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. 
	NOTE: Cloning of the IDV should happen outside of the IDP&#39;s top-level directory.</li>
	<li>Follow the install instructions in the IDV&#39;s README file.</li>
</ol>
2. Node finding
<ol>
	<li>Create a new IDP configuration file that parametrizes the Node Finding step. 
	NOTE: All quotation marks in the following steps should not be typed out. The quotation marks are only used here as a delimiter between the protocol text and what is to be typed out.
	<ol>
		<li>Add the main IDP arguments to the configuration file.</li>
		<li>Open a new text file in a text editor and type &#34;data_file =&#34;, &#34;annotation_file =&#34;, &#34;output_dir =&#34;, &#34;num_proc =&#34;, and &#34;IDVconnection = True&#34; on individual lines.</li>
		<li>For &#34;data_file&#34;, after the equal to sign, type the path to and name of the respective time series file and type a comma after the name. Separate each data by a comma, if more than one time series data set is being used. See Supplemental File 1 and Supplemental File 2 for an example of time series gene expression files.</li>
		<li>Type the path to and name of the annotation file for &#34;annotation_file&#34;, after the equal to sign. See Supplemental File 3 for an example of an annotation file.</li>
		<li>For &#34;output_file&#34;, after the equal to sign, type the path to and name of the folder where results will be saved.</li>
		<li>After the equal to sign, for &#34;num_proc&#34;, type the number of processes the IDP should use.</li>
		<li>Add Node Finding arguments to the configuration file.</li>
		<li>In the same text file as in step 2.1.1, type in the order presented &#34;[dlxjtk_arguments]&#34;, &#34;periods =&#34;, and &#34;dlxjtk_cutoff =&#34; on individual lines. Place these after the main arguments.</li>
		<li>For &#34;periods&#34;, after the equal to sign, if one-time series data set is being used, type each period length separated by commas. For more than one time series data set, type each set of period lengths as before but place square brackets around each set and place a comma between the sets.</li>
		<li>After the equal to sign, for &#34;dlxjtk_cutoff&#34;, type an integer specifying the maximum number of genes to retain in the gene_list_file output by de Lichtenberg by JTK_CYCLE (DLxJTK) (Table 1). 
		NOTE: It is highly recommended to review the dlxjtk_arguments sections in the IDP README to get a better understanding of each argument. See Supplemental File 4 for an example of a configuration file with the Node Finding arguments specified.</li>
	</ol>
	</li>
	<li>In the terminal, move into the IDP directory, named inherent_dynamics_pipeline.</li>
	<li>In the terminal, enter the command: conda activate dat2net</li>
	<li>Run the IDP using the configuration file created in step 2.1 by running this command in the terminal, where &#60;config file name&#62; is the name of the file: python src/dat2net.py &#60;config file name&#62;</li>
	<li>In the terminal, move to the directory named inherent_dynamics_visualizer&#160;and enter the command: ./viz_results.sh &#60;results_directory&#62; 
	NOTE: &#60;results_directory&#62; will point to the directory used as the output directory for the IDP.</li>
	<li>In a web browser, enter&#160;http://localhost:8050/ as the URL.</li>
	<li>With the IDV now open in the browser, click on the Node Finding tab and select the node finding folder of interest from the dropdown menu.</li>
	<li>Manually curate a new gene list from the gene list table in the IDV to be used for subsequent IDP steps.
	<ol>
		<li>To extend or shorten the gene list table, click on the up or down arrows or manually enter in an integer between 1 and 50 in the box next to Gene expression of DLxJTK-ranked genes. Top:.</li>
		<li>In the gene list table, click on the box beside a gene to view its gene expression profile in a line graph. Multiple genes can be added.</li>
		<li>Optionally specify the number of equally sized bins to compute and order genes by the time interval containing their peak expression, by inputting an integer into the input box above the gene list table labeled Input integer to divide the first cycle into bins:. 
		NOTE: This option is specific to oscillatory dynamics and might not be applicable to other types of dynamics.</li>
		<li>Select a heatmap viewing preference by clicking on an option under Order Genes By: First Cycle Max Expression (Table 1) which orders genes based on the time of the gene-expression peak in the first cycle. 
		NOTE: DLxJTK Rank orders genes based on the periodicity ranking from the DLxJTK algorithm of the IDP.</li>
		<li>Click on the Download Gene List button to download the gene list into the file format needed for the Edge Finding step. See Supplemental File 5 for an example of a gene list file.</li>
	</ol>
	</li>
	<li>In the Editable Gene Annotation Table, label a gene as a target, a regulator, or both in the annotation file for the Edge Finding step in a new Edge Finding run. If a gene is a regulator, label the gene as an activator, repressor, or both.
	<ol>
		<li>To label a gene as an activator, click on the cell in the tf_act column and change the value to 1. To label a gene as a repressor, change the value in the tf_rep column to 1. A gene will be allowed to act as both an activator and a repressor in the Edge Finding step by setting the values in both the tf_act and tf_rep columns to 1.</li>
		<li>To label a gene as a target, click on the cell in the target column and change the value to 1.</li>
	</ol>
	</li>
	<li>Click on the Download Annot. File button to download the annotation file into the file format needed for the Edge Finding step.</li>
</ol>
3. Edge finding
<ol>
	<li>Create a new IDP configuration file that parametrizes the Edge Finding step.
	<ol>
		<li>Add the main IDP arguments to the configuration file. Open a new text file in a text editor and repeat step 2.1.1.</li>
		<li>Add Edge Finding arguments to the configuration file.</li>
		<li>In the same text file as in step 3.1.1, type in the order presented &#34;[lempy_arguments]&#34;, &#34;gene_list_file =&#34;, &#34;[netgen_arguments]&#34;, &#34;edge_score_column =&#34;, &#34;edge_score_thresho =&#34;, &#34;num_edges_for_list =&#34;, &#34;seed_threshold =&#34;, and &#34;num_edges_for_seed =&#34; on individual lines. These should go below the main arguments.</li>
		<li>For &#34;gene_list_file&#34;, after the equal to sign, enter the path to and name of the gene list file generated in step 2.8.5.</li>
		<li>For &#34;edge_score_column&#34;, after the equal to sign, enter either &#34;pld&#34; or &#34;norm_loss&#34; to specify which data frame column from the lempy output is used to filter the edges.</li>
		<li>Select either &#34;edge_score_threshold&#34; or &#34;num_edges_for_list&#34;, and delete the other. If &#34;edge_score_threshold&#34; was selected, enter a number between 0 and 1. This number will be used to filter edges based on the column specified in step 3.1.5.
		<ol>
			<li>If &#34;num_edges_for_list&#34; was selected, enter a value equal to or less than the number of possible edges. This number will be used to filter the edges based on how they are ranked in the column specified in step 3.1.5. The edges left over will be used to build networks in Network Finding.</li>
		</ol>
		</li>
		<li>Select either &#34;seed_threshold&#34; or &#34;num_edges_for_seed&#34; and delete the other. If &#34;seed_threshold&#34; was selected, enter a number between 0 and 1. This number will be used to filter edges based on the column specified in step 3.1.5.
		<ol>
			<li>If &#34;num_edges_for_seed&#34; was selected, enter a value equal to or less than the number of possible edges. This number will be used to filter the edges based on how they are ranked in the column specified in step 3.1.5. The edges left over will be used to build the seed network (Table 1) used in Network Finding. 
			NOTE: It is highly recommended to review the lempy_arguments and netgen_arguments sections in the IDP README to get a better understanding of each argument. See Supplemental File 7 for an example of a configuration file with the Edge finding arguments specified.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Repeat steps 2.2 and 2.3.</li>
	<li>Run the IDP using the configuration file created in step 3.1 by running this command in the terminal, where &#60;config file name&#62; is the name of the file: python src/dat2net.py &#60;config file name&#62;</li>
	<li>If the IDV is still running, stop it by pressing Control C in the terminal window to stop the program. Repeat steps 2.5 and 2.6.</li>
	<li>With the IDV open in the browser, click on the Edge Finding tab and select the edge finding folder of interest from the drop-down menu. 
	NOTE: If multiple datasets are used in Edge Finding, then make sure to select the last dataset that was used in the Local Edge Machine (LEM) analysis (Table 1). It is important when selecting edges for the seed network or edge list based on LEM results to look at the last time series data listed in the configuration file as this output incorporates all preceding datafiles in its inference of regulatory relationships between nodes.</li>
	<li>To extend or shorten the edge table, manually enter an integer in the input box under Number of Edges:.</li>
	<li>Optionally filter edges on the LEM ODE parameters. Click and drag to move either the left side or the right side of each parameter&#39;s slider to remove edges from the edge table that have parameters outside of their new allowed parameter bounds.</li>
	<li>Optionally create a new seed network if a different seed network is wanted than the one proposed by the IDP. See Supplemental File 8 for an example of a seed network file.
	<ol>
		<li>Select either From Seed to select the seed network or From Selection from the dropdown menu under Network:.</li>
		<li>Deselect/select edges from the edge table by clicking the corresponding checkboxes adjacent to each edge to remove/add edges from the seed network.</li>
	</ol>
	</li>
	<li>Click on the Download DSGRN NetSpec button to download the seed network in the Dynamic Signatures Generated by Regulatory Networks (DSGRN) (Table 1) network specification format.</li>
	<li>Select additional nodes and edges to be used in the Network Finding step.
	<ol>
		<li>Select edges from the edge table by clicking the corresponding checkboxes to include in the edge list file used in Network Finding.</li>
		<li>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. See Supplemental File 9 and Supplemental File 10 for examples of edge and node list files, respectively. 
		&#8203;NOTE: The node list must contain all the nodes in the edge list file, so the IDV automatically creates the node list file based on the selected edges. Two options are available for viewing the edges in Edge Finding. The LEM Summary Table option presents the edges as a ranked list of the top 25 edges. Top-Line LEM Table presents the edges in a concatenated list of the top three ranked edges for each possible regulator. The number of edges viewed for each option can be adjusted by the user by changing the number in the Number of Edges input box.</li>
	</ol>
	</li>
</ol>
4. Network finding
<ol>
	<li>Create a new IDP configuration file that parametrizes the Network Finding step.
	<ol>
		<li>Add the main IDP arguments to the configuration file. Open a new text file in a text editor and repeat step 2.1.1.</li>
		<li>Add Network Finding arguments to the configuration file.</li>
		<li>In the same text file as in step 4.1.1, type in the order presented &#34;[netper_arguments]&#34;, &#34;edge_list_file =&#34;, &#34;node_list_file =&#34;, &#34;seed_net_file =&#34;, &#34;range_operations =&#34;, &#34;numneighbors =&#34;, &#34;maxparams =&#34;, &#34;[[probabilities]]&#34;, &#34;addNode =&#34;, &#34;addEdge =&#34;, &#34;removeNode =&#34;, and &#34;removeEdge =&#34; on individual lines, below the main arguments.</li>
		<li>For &#34;seed_net_file&#34;, &#34;edge_list_file&#34; and &#34;node_list_file&#34;, after the equal sign, enter the path to and name of the seed network file and the edge and node list files generated in steps 3.9 and 3.10.2.</li>
		<li>After the equal to sign, for &#34;range_operations&#34;, type two numbers separated by a comma. The first and second numbers are the minimum and the maximum number of addition or removal of nodes or edges per network made, respectively.</li>
		<li>For &#34;numneighbors&#34;, after the equal to sign, enter a number that represents how many networks to find in Network Finding.</li>
		<li>For &#34;maxparams&#34;, after the equal to sign, enter a number that represents the maximum number of DSGRN parameters to allow for a network.</li>
		<li>Enter values between 0 and 1 for each of these arguments: &#34;addNode&#34;, &#34;addEdge&#34;, &#34;removeNode&#34;, and &#34;removeEdge&#34;, after the equal to sign. The numbers must sum to 1. 
		NOTE: It is highly recommended to review the netper_arguments and netquery_arguments sections in the IDP README to get a better understanding of each argument. See Supplemental File 11 and Supplemental File 12 for examples of a configuration file with the Network Finding arguments specified.</li>
	</ol>
	</li>
	<li>Repeat steps 2.2 and 2.3.</li>
	<li>Run the IDP using the configuration file created in step 4.1 by running this command in the terminal, where &#60;config file name&#62; is the name of the file: python src/dat2net.py &#60;config file name&#62;</li>
	<li>If the IDV is still running, stop it by pressing Control C in the terminal window to stop the program. Repeat steps 2.5 and 2.6.</li>
	<li>With the IDV open in the browser, click on the Network Finding tab and select the network finding folder of interest.</li>
	<li>Select a network or set of networks to generate an edge prevalence table (Table 1) and to view the networks along with their respective query results.
	<ol>
		<li>Two options are available for selecting networks: Option 1 - 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. Option 2 - Click and drag over the scatterplot to draw a box around the networks to be included. After selection or input bounds are entered, press the Get Edge Prevalence From Selected Networks button. 
		NOTE: If more than one DSGRN query was specified, use the radio buttons labeled with the query type to switch between each query&#39;s results. The same applies if more than one epsilon (noise level) was specified.</li>
	</ol>
	</li>
	<li>Click the arrows beneath the edge prevalence table to move to the next page of the table. Press Download Table to download the edge prevalence table.</li>
	<li>Input an integer in the Network Index input box to display a single network from the selection made in step 4.6. Click on Download DSGRN NetSpec to download the displayed network in the DSGRN network specification format.</li>
	<li>Search networks for similarity to a specified motif or network of interest.
	<ol>
		<li>Use the checkboxes corresponding to each edge to select edges to be included in the network or motif used for the similarity analysis. Click on Submit to create the similarity scatterplot for the selected motif or network. 
		NOTE: Use the arrows in the edge list to sort alphabetically and the arrows beneath the table to move to the next page of the table.</li>
		<li>Click and drag over the scatterplot to draw a box around the networks to be included to select a network or set of networks to generate an edge prevalence table and to view the networks along with their respective query results. 
		NOTE: If more than one DSGRN query was specified, use the radio buttons labeled with the query type to switch between each query&#39;s results. The same applies if more than one epsilon (noise level) was specified.</li>
		<li>Repeat steps 4.7 and 4.8 to download the edge prevalence table and the displayed network for the similarity analysis, respectively.</li>
	</ol>
	</li>
</ol>

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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 ...

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Research

JoVE Journal

Biology

2.0K Views.  Duke University. 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.

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

Regulatory T cells: Therapeutic Potential for Treating Transplant Rejection and Type I Diabetes

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

The use of siRNA mediated gene knockdown is continuing to be an important tool in studies of gene expression.  siRNA studies are being conducted not only to study the effects of downregulating single genes, but also to interrogate signaling pathways and other complex interaction networks.  These pathway analyses require both the use of relevant cellular models and methods that cause less perturbation to the cellular physiology.  Electroporation is increasingly being used as an effective way to introduce siRNA and other nucleic acids into difficult to transfect cell lines and primary cells without altering the signaling pathway under investigation. There are multiple critical steps to a successful siRNA experiment, and there are ways to simplify the work while improving the data quality at several experimental stages.  To help you get started with your siRNA mediated gene knockdown project, we will demonstrate how to perform a pathway study complete from collecting and counting the cells prior to electroporation through post transfection real-time PCR gene expression analysis.  The following study investigates the role of the transcriptional activator STAT6 in IL-4 dependent gene expression of CCL17 in a Burkitt lymphoma cell line (Namalwa).  The techniques demonstrated are useful for a wide range of siRNA-based experiments on both adherent and suspension cells.  We will also show how to streamline cell counting with the TC10 automated cell counter, how to electroporate multiple samples simultaneously using the MXcell electroporation system, and how to simultaneously assess RNA quality and quantity with the Experion automated electrophoresis system.

This procedure describes a quick and easy workflow to introduce siRNA into difficult to transfect cell lines and follow gene expression by real-time PCR. Use of an automated cell counter, multi-well electroporation plate, and automated electrophoresis station provide quick and reliable results without the need for expensive robotic handling.

The use of siRNA mediated gene knockdown is continuing to be an important tool in studies of gene expression.  siRNA studies are being conducted not only ...

Using SecM Arrest Sequence as a Tool to Isolate Ribosome Bound Polypeptides

Extensive research has provided ample evidences suggesting that protein folding in the cell is a co-translational process1-5. However, the exact pathway that polypeptide chain follows during co-translational folding to achieve its functional form is still an enigma. In order to understand this process and to determine the exact conformation of the co-translational folding intermediates, it is essential to develop techniques that allow the isolation of RNCs carrying nascent chains of predetermined sizes to allow their further structural analysis.
SecM (secretion monitor) is a 170 amino acid E. coli protein that regulates expression of the downstream SecA (secretion driving) ATPase in the secM-secA operon6. Nakatogawa and Ito originally found that a 17 amino acid long sequence (150-FSTPVWISQAQGIRAGP-166) in the C-terminal region of the SecM protein is sufficient and necessary to cause stalling of SecM elongation at Gly165, thereby producing peptidyl-glycyl-tRNA stably bound to the ribosomal P-site7-9. More importantly, it was found that this 17 amino acid long sequence can be fused to the C-terminus of virtually any full-length and/or truncated protein thus allowing the production of RNCs carrying nascent chains of predetermined sizes7. Thus, when fused or inserted into the target protein, SecM stalling sequence produces arrest of the polypeptide chain elongation and generates stable RNCs both in vivo in E. coli cells and in vitro in a cell-free system. Sucrose gradient centrifugation is further utilized to isolate RNCs.
The isolated RNCs can be used to analyze structural and functional features of the co-translational folding intermediates. Recently, this technique has been successfully used to gain insights into the structure of several ribosome bound nascent chains10,11. Here we describe the isolation of bovine Gamma-B Crystallin RNCs fused to SecM and generated in an in vitro translation system.

We describe here a technique that is now routinely used to isolate stably bound ribosome nascent chain complexes (RNCs). This technique takes advantage of the discovery that a 17 amino acid long SecM "arrest sequence" can halt translation elongation in a prokaryotic (E. coli) system, when inserted into (or fused to the C-terminus) of virtually any protein.

Extensive research has provided ample evidences suggesting that protein folding in the cell is a co-translational process1-5. However, the exact pathway ...

Selective Capture of 5-hydroxymethylcytosine from Genomic DNA

5-methylcytosine (5-mC) constitutes ~2-8% of the total cytosines in human genomic DNA and impacts a broad range of biological functions, including gene expression, maintenance of genome integrity, parental imprinting, X-chromosome inactivation, regulation of development, aging, and cancer1. Recently, the presence of an oxidized 5-mC, 5-hydroxymethylcytosine (5-hmC), was discovered in mammalian cells, in particular in embryonic stem (ES) cells and neuronal cells2-4. 5-hmC is generated by oxidation of 5-mC catalyzed by TET family iron (II)/&alpha;-ketoglutarate-dependent dioxygenases2, 3. 5-hmC is proposed to be involved in the maintenance of embryonic stem (mES) cell, normal hematopoiesis and malignancies, and zygote development2, 5-10. To better understand the function of 5-hmC, a reliable and straightforward sequencing system is essential. Traditional bisulfite sequencing cannot distinguish 5-hmC from 5-mC11. To unravel the biology of 5-hmC, we have developed a highly efficient and selective chemical approach to label and capture 5-hmC, taking advantage of a bacteriophage enzyme that adds a glucose moiety to 5-hmC specifically12.
Here we describe a straightforward two-step procedure for selective chemical labeling of 5-hmC. In the first labeling step, 5-hmC in genomic DNA is labeled with a 6-azide-glucose catalyzed by &beta;-GT, a glucosyltransferase from T4 bacteriophage, in a way that transfers the 6-azide-glucose to 5-hmC from the modified cofactor, UDP-6-N3-Glc (6-N3UDPG). In the second step, biotinylation, a disulfide biotin linker is attached to the azide group by click chemistry. Both steps are highly specific and efficient, leading to complete labeling regardless of the abundance of 5-hmC in genomic regions and giving extremely low background. Following biotinylation of 5-hmC, the 5-hmC-containing DNA fragments are then selectively captured using streptavidin beads in a density-independent manner. The resulting 5-hmC-enriched DNA fragments could be used for downstream analyses, including next-generation sequencing.
Our selective labeling and capture protocol confers high sensitivity, applicable to any source of genomic DNA with variable/diverse 5-hmC abundances. Although the main purpose of this protocol is its downstream application (i.e., next-generation sequencing to map out the 5-hmC distribution in genome), it is compatible with single-molecule, real-time SMRT (DNA) sequencing, which is capable of delivering single-base resolution sequencing of 5-hmC.

Described is a two-step labeling process using &beta;-glucosyltransferase (&beta;-GT) to transfer an azide-glucose to 5-hmC, followed by click chemistry to transfer a biotin linker for easy and density-independent enrichment. This efficient and specific labeling method enables enrichment of 5-hmC with extremely low background and high-throughput epigenomic mapping via next-generation sequencing.

5-methylcytosine (5-mC) constitutes ~2-8% of the total cytosines in human genomic DNA and impacts a broad range of biological functions, including gene ...

DNA-affinity-purified Chip (DAP-chip) Method to Determine Gene Targets for Bacterial Two component Regulatory Systems

In vivo methods such as ChIP-chip are well-established techniques used to determine global gene targets for transcription factors. However, they are of limited use in exploring bacterial two component regulatory systems with uncharacterized activation conditions. Such systems regulate transcription only when activated in the presence of unique signals. Since these signals are often unknown, the in vitro microarray based method described in this video article can be used to determine gene targets and binding sites for response regulators. This DNA-affinity-purified-chip method may be used for any purified regulator in any organism with a sequenced genome. The protocol involves allowing the purified tagged protein to bind to sheared genomic DNA and then affinity purifying the protein-bound DNA, followed by fluorescent labeling of the DNA and hybridization to a custom tiling array. Preceding steps that may be used to optimize the assay for specific regulators are also described. The peaks generated by the array data analysis are used to predict binding site motifs, which are then experimentally validated. The motif predictions can be further used to determine gene targets of orthologous response regulators in closely related species. We demonstrate the applicability of this method by determining the gene targets and binding site motifs and thus predicting the function for a sigma54-dependent response regulator DVU3023 in the environmental bacterium Desulfovibrio vulgaris Hildenborough.

DNA-affinity-purified Chip DAP-chip Method to Determine Gene Targets for Bacterial Two component Regulatory Systems

This video article describes an in vitro microarray based method to determine the gene targets and binding sites for two component system response regulators.

In vivo methods such as ChIP-chip are well-established techniques used to determine global gene targets for transcription factors. However, they are of ...

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

Gene expression and its regulation are very important to understand the behavior of cells under different conditions. Various techniques are used nowadays to study gene expression, but most are limited in terms of providing an overall picture of the expression of the whole transcriptome. DNA microarrays offer a fast and economic research technology, which gives a full overview of global gene expression and have a vast number of applications including identification of novel genes and transcription factor binding sites, characterization of transcriptional activity of the cells and also help in analyzing thousands of genes (in a single experiment). In the present study, the conditions for bacterial transcriptome analysis from cell harvest to DNA microarray analysis have been optimized. Taking into account the time, costs and accuracy of the experiments, this technology platform proves to be very useful and universally applicabale for studying bacterial transcriptomes. Here, we perform DNA microarray analysis with Streptococcus pneumoniae as a case-study by comparing the transcriptional responses of S. pneumoniae grown in the presence of varying L-serine concentrations in the medium. Total RNA was isolated by using a Macaloid method using an RNA isolation kit and the quality of RNA was checked by using an RNA quality check kit. cDNA was prepared using reverse transcriptase and the cDNA samples were labelled using one of two amine-reactive fluorescent dyes. Homemade DNA microarray slides were used for hybridization of the labelled cDNA samples and microarray data were analyzed by using a cDNA microarray data pre-processing framework (Microprep). Finally, Cyber-T was used to analyze the data generated using Microprep for the identification of statistically significant differentially expressed genes. Furthermore, in-house built software packages (PePPER, FIVA, DISCLOSE, PROSECUTOR, Genome2D) were used to analyze data.

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

This manuscript describes the use of state-of-the-art technology provided by DNA-microarrays. Microarrays provide an overview of the transcriptomic changes in bacteria incurred under a specific condition. Moreover, we highlight the ease by which large amounts of data can be analyzed by using convenient in-house developed software packages.

Gene expression and its regulation are very important to understand the behavior of cells under different conditions. Various techniques are used nowadays ...

Visualizing Stromule Frequency with Fluorescence Microscopy

Stromules, or "stroma-filled tubules", are narrow, tubular extensions from the surface of the chloroplast that are universally observed in plant cells but whose functions remain mysterious. Alongside growing attention on the role of chloroplasts in coordinating plant responses to stress, interest in stromules and their relationship to chloroplast signaling dynamics has increased in recent years, aided by advances in fluorescence microscopy and protein fluorophores that allow for rapid, accurate visualization of stromule dynamics. Here, we provide detailed protocols to assay stromule frequency in the epidermal chloroplasts of Nicotiana benthamiana, an excellent model system for investigating chloroplast stromule biology. We also provide methods for visualizing chloroplast stromules in vitro by extracting chloroplasts from leaves. Finally, we outline sampling strategies and statistical approaches to analyze differences in stromule frequencies in response to stimuli, such as environmental stress, chemical treatments, or gene silencing. Researchers can use these protocols as a starting point to develop new methods for innovative experiments to explore how and why chloroplasts make stromules.

Protocols to investigate the dynamics of chloroplast stromules, the stroma-filled tubules that extend from the surface of chloroplasts, are described.

Stromules, or "stroma-filled tubules", are narrow, tubular extensions from the surface of the chloroplast that are universally observed in plant cells but ...

A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses

Soybean (Glycine max (L.) Merr.) is an important crop species and has become a legume model for the studies of genetic and biochemical pathways. Therefore, it is important to establish an efficient transient gene expression system in soybean. Here, we report a simple protocol for the preparation of soybean protoplasts and its application for transient functional analyses. We found that young unifoliate leaves from soybean seedlings resulted in large quantities of high quality protoplasts. By optimizing a PEG-calcium-mediated transformation method, we achieved high transformation efficiency using soybean unifoliate protoplasts. This system provides an efficient and versatile model for examination of complex regulatory and signaling mechanisms in live soybean cells and may help to better understand diverse cellular, developmental and physiological processes of legumes.

We developed a simple and efficient protocol for the preparation of large quantities of soybean protoplasts to study complex regulatory and signaling mechanisms in live cells.

Soybean (Glycine max (L.) Merr.) is an important crop species and has become a legume model for the studies of genetic and biochemical pathways. ...

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

The development of transgenic invertebrate preparations in which the activity of specifiable sets of neurons can be recorded and manipulated with light represents a revolutionary advance for studies of the neural basis of behavior. However, a downside of this development is its tendency to focus investigators on a very small number of &#8220;designer&#8221; organisms (e.g., C. elegans and Drosophila), potentially negatively impacting the pursuit of comparative studies across many species, which is needed for identifying general principles of network function. The present article illustrates how optical recording with voltage-sensitive dyes in the brains of non-transgenic gastropod species can be used to rapidly (i.e., within the time course of single experiments) reveal features of the functional organization of their neural networks with single-cell resolution. We outline in detail the dissection, staining, and recording methods used by our laboratory to obtain action potential traces from dozens to ~150 neurons during behaviorally relevant motor programs in the CNS of multiple gastropod species, including one new to neuroscience &#8211; the nudibranch Berghia stephanieae. Imaging is performed with absorbance voltage-sensitive dyes and a 464-element photodiode array that samples at 1,600 frames/second, fast enough to capture all action potentials generated by the recorded neurons. Multiple several-minute recordings can be obtained per preparation with little to no signal bleaching or phototoxicity. The raw optical data collected through the methods described can subsequently be analyzed through a variety of illustrated methods. Our optical recording approach can be readily used to probe network activity in a variety of non-transgenic species, making it well suited for comparative studies of how brains generate behavior.

This protocol presents a method for imaging neuronal population activity with single-cell resolution in non-transgenic invertebrate species using absorbance voltage-sensitive dyes and a photodiode array. This approach enables a rapid workflow, wherein imaging and analysis can be pursued over the course of a single day.

The development of transgenic invertebrate preparations in which the activity of specifiable sets of neurons can be recorded and manipulated with light ...

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus on one or more gene families that are significant to the organism and study. In this protocol, we outline bioinformatic steps to generate a phylogeny and to quantify the expression of genes of interest. Phylogenetic trees can give insight into how genes are evolving within and between species as well as reveal orthology. These results can be enhanced using RNA-seq data to compare the expression of these genes in different individuals or tissues. Studies of molecular evolution and expression can reveal modes of evolution and conservation of gene function between species. The characterization of a gene family can serve as a springboard for future studies and can highlight an important gene family in a new genome or transcriptome paper.

The purpose of this protocol is to investigate the evolution and expression of candidate genes using RNA sequencing data.

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...