intelligence, genomics and text mining. Organizing such data along lines of therapeutic precedence creates clearly distinct 'zones' of pharmaceutical opportunity, ranging from small-molecule repurposing to biotherapeutic prospects and gene family exploitation. Mapping content in this way also provides a visual alerting mechanism that evaluates new evidence in the context of old, reducing information overload by filtering redundant information. In addition, we argue the need for more tools in this space and highlight the role that data standards, new technologies and increased collaboration might have in achieving this aim.

Efficient discovery of anti-inflammatory small-molecule combinations using evolutionary computing

The control of biochemical fluxes is distributed, and to perturb complex intracellular networks effectively it is often necessary to modulate several steps simultaneously. However, the number of possible permutations leads to a combinatorial explosion in the number of experiments that would have to be performed in a complete analysis. We used a multiobjective evolutionary algorithm to optimize reagent combinations from a dynamic chemical library of 33 compounds with established or predicted targets in the regulatory network controlling IL-1β expression. The evolutionary algorithm converged on excellent solutions within 11 generations, during which we studied just 550 combinations out of the potential search space of ~9 billion. The top five reagents with the greatest contribution to combinatorial effects throughout the evolutionary algorithm were then optimized pairwise. A p38 MAPK inhibitor together with either an inhibitor of IκB kinase or a chelator of poorly liganded iron yielded synergistic inhibition of macrophage IL-1β expression. Evolutionary searches provide a powerful and general approach to the discovery of new combinations of pharmacological agents with therapeutic indices potentially greater than those of single drugs.

Genetic design automation: engineering fantasy or scientific renewal?

The aim of synthetic biology is to make genetic systems more amenable to engineering, which has naturally led to the development of computer-aided design (CAD) tools. Experimentalists still primarily rely on project-specific ad hoc workflows instead of domain-specific tools, which suggests that CAD tools are lagging behind the front line of the field. Here, we discuss the scientific hurdles that have limited the productivity gains anticipated from existing tools. We argue that the real value of efforts to develop CAD tools is the formalization of genetic design rules that determine the complex relationships between genotype and phenotype.

How stands the Tree of Life a century and a half after The Origin?

We examine the Tree of Life (TOL) as an evolutionary hypothesis and a heuristic. The original TOL hypothesis has failed but a new "statistical TOL hypothesis" is promising. The TOL heuristic usefully organizes data without positing fundamental evolutionary truth.

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy

Being born small for gestational age (SGA) confers increased risks of perinatal morbidity and mortality and increases the risk of cardiovascular complications and diabetes in later life. Accumulating evidence suggests that the etiology of SGA is usually associated with poor placental vascular development in early pregnancy. We examined metabolomic profiles using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) in three independent studies: (a) venous cord plasma from normal and SGA babies, (b) plasma from a rat model of placental insufficiency and controls, and (c) early pregnancy peripheral plasma samples from women who subsequently delivered a SGA baby and controls. Multivariate analysis by cross-validated Partial Least Squares Discriminant Analysis (PLS-DA) of all 3 studies showed a comprehensive and similar disruption of plasma metabolism. A multivariate predictive model combining 19 metabolites produced by a Genetic Algorithm-based search program gave an Odds Ratio for developing SGA of 44, with an area under the Receiver Operator Characteristic curve of 0.9. Sphingolipids, phospholipids, carnitines, and fatty acids were among this panel of metabolites. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of SGA offers insight into disease pathogenesis and offers the promise of a robust presymptomatic screening test.

Giant sucking sound: can physiology fill the intellectual void left by the reductionists?

Molecular reductionism has so far failed to deliver the broad-based therapeutic insights that were initially hoped for. This form of reductionism is now being replaced by so-called "systems biology." This is a nebulously defined approach and/or discipline, with some versions of it relying excessively on hypothesis-neutral approaches and only minimally informed by key physiological concepts such as homeostasis and regulation. In this context, physiology is uniquely positioned to continue to provide impressive levels of both biological and therapeutic insight by using hypothesis-driven "classical" approaches and concepts to help frame what might be described as the "pieces of the puzzle" that emerge from molecular reductionism. The strength of physiology as a "bridge" between reductionism and epidemiology, along with its unparalleled ability to generate therapeutic insights and opportunities justifies increased attention and emphasis on our discipline into the future. Arguments relevant to this set of assertions are advanced and this paper, which was based on the 2011 Adolph Lecture, represents an effort to fill the intellectual void left by reductionism and improve scientific progress.

Calculating life? Duelling discourses in interdisciplinary systems biology

A high profile context in which physics and biology meet today is in the new field of systems biology. Systems biology is a fascinating subject for sociological investigation because the demands of interdisciplinary collaboration have brought epistemological issues and debates front and centre in discussions amongst systems biologists in conference settings, in publications, and in laboratory coffee rooms. One could argue that systems biologists are conducting their own philosophy of science. This paper explores the epistemic aspirations of the field by drawing on interviews with scientists working in systems biology, attendance at systems biology conferences and workshops, and visits to systems biology laboratories. It examines the discourses of systems biologists, looking at how they position their work in relation to previous types of biological inquiry, particularly molecular biology. For example, they raise the issue of reductionism to distinguish systems biology from molecular biology. This comparison with molecular biology leads to discussions about the goals and aspirations of systems biology, including epistemic commitments to quantification, rigor and predictability. Some systems biologists aspire to make biology more similar to physics and engineering by making living systems calculable, modelable and ultimately predictable-a research programme that is perhaps taken to its most extreme form in systems biology's sister discipline: synthetic biology. Other systems biologists, however, do not think that the standards of the physical sciences are the standards by which we should measure the achievements of systems biology, and doubt whether such standards will ever be applicable to 'dirty, unruly living systems'. This paper explores these epistemic tensions and reflects on their sociological dimensions and their consequences for future work in the life sciences.

The relationship among sexual attitudes, sexual fantasy, and religiosity

Recent research on the impact of religiosity on sexuality has highlighted the role of the individual, and suggests that the effects of religious group and sexual attitudes and fantasy may be mediated through individual differences in spirituality. The present study investigated the role of religion in an ethnically diverse young adult sample (N = 1413, 69% women) using religious group as well as several religiosity domains: spirituality, intrinsic religiosity, paranormal beliefs, and fundamentalism. Differences between religious groups in conservative sexual attitudes were statistically significant but small; as predicted, spirituality mediated these effects. In contrast to the weak effects of religious group, spirituality, intrinsic religiosity, and fundamentalism were strong predictors of women's conservative sexual attitudes; for men, intrinsic religiosity predicted sexual attitude conservatism but spirituality predicted attitudinal liberalism. For women, both religious group and religiosity domains were significant predictors of frequency of sexual fantasies while, for men, only religiosity domains were significant predictors. These results indicate that individual differences in religiosity domains were better predictors of sexual attitudes and fantasy than religious group and that these associations are moderated by gender.

An integrated approach to characterize genetic interaction networks in yeast metabolism

Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ∼185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.

High-content affinity-based proteomics: unlocking protein biomarker discovery

Single protein biomarkers measured with antibody-based affinity assays are the basis of molecular diagnostics in clinical practice today. There is great hope in discovering new protein biomarkers and combinations of protein biomarkers for advancing medicine through monitoring health, diagnosing disease, guiding treatment, and developing new therapeutics. The goal of high-content proteomics is to unlock protein biomarker discovery by measuring many (thousands) or all (∼23,000) proteins in the human proteome in an unbiased, data-driven approach. High-content proteomics has proven technically difficult due to the diversity of proteins, the complexity of relevant biological samples, such as blood and tissue, and large concentration ranges (in the order of 10(12) in blood). Mass spectrometry and affinity methods based on antibodies have dominated approaches to high-content proteomics. For technical reasons, neither has achieved adequate simultaneous performance and high-content. Here we review antibody-based protein measurement, multiplexed antibody-based protein measurement, and limitations of antibodies for high-content proteomics due to their inherent cross-reactivity. Finally, we review a new affinity-based proteomic technology developed from the ground up to solve the problem of high content with high sensitivity and specificity. Based on a new generation of slow off-rate modified aptamers (SOMAmers), this technology is unlocking biomarker discovery.

Metabolomics, pathway regulation, and pathway discovery

Metabolomics is a data-based research strategy, the aims of which are to identify biomarker pictures of metabolic systems and metabolic perturbations and to formulate hypotheses to be tested. It involves the assay by mass spectrometry or NMR of many metabolites present in the biological system investigated. In this minireview, we outline studies in which metabolomics led to useful biomarkers of metabolic processes. We also illustrate how the discovery potential of metabolomics is enhanced by associating it with stable isotopic techniques.

Ten questions about systems biology

In this paper we raise 'ten questions' broadly related to 'omics', the term systems biology, and why the new biology has failed to deliver major therapeutic advances for many common diseases, especially diabetes and cardiovascular disease. We argue that a fundamentally narrow and reductionist perspective about the contribution of genes and genetic variants to disease is a key reason 'omics' has failed to deliver the anticipated breakthroughs. We then point out the critical utility of key concepts from physiology like homeostasis, regulated systems and redundancy as major intellectual tools to understand how whole animals adapt to the real world. We argue that a lack of fluency in these concepts is a major stumbling block for what has been narrowly defined as 'systems biology' by some of its leading advocates. We also point out that it is a failure of regulation at multiple levels that causes many common diseases. Finally, we attempt to integrate our critique of reductionism into a broader social framework about so-called translational research in specific and the root causes of common diseases in general. Throughout we offer ideas and suggestions that might be incorporated into the current biomedical environment to advance the understanding of disease through the perspective of physiology in conjunction with epidemiology as opposed to bottom-up reductionism alone.

Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets

MOTIVATION

The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chemically identifying metabolites from mass spectrometric signals present in complex datasets.

RESULTS

Three workflows have been developed to allow for the rapid, automated and high-throughput annotation and putative metabolite identification of electrospray LC-MS-derived metabolomic datasets. The collection of workflows are defined as PUTMEDID_LCMS and perform feature annotation, matching of accurate m/z to the accurate mass of neutral molecules and associated molecular formula and matching of the molecular formulae to a reference file of metabolites. The software is independent of the instrument and data pre-processing applied. The number of false positives is reduced by eliminating the inaccurate matching of many artifact, isotope, multiply charged and complex adduct peaks through complex interrogation of experimental data.

AVAILABILITY

The workflows, standard operating procedure and further information are publicly available at http://www.mcisb.org/resources/putmedid.html.

CONTACT

warwick.dunn@manchester.ac.uk.

Why does yeast ferment? A flux balance analysis study

Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using physicochemical constraints to describe the potential behaviour of an organism. FBA (flux balance analysis) highlights flux patterns through a network that serves to achieve a particular objective and requires a minimal amount of data to make quantitative inferences about network behaviour. Even though FBA is a powerful tool for system predictions, its general formulation sometimes results in unrealistic flux patterns. A typical example is fermentation in yeast: ethanol is produced during aerobic growth in excess glucose, but this pattern is not present in a typical FBA solution. In the present paper, we examine the issue of yeast fermentation against respiration during growth. We have studied a number of hypotheses from the modelling perspective, and novel formulations of the FBA approach have been tested. By making the observation that more respiration requires the synthesis of more mitochondria, an energy cost related to mitochondrial synthesis is added to the FBA formulation. Results, although still approximate, are closer to experimental observations than earlier FBA analyses, at least on the issue of fermentation.

Yeast systems biology: the challenge of eukaryotic complexity

In this chapter, we present an up-to-date view of the optimal characteristics of the yeast Saccharomyces cerevisiae as a model eukaryote for systems biology studies, with main molecular mechanisms, biological networks, and sub-cellular organization essentially conserved in all eukaryotes, derived from a complex common ancestor. The existence of advanced tools for molecular studies together with high-throughput experimental and computational methods, most of them being implemented and validated in yeast, with new ones being developed, is opening the way to the characterization of the core modular architecture and complex networks essential to all eukaryotes. Selected examples of the latest discoveries in eukaryote complexity and systems biology studies using yeast as a reference model and their applications in biotechnology and medicine are presented.

Mass spectrometry in systems biology an introduction

The qualitative detection, quantification, and structural characterization of analytes in biological systems are important requirements for objectives to be fulfilled in systems biology research. One analytical tool applied to a multitude of systems biology studies is mass spectrometry, particularly for the study of proteins and metabolites. Here, the role of mass spectrometry in systems biology will be assessed, the advantages and disadvantages discussed, and the instrument configurations available described. Finally, general applications will be briefly reviewed.

Sample preparation related to the intracellular metabolome of yeast methods for quenching, extraction, and metabolite quantitation

The determination of intracellular metabolite concentrations in Saccharomyces cerevisiae cell systems requires appropriate experimental methods to (a) collect cells and rapidly inhibit metabolism (quenching), (b) fracture cell walls and extract metabolites from within the cellular envelope(s), and (c) detect and quantify metabolites. A range of methods are applied for each of these processes, and no single method is appropriate for all metabolites. For example, the physicochemical diversity of metabolites, including solubility in water or organic solvents, is large. No single extraction solvent is appropriate for all metabolites reported in S. cerevisiae, and multiple solvent systems for extraction employing water, methanol, and chloroform at different pH are recommended for targeted extraction of metabolites. In this chapter, methods for the targeted study of organic acids present in the tricarboxylic acid cycle will be described. These include (a) the quenching of metabolism in batch cell cultures, (b) a single extraction method which provides the extraction of a wide diversity of metabolites, and (c) an analytical method applying gas chromatography-mass spectrometry for targeted analysis of six organic acids present in the tricarboxylic acid cycle metabolic pathway.

Fit-for-purpose quenching and extraction protocols for metabolic profiling of yeast using chromatography-mass spectrometry platforms

Metabolomics involves the investigation of the intracellular (endometabolome) and extracellular (exometabolome) pools of metabolites in biological systems. Methods to sample the exometabolome and to quench metabolism and extract intracellular metabolites for the model eukaryote Saccharomyces cerevisiae are presented here. These methods have been developed and validated to provide a fit-for-purpose protocol for global analyses of the S. cerevisiae metabolome. The protocol allows the extraction of a wide variety of metabolite classes and provides reproducible results to allow relative and semi-quantitative comparisons between samples of different origin. For exometabolome studies, fast sampling and separation of cells by syringe filtration is recommended. For endometabolome studies, fast quenching of intracellular metabolism is performed using a 60:40 (v/v) methanol:aqueous ammonium hydrogen carbonate solution at -48 °C. Extraction of intracellular metabolites is performed using multiple freeze/thaw cycles in a 60:40 (v/v) methanol:water solution at temperatures lower than 0 °C.

Review: The effects of oxygen on normal and pre-eclamptic placental tissue--insights from metabolomics

Placental dysfunction is central to many complications of human pregnancy including pre-eclampsia (PE), intra-uterine growth restriction (IUGR) and stillbirth. The precise molecular pathophysiology of placental dysfunction in these conditions is not known, although oxidative and nitrative stresses have been implicated. Metabolites are low molecular weight chemicals which play an important role in biological function, primarily through metabolism and regulation of biological processes. The holistic study of metabolites, defined as metabolomics or metabolic profiling, has the objective to detect and identify all, or a large complement of all metabolites. Metabolomics is applied to discover new knowledge regarding biological processes and systems. We hypothesised that a metabolomic strategy could (1) provide a reproducible technique to investigate the intracellular metabolism of placental tissue and also metabolites consumed from or secreted in to the extracellular 'metabolic footprint' of in vitro culture systems (2) identify metabolic related differences in placental tissue culture systems subjected to perturbations in oxygen tension and from pregnancies complicated by PE. We review our early studies which demonstrate that a reproducible experimental protocol is required, including the preparation of culture medium and the site of the placenta applied for sampling tissue. We have detected changes in the intracellular metabolome and metabolic footprint of placental tissue in response to altered oxygen tension and PE. We have demonstrated that placental tissue from uncomplicated pregnancies cultured in 1% oxygen (hypoxia) had metabolic similarities to explants from PE pregnancies cultured at 6% oxygen (normoxia). Metabolites requiring further study include lipids, glutamate and glutamine and metabolites related to tryptophan, leukotriene and prostaglandin metabolism. Metabolomics has the potential to identify changes in clinical conditions, such as PE, that are associated with placental molecular pathophysiology.

From genetic to genomic regulation: iterativity in microRNA research

The discovery and ongoing investigation of microRNAs (miRNAs) suggest important conceptual and methodological lessons for philosophers and historians of biology. This paper provides an account of miRNA research and the shift from viewing these tiny regulatory entities as minor curiosities to seeing them as major players in the post-transcriptional regulation of genes. Conceptually, the study of miRNAs is part of a broader change in understandings of genetic regulation, in which simple switch-like mechanisms were reinterpreted as aspects of complex cellular and genome-wide processes. Among them are the activities of small RNAs, previously regarded as non-functional. Methodologically, the miRNA story suggests new ways of characterizing biological research that should prove helpful to philosophers of science who seek to develop more pluralistic, pragmatic models of scientific inquiry. miRNA research displays iterative movements between multiple modes of investigation that include not only the proposal and testing of hypotheses but also exploratory, technology-oriented and question-driven modes of research. As an exemplary story of scientific discovery and development, the miRNA case illustrates transitions from genetics to genomics and systems biology, and it shows how diverse configurations of research practice are related to major scientific advances.

Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson's, Huntington's, Alzheimer's, prions, bactericides, chemical toxicology and others as examples

Exposure to a variety of toxins and/or infectious agents leads to disease, degeneration and death, often characterised by circumstances in which cells or tissues do not merely die and cease to function but may be more or less entirely obliterated. It is then legitimate to ask the question as to whether, despite the many kinds of agent involved, there may be at least some unifying mechanisms of such cell death and destruction. I summarise the evidence that in a great many cases, one underlying mechanism, providing major stresses of this type, entails continuing and autocatalytic production (based on positive feedback mechanisms) of hydroxyl radicals via Fenton chemistry involving poorly liganded iron, leading to cell death via apoptosis (probably including via pathways induced by changes in the NF-κB system). While every pathway is in some sense connected to every other one, I highlight the literature evidence suggesting that the degenerative effects of many diseases and toxicological insults converge on iron dysregulation. This highlights specifically the role of iron metabolism, and the detailed speciation of iron, in chemical and other toxicology, and has significant implications for the use of iron chelating substances (probably in partnership with appropriate anti-oxidants) as nutritional or therapeutic agents in inhibiting both the progression of these mainly degenerative diseases and the sequelae of both chronic and acute toxin exposure. The complexity of biochemical networks, especially those involving autocatalytic behaviour and positive feedbacks, means that multiple interventions (e.g. of iron chelators plus antioxidants) are likely to prove most effective. A variety of systems biology approaches, that I summarise, can predict both the mechanisms involved in these cell death pathways and the optimal sites of action for nutritional or pharmacological interventions.

Identification of putative stage-specific grapevine berry biomarkers and omics data integration into networks

The analysis of grapevine (Vitis vinifera) berries at the transcriptomic, proteomic, and metabolomic levels can provide great insight into the molecular events underlying berry development and postharvest drying (withering). However, the large and very different data sets produced by such investigations are difficult to integrate. Here, we report the identification of putative stage-specific biomarkers for berry development and withering and, to our knowledge, the first integrated systems-level study of these processes. Transcriptomic, proteomic, and metabolomic data were integrated using two different strategies, one hypothesis free and the other hypothesis driven. A multistep hypothesis-free approach was applied to data from four developmental stages and three withering intervals, with integration achieved using a hierarchical clustering strategy based on the multivariate bidirectional orthogonal projections to latent structures technique. This identified stage-specific functional networks of linked transcripts, proteins, and metabolites, providing important insights into the key molecular processes that determine the quality characteristics of wine. The hypothesis-driven approach was used to integrate data from three withering intervals, starting with subdata sets of transcripts, proteins, and metabolites. We identified transcripts and proteins that were modulated during withering as well as specific classes of metabolites that accumulated at the same time and used these to select subdata sets of variables. The multivariate bidirectional orthogonal projections to latent structures technique was then used to integrate the subdata sets, identifying variables representing selected molecular processes that take place specifically during berry withering. The impact of this holistic approach on our knowledge of grapevine berry development and withering is discussed.

Further developments towards a genome-scale metabolic model of yeast

BACKGROUND

To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.

RESULTS

We have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites--significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions.

CONCLUSIONS

We report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

PathText: a text mining integrator for biological pathway visualizations

Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations.

Contact:brian@monrovian.com.

The role of metabolites and metabolomics in clinically applicable biomarkers of disease

Metabolomics allows the simultaneous and relative quantification of thousands of different metabolites within a given sample using sensitive and specific methodologies such as gas or liquid chromatography coupled to mass spectrometry, typically in discovery phases of studies. Biomarkers are biological characteristics that are objectively measured and evaluated as indicators of normal biological processes, pathological processes or pharmacologic responses to a therapeutic intervention. Biomarkers are widely used in clinical practice for the diagnosis, assessment of severity and response to therapy in a number of clinical disease states. In human studies, metabolomics has been applied to define biomarkers related to prognosis or diagnosis of a disease or drug toxicity/efficacy and in doing so hopes to provide greater pathophysiological understanding of disease or therapeutic toxicity/efficacy. This review discusses the application of metabolomics in the discovery and subsequent application of biomarkers in the diagnosis and management of inborn errors of metabolism, cardiovascular disease and cancer. We critically appraise how novel biomarkers discovered through metabolomic analysis may be utilized in future clinical practice by addressing the following three fundamental questions: (1) Can the clinician measure them? (2) Do they add new information? (3) Do they help the clinician to manage patients? Although a number of novel biomarkers have been discovered through metabolomic studies of human diseases in the last decade, none have currently made the transition to routine use in clinical practice. Metabolites identified from these early studies will need to form the basis of larger, prospective, externally validated studies in clinical cohorts for their future use as biomarkers. At this stage, the absolute quantification of these biomarkers will need to be assessed epidemiologically, as will the ultimate deployment in the clinic via routine biochemistry, dip stick or similar rapid at- or near-patient care technologies.

Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers

Preeclampsia is a pregnancy-specific syndrome that causes substantial maternal and fetal morbidity and mortality. The etiology is incompletely understood, and there is no clinically useful screening test. Current metabolomic technologies have allowed the establishment of metabolic signatures of preeclampsia in early pregnancy. Here, a 2-phase discovery/validation metabolic profiling study was performed. In the discovery phase, a nested case-control study was designed, using samples obtained at 15±1 weeks' gestation from 60 women who subsequently developed preeclampsia and 60 controls taking part in the prospective Screening for Pregnancy Endpoints cohort study. Controls were proportionally population matched for age, ethnicity, and body mass index at booking. Plasma samples were analyzed using ultra performance liquid chromatography-mass spectrometry. A multivariate predictive model combining 14 metabolites gave an odds ratio for developing preeclampsia of 36 (95% CI: 12 to 108), with an area under the receiver operator characteristic curve of 0.94. These findings were then validated using an independent case-control study on plasma obtained at 15±1 weeks from 39 women who subsequently developed preeclampsia and 40 similarly matched controls from a participating center in a different country. The same 14 metabolites produced an odds ratio of 23 (95% CI: 7 to 73) with an area under receiver operator characteristic curve of 0.92. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of preeclampsia offers insight into disease pathogenesis and offers the tantalizing promise of a robust presymptomatic screening test.

Symbiodinium-invertebrate symbioses and the role of metabolomics

Symbioses play an important role within the marine environment. Among the most well known of these symbioses is that between coral and the photosynthetic dinoflagellate, Symbiodinium spp. Understanding the metabolic relationships between the host and the symbiont is of the utmost importance in order to gain insight into how this symbiosis may be disrupted due to environmental stressors. Here we summarize the metabolites related to nutritional roles, diel cycles and the common metabolites associated with the invertebrate-Symbiodinium relationship. We also review the more obscure metabolites and toxins that have been identified through natural products and biomarker research. Finally, we discuss the key role that metabolomics and functional genomics will play in understanding these important symbioses.