October 7, 2022

Event Goal: Host 10 talks and a self-catered networking lunch on research at the intersection of synthetic biology and machine learning. Talks will be 20 minutes each, with 10 minutes of question and discussion time.

Event Location: Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, House 18, Room “Maud-Menten”.

Agenda

10:00 Ioana Gherman, University of Bristol
Accelerating whole-cell modelling with surrogate machine learning models

10:30 Dilan Pathirana, University of Bonn
Efficient brute-force model selection by iterative elimination of less-useful moel subspaces

11:00 Evangelos-Marios Nikolados, University of Edinburgh
Accuracy and data efficiency in deep learning sequence-to-expression models

11:30 St. Elmo Wilken, Heinrich Heine Universität Düsseldorf
Interrogating the effect of enzyme kinetics on metabolism using diferentiable constraint-based models

12:00 Jordan Conolly, Newcastle University
Challenges and Prospects of Machine Learning applied to Nucleic Acid Origami

12:30 Networking Lunch

1:30 Charlotte Merzbacher, University of Edinburgh
A fast and scalable machine learning approach for dynamic metabolic engineering

2:00 Xavier Hernandez-Allas, Centre for Genomic Regulation, Barcelona
Tissue-specific codon usage: from systems to synthetic biology

2:30 Melania Nowicka, Freie Universität Berlin
Learning synthetic cell classifier designs with genetic algorithms and logic programming

3:00 Yu Wang, KTH Royal Institute of Technology, Stockholm
Feasibility of learning interactions between synthetic circuit and host cell from perturbation experiments

3:30 Maren Philipps, University of Bonn
Hybrid mechanistic and deep learning modeling in systems biology

4:00 Event ends