The function () can obtain the topology with interaction signs of a BoolNet network. There are multiple options to integrate BoolNet and rGriffin ( Martinez-Sanchez, Muñoz, Carrillo, Azpeitia, et al., 2019). Then we can use Griffin to find the networks that behave according with our biological information. Library('rGriffin') # Loading required package: rJava genes = c('a','b','c') # devtools::install_github('mar-esther23/rgriffin') rGriffin ( Martinez-Sanchez, Muñoz, Carrillo, Azpeitia, et al., 2019) can include other types of information like transition between cell type, cycles, transitions between cell types or mutant cell types. We can then use this information to create a query. This dataframe is the attractors of the network.
There might exist a third cell type that has not been fully characterized where we know that the cell expresses no a or c but we have NO information on b. For example, there is a cell type that expresses b, but not a or c and an other cell type that expresses c, but not a or b. Suppose we also have some information of what cell types have been observed. This dataframe is the topology of the network. We can add this interactions to the table as “OPU” (optional, positive, unambiguous). We also suspect that b and c may have positive self-regulatory loops. We know that a activates b and that b and c inhibit each other. Let us suppose a cell, we know that this cell has three proteins called a, b and c.
The rGriffin ( Martinez-Sanchez, Muñoz, Carrillo, Azpeitia, et al., 2019) package includes a number of functions to interact with the BoolNet package.
Using a SAT engine, Griffin explores the Boolean Network search space, finding all satisfying assignments that are compatible with the specified constraints. Griffin takes as inputs biologically meaningful constraints and turns them into a symbolic representation. The rGriffin ( Martinez-Sanchez, Muñoz, Carrillo, Azpeitia, et al., 2019) package is an R connector to Griffin (Gene Regulatory Interaction Formulator For Inquiring Networks), a java library for inference and analysis of Boolean Network models. This Boolean networks can then be used to study the biological system. rGriffin ( Martinez-Sanchez, Muñoz, Carrillo, Azpeitia, et al., 2019) uses available biological information (regulatory interactions, cell types, mutants) codified as a set of restrictions and returns the Boolean Networks that satisfy that restrictions. However, inferring the regulatory network and its functions is complex problem, as the available information is often incomplete. Boolean networks allow us to give a mechanistic explanation to how cell types emerge from regulatory networks.
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