A General Model for Binary Cell Fate Decision Gene Circuits with Degeneracy: Indeterminacy and Switch Behavior in the Absence of Cooperativity

BACKGROUND: The gene regulatory circuit motif in which two opposing fate-determining transcription factors inhibit each other but activate themselves has been used in mathematical models of binary cell fate decisions in multipotent stem or progenitor cells. This simple circuit can generate multistability and explains the symmetric "poised" precursor state in which both factors are present in the cell at equal amounts as well as the resolution of this indeterminate state as the cell commits to either cell fate characterized by an asymmetric expression pattern of the two factors. This establishes the two alternative stable attractors that represent the two fate options. It has been debated whether cooperativity of molecular interactions is necessary to produce such multistability. PRINCIPAL FINDINGS: Here we take a general modeling approach and argue that this question is not relevant. We show that non-linearity can arise in two distinct models in which no explicit interaction between the two factors is assumed and that distinct chemical reaction kinetic formalisms can lead to the same (generic) dynamical system form. Moreover, we describe a novel type of bifurcation that produces a degenerate steady state that can explain the metastable state of indeterminacy prior to cell fate decision-making and is consistent with biological observations. CONCLUSION: The general model presented here thus offers a novel principle for linking regulatory circuits with the state of indeterminacy characteristic of multipotent (stem) cells

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure–activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure–property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models

Cardio-metabolic impact of changing sitting, standing, and stepping in the workplace

According to cross-sectional and acute experimental evidence, reducing sitting time should improve cardio-metabolic health risk biomarkers. Furthermore, the improvements obtained may depend on whether sitting is replaced with standing or ambulatory activities. Based on data from the Stand Up Victoria multi-component workplace intervention, we examined this issue using compositional data analysis - a method that can examine and compare all activity changes simultaneously.Participants receiving the intervention (n=136 ≥0.6 full-time equivalent desk-based workers, 65% women, mean±SD age=44.6 ±9.1 years from seven worksites) were asked to improve whole-of-day activity by standing up, sitting less and moving more. Their changes in the composition of daily waking hours (activPAL-assessed sitting, standing, stepping) were quantified, then tested for associations with concurrent changes in cardio-metabolic risk (CMR) scores and 14 biomarkers concerning body composition, glucose, insulin and lipid metabolism. Analyses were by mixed models, accounting for clustering (3 months, n=105-120; 12 months, n=80-97).Sitting reduction was significantly (

Optimizing identification of clinically relevant gram-positive organisms by use of the bruker biotyper matrix-assisted laser desorption ionization-time of flight mass spectrometry system

Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) can be used as a method for the rapid identification of microorganisms. This study evaluated the Bruker Biotyper (MALDI-TOF MS) system for the identification of clinically relevant Gram-positive organisms. We tested 239 aerobic Gram-positive organisms isolated from clinical specimens. We evaluated 4 direct-smear methods, including “heavy” (H) and “light” (L) smears, with and without a 1-μl direct formic acid (FA) overlay. The quality measure assigned to a MALDI-TOF MS identification is a numerical value or “score.” We found that a heavy smear with a formic acid overlay (H+FA) produced optimal MALDI-TOF MS identification scores and the highest percentage of correctly identified organisms. Using a score of ≥2.0, we identified 183 of the 239 isolates (76.6%) to the genus level, and of the 181 isolates resolved to the species level, 141 isolates (77.9%) were correctly identified. To maximize the number of correct identifications while minimizing misidentifications, the data were analyzed using a score of ≥1.7 for genus- and species-level identification. Using this score, 220 of the 239 isolates (92.1%) were identified to the genus level, and of the 181 isolates resolved to the species level, 167 isolates (92.2%) could be assigned an accurate species identification. We also evaluated a subset of isolates for preanalytic factors that might influence MALDI-TOF MS identification. Frequent subcultures increased the number of unidentified isolates. Incubation temperatures and subcultures of the media did not alter the rate of identification. These data define the ideal bacterial preparation, identification score, and medium conditions for optimal identification of Gram-positive bacteria by use of MALDI-TOF MS

Materiomics: a toolkit for developing new biomaterials

Learning Objectives • To understand the what materiomics is and why it is required • To become familiar with the various approaches used to design materiomic experiments • To learn what a polymer microarray is, what it is used for and how it is produced • To appreciate the complexity of material-biological interactions • To become familiar with computational modelling methods as applied to biomaterials • To gain an insight into how materiomics has and will continue to benefit tissue engineering Scope of the chapter The materials that are employed in regenerative medicine often react unfavourably with in vivo (induce clotting, promote bacterial infection). There is a need to develop new materials that provide the required cell response, but how is this best achieved considering the huge number of polymeric materials that could be synthesised? This chapter is a description of how materials discovery should most effectively be carried out in the developing paradigm of materiomics. We define and describe the components of this approach and methodology with the aim of providing a starting point for new users to effectively ‘dock’ into the existing research

Validity of self-reported measures of workplace sitting time and breaks in sitting time

CLARK, B. K., A. A. THORP, E. A. H. WINKLER, P. A. GARDINER, G. N. HEALY, N. OWEN, and D. W. DUNSTAN. Validity of Self-Reported Measures of Workplace Sitting Time and Breaks in Sitting Time. Med. Sci. Sports Exerc., Vol. 43, No. 10, pp. 1907-1912, 2011. Purpose: To understand the prevalence and potential health effect of prolonged workplace sedentary (sitting) time, valid measures are required. Here, we examined the criterion validity of a brief self-reported measure of workplace sitting time and breaks in sitting time. Methods: An interviewer-administered questionnaire was used to assess workplace sitting time (h.d(-1)) and breaks from sitting per hour at work in a convenience sample of 121 full-time workers (36% men, mean age = 37 yr, 53% office based). These self-reported measures were compared with accelerometer-derived sedentary time (hours per day, = 100 counts per minute) during work hours. Results: Self-reported sitting time was significantly correlated with accelerometer-derived sedentary time (Pearson r = 0.39, 95% confidence interval = 0.22-0.53), with an average sitting time 0.45 h.d(-1) higher than average sedentary time. Bland-Altman plots and regression analysis showed positive associations between the difference in sitting and sedentary time and the average of sitting and sedentary time (mean difference = -2.75 h + 0.47 x average sitting and sedentary time; limits of agreement = +/- 2.25 h.d(-1)). The correlation of self-reported breaks per sitting hour with accelerometer-derived breaks per sedentary hour was also statistically significant (Spearman r(s) = 0.26, 95% confidence interval = 0.11-0.44). Conclusions: This study is the first to examine the criterion validity of an interviewer-administered questionnaire measure of workplace sitting time and breaks in sitting time using objective criterion measures. The workplace sitting measure has acceptable properties for use in observational studies concerned with sedentary behavior in groups of workers; however, the wide limits of agreement suggest caution in estimating individuals' sitting time with high precision. Using self-reported measures to capture patterns of workplace sitting (such as breaks in sitting time) requires further development

Beware of R 2 : Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

The statistical metrics used to characterize the external predictivity of a model, i.e., how well it predicts the properties of an independent test set, have proliferated over the past decade. This paper clarifies some apparent confusion over the use of the coefficient of determination, R2, as a measure of model fit and predictive power in QSAR and QSPR modelling

Nonequilibrium and Nonlinear Dynamics in Geomaterials I : The Low Strain Regime

Members of a wide class of geomaterials are known to display complex and fascinating nonlinear and nonequilibrium dynamical behaviors over a wide range of bulk strains, down to surprisingly low values, e.g., 10^{-7}. In this paper we investigate two sandstones, Berea and Fontainebleau, and characterize their behavior under the influence of very small external forces via carefully controlled resonant bar experiments. By reducing environmental effects due to temperature and humidity variations, we are able to systematically and reproducibly study dynamical behavior at strains as low as 10^{-9}. Our study establishes the existence of two strain thresholds, the first, epsilon_L, below which the material is essentially linear, and the second, epsilon_M, below which the material is nonlinear but where quasiequilibrium thermodynamics still applies as evidenced by the success of Landau theory and a simple macroscopic description based on the Duffing oscillator. At strains above epsilon_M the behavior becomes truly nonequilibrium -- as demonstrated by the existence of material conditioning -- and Landau theory no longer applies. The main focus of this paper is the study of the region below the second threshold, but we also comment on how our work clarifies and resolves previous experimental conflicts, as well as suggest new directions of research.Comment: 14 pages, 15 figure

Materials for stem cell factories of the future

The materials community is now identifying polymeric substrates that could permit translation of human pluripotent stem cells (hPSCs) from lab-based research to industrial scale biomedicine. Well defined materials are required to allow cell banking and to provide the raw material for reproducible differentiation into lineages for large scale drug screening programs and clinical use, wherein >1 billion cells for each patient are needed to replace losses during heart attack, multiple sclerosis and diabetes. Producing this number of cells for one patient is challenging and a rethink is needed to scalable technology with the potential to meet the needs of millions of patients a year. Here we consider the role of materials discovery, an emerging area of materials chemistry that is in a large part driven by the challenges posed by biologists to materials scientists1-4