This work was funded by the NIH grant R01 GM126555-01 and NSF grant DMS-1839299.

1. Install the IDP and IDV
NOTE: This section assumes that docker, conda, pip, and git are installed already (Table of materials).
<ol>
	<li>In a terminal, enter the command: git clone&#160;https://gitlab.com/biochron/inherent_dynamics_pipeline.git.</li>
	<li>Follow the install instructions in the IDP&#39;s README file.</li>
	<li>In a terminal, enter the command: git clone&#160;https://gitlab.com/bertfordley/inherent_dynamics_visualizer.git.</em...

<ol>
	<li>Karlebach, G., Shamir, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+and+analysis+of+gene+regulatory+networks.">Modelling and analysis of gene regulatory networks.</a> Nature Reviews Molecular Cell Biology. 9 (10), 770-780 (2008).</li><li>Aij&#246;, T., L&#228;hdesm&#228;ki, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Learning+gene+regulatory+networks+from+gene+expression+measurements+using+non-parametric+molecular+kinetics.">Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics.</a> Bioinformatics. 25 (22), 2937-2944 (2009).</li><li>Huynh-Thu, V. A., Sanguinetti, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combining+tree-based+and+dynamical+systems+for+the+inference+of+gene+regulatory+networks.">Combining tree-based and dynamical systems for the inference of gene regulatory networks.</a> Bioinformatics. 31 (10), 1614-1622 (2015).</li><li>Oates, C. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Causal+network+inference+using+biochemical+kinetics.">Causal network inference using biochemical kinetics.</a> Bioinformatics. 30 (17), 468-474 (2014).</li><li>Marbach, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wisdom+of+crowds+for+robust+gene+network+inference.">Wisdom of crowds for robust gene network inference.</a> Nature Methods. 9 (8), 796-804 (2012).</li><li>. Inherent Dynamics Pipeline Available from: <a target="_blank" href="https://gitlab.com/biochron/inherent_dynamics_pipeline">https://gitlab.com/biochron/inherent_dynamics_pipeline</a> (2021)</li><li>Motta, F. C., Moseley, R. C., Cummins, B., Deckard, A., Haase, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Conservation+of+dynamic+characteristics+of+transcriptional+regulatory+elements+in+periodic+biological+processes.">Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes.</a> bioRxiv. , (2020).</li><li>. LEMpy Available from: <a target="_blank" href="https://gitlab.com/biochron/lempy">https://gitlab.com/biochron/lempy</a> (2021)</li><li>McGoff, K. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+local+edge+machine:+inference+of+dynamic+models+of+gene+regulation.">The local edge machine: inference of dynamic models of gene regulation.</a> Genome Biology. 17, 214 (2016).</li><li>Cummins, B., Gedeon, T., Harker, S., Mischaikow, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Model+rejection+and+parameter+reduction+via+time+series.">Model rejection and parameter reduction via time series.</a> SIAM Journal on Applied Dynamical Systems. 17 (2), 1589-1616 (2018).</li><li>Cummins, B., Gedeon, T., Harker, S., Mischaikow, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Database+of+Dynamic+Signatures+Generated+by+Regulatory+Networks+(DSGRN).">Database of Dynamic Signatures Generated by Regulatory Networks (DSGRN).</a> Lecture Notes in Computer Science. (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). , 300-308 (2017).</li><li>Cummins, B., Gedeon, T., Harker, S., Mischaikow, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DSGRN:+Examining+the+dynamics+of+families+of+logical+models.">DSGRN: Examining the dynamics of families of logical models.</a> Frontiers in Physiology. 9. 9, 549 (2018).</li><li>. DSGRN Available from: <a target="_blank" href="https://github.com/marciogameiro/DSGRN">https://github.com/marciogameiro/DSGRN</a> (2021)</li><li>. Dsgm_Net_Gen Available from: <a target="_blank" href="https://github.com/breecummins/dsgrn_net_gen">https://github.com/breecummins/dsgrn_net_gen</a> (2021)</li><li>. Dsgrn_Net_Query Available from: <a target="_blank" href="https://github.com/breecummins/dsgrn_net_query">https://github.com/breecummins/dsgrn_net_query</a> (2021)</li><li>Orlando, D. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+control+of+cell-cycle+transcription+by+coupled+CDK+and+network+oscillators.">Global control of cell-cycle transcription by coupled CDK and network oscillators.</a> Nature. 453 (7197), 944-947 (2008).</li><li>Monteiro, P. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=YEASTRACT+:+a+portal+for+cross-species+comparative+genomics+of+transcription+regulation+in+yeasts.">YEASTRACT+: a portal for cross-species comparative genomics of transcription regulation in yeasts.</a> Nucleic Acids Research. 48 (1), 642-649 (2020).</li><li>de Bruin, R. A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Constraining+G1-specific+transcription+to+late+G1+phase:+The+MBF-associated+corepressor+Nrm1+acts+via+negative+feedback.">Constraining G1-specific transcription to late G1 phase: The MBF-associated corepressor Nrm1 acts via negative feedback.</a> Molecular Cell. 23 (4), 483-496 (2006).</li><li>Horak, C. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Complex+transcriptional+circuitry+at+the+G1/S+transition+in+Saccharomyces+cerevisiae.">Complex transcriptional circuitry at the G1/S transition in Saccharomyces cerevisiae.</a> Genes &amp; Development. 16 (23), 3017-3033 (2002).</li><li>Cherry, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Saccharomyces+genome+database:+The+genomics+resource+of+budding+yeast.">Saccharomyces genome database: The genomics resource of budding yeast.</a> Nucleic Acids Research. 40, 700-705 (2012).</li><li>Zhu, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two+yeast+forkhead+genes+regulate+the+cell+cycle+and+pseudohyphal+growth.">Two yeast forkhead genes regulate the cell cycle and pseudohyphal growth.</a> Nature. 406 (6791), 90-94 (2000).</li><li>Loy, C. J., Lydall, D., Surana, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NDD1,+a+high-dosage+suppressor+of+cdc28-1N,+is+essential+for+expression+of+a+subset+of+late-S-phase-specific+genes+in+saccharomyces+cerevisiae.">NDD1, a high-dosage suppressor of cdc28-1N, is essential for expression of a subset of late-S-phase-specific genes in saccharomyces cerevisiae.</a> Molecular and Cellular Biology. 19 (5), 3312-3327 (1999).</li><li>Cho, C. Y., Kelliher, C. M., Hasse, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cell-cycle+transcriptional+network+generates+and+transmits+a+pulse+of+transcription+once+each+cell+cycle.">The cell-cycle transcriptional network generates and transmits a pulse of transcription once each cell cycle.</a> Cell Cycle. 18 (4), 363-378 (2019).</li><li>Smith, L. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+intrinsic+oscillator+drives+the+blood+stage+cycle+of+the+malaria+parasite+Plasmodium+falciparum.">An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum.</a> Science. 368 (6492), 754-759 (2020).</li></ol>

The authors have nothing to disclose.

The inference of GRNs is an important challenge in systems biology. The IDP generates model GRNs from gene expression data using a sequence of tools that utilize the data in increasingly complex ways. Each step requires decisions about how to process the data and what elements (genes, functional interactions) will be passed to the next layer of the IDP. The impacts of these decisions on IDP results are not as obvious. To help in this regard, the IDV provides useful interactive visualizations of the outputs from individua...

Many important biological processes, such as cell differentiation and environmental response, are governed by sets of genes that interact with each other in a gene regulatory network (GRN). These GRNs produce the transcriptional dynamics needed for activating and maintaining the phenotype they control, so identifying the components and topological structure of the GRN is key to understanding many biological processes and functions. A GRN may be modeled as a set of interacting genes and/or gene products described by a network whose nodes are the genes and whose edges describe the direction and form of interaction (e.g., activation/repression of transcription, post-translational modification, etc.)1. Interactions can then be expressed as parameterized mathematical models describing the impact a regulating gene has on the production of its target(s)2,3,4. Inference of a GRN model requires both an inference of the structure of the interaction network and estimation of the underlying interaction parameters. A variety of computational inference methods have been developed that ingest time series gene expression data and output GRN models5. Recently, a new GRN inference method was developed, called the Inherent Dynamics Pipeline (IDP), that utilizes time series gene expression data to produce GRN models with labeled regulator-target interactions that are capable of producing dynamics that match the observed dynamics in the gene expression data6. The IDP is a suite of tools connected linearly into a pipeline and can be broken down into three steps: a Node Finding step that ranks genes based on gene expression characteristics known or suspected to be related to the function of the GRN7,8, an Edge Finding step that ranks pairwise regulatory relationships8,9, and a Network Finding step that produces GRN models that are capable of producing the observed dynamics10,11,12,13,14,15.
Like most computational methods, the IDP requires a set of user-specified arguments that dictate how the input data is analyzed, and different sets of arguments can produce different results on the same data. For example, several methods, including the IDP, contain arguments that apply some threshold on the data, and increasing/decreasing this threshold between successive runs of the particular method can result in dissimilar results between runs (see Supplement Note 10: Network inference methods of5). Understanding how each argument may impact the analysis and subsequent results is important for achieving high confidence in the results. Unlike most GRN inference methods, the IDP consists of multiple computational tools, each having its own set of arguments that a user must specify and each having its own results. While the IDP provides extensive documentation on how to parameterize each tool, the interdependency of each tool on the output of the previous step makes parameterizing the entire pipeline without intermediate analyses challenging. For instance, arguments in the Edge and Network Finding steps are likely to be informed by prior biological knowledge, and so will depend on the dataset and/or organism. To interrogate intermediate results, a basic understanding of programming, as well as a deep understanding of all the result files and their contents from the IDP, would be needed.
The Inherent Dynamics Visualizer (IDV) is an interactive visualization package that runs in a user&#39;s browser window and provides a way for users of the IDP to assess the impact of their argument choices on results from any step in the IDP. The IDV navigates a complicated directory structure produced by the IDP and gathers the necessary data for each step and presents the data in intuitive and interactive figures and tables for the user to explore. After exploring these interactive displays, the user can produce new data from an IDP step that can be based on more informed decisions. These new data can then be immediately used in the next respective step of the IDP. Additionally, exploration of the data can help determine whether an IDP step should be rerun with adjusted parameters. The IDV can enhance the use of the IDP, as well as make the use of the IDP more intuitive and approachable, as demonstrated by investigating the core oscillator GRN of the yeast cell-cycle. The following protocol includes IDP results from a fully parameterized IDP run versus an approach that incorporates the IDV after runs of each IDP step, i.e., Node, Edge, and Network Finding.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Docker</td>
			<td></td>
			<td></td>
			<td>https://docs.docker.com/get-docker/</td>
		</tr>
		<tr>
			<td>Git</td>
			<td></td>
			<td></td>
			<td>https://git-scm.com/</td>
		</tr>
		<tr>
			<td>Inherent Dynamics Pipeline</td>
			<td></td>
			<td></td>
			<td>https://gitlab.com/biochron/inherent_dynamics_pipeline</td>
		</tr>
		<tr>
			<td>Inherent Dynamics Visualizer</td>
			<td></td>
			<td></td>
			<td>https://gitlab.com/bertfordley/inherent_dynamics_visualizer</td>
		</tr>
		<tr>
			<td>Miniconda</td>
			<td></td>
			<td></td>
			<td>https://docs.conda.io/en/latest/miniconda.html</td>
		</tr>
		<tr>
			<td>Pip</td>
			<td></td>
			<td></td>
			<td>https://pip.pypa.io/en/stable/</td>
		</tr>
	</tbody>
</table>

inherent dynamics visualizer, an interactive application for evaluating and visualizing outputs from a gene regulatory network inference pipeline

Developing gene regulatory network models is a major challenge in systems biology. Several computational tools and pipelines have been developed to tackle this challenge, including the newly developed Inherent Dynamics Pipeline. The Inherent Dynamics Pipeline consists of several previously published tools that work synergistically and are connected in a linear fashion, where the output of one tool is then used as input for the following tool. As with most computational techniques, each step of the Inherent Dynamics Pipeline requires the user to make choices about parameters that don't have a precise biological definition. These choices can substantially impact gene regulatory network models produced by the analysis. For this reason, the ability to visualize and explore the consequences of various parameter choices at each step can help increase confidence in the choices and the results.The Inherent Dynamics Visualizer is a comprehensive visualization package that streamlines the process of evaluating parameter choices through an interactive interface within a web browser. The user can separately examine the output of each step of the pipeline, make intuitive changes based on visual information, and benefit from the automatic production of necessary input files for the Inherent Dynamics Pipeline. The Inherent Dynamics Visualizer provides an unparalleled level of access to a highly intricate tool for the discovery of gene regulatory networks from time series transcriptomic data.

The steps described textually above and graphically in Figure 1 were applied to the core oscillating GRN of the yeast cell-cycle to see if it is possible to discover functional GRN models that are capable of producing the dynamics observed in time series gene expression data collected in a yeast cell-cycle study16. To illustrate how the IDV can clarify and improve IDP output, the results, after performing this analysis in two ways, were compared: 1) running all steps ...

Watch this Scientific Journal Video about Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline at JoVE.com

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

The Inherent Dynamics Visualizer is an interactive visualization package that connects to a gene regulatory network inference tool for enhanced, streamlined generation of functional network models. The visualizer can be used to make more informed decisions for parameterizing the inference tool, thus increasing confidence in the resulting models.

Developing gene regulatory network models is a major challenge in systems biology. Several computational tools and pipelines have been developed to tackle ...

The Inherent Dynamics Visualizer makes working with several inference methods and understanding their results easier for researchers which in turn allows for the accelerated production of functional network models. The main advantage of the Inherent Dynamics Visualizer is visualizing and exploring intermediate results for informing parameters and input file contents of downstream steps. The Inherent Dynamics Visualizer facilitates exploration and understanding of results from several inference tools.However, we recommend referencing each tool's documentation to better understand how parameter choices impact results. In a new IDP configuration file that parameterizes the node finding step, type data file equal to, annotation file equal to, output file equal to, number process equal to, and IDVconnection equal to true on individual lines. After the equal to sign for data file, type the path to and name of the respective time series file with a comma after the name.For annotation file, type the path to and name of the annotation file. For output file, type the path to and name of the results folder. And for number process, type the number of processes the IDP should use.In the same text file after the main arguments, type in the order presented DLxJTK arguments in square brackets periods equal to and DLxJTK cutoff equal to on individual lines. For periods, if one time series dataset is being used, type each period length separated by commas after the equal to sign. For more than one time series dataset, type each set of period lengths as before, but place square brackets around each set and place a comma between the sets.For DLxJTK cutoff, after the equal to sign, type an integer specifying the maximum number of genes to retain in the gene list file output by De Lichtenburg by JTK-CYCLE. Next, run the IDP using the created configuration file by running the indicated command with the appropriate filename in the terminal. In the terminal, move to the directory named Inherent Dynamics Visualizer and enter the indicated command.Then in a web browser, enter the URL shown. Next, click on the node finding tab and select the node finding folder of interest from the dropdown menu. To extend or shorten the gene list table, click on the up or down arrows or manually enter an integer between one and 50 in the box next to gene expression of DLxJTK ranked genes.In the gene list table, click on the box beside a gene to view its gene expression profile and a line graph. Multiple genes can be added. Then download the gene list into the file format needed for the edge finding step by clicking on the download gene list button.In the editable gene annotation table, label a gene as a target, a regulator, or both. If a gene is a regulator, label the gene as an activator, repressor, or both. Finally, click on the download annotation file button to download the annotation file into the file format needed for the edge finding step.In a new IDP configuration file that parameterizes the edge finding step, for gene list file, enter the path to and name of the generated gene list file after the equal to sign. For edge score column, enter either PLD or norm loss to specify which dataframe column from the LEM py output is used to filter the edges. Next, select either edge score threshold or num edges for list and delete the other.If edge score threshold was selected, enter a number between zero and one. If num edges for list was selected, enter a value equal to or less than the number of possible edges. Then select either seed threshold or num edges for seed and delete the other.If seed threshold was selected, enter a number between zero and one. If num edges for seed was selected, enter a value equal to or less than the number of possible edges. Next, run the IDP using the created configuration file as demonstrated earlier.Select or deselect edges from the edge table by clicking the corresponding checkboxes adjacent to each edge to add or remove edges from the seed network. Then click on the download DSGRN network specification button to download the seed network in the DSGRN network specification format. After selecting the edges to be included in the edge list file used in the network finding by clicking the corresponding checkboxes from the edge table, click on download node and edge lists to download the node list and edge list files in the format required for their use in network finding.In a new IDP configuration file that parameterizes the network finding step, for seed net file, edge list file, and node list file, enter the path to and name of the seed network file and the edge and node list files. For range operations, type two numbers separated by a comma after the equal to sign. The first and second numbers are the minimum and the maximum number of addition or removal of nodes or edges per network made respectively.For num neighbors, enter a number that represents how many networks to find in the network finding. And for max params, enter a number that represents the maximum number of DSGRN parameters to allow for a network. For add node, add edge, remove node, and remove edge, enter values between zero and one after the equal to sign.The numbers must sum to one. Then run the IDP using the created configuration file as demonstrated earlier. To generate an edge prevalence table, select networks using the following two options.For option one, input lower and upper bounds on query results by inputting minimum and maximum values in the input boxes corresponding to the x-axis and y-axis of the plot. For option two, click and drag over the scatterplot to draw a box around the networks to be included. After selection or entering of input bounds, click the get edge prevalence from selected networks button.Next, input an integer in the network index input box to display a single network from the selection. Then click on download DSGRN network specification to download the display network in the DSGRN network specification format. Using the checkboxes corresponding to each edge, select edges to be included in the network or motif used for the similarity analysis.Then click on submit to create the similarity scatterplot for the selected motif or network. To select a network or set of networks, click and drag over the scatterplot. Draw a box around the networks to be included to generate an edge prevalence table and to view the networks along with the respective query results.Download the edge prevalence table by clicking download table. Then download the display network for similarity analysis by clicking on download DSGRN network specification as demonstrated earlier. This protocol was applied to the core oscillator gene regulatory network of the yeast cell cycle.Results from running every step of the IDP consecutively without using the IDV between steps are shown here. This fully parameterized run of the IDP produced results for node and edge finding. Yet in network finding, no model admissible networks were discovered.A set of known yeast cell cycle regulators was then selected from the Saccharomyces genome database and known regulatory relationships between genes were extracted from yeast tracked. The gene list table was extended to find the remaining gene in the gene regulatory network model and genes were deselected to remove genes not found in the same model. A new IDP configuration file was parameterized for the edge finding step with the new gene list and annotation file, and results were loaded in into the IDV.Edges without experimental evidence were removed from the seed network. After creating a seed network well-supported by experimental evidence, 37 model admissible networks were found in the network finding step, of which 24 can stably oscillate. Of these 24 networks, the best performers were two networks that matched the data at 50%of their stably oscillating model parameters.When the ability to remove edges during network generation was added, 612 networks were found with 67%of these networks having the capacity to oscillate stably. Interestingly, 82 networks capable of stable oscillatory dynamics were not capable of producing dynamics similar to those seen in the data. And of the 411 networks, 124 exhibited robust matches to the data.Biologically feasible parameter space is unknown to DSGRN, but incorporating biological information in node and edge finding helps constrain network finding to biologically reasonable regions in the space of all networks. ODE modeling of the networks using parameters from the edge finding step can be performed to further test the functionality of the networks in silico.

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2.0K Görüntüleme.  Duke University. Inherent Dynamics Visualizer, çeşitli çıkarım yöntemleriyle çalışmayı ve sonuçlarını araştırmacıların anlamasını kolaylaştırır ve bu da işlevsel ağ modellerinin üretiminin hızlandırılmasına olanak tanır. Inherent Dynamics Visualizer'ın ana avantajı, aşağı akış adımlarının parametrelerini ve giriş dosyası içeriğini bilgilendirmek için ara sonuçları görselleştirmek ve keşfetmektir. Inherent Dynamics Visualizer, çeşitli çıkarım araçlarında...

Video: Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline