CLC Seminar - Constraint-based Abstraction of Reaction Networks to Boolean Networks

Feb 26, 2024

05:00 PM

Jessup Hall, 1

5 West Jefferson Street, Iowa City, IA 52240

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Athénaïs Vaginay portrait - submitted


Athénaïs Vaginay


Reaction networks and Boolean networks are two prominent approaches for modeling biological systems. They rely on drastically different philosophies: the former consists of a detailed description with continuous time and values, and the latter uses discrete steps and Boolean values. The automatic conversion of reaction networks to Boolean networks is an important challenge of symbolic systems biology, which I address with the SBML2BNET pipeline.

My presentation delves into the pivotal role of formal methods during the two steps of the conversion process: (1) in the definition of the properties of the input reaction network we want to preserve and (2) the synthesis of Boolean networks complying with these properties. The evaluation of SBML2BNET on synthetic examples and on real-world reaction networks from the repository BioModels has demonstrated its effectiveness. The perspectives mainly concern the practical relevance of the Boolean networks we synthesize, as well as the formal exploration of the relationship with other semantics of reaction networks.


Athénaïs Vaginay is a computational biologist working in symbolic systems biology. She received a PhD in Computer Science from the University of Lorraine, France for a thesis on the generation of boolean network models from reaction networks. In April she will start a postdoc in the research group of Loïc Paulevé at the LaBRI laboratory at the University of Bordeaux.

The Computational Logic Center at The University of Iowa seeks to advance the theory and practice of correct software development, by applying techniques from logic, programming languages, and automated theorem proving.

Our primary research areas are foundations of programming languages, type theory, computer-aided verification, automated reasoning, computer security and privacy.

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