Developing a logical model of yeast metabolism

With the completion of the sequencing of genomes of increasing numbers of organisms, the focus of biology is moving to determining the role of these genes (functional genomics). To this end it is useful to view the cell as a biochemical machine: it consumes simple molecules to manufacture more complex ones by chaining together biochemical reactions into long sequences referred to as em metabolic pathways. Such metabolic pathways are not linear but often interesect to form complex networks. Genes play a fundamental role in these networks by providing the information to synthesise the enzymes that catalyse biochemical reactions. Although developing a complete model of metabolism is of fundamental importance to biology and medicine, the size and complexity of the network has proven beyond the capacity of human reasoning. This paper presents the first results of the Robot Scientist research programme that aims to automatically discover the function of genes in the metabolism of the yeast em Saccharomyces cerevisiae. Results include: (1) the first logical model of metabolism;(2) a method to predict phenotype by deductive inference; and (3) a method to infer reactions and gene function by aductive inference. We describe the em in vivo experimental set-up which will allow these em in silico predictions to be automatically tested by a laboratory robot

Combining inductive logic programming, active learning and robotics to discover the function of genes

The paper is addressed to AI workers with an interest in biomolecular genetics and also to biomolecular geneticists interested in what AI tools may do for them. The authors are engaged in a collaborative enterprise aimed at partially automating some aspects of scientific work. These aspects include the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. As a potential component of the reasoning carried out by an "artificial scientist" this paper describes ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. In simulated yeast growth tests ASE-Progol was used to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy of around 88% was reduced by five orders of magnitude when trials were selected by ASE-Progol rather than being sampled at random. While the naive strategy of always choosing the cheapest trial from the set of candidate trials led to lower cumulative costs than ASE-Progol, both the naive strategy and the random strategy took significantly longer to converge upon a final hypothesis than ASE-Progol. For example to reach an accuracy of 80%, ASE-Progol required 4 days while random sampling required 6 days and the naive strategy required 10 days

A generic approach for the development of short-term predictions of Escherichia coli and biotoxins in shellfish

Microbiological contamination or elevated marine biotoxin concentrations within shellfish can result in temporary closure of shellfish aquaculture harvesting, leading to financial loss for the aquaculture business and a potential reduction in consumer confidence in shellfish products. We present a method for predicting short-term variations in shellfish concentrations of Escherichia coli and biotoxin (okadaic acid and its derivates dinophysistoxins and pectenotoxins). The approach was evaluated for 2 contrasting shellfish harvesting areas. Through a meta-data analysis and using environmental data in situ, satellite observations and meteorological nowcasts and forecasts), key environmental drivers were identified and used to develop models to predict E. coli and biotoxin concentrations within shellfish. Models were trained and evaluated using independent datasets, and the best models were identified based on the model exhibiting the lowest root mean square error. The best biotoxin model was able to provide 1 wk forecasts with an accuracy of 86%, a 0% false positive rate and a 0% false discovery rate (n = 78 observations) when used to predict the closure of shellfish beds due to biotoxin. The best E. coli models were used to predict the European hygiene classification of the shellfish beds to an accuracy of 99% (n = 107 observations) and 98% (n = 63 observations) for a bay (St Austell Bay) and an estuary (Turnaware Bar), respectively. This generic approach enables high accuracy short-term farm-specific forecasts, based on readily accessible environmental data and observations

D-brane Inspired Fermion Mass Textures

In this paper, the issues of the quark mass hierarchies and the Cabbibo Kobayashi Maskawa mixing are analyzed in a class of intersecting D-brane configurations with Standard Model gauge symmetry. The relevant mass matrices are constructed taking into account the constraints imposed by extra abelian symmetries and anomaly cancelation conditions. Possible mass generating mechanisms including perturbative as well as non-perturbative effects are discussed and specific patterns of mass textures are found characterized by the hierarchies of the scales where the various sources contribute. It is argued that the Cholesky decomposition of the mass matrices is the most appropriate way to determine the properties of these fermion mass patterns, while the associated triangular mass matrix form provides a unified description of all phenomenologically equivalent symmetric and non-symmetric mass matrices. An elegant analytic formula is derived for the Cholesky triangular form of the mass matrices where the entries are given as simple functions of the mass eigenstates and the diagonalizing transformation entries. Finally, motivated by the possibility of vanishing zero Yukawa mass entries in several D-brane and F-theory constructions due to the geometry of the internal space, we analyse in detail all possible texture-zeroes mass matrices within the proposed new context. These new texture-zeroes are compared to those existing in the literature while D-brane inspired cases are worked out in detail.Comment: 58 pages, 7 figure

Global parameter search reveals design principles of the mammalian circadian clock

Background: Virtually all living organisms have evolved a circadian (~24 hour) clock that controls physiological and behavioural processes with exquisite precision throughout the day/night cycle. The suprachiasmatic nucleus (SCN), which generates these ~24 h rhythms in mammals, consists of several thousand neurons. Each neuron contains a gene-regulatory network generating molecular oscillations, and the individual neuron oscillations are synchronised by intercellular coupling, presumably via neurotransmitters. Although this basic mechanism is currently accepted and has been recapitulated in mathematical models, several fundamental questions about the design principles of the SCN remain little understood. For example, a remarkable property of the SCN is that the phase of the SCN rhythm resets rapidly after a 'jet lag' type experiment, i.e. when the light/ dark (LD) cycle is abruptly advanced or delayed by several hours. Results: Here, we describe an extensive parameter optimization of a previously constructed simplified model of the SCN in order to further understand its design principles. By examining the top 50 solutions from the parameter optimization, we show that the neurotransmitters' role in generating the molecular circadian rhythms is extremely important. In addition, we show that when a neurotransmitter drives the rhythm of a system of coupled damped oscillators, it exhibits very robust synchronization and is much more easily entrained to light/dark cycles. We were also able to recreate in our simulations the fast rhythm resetting seen after a 'jet lag' type experiment. Conclusion: Our work shows that a careful exploration of parameter space for even an extremely simplified model of the mammalian clock can reveal unexpected behaviours and non-trivial predictions. Our results suggest that the neurotransmitter feedback loop plays a crucial role in the robustness and phase resetting properties of the mammalian clock, even at the single neuron level

Difficulties in recruitment for a randomized controlled trial involving hysterosalpingography

BACKGROUND: The usefulness of hysterosalpingography (HSG) as routine investigation in the fertility work-up prior to laparoscopy and dye had been assessed in a randomized controlled trial. Recruiting subjects to the study was more difficult than anticipated. The objective of this study was to explore possible reasons for non-participation in the trial. METHODS: All newly referred subfertile women admitted to the Reproductive Medicine Clinic of Leiden University Medical Centre between 1 April 1997 and 31 December 1999, were eligible for the study. The reasons for non-participation were evaluated by scrutinizing the medical records. RESULTS: Out of 759 women, a total of 127 (17%) agreed to participate in the trial. The most important reason for non-participation was because of exclusion criteria (73%). Other reasons were inattentive clinicians (3%) and patient-associated reasons (24%). Patient refusal and indecisiveness to enroll in the study were the most common patient-associated reasons. The most frequently stated reason for trial refusal was reluctance to undergo laparoscopy and dye mainly due to issues related to anesthesia and scheduling of procedure. CONCLUSION: Almost three-quarters of recruitment difficulties in this study were due to unavoidable reasons. To overcome the remaining avoidable reasons for non-participation, attention should be paid to appropriate instruction of the study protocol to the participating doctors and to provide adequate information, in layman's terms, to the patients. Reminding patients by notes or telephone calls for attending the clinic are helpful. It may be contingent upon tracing the reasons of clinicians and patients for non-participation to improve enrollment during a trial

Utility of Repeated Praziquantel Dosing in the Treatment of Schistosomiasis in High-Risk Communities in Africa: A Systematic Review

Infection by Schistosoma worms causes serious disease among people who live in areas of Africa, South America, and Asia where these parasites are regularly transmitted. Although yearly treatment with the drug praziquantel is fairly effective in reducing or eliminating active infection, it does not cure everyone, and reinfection remains a continuing problem in high-risk communities. Studies have suggested that a repeat dose of praziquantel, given 2 to 8 weeks after the first dose, can improve cure rates and reduce remaining intensity of infections in population-based programs. Our systematic review of published research found that, on average, in Africa, such repeated dosing appears to offer particular advantages in the treatment of S. mansoni, the cause of intestinal schistosomiasis, but there was less consistent improvement after double-dosing for S. haematobium, the cause of urogenital schistosomiasis. Based on this evidence, we used a calibrated life-path model to predict the costs and benefits of a single-dose vs. a double-dose strategy in a typical high-risk community. Our projections suggest cost-effective incremental benefits from double dosing in terms of i) limiting a person's total years spent infected and ii) limiting the number of years they spend with heavy infection, with consequent improvements in quality of life

The edges of understanding

A culture's icons are a window onto its soul. Few would disagree that, in the culture of molecular biology that dominated much of the life sciences for the last third of the 20th century, the dominant icon was the double helix. In the present, post-modern, 'systems biology' era, however, it is, arguably, the hairball

Association tests for rare and common variants based on genotypic and phenotypic measures of similarity between individuals

Genome-wide association studies have helped us identify thousands of common variants associated with several widespread complex diseases. However, for most traits, these variants account for only a small fraction of phenotypic variance or heritability. Next-generation sequencing technologies are being used to identify additional rare variants hypothesized to have higher effect sizes than the already identified common variants, and to contribute significantly to the fraction of heritability that is still unexplained. Several pooling strategies have been proposed to test the joint association of multiple rare variants, because testing them individually may not be optimal. Within a gene or genomic region, if there are both rare and common variants, testing their joint association may be desirable to determine their synergistic effects. We propose new methods to test the joint association of several rare and common variants with binary and quantitative traits. Our association test for quantitative traits is based on genotypic and phenotypic measures of similarity between pairs of individuals. For the binary trait or case-control samples, we recently proposed an association test based on the genotypic similarity between individuals. Here, we develop a modified version of this test for rare variants. Our tests can be used for samples taken from multiple subpopulations. The power of our test statistics for case-control samples and quantitative traits was evaluated using the GAW17 simulated data sets. Type I error rates for the proposed tests are well controlled. Our tests are able to identify some of the important causal genes in the GAW17 simulated data sets