A mechanical model of early somite segmentation

Somitogenesis is often described using the clock-and-wavefront (CW) model, which does not explain how molecular signaling rearranges the pre-somitic mesoderm (PSM) cells into somites. Our scanning electron microscopy analysis of chicken embryos reveals a caudally-progressing epithelialization front in the dorsal PSM that precedes somite formation. Signs of apical constriction and tissue segmentation appear in this layer 3-4 somite lengths caudal to the last-formed somite. We propose a mechanical instability model in which a steady increase of apical contractility leads to periodic failure of adhesion junctions within the dorsal PSM and positions the future inter-somite boundaries. This model produces spatially periodic segments whose size depends on the speed of the activation front of contraction (F), and the buildup rate of contractility (Λ). The Λ/F ratio determines whether this mechanism produces spatially and temporally regular or irregular segments, and whether segment size increases with the front speed

DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

<p>Abstract</p> <p>Background</p> <p>High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.</p> <p>Results</p> <p>We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called D<smcaps>A</smcaps>D<smcaps>A</smcaps>, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that D<smcaps>A</smcaps>D<smcaps>A</smcaps> outperforms existing methods in prioritizing candidate disease genes.</p> <p>Conclusions</p> <p>These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. D<smcaps>A</smcaps>D<smcaps>A</smcaps> is implemented in Matlab and is freely available at <url>http://compbio.case.edu/dada/</url>.</p

Computer Simulation of Cellular Patterning Within the Drosophila Pupal Eye

We present a computer simulation and associated experimental validation of assembly of glial-like support cells into the interweaving hexagonal lattice that spans the Drosophila pupal eye. This process of cell movements organizes the ommatidial array into a functional pattern. Unlike earlier simulations that focused on the arrangements of cells within individual ommatidia, here we examine the local movements that lead to large-scale organization of the emerging eye field. Simulations based on our experimental observations of cell adhesion, cell death, and cell movement successfully patterned a tracing of an emerging wild-type pupal eye. Surprisingly, altering cell adhesion had only a mild effect on patterning, contradicting our previous hypothesis that the patterning was primarily the result of preferential adhesion between IRM-class surface proteins. Instead, our simulations highlighted the importance of programmed cell death (PCD) as well as a previously unappreciated variable: the expansion of cells' apical surface areas, which promoted rearrangement of neighboring cells. We tested this prediction experimentally by preventing expansion in the apical area of individual cells: patterning was disrupted in a manner predicted by our simulations. Our work demonstrates the value of combining computer simulation with in vivo experiments to uncover novel mechanisms that are perpetuated throughout the eye field. It also demonstrates the utility of the Glazier–Graner–Hogeweg model (GGH) for modeling the links between local cellular interactions and emergent properties of developing epithelia as well as predicting unanticipated results in vivo

Collective Motion of Cells Mediates Segregation and Pattern Formation in Co-Cultures

Pattern formation by segregation of cell types is an important process during embryonic development. We show that an experimentally yet unexplored mechanism based on collective motility of segregating cells enhances the effects of known pattern formation mechanisms such as differential adhesion, mechanochemical interactions or cell migration directed by morphogens. To study in vitro cell segregation we use time-lapse videomicroscopy and quantitative analysis of the main features of the motion of individual cells or groups. Our observations have been extensive, typically involving the investigation of the development of patterns containing up to 200,000 cells. By either comparing keratocyte types with different collective motility characteristics or increasing cells' directional persistence by the inhibition of Rac1 GTP-ase we demonstrate that enhanced collective cell motility results in faster cell segregation leading to the formation of more extensive patterns. The growth of the characteristic scale of patterns generally follows an algebraic scaling law with exponent values up to 0.74 in the presence of collective motion, compared to significantly smaller exponents in case of diffusive motion

A pilot study of an integrated mental health, social and medical model for diabetes care in an inner‐city setting: three dimensions for diabetes (3DFD)

Aims We examined the effectiveness of a service innovation, Three Dimensions for Diabetes (3DFD), that consisted of a referral to an integrated mental health, social care and diabetes treatment model, compared with usual care in improving biomedical and health economic outcomes. Methods Using a non‐randomized control design, the 3DFD model was offered in two inner‐city boroughs in London, UK, where diabetes health professionals could refer adult residents with diabetes, suboptimal glycaemic control [HbA1c ≥ 75 mmol/mol (≥ 9.0%)] and mental health and/or social problems. In the usual care group, there was no referral pathway and anonymized data on individuals with HbA1c ≥ 75 mmol/mol (≥ 9.0%) were collected from primary care records. Change in HbA1c from baseline to 12 months was the primary outcome, and change in healthcare costs and biomedical variables were secondary outcomes. Results 3DFD participants had worse glycaemic control and higher healthcare costs than control participants at baseline. 3DFD participants had greater improvement in glycaemic control compared with control participants [−14 mmol/mol (−1.3%) vs. −6 mmol/mol (−0.6%) respectively, P < 0.001], adjusted for confounding. Total follow‐up healthcare costs remained higher in the 3DFD group compared with the control group (mean difference £1715, 95% confidence intervals 591 to 2811), adjusted for confounding. The incremental cost‐effectiveness ratio was £398 per mmol/mol unit decrease in HbA1c, indicating the 3DFD intervention was more effective and costed more than usual care. Conclusions A biomedical, psychological and social criteria‐based referral system for identifying and managing high‐cost and high‐risk individuals with poor glycaemic control can lead to improved health in all three dimensions

Ontogenetic phase shifts in metabolism in a flounder Paralichthys olivaceus

Size-scaling metabolism is widely considered to be of significant importance in biology and ecology. Thus, allometric relationships between metabolic rate (VO2) and body mass (M), V O25aiMb, have long been a topic of interest and speculation. It has been proposed that intraspecifically metabolic rate scales isometrically or near isometrically with body mass during the early life history in fishes, invertebrates, birds and mammals. We developed a new perspective on intraspecific size-scaling metabolism through determination of metabolic rate in the Japanese flounder, Paralichthys olivaceus, during their early life stages spanning approximately four orders of magnitude in body mass. With the increase of body mass, the Japanese flounder had four distinct negative allometric phases in which three stepwise increases in scaling constants (ai, i51?4), i.e. ontogenetic phase shifts in metabolism, occurred with growth during its early life stages at around 0.002, 0.01 and 0.2 g, maintaining each scaling exponent constant in each phase (b50.831).These shifts in metabolism during the early life stages are similar to the tiger puffer, Takifugu rubripes. Our results indicate that ontogenetic phase shifts in metabolism are key to understanding intraspecific size-scaling metabolism in fishes

A random forest approach to the detection of epistatic interactions in case-control studies

<p>Abstract</p> <p>Background</p> <p>The key roles of epistatic interactions between multiple genetic variants in the pathogenesis of complex diseases notwithstanding, the detection of such interactions remains a great challenge in genome-wide association studies. Although some existing multi-locus approaches have shown their successes in small-scale case-control data, the "combination explosion" course prohibits their applications to genome-wide analysis. It is therefore indispensable to develop new methods that are able to reduce the search space for epistatic interactions from an astronomic number of all possible combinations of genetic variants to a manageable set of candidates.</p> <p>Results</p> <p>We studied case-control data from the viewpoint of binary classification. More precisely, we treated single nucleotide polymorphism (SNP) markers as categorical features and adopted the random forest to discriminate cases against controls. On the basis of the gini importance given by the random forest, we designed a sliding window sequential forward feature selection (SWSFS) algorithm to select a small set of candidate SNPs that could minimize the classification error and then statistically tested up to three-way interactions of the candidates. We compared this approach with three existing methods on three simulated disease models and showed that our approach is comparable to, sometimes more powerful than, the other methods. We applied our approach to a genome-wide case-control dataset for Age-related Macular Degeneration (AMD) and successfully identified two SNPs that were reported to be associated with this disease.</p> <p>Conclusion</p> <p>Besides existing pure statistical approaches, we demonstrated the feasibility of incorporating machine learning methods into genome-wide case-control studies. The gini importance offers yet another measure for the associations between SNPs and complex diseases, thereby complementing existing statistical measures to facilitate the identification of epistatic interactions and the understanding of epistasis in the pathogenesis of complex diseases.</p

A Computational Approach to Understand In Vitro Alveolar Morphogenesis

Primary human alveolar type II (AT II) epithelial cells maintained in Matrigel cultures form alveolar-like cysts (ALCs) using a cytogenesis mechanism that is different from that of other studied epithelial cell types: neither proliferation nor death is involved. During ALC formation, AT II cells engage simultaneously in fundamentally different, but not fully characterized activities. Mechanisms enabling these activities and the roles they play during different process stages are virtually unknown. Identifying, characterizing, and understanding the activities and mechanisms are essential to achieving deeper insight into this fundamental feature of morphogenesis. That deeper insight is needed to answer important questions. When and how does an AT cell choose to switch from one activity to another? Why does it choose one action rather than another? We report obtaining plausible answers using a rigorous, multi-attribute modeling and simulation approach that leveraged earlier efforts by using new, agent and object-oriented capabilities. We discovered a set of cell-level operating principles that enabled in silico cells to self-organize and generate systemic cystogenesis phenomena that are quantitatively indistinguishable from those observed in vitro. Success required that the cell components be quasi-autonomous. As simulation time advances, each in silico cell autonomously updates its environment information to reclassify its condition. It then uses the axiomatic operating principles to execute just one action for each possible condition. The quasi-autonomous actions of individual in silico cells were sufficient for developing stable cyst-like structures. The results strengthen in silico to in vitro mappings at three levels: mechanisms, behaviors, and operating principles, thereby achieving a degree of validation and enabling answering the questions posed. We suggest that the in silico operating principles presented may have a biological counterpart and that a semiquantitative mapping exists between in silico causal events and in vitro causal events

Systematic Evaluation of the Descriptive and Predictive Performance of Paediatric Morphine Population Models

Purpose: A framework for the evaluation of paediatric population models is proposed and applied to two different paediatric population pharmacokinetic models for morphine. One covariate model was based on a systematic covariate analysis, the other on fixed allometric scaling principles. Methods: The six evaluation criteria in the framework were 1) number of parameters and condition number, 2) numerical diagnostics, 3) prediction-based diagnostics, 4) η-shrinkage, 5) simulation-based diagnostics, 6) diagnostics of individual and population parameter estimates versus covariates, including measurements of bias and precision of the population values compared to the observed individual values. The framework entails both an internal and external model evaluation procedure. Results: The application of the framework to the two models resulted in the detection of overparameterization and misleading diagnostics based on individual predictions caused by high shrinkage. The diagnostic of individual and population parameter estimates versus covariates proved to be highly informative in assessing obtained covariate relationships. Based on the framework, the systematic covariate model proved to be superior over the fixed allometric model in terms of predictive performance. Conclusions: The proposed framework is suitable for the evaluation of paediatric (covariate) models and should be applied to corroborate the descriptive and predictive properties of these models