Florida Charter Schools: Hot and Humid with Passing Storms

This report examines the history of Florida's charter school initiative, results to date, and areas where the state can improve

Magnetic Flux Expulsion in Star Formation

Stars form in dense cores of magnetized molecular clouds. If the magnetic flux threading the cores is dragged into the stars, the stellar field would be orders of magnitude stronger than observed. This well-known "magnetic flux problem" demands that most of the core magnetic flux be decoupled from the matter that enters the star. We carry out the first exploration of what happens to the decoupled magnetic flux in 3D, using an MHD version of the ENZO adaptive mesh refinement code. The field-matter decoupling is achieved through a sink particle treatment, which is needed to follow the protostellar accretion phase of star formation. We find that the accumulation of the decoupled flux near the accreting protostar leads to a magnetic pressure buildup. The high pressure is released anisotropically, along the path of least resistance. It drives a low-density expanding region in which the decoupled magnetic flux is expelled. This decoupling-enabled magnetic structure has never been seen before in 3D MHD simulations of star formation. It generates a strong asymmetry in the protostellar accretion flow, potentially giving a kick to the star. In the presence of an initial core rotation, the structure presents an obstacle to the formation of a rotationally supported disk, in addition to magnetic braking, by acting as a rigid magnetic wall that prevents the rotating gas from completing a full orbit around the central object. We conclude that the decoupled magnetic flux from the stellar matter can strongly affect the protostellar collapse dynamics

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

Background: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. Results: We present the Systems Biology Markup Language (SBML) Qualitative Models Package (“qual”), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. Conclusions: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks

Pathogenic Huntingtin Repeat Expansions in Patients with Frontotemporal Dementia and Amyotrophic Lateral Sclerosis.

We examined the role of repeat expansions in the pathogenesis of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) by analyzing whole-genome sequence data from 2,442 FTD/ALS patients, 2,599 Lewy body dementia (LBD) patients, and 3,158 neurologically healthy subjects. Pathogenic expansions (range, 40-64 CAG repeats) in the huntingtin (HTT) gene were found in three (0.12%) patients diagnosed with pure FTD/ALS syndromes but were not present in the LBD or healthy cohorts. We replicated our findings in an independent collection of 3,674 FTD/ALS patients. Postmortem evaluations of two patients revealed the classical TDP-43 pathology of FTD/ALS, as well as huntingtin-positive, ubiquitin-positive aggregates in the frontal cortex. The neostriatal atrophy that pathologically defines Huntington's disease was absent in both cases. Our findings reveal an etiological relationship between HTT repeat expansions and FTD/ALS syndromes and indicate that genetic screening of FTD/ALS patients for HTT repeat expansions should be considered

COMPUTATIONAL MODELING INTERVENTION: USING DYNAMICAL MODELS TO TEACH COMPLEX BIOLOGICAL PROCESSES

The Unites States, while being one of the richest countries in the world, ranks 17th in science proficiency out of 31 countries. New techniques for teaching are needed if the US wants to keep up with increasing global competition. Often the problem with traditional methods for teaching biological processes is that they present the material as linear or causal, when in fact there is a much wider network at play. For example, cellular respiration is often represented as a linear process that always starts with glucose and always ends with ATP and water. However, there are several entry and exit points of cellular respiration that interact with other metabolic pathways and are additionally controlled by allosteric regulation based on cellular conditions. This sort of big-picture view is rarely seen in general biology and thus gives a misleading representation of how the cell undergoes cellular respiration.Computer modeling and technology-based methods for analyzing scientific information is quickly becoming the norm in advanced scientific labs, a trend that should also be reflected in the classroom. Exposing students to computer modeling programs now allows them to be better equipped for this new era of data processing based on bioinformatics. In addition, modeling offers a way for students to visualize behavioral relationships of biological processes, such as gene transcription. This enables students to master material instead of just memorizing it. This research group aims to examine the usefulness of computational modeling intervention (CMI) for the teaching of basic to advanced biological processes through the use of the interactive modeling program, Cell Collective

A Comprehensive, Multi-Scale Dynamical Model of ErbB Receptor Signal Transduction in Human Mammary Epithelial Cells

The non-receptor tyrosine kinase Src and receptor tyrosine kinase epidermal growth factor receptor (EGFR/ErbB1) have been established as collaborators in cellular signaling and their combined dysregulation plays key roles in human cancers, including breast cancer. In part due to the complexity of the biochemical network associated with the regulation of these proteins as well as their cellular functions, the role of Src in EGFR regulation remains unclear. Herein we present a new comprehensive, multi-scale dynamical model of ErbB receptor signal transduction in human mammary epithelial cells. This model, constructed manually from published biochemical literature, consists of 245 nodes representing proteins and their post-translational modifications sites, and over 1,000 biochemical interactions. Using computer simulations of the model, we find it is able to reproduce a number of cellular phenomena. Furthermore, the model predicts that overexpression of Src results in increased endocytosis of EGFR in the absence/low amount of the epidermal growth factor (EGF). Our subsequent laboratory experiments also suggest increased internalization of EGFR upon Src overexpression under EGF-deprived conditions, further supporting this model-generated hypothesis

Bio-Logic Builder: A Non-Technical Tool for Building Dynamical, Qualitative Models

Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio- Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism) of biological regulatory mechanisms in a modular and non-technical fashion. As part of the user interface, generalized ‘‘biologic’’ modules have been defined to provide users with the building blocks for many biological processes. To build/modify computational models, experimentalists provide purely qualitative information about a particular regulatory mechanisms as is generally found in the laboratory. The Bio-Logic Builder subsequently converts the provided information into a mathematical representation described with Boolean expressions/rules. We used this tool to build a number of dynamical models, including a 130-protein large-scale model of signal transduction with over 800 interactions, influenza A replication cycle with 127 species and 200+ interactions, and mammalian and budding yeast cell cycles. We also show that any and all qualitative regulatory mechanisms can be built using this tool