Cell-type-specific long-range looping interactions identify distant regulatory elements of the CFTR gene

Identification of regulatory elements and their target genes is complicated by the fact that regulatory elements can act over large genomic distances. Identification of long-range acting elements is particularly important in the case of disease genes as mutations in these elements can result in human disease. It is becoming increasingly clear that long-range control of gene expression is facilitated by chromatin looping interactions. These interactions can be detected by chromosome conformation capture (3C). Here, we employed 3C as a discovery tool for identification of long-range regulatory elements that control the cystic fibrosis transmembrane conductance regulator gene, CFTR. We identified four elements in a 460-kb region around the locus that loop specifically to the CFTR promoter exclusively in CFTR expressing cells. The elements are located 20 and 80 kb upstream; and 109 and 203 kb downstream of the CFTR promoter. These elements contain DNase I hypersensitive sites and histone modification patterns characteristic of enhancers. The elements also interact with each other and the latter two activate the CFTR promoter synergistically in reporter assays. Our results reveal novel long-range acting elements that control expression of CFTR and suggest that 3C-based approaches can be used for discovery of novel regulatory elements.     

Heterochromatin regulates cell type-specific long-range chromatin interactions essential for directed recombination

Mating-type switching in Schizosaccharomyces pombe involves replacing genetic information at the expressed mat1 locus with sequences copied from one of two silent donor loci, mat2-P or mat3-M, located within a 20-kb heterochromatic domain. Donor selection is dictated by cell type: mat2 is the preferred donor in M cells, and mat3 is the preferred donor in P cells. Here we show that a recombination-promoting complex (RPC) containing Swi2 and Swi5 proteins exhibits cell type-specific localization pattern at the silent mating-type region and this differential localization modulates donor preference during mating-type switching. In P cells, RPC localization is restricted to a recombination enhancer located adjacent to mat3, but in M cells, RPC spreads in cis across the entire silent mating-type interval in a heterochromatin-dependent manner. Our analyses implicate heterochromatin in long-range regulatory interactions and suggest that heterochromatin imposes at the mating-type region structural organization that is important for the donor-choice mechanism.     

A closer look at long-range chromosomal interactions

Higher-order chromosome organization is emerging as a major determinant of gene regulation. Although the structure of chromatin at the level of individual nucleosomes has been studied in considerable detail, less is known about higher levels of organization. Two new methods have been developed that can be used to obtain detailed information about the higher-order folding of chromatin. Using these methods, long-range looping interactions have been shown to occur upon activation of the murine beta-globin locus, explaining the long-standing question of how gene regulatory elements can act at large genomic distances from their target genes.     

Synorth: exploring the evolution of synteny and long-range regulatory interactions in vertebrate genomes

Genomic regulatory blocks are chromosomal regions spanned by long clusters of highly conserved noncoding elements devoted to long-range regulation of developmental genes, often immobilizing other, unrelated genes into long-lasting syntenic arrangements. Synorth is a web resource for exploring and categorizing the syntenic relationships in genomic regulatory blocks across multiple genomes, tracing their evolutionary fate after teleost whole genome duplication at the level of genomic regulatory block loci, individual genes, and their phylogenetic context.     

An integrated approach to patient-specific predictive modeling for single ventricle heart palliation

In patients with congenital heart disease and a single ventricle (SV), ventricular support of the circulation is inadequate, and staged palliative surgery (usually 3 stages) is needed for treatment. In the various palliative surgical stages individual differences in the circulation are important and patient-specific surgical planning is ideal. In this study, an integrated approach between clinicians and engineers has been developed, based on patient-specific multi-scale models, and is here applied to predict stage 2 surgical outcomes. This approach involves four distinct steps: (1) collection of pre-operative clinical data from a patient presenting for SV palliation, (2) construction of the pre-operative model, (3) creation of feasible virtual surgical options which couple a three-dimensional model of the surgical anatomy with a lumped parameter model (LPM) of the remainder of the circulation and (4) performance of post-operative simulations to aid clinical decision making. The pre-operative model is described, agreeing well with clinical flow tracings and mean pressures. Two surgical options (bi-directional Glenn and hemi-Fontan operations) are virtually performed and coupled to the pre-operative LPM, with the hemodynamics of both options reported. Results are validated against postoperative clinical data. Ultimately, this work represents the first patient-specific predictive modeling of stage 2 palliation using virtual surgery and closed-loop multi-scale modeling.     

A modular approach for modeling the cell cycle based on functional response curves

Modeling biochemical reactions by means of differential equations often results in systems with a large number of variables and parameters. As this might complicate the interpretation and generalization of the obtained results, it is often desirable to reduce the complexity of the model. One way to accomplish this is by replacing the detailed reaction mechanisms of certain modules in the model by a mathematical expression that qualitatively describes the dynamical behavior of these modules. Such an approach has been widely adopted for ultrasensitive responses, for which underlying reaction mechanisms are often replaced by a single Hill function. Also time delays are usually accounted for by using an explicit delay in delay differential equations. In contrast, however, S-shaped response curves, which by definition have multiple output values for certain input values and are often encountered in bistable systems, are not easily modeled in such an explicit way. Here, we extend the classical Hill function into a mathematical expression that can be used to describe both ultrasensitive and S-shaped responses. We show how three ubiquitous modules (ultrasensitive responses, S-shaped responses and time delays) can be combined in different configurations and explore the dynamics of these systems. As an example, we apply our strategy to set up a model of the cell cycle consisting of multiple bistable switches, which can incorporate events such as DNA damage and coupling to the circadian clock in a phenomenological way.     

Protocols for data generation for predictive modeling

Summary Essential to the development of useful predictive models for foods is the collection of appropriate data. Within the UK Predictive Food Microbiology Program, a series of documents have been produced to aid the standardization of data collection by a number of laboratories. Documents include a protocols form, notes on experimental design, notes on the accuracy of counts by plating, the preferred method of calculating a plate count value, preferred MPN tables and the preferred methods of recording data values. These documents have proven useful in aiding collaboration between laboratories.     

A multiphysics approach for modeling early atherosclerosis

This work is devoted to the development of a mathematical model of the early stages of atherosclerosis incorporating processes of all time scales of the disease and to show their interactions. The cardiovascular mechanics is modeled by a fluid–structure interaction approach coupling a non-Newtonian fluid to a hyperelastic solid undergoing anisotropic growth and a change of its constitutive equation. Additionally, the transport of low-density lipoproteins and its penetration through the endothelium is considered by a coupled set of advection–diffusion-reaction equations. Thereby, the permeability of the endothelium is wall-shear stress modulated resulting in a locally varying accumulation of foam cells triggering a novel growth and remodeling formulation. The model is calibrated and applied to an murine-specific case study, and a qualitative validation of the computational results is performed. The model is utilized to further investigate the influence of the pulsatile blood flow and the compliance of the artery wall to the atherosclerotic process. The computational results imply that the pulsatile blood flow is crucial, whereas the compliance of the aorta has only a minor influence on atherosclerosis. Further, it is shown that the novel model is capable to produce a narrowing of the vessel lumen inducing an adaption of the endothelial permeability pattern.     

Design of experiments for predictive microbial modeling

Summary Good predictive microbial models can be built with appropriate data from well-designed experiments. Anyone setting up an experiment should consider the sources of variability, possible screening experiments, optimum spacing between points on a continuous scale, and the most appropriate type of design, e.g. factorial, screening, or central composite.     

A hybrid agent-based approach for modeling microbiological systems

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10³ cells and 1.2×10⁶ molecules. The model produces cell migration patterns that are comparable to laboratory observations.     

A linearized constraint-based approach for modeling signaling networks

With the unparalleled increase in the availability of biological data over the last couple of decades, accurate and computable models are becoming increasingly important for unraveling complex biological phenomena. Past efforts to model signaling networks have utilized various computational methods, including Boolean and constraint-based modeling (CBM) approaches. These approaches are based on solving mixed integer linear programs; hence, they may not scale up for the analysis of large networks and are not amenable for applications based on sampling the full spectrum of the solution space. Here we propose a new CBM approach that is fully linear and does not involve integer variables, thereby overcoming the aforementioned limitations. We describe a novel optimization procedure for model construction and demonstrate the utility of our approach on a reconstructed model of the human epidermal growth factor receptor (EGFR) pathway, spanning 322 species and 211 connections. We compare our model's predictions to experimental phosphorylation data and to the predictions inferred via an additional Boolean-based EGFR signaling model. Our results show high prediction accuracy (75%) and high similarity to the Boolean model. Considering the marked computational advantages in terms of scalability and sampling utilization obtained by having a linear mode, these results demonstrate the potential promise of this framework for the study of cellular signaling.