Evolutionary Cellular Computing for Environmental Synthetic Biology
The ECCO project aims to create self-adaptive living cellular computers for bioremediation, enhancing robustness and efficiency through intra- and multi-cellular reconfigurability.
Projectdetails
Introduction
This project will build living evolutionary cellular computers and showcase them as intelligent bioremediation agents. Current synthetic genetic networks that perform human-defined computations must remain unchanged—as initially designed—in order to perform well. This is a problem since biological substrates adapt and evolve, compromising durability, robustness, and computing power.
Objectives
We will exploit the intrinsic dynamic features of living systems. ECCO’s biocomputers will be able to self-adapt and reconfigure at run-time. They will show unprecedented levels of robustness and efficiency—far beyond current technological limits.
Intra-Cellular Evolvability
To this end, we will tackle intra-cellular evolvability and multi-cellular reconfigurability. At the intra-cellular level, we will upgrade current genetic circuitry with pre-defined mutation, evaluation, and selection dynamics. Circuits will optimize themselves.
Multi-Cellular Reconfigurability
At the multi-cellular level, we will design cellular consortia able to reconfigure their structure—therefore changing their functionality—according to environmental needs, thus adaptive.
Integration of Theory and Experimentation
The ECCO project will integrate theoretical developments with in-vivo experimentation. The soil bacteria Pseudomonas putida will be used as a host to illustrate the capabilities of evolutionary genetic circuits.
Demonstration of Efficiency
To demonstrate long-run efficiency, bacteria will be used to colonize the root of the plant Arabidopsis thaliana—a much more complex environment than the pristine laboratory conditions where circuits are often characterized.
Reconfigurability and Pollution
Reconfigurability will be achieved by building a multicellular computer able to switch between metal and aromatic removal circuits—two important pollutants.
Conclusion
Evolution, adaptation, and reconfigurability are elusive to conventional computers; conveniently, these are intrinsic properties of living organisms. The ECCO will benefit from this in order to engineer living computers that unlock applications in novel domains—from synthetic agriculture to precision bioremediation.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 2.131.809 |
Totale projectbegroting | € 2.131.809 |
Tijdlijn
Startdatum | 1-10-2022 |
Einddatum | 30-9-2027 |
Subsidiejaar | 2022 |
Partners & Locaties
Projectpartners
- AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICASpenvoerder
- UNIVERSIDAD POLITECNICA DE MADRID
Land(en)
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From single cells to microbial consortia: bridging the gaps between synthetic circuit design and emerging dynamics of heterogeneous populations
The project aims to develop mathematical methods to control synthetic gene circuits in microbial populations, enhancing functionality and bioproduction of challenging proteins through population dynamics.
Engineering biological timers and their applications
The project aims to engineer programmable molecular timers in E. coli for autonomous control of cellular actions, enhancing applications in bioproduction and biosensing.
Mapping vast functional landscapes with single-species resolution: a new approach for precision engineering of microbial consortia
ECOPROSPECTOR aims to optimize microbial community composition for enhanced starch hydrolysis using machine learning and evolutionary theories, bridging ecology and biotechnology.
Engineering, Analysis and Control of Biomolecular Circuits Under Uncertainty
CellWise aims to enhance predictability in Synthetic Biology by developing mathematical tools for engineering complex biomolecular circuits, focusing on cell cognition and decision-making under uncertainty.
Evolving Organs-on-Chip from developmental engineering to “mechanical re-evolution”
EvOoC develops smart Organs-on-Chip platforms that utilize mechanical forces and machine learning to enhance tissue regeneration and disease modeling for innovative therapeutic solutions.
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