The Inferred Cardiogenic Gene Regulatory Network in the Mammalian Heart

Cardiac development is a complex, multiscale process encompassing cell fate adoption, differentiation and morphogenesis. To elucidate pathways underlying this process, a recently developed algorithm to reverse engineer gene regulatory networks was applied to time-course microarray data obtained from the developing mouse heart. Approximately 200 genes of interest were input into the algorithm to generate putative network topologies that are capable of explaining the experimental data via model simulation. To cull specious network interactions, thousands of putative networks are merged and filtered to generate scale-free, hierarchical networks that are statistically significant and biologically relevant. The networks are validated with known gene interactions and used to predict regulatory pathways important for the developing mammalian heart. Area under the precision-recall curve and receiver operator characteristic curve are 9% and 58%, respectively. Of the top 10 ranked predicted interactions, 4 have already been validated. The algorithm is further tested using a network enriched with known interactions and another depleted of them. The inferred networks contained more interactions for the enriched network versus the depleted network. In all test cases, maximum performance of the algorithm was achieved when the purely data-driven method of network inference was combined with a data-independent, functional-based association method. Lastly, the network generated from the list of approximately 200 genes of interest was expanded using gene-profile uniqueness metrics to include approximately 900 additional known mouse genes and to form the most likely cardiogenic gene regulatory network. The resultant network supports known regulatory interactions and contains several novel cardiogenic regulatory interactions. The method outlined herein provides an informative approach to network inference and leads to clear testable hypotheses related to gene regulation

The feasibility of genome-scale biological network inference using Graphics Processing Units

Abstract Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called ‘big data’ applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications. For example, both inverse identification and forward simulations of genome-scale gene regulatory network models pose compute-intensive problems. This issue is addressed here by combining the processing power of Graphics Processing Units (GPUs) and a parallel reverse engineering algorithm for inference of regulatory networks. It is shown that, given an appropriate data set, information on genome-scale networks (systems of 1000 or more state variables) can be inferred using a reverse-engineering algorithm in a matter of days on a small-scale modern GPU cluster.https://deepblue.lib.umich.edu/bitstream/2027.42/136186/1/13015_2017_Article_100.pd

Reverse Engineering of Genome-Scale Biological Networks

<p>Availability of genome-scale data sets in biology present a great opportunity as well as a challenge for computational biologists. Simulation and model based analysis on such large-scale dynamical systems pose compute-intensive problems. A reverse-engineering algorithm optimized for parallel architectures has been developed to study these dynamical systems. The parallel architecture and processing power of Graphics processing units (GPUs) provide a platform to carry out genome-scale simulations. We show that genome-scale networks can be inferred using this reverse-engineering algorithm in a matter of days on a single Tesla K20 GPU.</p

Apparent Critical Micelle Concentrations in Block Copolymer/Ionic Liquid Solutions: Remarkably Weak Dependence on Solvophobic Block Molecular Weight

The effects of block copolymer molecular weight (MW) and composition on the critical micelle concentration (CMC) were studied using ionic liquids (ILs) as model solvents. Pyrene fluorescence was used to measure CMCs as a function of block MW for three polystyrene–poly­(ethylene oxide) (PS–PEO) samples and three PS–poly­(methyl methacrylate) (PS–PMMA) samples in 1-ethyl-3-methylimidazolium bis­(trifluoromethylsulfonyl)­amide. The CMC decreased by a modest factor of 1.5 in the PS–PEO series, in which the solvophobic PS block MW remained unchanged (20 000) while the PEO block MW was decreased from 13 000 to 5000. This result correlated reasonably well with calculations from self-consistent-field (SCF) theory. A greater decrease (factor of 5) was seen in the PS–PMMA series, where the solvophobic PS block MW was varied from 3000 to 11 000 while maintaining a constant overall MW (ca. 15 000). However, this decrease was much weaker than that predicted by SCF calculations. A compilation of literature CMC data for amphiphilic block copolymers in water generally reveals a strong dependence on solvophobic block degree of polymerization <i>N</i> for low <i>N</i>, but a much weaker dependence for longer solvophobic blocks. From master plots of the compiled data, a scaling parameter shift from CMC ∼ exp­(−<i>cN</i>) to CMC ∼ exp­(−<i>cN</i>^1/3) was found above a critical solvophobic block <i>N</i>. The parameter <i>c</i> correlates with the χ parameter between the solvophobic block and the solvent. The weaker <i>N</i> dependence was found to fit the IL data very well. While such a change in MW dependence has previously been attributed to the collapse of unimer solvophobic blocks, we also discuss the potential role of kinetic limitations

Reverse Engineering the Cardiogenic Gene Regulatory Network in the Mammalian Heart

<p>Little is known about the underlying regulatory network responsible for the development<br>of the mammalian heart. To address this issue, we utilize our recently developed algorithm<br>to reverse engineer the cardiogenic gene regulatory network using time-series microarray<br>data obtained from the developing mouse heart. The subnetwork ensembles generated by<br>the algorithm consist of topologies capable of explaining the experimental data via model<br>simulation. After pooling these subnetwork topologies together and applying an<br>appropriate cutoff metric to the gene interaction list, a scale-free, hierarchical network<br>emerges. The network is validated with known gene interactions and used to identify new<br>regulatory interactions and network hubs critical to the developing mammalian heart. The<br>candidate gene interactions identified by the algorithm is prioritized using semantic<br>similarity and gene profile uniqueness metrics to produce a list of testable interaction<br>pairs. Among the top 25 gene pairs identified, significant fraction have already been<br>validated. Furthermore, the network was expanded using the same semantic similarity<br>and gene profile metrics to include all genes in the mouse genome to form the most likely<br>cardiogenic gene regulatory network predicted by the algorithm. The method outlined<br>herein provides an informative approach to network inference and leads to clear testable<br>hypotheses related to gene regulation. Massively parallel architecture of GPUs have been<br>tested to study genome-scale problems.</p

Hierarchical clustering of the mouse heart gene expression input dataset.

<p>After E8, the data are representative of gene expression in the left ventricle. The cardiogenic program is seen to propagate through the network yielding elevated expression of the typical cardiomyocyte markers by the Adult stage. The CG list profiles were clustered using the MATLAB clustering algorithm using the Pearson correlation and complete linkage metrics. The rows corresponding to the example genes for the known early stage transcription factors, <i>Oct4</i>, <i>Nanog</i>, <i>Sox2</i> and <i>T</i>, developmental genes, <i>Nkx2–5</i>, <i>Myl7</i>, <i>Notch1</i> and <i>Myog</i>, and ventricular cardiac specific markers, <i>Ttn</i>, <i>Myh6</i>, <i>Myh7</i> and <i>Ckm</i>, are highlighted in yellow on the right.</p

Network inference method flowchart.

<p>Phase 1 consists of constructing a scaffold network using a set of chosen genes thought to sufficiently represent the cardiogenesis process. Two independent metrics based on the interaction frequencies generated by the inference algorithm, the confidence metric, and gene ontology, the semantic similarity metric, are used to filter the network and remove spurious interactions. Phase 2 involves expansion of this scaffold network using a cluster expansion technique to produce a more complete network that best characterizes the regulatory interactions during cardiogenesis as inferred from the data. The gene interactions are further prioritized using a gene-profile uniqueness metric, the cluster product, to generate an experimentally realizable set of predictions.</p

Network Expansion.

<p>The inferred network using the CG list was used as a scaffold and extended to include genes from the entire mouse genome by expression profile similarity. Representative annotations using the Gene Ontology database are shown by node color. All annotations are relevant to cardiogenesis with some more specific than others. Edge color and thickness are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100842#pone-0100842-g003" target="_blank">Figure 3</a>. Directional arrows are omitted for clarity. The gene interactions shown are the edges with fidelity scores greater than 2.5. GO term acronyms: MCO, multicellular organismal development; HD, heart development; ASM, anatomical structure morphogenesis; TD, tissue development; NRGE, negative regulation of gene expression; SMTD, striated muscle tissue development; CD, cell differentiation; ESO, extracellular structure organization; SMMP, small molecule metabolic process; OD, organ development; SD, system development; CDP, cellular developmental process; MTD, muscle tissue development; CSD, cardiovascular system development; O, other. White nodes have no annotation ascribed. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100842#pone.0100842.s002" target="_blank">Tables S2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100842#pone.0100842.s003" target="_blank">S3</a>.</p