Computation and AI to unlock and engineer biological complexity
At the SMILIES group, we explore computational systems biology as a powerful approach to understanding and engineering biological complexity. By integrating multimodal and multiomic data analysis, multiscale simulation, AI, and optimization, our research develops computational methods for analyzing, modeling, designing, and improving biological systems and bioprocesses.
Single-Cell Multi-Omics
Hybrid Multiscale Modeling
Generative Biofabrication
Intelligent Agriculture
Computational Neuroscience
Funded projects
Saisei PRIN Project
Saisei – Multi-Scale Protocols Generation for Intelligent Biofabrication is a PRIN 2022 project funded by the Italian Ministry for Universities and Research (MUR). The Saisei
Cultura Regenerating Systems PoC-OFF Project
Cultura Regenerating Systems – A Proof of Concept project funded by the Ministry of Economic Development of the Italian Government. The Cultura Regenerating Systems PoC-OFF
cultūrā PoC LIFTT Project
Metodo di simulazione via computer dell’ontogenesi di un sistema biologico e, opzionalmente, di generazione di un protocollo di coltura – A Proof of Concept project
Repositories on GitHub
atlas-smilies
Python package for multi-omic trajectory inference from paired single-cell RNA-seq and ATAC-seq data.
PhysiBench
Curated intracellular Boolean models and multiscale simulation trajectories for computational biology benchmarking.
GH-Co-Accessibility
Pipeline for analyzing GeneHancer regulatory elements, including CIRCE-based co-accessibility networks, GeneHancer–gene associations, cell-type-specific regulatory elements, and cross-cell-type gene–GeneHancer correlation patterns.
atlas-experiments
Code and workflows to reproduce the results from the ATLAS multi-omic trajectory inference paper.
PhysiSandS
PhysiS&S implements the Start & Stop add-on for PhysiCell and PhysiBoSS, enabling simulations to pause based on time or cell conditions, save system-state snapshots, and resume from saved states. This repository includes code and instructions to reproduce the results from the Start&Stop paper.
spike-train-scalograms
Spike train scalograms with fine-tuned CNNs for neuronal cell type classification from electrophysiological recordings.
META2
META² is a meta-model for predicting the performance of meta-heuristic algorithms on arbitrary optimization problems. This repository provides instructions to build, use, and extend the model.
BiSDL
Biology Systems Description Language (BiSDL): a high-level modeling language for biological systems and processes.
LSTMeta-TNF
Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Resistance In Vitro.
rLotos
rLotos: deep reinforcement learning for design space exploration of simulated epithelial sheet biofabrication.
nwn-snakes
Extension of the SNAKES Petri net library adding support for Nets-within-Nets modeling.
RLoscillators
Reinforcement learning for optimizing synthetic oscillatory biological networks.
MAGA
Meta-analysis of gene activity (MAGA) contributions and correlation with gene expression through GAGAM.
NSS
Neuronal Spike Shapes (NSS): analysis of electrophysiological cell profiles from action potential waveforms.
GRAIGH
Gene Regulation Accessibility Integrating GeneHancer (GRAIGH): integrating GeneHancer elements with scATAC-seq data.
MODA
MODA: guidelines and resources for joint scRNA-seq and scATAC-seq data analysis.
GAGAM
Genomic Annotated Gene Activity Matrix (GAGAM) for integrating scATAC-seq accessibility and scRNA-seq expression.
patch-seq-sample-enrichment
Sample size enrichment for single-cell multimodal low-throughput Patch-seq datasets.
Coherence
Coherence: evolutionary design space exploration and co-simulation for optimized biofabrication protocols.
NWN_CElegans_VPC_model
Nets-within-Nets Petri net model of C. elegans vulval precursor cell specification.
Publications
- Bardini, R., & Di Carlo, S. (2024). Computational methods for biofabrication in tissue engineering and regenerative medicine-a literature review. Computational and Structural Biotechnology Journal. https://doi.org/10.1016/j.csbj.2023.12.035
- Castrignanò, A., Bardini, R., Savino, A., & Di Carlo, S. (2024). A methodology combining reinforcement learning and simulation to optimize the in silico culture of epithelial sheets. Journal of Computational Science, 76, 102226. https://doi.org/10.1016/j.jocs.2024.102226
- Giannantoni, L., Bardini, R., & Di Carlo, S. (2022). A Methodology for Co-simulation-Based Optimization of Biofabrication Protocols. In Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) 2022 (pp. 179-192). Gran Canaria. https://doi.org/10.1007/978-3-031-07802-6_16
- Martini, L., Bardini, R., Savino, A., & Di Carlo, S. (2022, June). GAGAM: A genomic annotation-based enrichment of scATAC-seq data for gene activity matrix. In International Work-Conference on Bioinformatics and Biomedical Engineering (pp. 18-32). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-07802-6_2
- Martini, L., Bardini, R., & Di Carlo, S. (2021, December). Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 253-258). IEEE. 10.1109/BIBM52615.2021.9669888
- Bardini, R., Benso, A., Politano, G., & Di Carlo, S. (2021). Nets-within-nets for modeling emergent patterns in ontogenetic processes. Computational and Structural Biotechnology Journal, 19, 5701-5721. https://doi.org/10.1016/j.csbj.2021.10.008
- Muggianu, F., Benso, A., Bardini, R., Hu, E., Politano, G., & Di Carlo, S. (2018, December). Modeling biological complexity using biology system description language (bisdl). In 2018 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 713-717). IEEE. 10.1109/BIBM.2018.8621533
- Bardini, R., Di Carlo, S., Politano, G., & Benso, A. (2018). Modeling antibiotic resistance in the microbiota using multi-level Petri Nets. BMC systems biology, 12(Suppl 6), 108. https://doi.org/10.1186/s12918-018-0627-1
- Bardini, R., Politano, G., Benso, A., & Di Carlo, S. (2018). Computational tools for applying multi-level models to synthetic biology. In Synthetic Biology: Omics Tools and Their Applications (pp. 95-112). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-10-8693-9_7
- Bardini, R., Politano, G., Benso, A., & Di Carlo, S. (2017, November). Using multi-level petri nets models to simulate microbiota resistance to antibiotics. In 2017 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 128-133). IEEE. 10.1109/BIBM.2017.8217637
- Bardini, R., Politano, G., Benso, A., & Di Carlo, S. (2017). Multi-level and hybrid modelling approaches for systems biology. Computational and structural biotechnology journal, 15, 396-402. https://doi.org/10.1016/j.csbj.2017.07.005
- Bardini, R., Benso, A., Di Carlo, S., Politano, G., & Savino, A. (2016, March). Using nets-within-nets for modeling differentiating cells in the epigenetic landscape. In International Conference on Bioinformatics and Biomedical Engineering (pp. 315-321). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-31744-1_28
Involved team
