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Damiano Ognissanti

dblp:417/2001 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › systems biology
dynamical system modeling
0.912025
PEtab.jl: advancing the efficiency and utility of dynamic modelling · Bioinform. 2025
Bioinformatics and computational biology › systems biology
parameter estimation
0.912025
PEtab.jl: advancing the efficiency and utility of dynamic modelling · Bioinform. 2025
Bioinformatics and computational biology
systems biology
0.912025
PEtab.jl: advancing the efficiency and utility of dynamic modelling · Bioinform. 2025
Bioinformatics and computational biology › systems biology › parameter estimation
identifiability analysis
0.312025
PEtab.jl: advancing the efficiency and utility of dynamic modelling · Bioinform. 2025

Methods — techniques the papers use, named apart from their topics

symbolic pre-processing · 0.9SBML import · 0.9ODE solver · 0.9
YearPublicationVenuePosition
2025 PEtab.jl: advancing the efficiency and utility of dynamic modelling
abstract
SUMMARY: Dynamic models represent a powerful tool for studying complex biological processes, ranging from cell signalling to cell differentiation. Building such models often requires computationally demanding modelling workflows, such as model exploration and parameter estimation. We developed two Julia-based tools: SBMLImporter.jl, an SBML importer, and PEtab.jl, an importer for parameter estimation problems in the PEtab format, designed to streamline modelling processes. These tools leverage Julia's high-performance computing capabilities, including symbolic pre-processing and advanced ODE solvers. PEtab.jl aims to be a Julia-accessible toolbox that supports the entire modelling pipeline from parameter estimation to identifiability analysis. AVAILABILITY AND IMPLEMENTATION: SBMLImporter.jl and PEtab.jl are implemented in the Julia programming language. Both packages are available on GitHub (github.com/sebapersson/SBMLImporter.jl and github.com/sebapersson/PEtab.jl) as officially registered Julia packages, installable via the Julia package manager. Each package is continuously tested and supported on Linux, macOS, and Windows.
Sebastian Persson, Fabian Fröhlich, Stephan Grein, Torkel Loman, Damiano Ognissanti, Viktor Hasselgren, Jan Hasenauer, Marija Cvijovic
Bioinform.5