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Modeling relaxation experiments with a mechanistic model of gene expression.
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- BMC Bioinformatics, 2024, v. 25, n. 1, p. 1, doi. 10.1186/s12859-024-05816-4
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TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.
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- BMC Bioinformatics, 2024, v. 25, n. 1, p. 1, doi. 10.1186/s12859-024-05855-x
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- Article
Stochastic modeling of a gene regulatory network driving B cell development in germinal centers.
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- PLoS ONE, 2024, v. 19, n. 3, p. 1, doi. 10.1371/journal.pone.0301022
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Differentiation is accompanied by a progressive loss in transcriptional memory.
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- BMC Biology, 2024, v. 22, n. 1, p. 1, doi. 10.1186/s12915-024-01846-9
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An image-guided microfluidic system for single-cell lineage tracking.
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- PLoS ONE, 2023, v. 18, n. 8, p. 1, doi. 10.1371/journal.pone.0288655
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One model fits all: Combining inference and simulation of gene regulatory networks.
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- PLoS Computational Biology, 2023, v. 19, n. 3, p. 1, doi. 10.1371/journal.pcbi.1010962
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scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.
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- In Silico Biology, 2023, v. 15, n. 1/2, p. 11, doi. 10.3233/ISB-220252
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- Article
Evidence for close molecular proximity between reverting and undifferentiated cells.
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- BMC Biology, 2022, v. 20, n. 1, p. 1, doi. 10.1186/s12915-022-01363-7
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- Article
Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level.
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- BMC Biology, 2022, v. 20, n. 1, p. 1, doi. 10.1186/s12915-022-01264-9
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- Article
Reduction of a stochastic model of gene expression: Lagrangian dynamics gives access to basins of attraction as cell types and metastabilty.
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- Journal of Mathematical Biology, 2021, v. 83, n. 5, p. 1, doi. 10.1007/s00285-021-01684-1
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- Article
Practical identifiability in the frame of nonlinear mixed effects models: the example of the in vitro erythropoiesis.
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- BMC Bioinformatics, 2021, v. 22, n. 1, p. 1, doi. 10.1186/s12859-021-04373-4
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- Article
Modeling and characterization of inter-individual variability in CD8 T cell responses in mice.
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- In Silico Biology, 2021, v. 14, n. 1/2, p. 13, doi. 10.3233/ISB-200205
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- Article
Drugs modulating stochastic gene expression affect the erythroid differentiation process.
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- PLoS ONE, 2019, v. 14, n. 11, p. 1, doi. 10.1371/journal.pone.0225166
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Erythroid differentiation displays a peak of energy consumption concomitant with glycolytic metabolism rearrangements.
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- PLoS ONE, 2019, v. 14, n. 9, p. 1, doi. 10.1371/journal.pone.0221472
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- Article
WASABI: a dynamic iterative framework for gene regulatory network inference.
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- BMC Bioinformatics, 2019, v. 20, n. 1, p. 1, doi. 10.1186/s12859-019-2798-1
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- Article
Model-Based Assessment of the Role of Uneven Partitioning of Molecular Content on Heterogeneity and Regulation of Differentiation in CD8 T-Cell Immune Responses.
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- Frontiers in Immunology, 2019, p. N.PAG, doi. 10.3389/fimmu.2019.00230
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Calibration, Selection and Identifiability Analysis of a Mathematical Model of the in vitro Erythropoiesis in Normal and Perturbed Contexts.
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- In Silico Biology, 2019, v. 13, n. 1/2, p. 55, doi. 10.3233/ISB-190471
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- Article
Automated cell cycle and cell size measurements for single-cell gene expression studies.
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- BMC Research Notes, 2018, v. 11, p. 1, doi. 10.1186/s13104-018-3195-y
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SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles.
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- Bioinformatics, 2018, v. 34, n. 2, p. 258, doi. 10.1093/bioinformatics/btx575
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- Article
Inferring gene regulatory networks from single-cell data: a mechanistic approach.
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- BMC Systems Biology, 2017, v. 11, p. 1, doi. 10.1186/s12918-017-0487-0
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- Article
Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment.
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- PLoS Biology, 2017, v. 15, n. 7, p. 1, doi. 10.1371/journal.pbio.2001867
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- Article
Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process.
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- PLoS Biology, 2016, v. 14, n. 12, p. 1, doi. 10.1371/journal.pbio.1002585
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IL-2 sensitivity and exogenous IL-2 concentration gradient tune the productive contact duration of CD8<sup>+</sup> T cell-APC: a multiscale modeling study.
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- BMC Systems Biology, 2016, v. 10, p. 1, doi. 10.1186/s12918-016-0323-y
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- Article
Temperature-induced variation in gene expression burst size in metazoan cells.
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- BMC Molecular Biology, 2015, v. 16, p. 1, doi. 10.1186/s12867-015-0048-2
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Stochastic Fluctuations and Distributed Control of Gene Expression Impact Cellular Memory.
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- PLoS ONE, 2014, v. 9, n. 12, p. 1, doi. 10.1371/journal.pone.0115574
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Finding maximal homogeneous clique sets.
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- Knowledge & Information Systems, 2014, v. 39, n. 3, p. 579, doi. 10.1007/s10115-013-0625-y
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- Article
Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts.
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- BMC Biology, 2013, v. 11, n. 1, p. 1, doi. 10.1186/1741-7007-11-15
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- Article
Differential miRNA expression profiles in proliferating or differentiated keratinocytes in response to gamma irradiation.
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- BMC Genomics, 2013, v. 14, n. 1, p. 1, doi. 10.1186/1471-2164-14-184
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- Article
Mathematical model of the primary CD8 T cell immune response: stability analysis of a nonlinear age-structured system.
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- Journal of Mathematical Biology, 2012, v. 65, n. 2, p. 263, doi. 10.1007/s00285-011-0459-8
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- Article
On the spontaneous stochastic dynamics of a single gene: complexity of the molecular interplay at the promoter.
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- BMC Systems Biology, 2010, v. 4, p. 2, doi. 10.1186/1752-0509-4-2
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Extracting Signature Motifs from Promoter Sets of Differentially Expressed Genes.
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- In Silico Biology, 2009, v. 9, n. 1/2, p. 17, doi. 10.3233/ISB-2009-0381
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- Article
Constraint-Based Knowledge Discovery from SAGE Data.
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- In Silico Biology, 2008, v. 8, n. 2, p. 157
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- Article
SQUAT: A web tool to mine human, murine and avian SAGE data.
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- BMC Bioinformatics, 2008, v. 9, p. 1, doi. 10.1186/1471-2105-9-378
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- Article
A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome.
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- BMC Genomics, 2008, v. 9, p. 1, doi. 10.1186/1471-2164-9-418
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- Article
Clustering Formal Concepts to Discover Biologically Relevant Knowledge from Gene Expression Data.
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- In Silico Biology, 2007, v. 7, n. 4/5, p. 467
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SAGE analysis of mosquito salivary gland transcriptomes during Plasmodium invasion.
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- Cellular Microbiology, 2007, v. 9, n. 3, p. 708, doi. 10.1111/j.1462-5822.2006.00822.x
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- Article
Broadening of DNA replication origin usage during metazoan cell differentiation.
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- EMBO Reports, 2006, v. 7, n. 8, p. 806, doi. 10.1038/sj.embor.7400736
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Modeling the emergence of multi-protein dynamic structures by principles of self-organization through the use of 3DSpi, a multi-agent-based software.
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- BMC Bioinformatics, 2005, v. 6, p. 228, doi. 10.1186/1471-2105-6-228
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- Article
Global transcription analysis of immature avian erythrocytic progenitors: from self-renewal to differentiation.
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- Oncogene, 2004, v. 23, n. 46, p. 7628, doi. 10.1038/sj.onc.1208061
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Identitag, a relational database for SAGE tag identification and interspecies comparison of SAGE libraries.
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- BMC Bioinformatics, 2004, v. 5, p. 143, doi. 10.1186/1471-2105-5-143
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- Article
The MEK-1/ERKs signalling pathway is differentially involved in the self-renewal of early and late avian erythroid progenitor cells.
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- Oncogene, 2003, v. 22, n. 58, p. 9205, doi. 10.1038/sj.onc.1207049
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- Article
THe v-erbA oncogene blocks expression of α2/β1 integrin a normal inhibitor of erythroid progenitor proliferation.
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- Oncogene, 2002, v. 21, n. 18, p. 2864, doi. 10.1038/sj.onc.1205411
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- Article
The v-ErbA oncoprotein quenches the activity of an erythroid-specific enhancer.
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- Oncogene, 2001, v. 20, n. 7, p. 775, doi. 10.1038/sj.onc.1204159
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TGF-ß cooperates with TGF-a to induce the self-renewal of normal erythrocytic progenitors: evidence for an autocrine mechanism.
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- EMBO Journal, 1999, v. 18, n. 10, p. 2764, doi. 10.1093/emboj/18.10.2764
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Role of the different RAR isoforms in controlling the erythrocytic differentiation sequence. Interference with the v-erbA and p135<sup>gag-myb-ets</sup> nuclear oncogenes.
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- Oncogene, 1998, v. 16, n. 5, p. 563, doi. 10.1038/sj.onc.1201550
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v-erbB oncogene expression accounts for most variations in protein synthesis after avian erythroblastosis virus infection of chicken embryo fibroblasts: A two-dimensional electrophoresis study.
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- Electrophoresis, 1992, v. 13, n. 1, p. 383, doi. 10.1002/elps.1150130178
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- Article