Visual setup of logical models of signaling and regulatory networks with ProMoT

BACKGROUND: The analysis of biochemical networks using a logical (Boolean) description is an important approach in Systems Biology. Recently, new methods have been proposed to analyze large signaling and regulatory networks using this formalism. Even though there is a large number of tools to set up models describing biological networks using a biochemical (kinetic) formalism, however, they do not support logical models. RESULTS: Herein we present a flexible framework for setting up large logical models in a visual manner with the software tool ProMoT. An easily extendible library, ProMoT's inherent modularity and object-oriented concept as well as adaptive visualization techniques provide a versatile environment. Both the graphical and the textual description of the logical model can be exported to different formats. CONCLUSION: New features of ProMoT facilitate an efficient set-up of large Boolean models of biochemical interaction networks. The modeling environment is flexible; it can easily be adapted to specific requirements, and new extensions can be introduced. ProMoT is freely available from

Comparing Signaling Networks between Normal and Transformed Hepatocytes Using Discrete Logical Models

Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of “omic” data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.National Institutes of Health (U.S.) (Grant GM68762)National Institutes of Health (U.S.) (Grant CA112967

A comprehensive modular map of molecular interactions in RB/E2F pathway

We present, here, a detailed and curated map of molecular interactions taking place in the regulation of the cell cycle by the retinoblastoma protein (RB/RB1). Deregulations and/or mutations in this pathway are observed in most human cancers. The map was created using Systems Biology Graphical Notation language with the help of CellDesigner 3.5 software and converted into BioPAX 2.0 pathway description format. In the current state the map contains 78 proteins, 176 genes, 99 protein complexes, 208 distinct chemical species and 165 chemical reactions. Overall, the map recapitulates biological facts from approximately 350 publications annotated in the diagram. The network contains more details about RB/E2F interaction network than existing large-scale pathway databases. Structural analysis of the interaction network revealed a modular organization of the network, which was used to elaborate a more summarized, higher-level representation of RB/E2F network. The simplification of complex networks opens the road for creating realistic computational models of this regulatory pathway

Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction

Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks

Development of constrained fuzzy logic for modeling biological regulatory networks and predicting contextual therapeutic effects

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 199-213).Upon exposure to environmental cues, protein modifications form a complex signaling network that dictates cellular response. In this thesis, we develop methods for using continuous logic-based models to aide our understanding of these signaling networks and facilitate data interpretation. We present a novel modeling framework called constrained fuzzy logic (cFL) that maintains a simple logic-based description of interactions with AND, OR, and NOT gates, but allows for intermediate species activities with mathematical functions relating input and output values (transfer functions). We first train a prior knowledge network (PKN) to data with cFL, which reveals what aspects of the dataset agree or disagree with prior knowledge. The cFL models are trained to a dataset describing signaling proteins in a hepatocellular carcinoma cell line after exposure to ligand cues in the presence or absence of small molecule inhibitors. We find that multiple models with differing topology and parameters explain the data equally well, and it is crucial to consider this non-identifiability during model training and subsequence analysis. Our trained models generate new biological understanding of network crosstalk as well as quantitative predictions of signaling protein activation. In our next applications of cFL, we explore the ability of models either constructed based solely on prior knowledge or trained to dedicated biochemical data to make predictions that answer the following questions: 1) What perturbations to species in the system are effective at accomplishing a clinical goal? and 2) In what environmental conditions are these perturbations effective? We find that we are able to make accurate predictions in both cases. Thus, we offer cFL as a flexible modeling methodology to assist data interpretation and hypothesis generation for choice of therapeutic targets.by Melody K. Morris.Ph.D