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TSNI specifications


Unique identifier OMICS_20018
Alternative names Time Series Network Identification, TSNI-Integral
Software type Application/Script
Interface Command line interface
Restrictions to use Academic or non-commercial use
Input data A matrix that contains the experimental expression values.
Operating system Unix/Linux
Programming languages MATLAB
Computer skills Advanced
Stability Stable
Maintained Yes




No version available


  • person_outline Diego di Bernardo
  • person_outline Caterina Missero

Additional information


Publications for Time Series Network Identification

TSNI citations


DTNI: a novel toxicogenomics data analysis tool for identifying the molecular mechanisms underlying the adverse effects of toxic compounds

PMCID: 5429357
PMID: 28032149
DOI: 10.1007/s00204-016-1922-5

[…] 2) an indirect gene–gene interaction (through one or more intermediates); (3) both genes affected by one or more of the studied compounds.The ROC curves for the simulations show that DTNI outperforms TSNI in inferring a network induced by a toxicant. Moreover, DTNI allows including data from multiple perturbations (compounds), while only one perturbation can be included within TSNI. Including mult […]


Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?

PLoS One
PMCID: 4435750
PMID: 25984725
DOI: 10.1371/journal.pone.0127364

[…] t typically available from in vivo time course studies and that also support the resolution required to simulate treatment kinetics. Based on our survey of methods, we found ODE-based algorithms like TSNI [], TSNI integral [] and the one proposed in Yeung et al. [], most suitable. The algorithm proposed in [] and TSNI use similar techniques of Singular Value Decomposition (SVD) and Principal Compo […]


CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks

PLoS One
PMCID: 3951243
PMID: 24622336
DOI: 10.1371/journal.pone.0090781

[…] to galactose), measured via quantitative real-time PCR (q-PCR) every minutes for up to hours, within five experiments. The performance of two additional algorithms utilizing sparsity and causality, TSNI and BANJO , were evaluated in using the same dataset. TSNI is an algorithm based on modeling networks via ordinary differential equations (ODE), while BANJO is an algorithm based on Bayesian ne […]


Network based elucidation of drug response: from modulators to targets

BMC Syst Biol
PMCID: 3878740
PMID: 24330611
DOI: 10.1186/1752-0509-7-139

[…] ts following inducible activation of the transcription factor using microarrays. TSNI was then applied to the collected GEPs to identify the direct targets of the transcription factor. More recently, TSNI was applied to identify the transcriptional target of Id proteins following inducible deletion of Id genes in murine Neuron Stem Cells [].Other methods using time-course gene expression profiles […]


Integrating external biological knowledge in the construction of regulatory networks from time series expression data

BMC Syst Biol
PMCID: 3465231
PMID: 22898396
DOI: 10.1186/1752-0509-6-101

[…] applied perturbations. ODE-based methods can be broadly classified into two categories, depending on whether the gene expressions are measured at steady state [-] or over time [-]. As an example, the TSNI (Time Series Network Identification) algorithm used ODEs to model time series expression data subject to an external perturbation []. To handle the dimensionality challenge (i.e. the number of ob […]


Reconstructing genome wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities

BMC Bioinformatics
PMCID: 3224099
PMID: 21668997
DOI: 10.1186/1471-2105-12-233

[…] s transcriptome data to develop dynamic models []. These include network identification by multiple regression [], microarray network identification [] and multi-scale time-correlation estimation []. time-series network identification [], directed information-based CLR []. Dynamic Bayesian network models use a Bayesian Framework to reconstruct gene regulatory networks [,].Time-series based algorit […]


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TSNI institution(s)
Telethon Institute of Genetics and Medicine, Naples, Italy; Department of Computer and Systems Engineering, University of Naples, Federico II, Naples, Italy; CEINGE Biotecnologie Avanzate, Napoli, Italy
TSNI funding source(s)
Supported by grants from the Italian Telethon Foundation (TDDP17TELB and TDDP51TELC) to D.d.B., the Italian Telethon Foundation (GGP06243), and the National Foundation for Ectodermal Dysplasia (NFED); by the European School of Molecular Medicine (SEMM), Naples, Italy; by the Italian Telethon Foundation, Seconda Universita’ degli Studi di Napoli (SUN) and Universita’ degli Studi “Federico II” di Napoli.

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