Abstract | To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. |
Applications | We extracted gene regulatory network data from the ENCODE leukemia cell line, K562, and gene and miRNA eXpression datasets for AML from TCGA. |
Introduction | At a high level, the gene regulatory network can be regarded as analogous to an electronic circuit insofar as both gene networks and electronic circuits have inputs and outputs related by certain rules. |
Introduction | While this model is not able to capture the very complex regulatory patterns that may be characterized by continuous models [12,13], it is computationally efficient, and it is comprehensive enough to meaningfully describe a large variety of regulatory networks on a genome-wide scale in multiple organisms. |
Introduction | By combining the activity of RFs and their respective targets on a genome-wide scale, a bigger picture emerges: the gene regulatory network . |
Loregic applications for other regulatory features | FFLs have been found to be important motifs in regulatory networks , With many interacting by following logic operations [11]. |
Results | Loregic takes as inputs two types of data: a regulatory network (defined by RFs and their target genes) and a binarized gene expression dataset across multiple samples. |
Results | Input gene regulatory network consisting of regulatory factors and their target genes; |
Results | Finally, Steps CF are repeated for all triplets in the regulatory network , and all logic-gate-consistent triplets are identified. |
Abstract | In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity. |
Combining computational and experimental approaches to delineate the pathways controlling TEM pro-angiogenic function | 1C): 1) experimental measurement of the responses of TEM differentiated in vitro to a set of ligands, 2) construction of a dynamic regulatory network based on these experimental data, 3) in silico prediction of the treatments altering TEM behavior, 4) experimental validation of computationally predicted treatments using ivdTEM and 5) validation the best predicted treatments in patient TEM (Fig. |
Construction of dynamical models from the experimental data using TEM differentiated in vitro | We used TEM differentiated in vitro to derive a dynamical regulatory network from experimental data obtained with a selected number of li-gands (Fig. |
Construction of dynamical models from the experimental data using TEM differentiated in vitro | Dynamical Boolean modeling was then performed by integrating the retained links into an algorithm for computing Minimal Intervention Set (MIS) of TEM regulatory network . |
Construction of dynamical models from the experimental data using TEM differentiated in vitro | Given a regulatory network , MIS patterns represent a set of simultaneous perturbations (or treatments) to force the network into a desired steady state, where a subset of nodes remain at a fixed expression level of either low or high [41,42]. |
Introduction | In a Boolean modeling approach, the nodes in a regulatory network represent the state of activation of a gene (protein, receptor or ligand) using discrete variables (On or Off). |
Introduction | Introducing perturbations in a biological regulatory network can change the attractors and even transition the system from one attractor to another one. |
Introduction | The Boolean steady state of the network has been shown to correspond to the cellular states for various regulatory networks in the past [3]. |
The plasticity of TEM predicted computationally was validated experimentally using TEM differentiated in vitro | Using the regulatory network model of TEM differentiated in vitro we predicted the minimal treatments required for transitioning tumor TEM to blood TEM and vice versa. |
Author Summary | By constructing a global energy landscape for a simplified yeast cell-cycle regulatory network , we provide a systematic study of this issue. |
Introduction | The cell-cycle regulatory network must also be robust and adaptive to external stresses and signal changes. |
Introduction | Our results demonstrate that the energy landscape of the cell cycle is globally attractive, and we show how the cell cycle regulatory network reduces fluctuations from its upstream process and enables long durations in the transition regime. |
Models | Based on the key regulatory network [17] and our previous study on budding yeast [22] , the cell cycle regulatory network can be simplified and separated into G1 /S, early M and late M modules, as shown in Fig. |
Supporting Information | The regulatory network of cell-cycle process in budding yeast. |
Supporting Information | (A) The regulatory network of key regulators in budding yeast cell-cycle process. |
Discussion | regulatory networks , the rank corresponds to the minimal number of input features on which the outputs depend. |
Discussion | A recent study, suggested that bow-ties in developmental gene regulatory network can evolve due to hierarchy in specificity [79]. |
Introduction | Many developmental gene regulatory networks have bow-tie structures in which a single intermediate gene (‘input-output’ or ‘selector’ gene) combines information from multiple patterning genes (the input layers) and then initiates a self-contained developmental program by regulating an array of output genes [5,6] that can produce a large variety of morphologies [17—20]. |
Introduction | In developmental gene regulatory networks , modulated expression of the ‘waist’ (‘input-output’ or ‘selector’) gene can result in markedly different phenotypes. |
Author Summary | From such a paradigmatic example it is clear that differentiation processes are the result of the interplay of complex regulatory networks acting inside the cell and external stimuli, coming from both the adjacent cells and the environment. |
Introduction | These processes are highly dynamical, directed by complex regulatory networks involving cell-to-cell interactions, and often triggered by external stimuli. |
Introduction | In this work, we develop a simple mathematical model by incorporating the recent experimental results on the genetic regulatory network of cyanobacteria into the theoretical machinery of system biology. |
Introduction | First we present the main actors of the basic regulatory network and the different dynamical interactions that take place during the differentiation process. |
Emotional Signatures Across Networks and Regions of Interest | Mean intensity values for each region/ network served as nodes in co-activation analyses. |
Network Co-activation Differences among Emotion Categories | By saving the average intensity values for each region/ network from each MCMC iteration, we were able to estimate the co-activation intensity as the correlation between average intensity values for each pair of regions. |
Supporting Information | Average co-activation Within and between each region/ network grouping, for comparison to global network efficiency values based on path length in Fig. |