Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data

<p>Abstract</p> <p>Background</p> <p>Genome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respective species.</p> <p>Results</p> <p>We reconstructed a constraint-based metabolic model of <it>Acinetobacter baylyi </it>ADP1, a soil bacterium of interest for environmental and biotechnological applications with large-spectrum biodegradation capabilities. Following initial reconstruction from genome annotation and the literature, we iteratively refined the model by comparing its predictions with the results of large-scale experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) large-scale growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262 were initially consistent with our experimental observations. Inconsistencies were systematically examined, leading in 65 cases to model corrections. The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps.</p> <p>Conclusion</p> <p>The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model.</p

A Survey of Ocean Simulation and Rendering Techniques in Computer Graphics

This paper presents a survey of ocean simulation and rendering methods in computer graphics. To model and animate the ocean's surface, these methods mainly rely on two main approaches: on the one hand, those which approximate ocean dynamics with parametric, spectral or hybrid models and use empirical laws from oceanographic research. We will see that this type of methods essentially allows the simulation of ocean scenes in the deep water domain, without breaking waves. On the other hand, physically-based methods use Navier-Stokes Equations (NSE) to represent breaking waves and more generally ocean surface near the shore. We also describe ocean rendering methods in computer graphics, with a special interest in the simulation of phenomena such as foam and spray, and light's interaction with the ocean surface

Prospective Tests on Biological Models of Acupuncture

The biological effects of acupuncture include the regulation of a variety of neurohumoral factors and growth control factors. In science, models or hypotheses with confirmed predictions are considered more convincing than models solely based on retrospective explanations. Literature review showed that two biological models of acupuncture have been prospectively tested with independently confirmed predictions: The neurophysiology model on the long-term effects of acupuncture emphasizes the trophic and anti-inflammatory effects of acupuncture. Its prediction on the peripheral effect of endorphin in acupuncture has been confirmed. The growth control model encompasses the neurophysiology model and suggests that a macroscopic growth control system originates from a network of organizers in embryogenesis. The activity of the growth control system is important in the formation, maintenance and regulation of all the physiological systems. Several phenomena of acupuncture such as the distribution of auricular acupuncture points, the long-term effects of acupuncture and the effect of multimodal non-specific stimulation at acupuncture points are consistent with the growth control model. The following predictions of the growth control model have been independently confirmed by research results in both acupuncture and conventional biomedical sciences: (i) Acupuncture has extensive growth control effects. (ii) Singular point and separatrix exist in morphogenesis. (iii) Organizers have high electric conductance, high current density and high density of gap junctions. (iv) A high density of gap junctions is distributed as separatrices or boundaries at body surface after early embryogenesis. (v) Many acupuncture points are located at transition points or boundaries between different body domains or muscles, coinciding with the connective tissue planes. (vi) Some morphogens and organizers continue to function after embryogenesis. Current acupuncture research suggests a convergence of the neurophysiology model, the connective tissue model and the growth control model. The growth control model of acupuncture set the first example of a biological model in integrative medicine with significant prediction power

Genome-scale models of bacterial metabolism: reconstruction and applications

Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety of computational methods exploiting metabolic models have been developed and applied to bacteria, yielding valuable insights into bacterial metabolism and evolution, and providing a sound basis for computer-assisted design in metabolic engineering. Recent advances in computational systems biology and high-throughput experimental technologies pave the way for the systematic reconstruction of metabolic models from genomes of new species, and a corresponding expansion of the scope of their applications. In this review, we provide an introduction to the key ideas of metabolic modeling, survey the methods, and resources that enable model reconstruction and refinement, and chart applications to the investigation of global properties of metabolic systems, the interpretation of experimental results, and the re-engineering of their biochemical capabilities

Acupuncture for attention deficit hyperactivity disorder (ADHD): study protocol for a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Attention-deficit/hyperactivity disorder (ADHD) is a common neuro-psychiatric problem, affecting 7-9% of children. Pharmacological interventions are widely used with behavioral treatments in ADHD. Still, the origin of ADHD is unclear, limiting pharmacological effectiveness and making adverse effects common. The use of complementary and alternative medicine (CAM) has increased, especially for developmental and behavioral disorders, such as ADHD. CAM is used by 60-65% of parents of children with ADHD to relieve ADHD-associated symptoms and to avoid the side effects of conventional medication. Acupuncture has been widely used to treat patients with ADHD, but the available evidence of its effectiveness is insufficient. Our aim was to evaluate the effectiveness and safety of acupuncture in patients (both and each treatment naive and conventional therapy children) with ADHD (any subtype) compared to the waitlist control.</p> <p>Methods/Design</p> <p>This study is a waitlist controlled open trial. We used a computer generated randomization scheme. This randomised, controlled trial had two parallel arms (acupuncture, and waitlist group). Each arm consisted of 40 participants. The acupuncture group received acupuncture treatment two times per week for a total of 12 sessions over 6 weeks. Post-treatment follow-up was performed 3 weeks later to complement the 12 acupuncture sessions. Participants in the waitlist group did not receive acupuncture treatments during the first six weeks but were only required to be assessed. After 6 weeks, the same treatments given to the acupuncture group were provided to the waitlist group. The primary outcome of this trial included differences in Korean version of ADHD-Rating Scale (K-ADHD-RS) before randomization, 3 weeks and 6 weeks after randomization, and 3 weeks after completing the treatment.</p> <p>Discussion</p> <p>Subjective measurements, like K-ADHD-RS, are commonly used in ADHD. Although these measurements have adequate reliability and validity, lack of objective assessment in ADHD may lead to some disputes, like parental placebo effects. More objective measurements, like Computerized Neurocognitive function Test (CNT) in this study, are needed in ADHD trials. Furthermore, this trial will provide evidence for the effectiveness of acupuncture as a treatment for ADHD.</p> <p>Trial Registration</p> <p>Clinical Research Information Service (CRiS) KCT0000019</p

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders