Software tools developed in the LSP


The Integrated Network and Dynamical Reasoning Assembler (INDRA) generates executable models of cell signaling pathway dynamics by using state-of-the-art Natural Language Processing systems to directly parse natural language. It can augment the models with structured information pulled from curated pathway databases. INDRA is developed under the DARPA Big Mechanism program.

GR Metrics

Recently we published alternative small molecule drug-response metrics that enable accurate response comparisons across cell lines and conditions with differing growth rates. To facilitate broad adoption of this new metric, we have also released software libraries for R, Python and MATLAB to calculate these new Growth-induced response (GR) metrics from endpoint dose-response data. Notably, our R code has been accepted for incorporation into Bioconductor.


A Python framework for Systems Biology modeling. PySB is a framework for building mathematical models of biochemical systems as Python programs. It abstracts the complex process of creating equations describing interactions among multiple proteins or other biomolecules into a simple and intuitive domain specific programming language.


An MCMC-based sampler of dynamic model parameters in which the landscape is treated in a Bayesian fashion.  The sampler requires specification of a parameter prior landscape, and experimental data (real or synthetic) for the fitting process.  BayesSB is maintained in Python but there is an legacy version in MATLAB.


A software tool for managing, transforming, visualizing, and modeling data, in particular the high-throughput data encountered in Systems Biology. Version 1 is currently implemented as a MATLAB toolbox, and version 2 is under development in Python.


Semantically Typed Data Cube (SDCube) is a data model, software library, and file format built on top of HDF5 and XML for storing structured numerical data produced by multi-factorial biology experiments.  An SDCube consists of an HDF5 file with groups and datasets created according to the modular, hierarchical SDCube data model, as well as an accompanying XML file containing metadata such as Minimum Information annotations and a description of the experimental factors. This concept was originally developed as part of the ImageRail software project, but will be incorporated into DataRail 2.0 and other tools as a core technology of LSP.


A flexible pipeline to model protein signalling networks trained to data using various logic formalisms. CellNOpt is used for creating logic-based models of signal transduction networks using different logic formalisms (Boolean, Fuzzy, or differential equations). CellNOpt uses information on signaling pathways encoded as a Prior Knowledge Network, and trains it against high-throughput biochemical data to create cell-specific models.