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

On the accuracy of language trees

Historical linguistics aims at inferring the most likely language phylogenetic tree starting from information concerning the evolutionary relatedness of languages. The available information are typically lists of homologous (lexical, phonological, syntactic) features or characters for many different languages. From this perspective the reconstruction of language trees is an example of inverse problems: starting from present, incomplete and often noisy, information, one aims at inferring the most likely past evolutionary history. A fundamental issue in inverse problems is the evaluation of the inference made. A standard way of dealing with this question is to generate data with artificial models in order to have full access to the evolutionary process one is going to infer. This procedure presents an intrinsic limitation: when dealing with real data sets, one typically does not know which model of evolution is the most suitable for them. A possible way out is to compare algorithmic inference with expert classifications. This is the point of view we take here by conducting a thorough survey of the accuracy of reconstruction methods as compared with the Ethnologue expert classifications. We focus in particular on state-of-the-art distance-based methods for phylogeny reconstruction using worldwide linguistic databases. In order to assess the accuracy of the inferred trees we introduce and characterize two generalizations of standard definitions of distances between trees. Based on these scores we quantify the relative performances of the distance-based algorithms considered. Further we quantify how the completeness and the coverage of the available databases affect the accuracy of the reconstruction. Finally we draw some conclusions about where the accuracy of the reconstructions in historical linguistics stands and about the leading directions to improve it.Comment: 36 pages, 14 figure

The Sec1/Munc18 protein Vps45 regulates cellular levels of its SNARE binding partners Tlg2 and Snc2 in Saccharomyces cerevisiae

Intracellular membrane trafficking pathways must be tightly regulated to ensure proper functioning of all eukaryotic cells. Central to membrane trafficking is the formation of specific SNARE (soluble N-ethylmeleimide-sensitive factor attachment protein receptor) complexes between proteins on opposing lipid bilayers. The Sec1/Munc18 (SM) family of proteins play an essential role in SNARE-mediated membrane fusion, and like the SNAREs are conserved through evolution from yeast to humans. The SM protein Vps45 is required for the formation of yeast endosomal SNARE complexes and is thus essential for traffic through the endosomal system. Here we report that, in addition to its role in regulating SNARE complex assembly, Vps45 regulates cellular levels of its SNARE binding partners: the syntaxin Tlg2 and the v-SNARE Snc2: Cells lacking Vps45 have reduced cellular levels of Tlg2 and Snc2; and elevation of Vps45 levels results in concomitant increases in the levels of both Tlg2 and Snc2. As well as regulating traffic through the endosomal system, the Snc v-SNAREs are also required for exocytosis. Unlike most vps mutants, cells lacking Vps45 display multiple growth phenotypes. Here we report that these can be reversed by selectively restoring Snc2 levels in vps45 mutant cells. Our data indicate that as well as functioning as part of the machinery that controls SNARE complex assembly, Vps45 also plays a key role in determining the levels of its cognate SNARE proteins; another key factor in regulation of membrane traffic

Using a logical model to predict the growth of yeast

<p>Abstract</p> <p>Background</p> <p>A logical model of the known metabolic processes in <it>S. cerevisiae </it>was constructed from iFF708, an existing Flux Balance Analysis (FBA) model, and augmented with information from the KEGG online pathway database. The use of predicate logic as the knowledge representation for modelling enables an explicit representation of the structure of the metabolic network, and enables logical inference techniques to be used for model identification/improvement.</p> <p>Results</p> <p>Compared to the FBA model, the logical model has information on an additional 263 putative genes and 247 additional reactions. The correctness of this model was evaluated by comparison with iND750 (an updated FBA model closely related to iFF708) by evaluating the performance of both models on predicting empirical minimal medium growth data/essential gene listings.</p> <p>Conclusion</p> <p>ROC analysis and other statistical studies revealed that use of the simpler logical form and larger coverage results in no significant degradation of performance compared to iND750.</p

Re-examination of the Controversial Coexistence of Traumatic Brain Injury and Posttraumatic Stress Disorder: Misdiagnosis and Self-Report Measures

The coexistence of traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) remains a controversial issue in the literature. To address this controversy, we focused primarily on the civilian-related literature of TBI and PTSD. Some investigators have argued that individuals who had been rendered unconscious or suffered amnesia due to a TBI are unable to develop PTSD because they would be unable to consciously experience the symptoms of fear, helplessness, and horror associated with the development of PTSD. Other investigators have reported that individuals who sustain TBI, regardless of its severity, can develop PTSD even in the context of prolonged unconsciousness. A careful review of the methodologies employed in these studies reveals that investigators who relied on clinical interviews of TBI patients to diagnose PTSD found little or no evidence of PTSD. In contrast, investigators who relied on PTSD questionnaires to diagnose PTSD found considerable evidence of PTSD. Further analysis revealed that many of the TBI patients who were initially diagnosed with PTSD according to self-report questionnaires did not meet the diagnostic criteria for PTSD upon completion of a clinical interview. In particular, patients with severe TBI were often misdiagnosed with PTSD. A number of investigators found that many of the severe TBI patients failed to follow the questionnaire instructions and erroneously endorsed PTSD symptoms because of their cognitive difficulties. Because PTSD questionnaires are not designed to discriminate between PTSD and TBI symptoms or determine whether a patient's responses are accurate or exaggerated, studies that rely on self-report questionnaires to evaluate PTSD in TBI patients are at risk of misdiagnosing PTSD. Further research should evaluate the degree to which misdiagnosis of PTSD occurs in individuals who have sustained mild TBI

Modulation of phosphofructokinase (PFK) from Setaria cervi, a bovine filarial parasite, by different effectors and its interaction with some antifilarials

<p>Abstract</p> <p>Background</p> <p>Phosphofructokinase (ATP: D-fructose-6-phosphate-1-phosphotransferase, EC 2.7.1.11, PFK) is of primary importance in the regulation of glycolytic flux. This enzyme has been extensively studied from mammalian sources but relatively less attention has been paid towards its characterization from filarial parasites. Furthermore, the information about the response of filarial PFK towards the anthelmintics/antifilarial compounds is lacking. In view of these facts, PFK from <it>Setaria cervi</it>, a bovine filarial parasite having similarity with that of human filarial worms, was isolated, purified and characterized.</p> <p>Results</p> <p>The <it>S. cervi </it>PFK was cytosolic in nature. The adult parasites (both female and male) contained more enzyme activity than the microfilarial (Mf) stage of <it>S. cervi</it>, which exhibited only 20% of total activity. The <it>S. cervi </it>PFK could be modulated by different nucleotides and the response of enzyme to these nucleotides was dependent on the concentrations of substrates (F-6-P and ATP). The enzyme possessed wide specificity towards utilization of the nucleotides as phosphate group donors. <it>S. cervi </it>PFK showed the presence of thiol group(s) at the active site of the enzyme, which could be protected from inhibitory action of para-chloromercuribenzoate (p-CMB) up to about 76% by pretreatment with cysteine or β-ME. The sensitivity of PFK from <it>S. cervi </it>towards antifilarials/anthelmintics was comparatively higher than that of mammalian PFK. With suramin, the Ki value for rat liver PFK was 40 times higher than PFK from <it>S. cervi</it>.</p> <p>Conclusions</p> <p>The results indicate that the activity of filarial PFK may be modified by different effectors (such as nucleotides, thiol group reactants and anthelmintics) in filarial worms depending on the presence of varying concentrations of substrates (F-6-P and ATP) in the cellular milieu. It may possess thiol group at its active site responsible for catalysis. Relatively, 40 times higher sensitivity of filarial PFK towards suramin as compared to the analogous enzyme from the mammalian system indicates that this enzyme could be exploited as a potential chemotherapeutic target against filariasis.</p

Prediction of the Onset of Disturbed Eating Behavior in Adolescent Girls With Type 1 Diabetes

OBJECTIVE—The purpose of this study was to identify predictors of the onset of disturbed eating behavior (DEB) in adolescent girls with type 1 diabetes

Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome

The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (CCORPS) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, CCORPS is applied to the problem of identifying structural features of the kinase ATP binding site that are informative of inhibitor binding. CCORPS is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, CCORPS is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors

Magnetic Resonance Water Proton Relaxation in Protein Solutions and Tissue: T1ρ Dispersion Characterization

BACKGROUND: Image contrast in clinical MRI is often determined by differences in tissue water proton relaxation behavior. However, many aspects of water proton relaxation in complex biological media, such as protein solutions and tissue are not well understood, perhaps due to the limited empirical data. PRINCIPAL FINDINGS: Water proton T(1), T(2), and T(1rho) of protein solutions and tissue were measured systematically under multiple conditions. Crosslinking or aggregation of protein decreased T(2) and T(1rho), but did not change high-field T(1). T(1rho) dispersion profiles were similar for crosslinked protein solutions, myocardial tissue, and cartilage, and exhibited power law behavior with T(1rho)(0) values that closely approximated T(2). The T(1rho) dispersion of mobile protein solutions was flat above 5 kHz, but showed a steep curve below 5 kHz that was sensitive to changes in pH. The T(1rho) dispersion of crosslinked BSA and cartilage in DMSO solvent closely resembled that of water solvent above 5 kHz but showed decreased dispersion below 5 kHz. CONCLUSIONS: Proton exchange is a minor pathway for tissue T(1) and T(1rho) relaxation above 5 kHz. Potential models for relaxation are discussed, however the same molecular mechanism appears to be responsible across 5 decades of frequencies from T(1rho) to T(1)