DREAMY - Distributed Algorithms for Microbiological Systems
ANR-funded research project
A key advantage of biological computing devices is their ability to sense, compute, and especially to respond to their biological environment, e.g., bacteria can be programmed to act as autonomous robots within the human body. Local presence of certain molecules in the environment allows sensing of neighboring cell types and acting accordingly, e.g., by activating an immune response. Current designs of synthetic circuits in bacteria, however, face severe resource limitations: each genetic part added to the cell imposes an additional burden, becoming progressively toxic for the cell.
The most common design techniques for biological logic gates rely on gene regulation via DNA-binding proteins, nucleic acid (DNA/RNA) interactions, or more recently the CRISPR machinery. Each comes with its own constraints: like limited availability of orthogonal signals for use within the cell (DNA-binding), small dynamic range (RNA-based), or reduced growth rates (the CRISPR machinery). This has led to recent efforts to distribute circuits among several cells to reduce the resource load per cell, taking the formative steps towards distributed bacterial circuits.
The DREAMY research project seeks to develop innovative solutions to the problem of building distributed circuits in bacteria from an algorithmic, theoretical perspective that contributes to real-world implementable solutions.
We organize the following related workshops and seminar series:
- CELLS - Workshop on Computing among Cells: co-located with the International Symposium on DIStributed Computing (DISC)
- HicDiesMeus - Working Group on Highly Constrained Discrete Agents for Modeling Natural Systems: seminar series on problems from physical, biological, and sociological domains within a distributed computing context
chip design, distributed algorithms
distributed algorithms, stochastic processes
Local Coordinator @ Micalis
genetic circuits, phage communication
AI, model discovery
Azammat Charaf Zadah
RL, distributed systems
timed concurrency, process mining
Luana Martins Ferraz
concurrency, partial orders, systems biology, ecology
Gabriel Le Bouder
stochastic cell processes, distributed computing
bioproduction, predictive models, optimization
control theory, cooperative control of multi-agent systems
Abhinav Vinayak Pujar
AI, data science
Kevin E Awoufack
single-cell analysis, AI
model discovery, AI
bio simulation, C++ development
dynamical systems, control theory
PhD Student (2022/23)
asynchronous circuit design, robust circuits
Letícia Levin Diniz
bio simulation, full-stack web development
microfluidics, embedded systems
Josephine L Ramirez
Thomas Sadigh Rezvani
stochastic models, cell models
AI, neural networks
- Fabricio Cravo, Matthias Függer, Thomas Nowak: An Allee-based Distributed Algorithm for Microbial Whole-Cell Sensors bioRxiv, 2023.
- Hagit Attiya, Armando Castañeda, Thomas Nowak: Topological Characterization of Task Solvability in General Models of Computation. arXiv, 2023. To appear at DISC 2023.
- Arman Ferdowsi, Matthias Függer, Thomas Nowak, Ulrich Schmid: Continuity of Thresholded Mode-Switched ODEs and Digital Circuit Delay Models. To appear at HSCC 2023, 2023.
- Matthias Függer, Christoph Lenzen, Ulrich Schmid: On Specifications and Proofs of Timed Circuits. arXiv, 2022.
- Amit Pathania, Corbin Hopper, Amir Pandi, Matthias Függer, Thomas Nowak, Manish Kushwaha: A synthetic communication system uncovers extracellular immunity that self-limits bacteriophage transmission. bioRxiv, 2022.
- Fabricio Cravo, Matthias Függer, Thomas Nowak, and Gayathri Prakash. Mobspy: A meta-species language for chemical reaction networks. Computational Methods in Systems Biology, 2022.
- Victoria Andaur, Janna Burman, Matthias Függer, Manish Kushwaha, Bilal Manssouri, Thomas Nowak, Joel Rybicki: Reaching Agreement in Competitive Microbial Systems. arXiv, 2021.
- Da-Jung Cho, Matthias Függer, Corbin Hopper, Manish Kushwaha, Thomas Nowak, and Quentin Soubeyran. Distributed computation with continual population growth. Distributed Computing, 2021.
We are grateful for the funding provided by ANR (grant ANR-21-CE48-0003), CNRS, Digicosme, INRAe, Institut Farman, RFSI, and Université Paris-Saclay.