Interactive server: https://cheminf.imada.sdu.dk/mod/
Understanding the emergence and evolution of complex systems is a core challenge in artificial life. Chemistry, as the foundation of biological organisation, offers a powerful lens through which to explore these processes. MØD is a computational tool developed by the University of Southern Denmark that enables the automated generation, simulation, and optimisation of chemical reaction networks, leveraging graph-based rewrite rules to represent molecular transformations.
This tutorial will introduce MØD as a versatile platform for exploring chemical systems in ways that intersect with artificial life research. Participants will learn how to generate derivation graphs - visual representations of reaction networks - and extend their analyses to stochastic simulations and pathway optimisation via the in-built flow query tool. MØD also offers a variety of functionality at a more molecular/mechanistic level, such as electron pushout diagrams for depicting the exact mechanisms behind the reaction. The tutorial will include bespoke examples that detail how to take advantage of MØD’s chemical exploration capabilities, for instance, in modelling mass spectrometry. By bridging chemistry, computation, and network modeling, MØD provides a functional framework for investigating prebiotic chemistry, protocell development, and other emergent properties of complex reaction networks.
While this introductory session is designed for researchers from diverse backgrounds, including biology, chemistry, and computational modeling, who are interested in applying powerful computational techniques for the modeling of chemical and biochemical systems, we recommend attendees have basic knowledge surrounding Python and the associated syntax, and experience using command line functionalities. Additional experience with text-based editors is useful but not necessary.
MØD documentation: https://jakobandersen.github.io/mod/index.html