Periodic Reporting for period 1 - ECCO (Evolutionary Cellular Computing for Environmental Synthetic Biology)
Reporting period: 2022-10-01 to 2025-03-31
The overarching goal of this project is to harness evolutionary dynamics in a rational and deliberate manner. While this may seem paradoxical—since evolution is inherently open-ended and not designed to achieve specific targets—we are identifying key elements of evolutionary processes that can be strategically leveraged. These insights enable us to engineer genetic circuits capable of performing computations over evolutionary timescales.
Genetic circuits consist of cascades of regulatory nodes, where genes express products (such as proteins) that, in turn, regulate the expression of subsequent genes in the chain. This "on/off" conceptual framework can be used to engineer basic computational devices, such as Boolean logic functions, constructed from DNA and proteins within living cells like bacteria. However, a significant challenge for the engineering of genetic logic circuits is evolution. Genetic circuits mutate and adapt to environmental circumstances, altering their composition and, consequently, their logical performance. As a result, the functionality of these circuits is often lost over time.
This is where the rational application of evolutionary dynamics becomes pivotal. By integrating principles of evolutionary dynamics into the engineering process, we aim to enhance the stability and adaptability of synthetic genetic circuits—traits that, until now, have been largely confined to natural genetic networks. Our goal is to engineer synthetic networks with these capabilities, making them more robust and versatile in the face of evolutionary pressures.
Computer science provides a rich source of inspiration for this endeavor. Evolutionary Computing has been a highly successful field for decades, leveraging principles of biological evolution to solve complex computational problems. Just as the pioneers of computer science drew inspiration from the workings of living systems, we now look to computer science as a guide for designing synthetic biocomputing devices within living organisms.
Genetic circuits consist of cascades of regulatory nodes, where genes express products (such as proteins) that, in turn, regulate the expression of subsequent genes in the chain. This "on/off" conceptual framework can be used to engineer basic computational devices, such as Boolean logic functions, constructed from DNA and proteins within living cells like bacteria. However, a significant challenge for the engineering of genetic logic circuits is evolution. Genetic circuits mutate and adapt to environmental circumstances, altering their composition and, consequently, their logical performance. As a result, the functionality of these circuits is often lost over time.
This is where the rational application of evolutionary dynamics becomes pivotal. By integrating principles of evolutionary dynamics into the engineering process, we aim to enhance the stability and adaptability of synthetic genetic circuits—traits that, until now, have been largely confined to natural genetic networks. Our goal is to engineer synthetic networks with these capabilities, making them more robust and versatile in the face of evolutionary pressures.
Computer science provides a rich source of inspiration for this endeavor. Evolutionary Computing has been a highly successful field for decades, leveraging principles of biological evolution to solve complex computational problems. Just as the pioneers of computer science drew inspiration from the workings of living systems, we now look to computer science as a guide for designing synthetic biocomputing devices within living organisms.
After two years, the project is progressing according to plan. One of the most challenging and typically high-risk aspects of such projects—the assembly of the research team—went remarkably smoothly from the outset. The ECCO team comprises a highly coherent and interdisciplinary group of scientists, including computer scientists, engineers, molecular biologists, biochemists, and physicists. We have successfully fostered a collaborative environment where scientific questions are addressed collectively across the laboratory.
Our workflow exemplifies this collaboration: after in-depth discussions, the theoreticians design the experiments, the experimentalists build and test them, and the entire team evaluates the outcomes to refine and iterate further.
In WP1, we are focusing on understanding the evolution of DNA sequences (circuits) and the boundaries of their evolutionary design space. Complex circuits requiring intricate behaviors, such as oscillations or bistability, have highly specific design constraints related to evolutionary deviations, mutations, and other factors.
In WP2, we have adapted advanced molecular tools that are now fully operational in our laboratory. Applying these tools to living cells promises to yield groundbreaking approaches for using evolution in synthetic biology.
Additionally, we have initiated experiments in WP3—ahead of schedule—on laboratory-controlled bioremediation setups involving plants, pollutants, and multiple bacterial strains. Our bacterial computers have demonstrated the ability to restore balance in collapsing environments, at least in our simplified lab experiments. Over the remaining project duration, we aim to determine whether these living evolutionary computations can become a transformative technology, as we predict.
Our workflow exemplifies this collaboration: after in-depth discussions, the theoreticians design the experiments, the experimentalists build and test them, and the entire team evaluates the outcomes to refine and iterate further.
In WP1, we are focusing on understanding the evolution of DNA sequences (circuits) and the boundaries of their evolutionary design space. Complex circuits requiring intricate behaviors, such as oscillations or bistability, have highly specific design constraints related to evolutionary deviations, mutations, and other factors.
In WP2, we have adapted advanced molecular tools that are now fully operational in our laboratory. Applying these tools to living cells promises to yield groundbreaking approaches for using evolution in synthetic biology.
Additionally, we have initiated experiments in WP3—ahead of schedule—on laboratory-controlled bioremediation setups involving plants, pollutants, and multiple bacterial strains. Our bacterial computers have demonstrated the ability to restore balance in collapsing environments, at least in our simplified lab experiments. Over the remaining project duration, we aim to determine whether these living evolutionary computations can become a transformative technology, as we predict.
To date, the project has resulted in seven publications. Three stand out for their impact and interdisciplinary nature:
1. Computing Beyond Turing Limits: A theoretical exploration of computational possibilities outside traditional Turing paradigms. DOI:10.1145/3635470
2. Genetic Engineering Tools: Novel tools for editing bacterial chromosomes, designed to advance molecular engineering. DOI:10.1093/synbio/ysad012
3. Laboratory Automation Repository: An open-source resource for automation protocols that bridges biology and engineering. DOI:10.1021/acssynbio.3c00397
These publications reflect the laboratory's strong interdisciplinary focus, situated at the intersection of computing, biology, and engineering.
1. Computing Beyond Turing Limits: A theoretical exploration of computational possibilities outside traditional Turing paradigms. DOI:10.1145/3635470
2. Genetic Engineering Tools: Novel tools for editing bacterial chromosomes, designed to advance molecular engineering. DOI:10.1093/synbio/ysad012
3. Laboratory Automation Repository: An open-source resource for automation protocols that bridges biology and engineering. DOI:10.1021/acssynbio.3c00397
These publications reflect the laboratory's strong interdisciplinary focus, situated at the intersection of computing, biology, and engineering.