activity
20242026
collaborators

10 papers

q-bio.MN2026

Systematic pathway comparison on the powerset of rule-based biochemical systems

Anne-Susann Abel, Sissel Banke, Erika M. Herrera Machado +4

Computational pathway design often focuses on evaluating selected pathways or optimizing fluxes in a fixed network, but gives less direct access to the combinatorial question of wh…

q-bio.MN2026

A Collision-based strategy for Network-free Exploration of Complex Molecular Networks

Adittya Pal, Rolf Fagerberg, Jakob Lykke Andersen +2

This work presents a stochastic exploration framework for large, implicitly defined chemical reaction spaces that are too large to be generated and stored as explicit molecular net…

cs.CE2026

Finding Pathways in Reaction Networks guided by Energy Barriers using Integer Linear Programming

Adittya Pal, Rolf Fagerberg, Jakob Lykke Andersen +2

Analyzing synthesis pathways for target molecules in a chemical reaction network annotated with information on the kinetics of individual reactions is an area of active study. This…

q-bio.QM2026

ChemRecon: a Consolidated Meta-Database Platform for Biochemical Data Integration

Casper Asbjørn Eriksen, Jakob Lykke Andersen, Rolf Fagerberg +1

In this paper, we present ChemRecon, a meta-database and Python interface for integrating and exploring biochemical data across multiple heterogeneous resources by consolidating co…

q-bio.QM2026

A Sensitivity Analysis Methodology for Rule-Based Stochastic Chemical Systems

Erika M. Herrera Machado, Jakob L. Andersen, Rolf Fagerberg +1

In this study, we introduce a sensitivity analysis methodology for stochastic systems in chemistry, where dynamics are often governed by random processes. Our approach is based on…

q-bio.MN2025

Rule-Based Gillespie Simulation of Chemical Systems

Erika M. Herrera Machado, Jakob L. Andersen, Rolf Fagerberg +3

The MØD computational framework implements rule-based generative chemistries as explicit transformations of graphs representing chemical structural formulae. Here, we expand MØD by…