March 26, 2019, Tuesday


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The BIMIB group developed several software tools.

pyTSA (pronounced as "pizza")

Analysis of dynamical systems is often done via simulation ensembles, under different parameter configurations, or by repeatedly sampling from a random process. In any case, automatizing time-series analysis is key to save time and focus on other tasks, e.g. model refinement.

pyTSA is a Python tool to make time-series analysis as intuitive as possible. Its scripts can be pipelined with any simulation tool outputting time-series, and intuitive commands allow to perform complex analyses in a intuitive way.

A brief description of pyTSA's features is available here; the source code, the project wiki and the examples are hosted on GitHub.

TRONCO - TRanslational ONCOlogy

An R suite for state-of-the-art algorithms for the reconstruction of causal models of cancer progressions from genomic cross-sectional data.

TRONCO's official website.


CABERNET - Cytoscape app for Augmented Boolean modEls of gene Regulatory NETwork - is a Cytoscape 3.2.0 app for the generation, the simulation, the analysis and the visualization of Boolean models of gene regulatory networks, particularly focused on the investigation of their robustness.

You can find the description of CABERNET and the link for download at CABERNET page.


GESTODifferent is a plugin for Cytoscape for the identification of Boolean gene regulatory networks describing the stochastic differentiation process. Please use CABERNET (it is an evolution of GESTODifferent).

You can find the description of GESTODifferent and the link for download on the Retronet page.


reHCstar is a SAT-based program to compute a haplotype configuration on pedigrees with recombinations, genotyping errors, and missing genotypes.

reHCstar is based on a reduction of the Haplotype Configuration with Recombinations and Errors problem to Boolean Satisfiability, which is then solved by a SAT solver. A haplotype configuration is finally recovered from the satisfying assignment.

A detailed description of the algorithm is provided by this technical report:
Yuri Pirola, Gianluca Della Vedova, Stefano Biffani, Alessandra Stella, and Paola Bonizzoni. Haplotype Inference on Pedigrees with Recombinations, Errors, and Missing Genotypes via SAT solvers. CoRR abs/1107.3724 (2011). Link

reHCstar has been released under the GNU General Public License and it is freely available from this page.

reHCstar has been designed and implemented by Yuri Pirola under the supervision of Paola Bonizzoni and Gianluca Della Vedova.


PIntron is a novel pipeline for computational gene-structure prediction based on spliced alignment of expressed sequences (ESTs and mRNAs).

PIntron is composed by four steps: Firstly, alternative alignments of expressed sequences to a reference genomic sequence are implicitly computed and represented in a graph (called embedding graph) by a novel fast spliced alignment procedure. Secondly, biologically meaningful alignments are extracted. Then, a consensus gene structure induced by the previously computed alignments is determined based on a parsimony principle. Finally, the resulting introns are reconciliated and classified according to general biological criteria.

Source code, pre-built executables for various architectures, and documentation are available at this page.

PIntron has been designed and implemented by Yuri Pirola and Raffaella Rizzi under the supervision of Paola Bonizzoni and Gianluca Della Vedova.


Heu-MCHC is a fast and accurate heuristic for the Minimum-Change Haplotype Configuration (MCHC) problem, i.e., a combinatorial formulation of the haplotype inference problem on pedigree data where the total number of recombinations and point mutations has to be minimized.

Please refer to the dedicated page for additional information and downloads.

Heu-MCHC has been designed and implemented by Yuri Pirola under the supervision of Tao Jiang.


BioSimWare is a novel software that provides a user-friendly framework for stochastic modelling, simulation and analysis of complex biological systems. It exploits a simple modelling approach to describe the interactions between molecules. Stochastic simulations of the emergent dynamics of the system can be easily run by setting initial conditions, such as amounts of molecular species an kinetic constants, and by modulating the reaction parameters to test different conditions.

BioSimWare also implements optimization techniques to perform the estimation of unknown parameters, which can be performed by providing an experimental/synthetic time-series curve of the dynamics of one or more molecular species. Statistical analysis and characterization of the dynamical properties can finally be performed.

BioSimWare has been implemented by Dario Pescini and Paolo Cazzaniga.

Download BioSimWare here

For additional information please contact: biosimware [at] disco [dot] unimib [dot] it