An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data

Regulatory networks play a central role in cellular behavior and decision making. Learning these regulatory networks is a major task in biology, and devising computational methods and mathematical models for this task is a major endeavor in bioinformatics. Boolean networks have been used extensively for modeling regulatory networks. In this model, the state of each gene can be either ‘on’ or ‘off’ and that next-state of a gene is updated, synchronously or asynchronously, according to a Boolean rule that is applied to the current-state of the entire system. Inferring a Boolean network from a set of experimental data entails two main steps: first, the experimental time-series data are discretized into Boolean trajectories, and then, a Boolean network is learned from these Boolean trajectories. In this paper, we consider three methods for data discretization, including a new one we propose, and three methods for learning Boolean networks, and study the performance of all possible nine combinations on four regulatory systems of varying dynamics complexities. We find that employing the right combination of methods for data discretization and network learning results in Boolean networks that capture the dynamics well and provide predictive power. Our findings are in contrast to a recent survey that placed Boolean networks on the low end of the ‘‘faithfulness to biological reality’’ and ‘‘ability to model dynamics’’ spectra. Further, contrary to the common argument in favor of Boolean networks, we find that a relatively large number of time points in the timeseries data is required to learn good Boolean networks for certain data sets. Last but not least, while methods have been proposed for inferring Boolean networks, as discussed above, missing still are publicly available implementations thereof. Here, we make our implementation of the methods available publicly in open source at http://bioinfo.cs.rice.edu/

Sources of Community Health Worker Motivation: A Qualitative Study in Morogoro Region, Tanzania.

There is a renewed interest in community health workers (CHWs) in Tanzania, but also a concern that low motivation of CHWs may decrease the benefits of investments in CHW programs. This study aimed to explore sources of CHW motivation to inform programs in Tanzania and similar contexts. We conducted semi-structured interviews with 20 CHWs in Morogoro Region, Tanzania. Interviews were digitally recorded, transcribed, and coded prior to translation and thematic analysis. The authors then conducted a literature review on CHW motivation and a framework that aligned with our findings was modified to guide the presentation of results. Sources of CHW motivation were identified at the individual, family, community, and organizational levels. At the individual level, CHWs are predisposed to volunteer work and apply knowledge gained to their own problems and those of their families and communities. Families and communities supplement other sources of motivation by providing moral, financial, and material support, including service fees, supplies, money for transportation, and help with farm work and CHW tasks. Resistance to CHW work exhibited by families and community members is limited. The organizational level (the government and its development partners) provides motivation in the form of stipends, potential employment, materials, training, and supervision, but inadequate remuneration and supplies discourage CHWs. Supervision can also be dis-incentivizing if perceived as a sign of poor performance. Tanzanian CHWs who work despite not receiving a salary have an intrinsic desire to volunteer, and their motivation often derives from support received from their families when other sources of motivation are insufficient. Policy-makers and program managers should consider the burden that a lack of remuneration imposes on the families of CHWs. In addition, CHWs' intrinsic desire to volunteer does not preclude a desire for external rewards. Rather, adequate and formal financial incentives and in-kind alternatives would allow already-motivated CHWs to increase their commitment to their work

Pharmacological Evaluation of the Long-Term Effects of Xanomeline on the M1 Muscarinic Acetylcholine Receptor

Xanomeline is a unique agonist of muscarinic receptors that possesses functional selectivity at the M1 and M4 receptor subtypes. It also exhibits wash-resistant binding to and activation of the receptor. In the present work we investigated the consequences of this type of binding of xanomeline on the binding characteristics and function of the M1 muscarinic receptor. Pretreatment of CHO cells that stably express the M1 receptor for 1 hr with increasing concentrations of xanomeline followed by washing and waiting for an additional 23 hr in control culture media transformed xanomeline-induced inhibition of [3H]NMS binding from monophasic to biphasic. The high-affinity xanomeline binding site exhibited three orders of magnitude higher affinity than in the case of xanomeline added directly to the binding assay medium containing control cells. These effects were associated with a marked decrease in maximal radioligand binding and attenuation of agonist-induced increase in PI hydrolysis and were qualitatively similar to those caused by continuous incubation of cells with xanomeline for 24 hr. Attenuation of agonist-induced PI hydrolysis by persistently-bound xanomeline developed with a time course that parallels the return of receptor activation by prebound xanomeline towards basal levels. Additional data indicated that blockade of the receptor orthosteric site or the use of a non-functional receptor mutant reversed the long-term effects of xanomeline, but not its persistent binding at an allosteric site. Furthermore, the long-term effects of xanomeline on the receptor are mainly due to receptor down-regulation rather than internalization

FORG3D: Force-directed 3D graph editor for visualization of integrated genome scale data

<p>Abstract</p> <p>Background</p> <p>Genomics research produces vast amounts of experimental data that needs to be integrated in order to understand, model, and interpret the underlying biological phenomena. Interpreting these large and complex data sets is challenging and different visualization methods are needed to help produce knowledge from the data.</p> <p>Results</p> <p>To help researchers to visualize and interpret integrated genomics data, we present a novel visualization method and bioinformatics software tool called FORG3D that is based on real-time three-dimensional force-directed graphs. FORG3D can be used to visualize integrated networks of genome scale data such as interactions between genes or gene products, signaling transduction, metabolic pathways, functional interactions and evolutionary relationships. Furthermore, we demonstrate its utility by exploring gene network relationships using integrated data sets from a <it>Caenorhabditis elegans </it>Parkinson's disease model.</p> <p>Conclusion</p> <p>We have created an open source software tool called FORG3D that can be used for visualizing and exploring integrated genome scale data.</p

The acute mania of King George III: A computational linguistic analysis.

We used a computational linguistic approach, exploiting machine learning techniques, to examine the letters written by King George III during mentally healthy and apparently mentally ill periods of his life. The aims of the study were: first, to establish the existence of alterations in the King's written language at the onset of his first manic episode; and secondly to identify salient sources of variation contributing to the changes. Effects on language were sought in two control conditions (politically stressful vs. politically tranquil periods and seasonal variation). We found clear differences in the letter corpus, across a range of different features, in association with the onset of mental derangement, which were driven by a combination of linguistic and information theory features that appeared to be specific to the contrast between acute mania and mental stability. The paucity of existing data relevant to changes in written language in the presence of acute mania suggests that lexical, syntactic and stylometric descriptions of written discourse produced by a cohort of patients with a diagnosis of acute mania will be necessary to support the diagnosis independently and to look for other periods of mental illness of the course of the King's life, and in other historically significant figures with similarly large archives of handwritten documents

Amino Acid Similarity Accounts for T Cell Cross-Reactivity and for “Holes” in the T Cell Repertoire

Background: Cytotoxic T cell (CTL) cross-reactivity is believed to play a pivotal role in generating immune responses but the extent and mechanisms of CTL cross-reactivity remain largely unknown. Several studies suggest that CTL clones can recognize highly diverse peptides, some sharing no obvious sequence identity. The emerging realization in the field is that T cell receptors (TcR) recognize multiple distinct ligands. Principal Findings: First, we analyzed peptide scans of the HIV epitope SLFNTVATL (SFL9) and found that TCR specificity is position dependent and that biochemically similar amino acid substitutions do not drastically affect recognition. Inspired by this, we developed a general model of TCR peptide recognition using amino acid similarity matrices and found that such a model was able to predict the cross-reactivity of a diverse set of CTL epitopes. With this model, we were able to demonstrate that seemingly distinct T cell epitopes, i.e., ones with low sequence identity, are in fact more biochemically similar than expected. Additionally, an analysis of HIV immunogenicity data with our model showed that CTLs have the tendency to respond mostly to peptides that do not resemble self-antigens. Conclusions: T cell cross-reactivity can thus, to an extent greater than earlier appreciated, be explained by amino acid similarity. The results presented in this paper will help resolving some of the long-lasting discussions in the field of T cel

Effects of Transcriptional Pausing on Gene Expression Dynamics

Stochasticity in gene expression affects many cellular processes and is a source of phenotypic diversity between genetically identical individuals. Events in elongation, particularly RNA polymerase pausing, are a source of this noise. Since the rate and duration of pausing are sequence-dependent, this regulatory mechanism of transcriptional dynamics is evolvable. The dependency of pause propensity on regulatory molecules makes pausing a response mechanism to external stress. Using a delayed stochastic model of bacterial transcription at the single nucleotide level that includes the promoter open complex formation, pausing, arrest, misincorporation and editing, pyrophosphorolysis, and premature termination, we investigate how RNA polymerase pausing affects a gene's transcriptional dynamics and gene networks. We show that pauses' duration and rate of occurrence affect the bursting in RNA production, transcriptional and translational noise, and the transient to reach mean RNA and protein levels. In a genetic repressilator, increasing the pausing rate and the duration of pausing events increases the period length but does not affect the robustness of the periodicity. We conclude that RNA polymerase pausing might be an important evolvable feature of genetic networks

Understanding implementation processes of clinical pathways and clinical practice guidelines in pediatric contexts: a study protocol

<p>Abstract</p> <p>Background</p> <p>Canada is among the most prosperous nations in the world, yet the health and wellness outcomes of Canadian children are surprisingly poor. There is some evidence to suggest that these poor health outcomes are partly due to clinical practice variation, which can stem from failure to apply the best available research evidence in clinical practice, otherwise known as knowledge translation (KT). Surprisingly, clinical practice variation, even for common acute paediatric conditions, is pervasive. Clinical practice variation results in unnecessary medical treatments, increased suffering, and increased healthcare costs. This study focuses on improving health outcomes for common paediatric acute health concerns by evaluating strategies that improve KT and reduce clinical practice variation.</p> <p>Design/Methods</p> <p>Using a multiple case study design, qualitative and quantitative data will be collected from four emergency departments in western Canada. Data sources will include: pre- and post-implementation focus group data from multidisciplinary healthcare professionals; individual interviews with the local champions, KT intervention providers, and unit/site leaders/managers; Alberta Context Tool (ACT) survey data; and aggregated patient outcome data. Qualitative and quantitative data will be systematically triangulated, and matrices will be built to do cross-case comparison. Explanations will be built about the success or lack of success of the clinical practice guidelines (CPG) and clinical pathways (CPs) uptake based upon the cross-case comparisons.</p> <p>Significance</p> <p>This study will generate new knowledge about the potential causal mechanisms and factors which shape implementation. Future studies will track the impact of the CPG/CPs implementation on children's health outcome, and healthcare costs.</p