Neuronal Spike Shapes (NSS): A straightforward approach to investigate heterogeneity in neuronal excitability states

Martini, Lorenzo; Amprimo, Gianluca; Di Carlo, Stefano; Olmo, Gabriella; Ferraris, Claudia; Savino, Alessandro; Bardini, Roberta

doi:10.1016/j.compbiomed.2023.107783

Cited by 4 publications

(2 citation statements)

References 71 publications

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“…Widely utilized for profiling thousands of single-cell transcriptional profiles, scRNA-seq enables the investigation of cellular heterogeneity based on gene expression [ 1 , 2 , 3 ]. Simultaneously, the emerging popularity of scATAC-seq proves invaluable.…”

Section: Introductionmentioning

confidence: 99%

Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation

Martini,

Bardini,

Savino

et al. 2024

Genes

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It is well known how sequencing technologies propelled cellular biology research in recent years, providing incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to simultaneously perform the mentioned sequencing modalities on the same cells. Yet, there still needs to be a clear and dedicated way to analyze these multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the transcription factors that bind to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TF expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types.

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Section: Introductionmentioning

confidence: 99%

Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation

Martini,

Bardini,

Savino

et al. 2024

Genes

Self Cite

View full text Add to dashboard Cite

show abstract

“…Widely utilized for profiling thousands of single-cell transcriptional profiles, scRNAseq enables the investigation of cellular heterogeneity based on gene expression [1] [2] [3]. Simultaneously, the emerging popularity of scATAC-seq proves invaluable.…”

Section: Introductionmentioning

confidence: 99%

Cross-omic Transcription Factors meta-analysis: an insight on TFs accessibility and expression correlation

Martini,

Bardini,

Savino

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

It is well-known how sequencing technologies propelled cellular biology research in recent years, giving an incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with Single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to perform the mentioned sequencing modalities simultaneously on the same cells. Yet, there still needs to be a clear and dedicated way to analyze this multi-modal data. One of the current methods is to calculate the GAM, which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well-defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the Transcription Factors that binds to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of GAGAM contributions, aiming to investigate the correlation between the TFs expression and motif information in the different functional genomic regions to understand the different TF dynamics involved in different cell types.

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Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

Giannantoni,

Bardini,

Savino

et al. 2024

Preprint

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Background: The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. Results: BiSDL's effectiveness is showcased through two case studies on complex multicellular systems: a bacterial consortium and a synthetic morphogen system. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. Conclusions: BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for the design and simulation of complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

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Neuronal Spike Shapes (NSS): A straightforward approach to investigate heterogeneity in neuronal excitability states

Cited by 4 publications

References 71 publications

Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation

Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation

Cross-omic Transcription Factors meta-analysis: an insight on TFs accessibility and expression correlation

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

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