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CONSYN SIGNED

Contextualizing biomolecular circuit models for synthetic biology

Total Cost €

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EC-Contrib. €

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Partnership

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Project "CONSYN" data sheet

The following table provides information about the project.

Coordinator
TECHNISCHE UNIVERSITAT DARMSTADT 

Organization address
address: KAROLINENPLATZ 5
city: DARMSTADT
postcode: 64289
website: www.tu-darmstadt.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 1˙996˙579 €
 EC max contribution 1˙996˙579 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-COG
 Funding Scheme ERC-COG
 Starting year 2018
 Duration (year-month-day) from 2018-04-01   to  2023-03-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITAT DARMSTADT DE (DARMSTADT) coordinator 1˙996˙579.00

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 Project objective

Synthetic biology is the bottom-up engineering of new molecular functionality inside a biological cell. Although it aims at a quantitative and compositional approach, most of today’s implementations of synthetic circuits are based on inefficient trial-and-error runs. This approach to circuit design does not scale well with circuit complexity and is against the basic paradigm of synthetic biology. This unsatisfactory state of affairs is partly due to the lack of the right computational methodology that can support the quantitative characterization of circuits and their significant context dependency, i.e., their change in behavior upon interactions with the host machinery and with other circuit elements. CONSYN will contribute computational methodology to overcome the trial-and-error approach and to ultimately turn synthetic circuit design into a rational bottom-up process that heavily relies on computational analysis before any actual biomolecular implementation is considered. In order to achieve this goal, we will work on the following agenda: (i) develop biophysical and statistical models of biomolecular contexts into which the synthetic circuit or synthetic part can be embedded in silico; (ii) devise new statistical inference methods that can deliver accurate characterization of circuits and their context dependency by making use of cutting-edge single-cell experimental data; (iii) derive new context-insensitive circuit designs through in silico sensitivity analysis and application of filtering theory; (iv) optimize protocols and measurement infrastructure using model-based experimental design yielding a better circuit and context characterization; (v) experimentally build synthetic circuits in vivo and in cell-free systems in order to validate and bring to life the above theoretical investigations. We are in the unique position to also address (v) in-house due to the experimental wetlab facilities in our group.

 Deliverables

List of deliverables.
Data Management Plan Open Research Data Pilot 2019-08-07 10:53:38

Take a look to the deliverables list in detail:  detailed list of CONSYN deliverables.

 Publications

year authors and title journal last update
List of publications.
2019 Altintan Derya Koeppl Heinz
Hybrid master equation for jump-diffusion approximation of biomolecular reaction networks
published pages: , ISSN: 1572-9125, DOI:
BIT Numerical Mathematics 2019-11-28
2019 Christian Wildner, Heinz Koeppl
Moment-Based Variational Inference for Markov Jump Processes
published pages: , ISSN: , DOI:
Proceedings of the 36th International Conference on Machine Learning 2019-11-07
2019 François-Xavier Lehr, Maleen Hanst, Marc Vogel, Jennifer Kremer, H. Ulrich Göringer, Beatrix Suess, Heinz Koeppl
Cell-Free Prototyping of AND-Logic Gates Based on Heterogeneous RNA Activators
published pages: 2163-2173, ISSN: 2161-5063, DOI: 10.1021/acssynbio.9b00238
ACS Synthetic Biology 8/9 2019-11-07
2019 Johannes Falk, Leo Bronstein, Maleen Hanst, Barbara Drossel, Heinz Koeppl
Context in synthetic biology: Memory effects of environments with mono-molecular reactions
published pages: 24106, ISSN: 0021-9606, DOI: 10.1063/1.5053816
The Journal of Chemical Physics 150/2 2019-11-07
2019 Dominik Lindner, Michael Schmidt, Heinz Koeppl
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
published pages: , ISSN: , DOI:
2019-11-07

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The information about "CONSYN" are provided by the European Opendata Portal: CORDIS opendata.

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