Modeling Spiral Galaxy Surface Luminosity to Explain Non-Uniform Inclination Distributions

The distribution of spiral and bar galaxy orientations is expected to be uniform. However, analysis of several major galaxy catalogs shows this is not always reflected in data. In an attempt to explain this discrepancy, we have developed a galaxy simulation code to compute the appearance of a spiral type galaxy as a function of its morphological parameters. We examine the dependence of observed brightness upon inclination angle by using smooth luminous mass density and interstellar medium (ISM) density distributions. The luminous mass component is integrated along a particular line of sight, thus producing a mass distribution, from which a surface luminosity profile is derived. The ISM component is integrated alongside the luminous mass component to account for light extinction. Using this model, we present simulated galaxy inclination distributions that account for potential selection effects

Galaxy Inclination and Surface Brightness

The distribution of spiral and bar galaxy inclination angles is expected to be uniform. However, analysis of several major galaxy catalogs shows this is not the case; galaxies oriented near edge-on are significantly more common in these catalogs. In an attempt to explain this discrepancy, we have developed a galaxy simulation code to compute the appearance of a spiral type galaxy as a function of its morphological parameters. We examine the dependence of observed brightness upon inclination angle by using smooth luminous mass density and interstellar medium (ISM) density distributions. The luminous mass component is integrated along a particular line of sight, thus producing a mass distribution, from which a surface luminosity profile is derived. The ISM component is integrated alongside the luminous mass component to account for light extinction. Preliminary data ignoring extinction demonstrate trends that match the observed distribution for small inclinations. We reproduce overall spiral galaxy morphology and outline the ongoing validation process. If the dependence of the total surface brightness on inclination strongly corresponds to the observed distribution of inclination angles, we can attribute much of the discrepancy to a geometrical selection effect

Boolean Networks as Predictive Models of Emergent Biological Behaviors

Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions (from molecules in gene regulatory networks to species in ecological networks) and the often-incomplete state of system knowledge (e.g., the unknown values of kinetic parameters for biochemical reactions). Boolean networks have emerged as a powerful tool for modeling these systems. We provide a methodological overview of Boolean network models of biological systems. After a brief introduction, we describe the process of building, analyzing, and validating a Boolean model. We then present the use of the model to make predictions about the system's response to perturbations and about how to control (or at least influence) its behavior. We emphasize the interplay between structural and dynamical properties of Boolean networks and illustrate them in three case studies from disparate levels of biological organization.Comment: Review, to appear in the Cambridge Elements serie

Identifying (un)controllable dynamical behavior in complex networks.

We present a technique applicable in any dynamical framework to identify control-robust subsets of an interacting system. These robust subsystems, which we call stable modules, are characterized by constraints on the variables that make up the subsystem. They are robust in the sense that if the defining constraints are satisfied at a given time, they remain satisfied for all later times, regardless of what happens in the rest of the system, and can only be broken if the constrained variables are externally manipulated. We identify stable modules as graph structures in an expanded network, which represents causal links between variable constraints. A stable module represents a system "decision point", or trap subspace. Using the expanded network, small stable modules can be composed sequentially to form larger stable modules that describe dynamics on the system level. Collections of large, mutually exclusive stable modules describe the system's repertoire of long-term behaviors. We implement this technique in a broad class of dynamical systems and illustrate its practical utility via examples and algorithmic analysis of two published biological network models. In the segment polarity gene network of Drosophila melanogaster, we obtain a state-space visualization that reproduces by novel means the four possible cell fates and predicts the outcome of cell transplant experiments. In the T-cell signaling network, we identify six signaling elements that determine the high-signal response and show that control of an element connected to them cannot disrupt this response

Global Nightly OH and O2 Mesospheric Airglow: Examining a Decade of Measurements Using the NASA SABER Satellite Sensor

The SABER instrument aboard the TIMED satellite is a multichannel radiometer and has been continuously measuring the altitude distribution of infrared airglow intensity in the mesosphere on a global basis since 2002. While the majority of these altitude distributions are Gaussian-like, a significant portion exhibit two or more local maxima, suggesting multiple airglow layers. To better understand the cause of this phenomenon, the global and temporal distributions of infrared OH andO2 scans resulting in multiple peak altitude profiles are being examined

Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks

Biomolecular network dynamics are thought to operate near the critical boundary between ordered and disordered regimes, where large perturbations to a small set of elements neither die out nor spread on average. A biomolecular automaton (e.g., gene, protein) typically has high regulatory redundancy, where small subsets of regulators determine activation via collective canalization. Previous work has shown that effective connectivity, a measure of collective canalization, leads to improved dynamical regime prediction for homogeneous automata networks. We expand this by (i) studying random Boolean networks (RBNs) with heterogeneous in-degree distributions, (ii) considering additional experimentally validated automata network models of biomolecular processes, and (iii) considering new measures of heterogeneity in automata network logic. We found that effective connectivity improves dynamical regime prediction in the models considered; in RBNs, combining effective connectivity with bias entropy further improves the prediction. Our work yields a new understanding of criticality in biomolecular networks that accounts for collective canalization, redundancy, and heterogeneity in the connectivity and logic of their automata models. The strong link we demonstrate between criticality and regulatory redundancy provides a means to modulate the dynamical regime of biochemical networks

Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.

Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN

Contains the <i>in silico</i> and <i>in vivo</i> datasets used to perform GRN inference, including transcriptional profiles, gene lengths, promoter strengths, and TF-DNA binding data, and the detailed results of each individual inferred GRNs in this study.

Contains the in silico and in vivo datasets used to perform GRN inference, including transcriptional profiles, gene lengths, promoter strengths, and TF-DNA binding data, and the detailed results of each individual inferred GRNs in this study.</p