Robotic technologies for Automated Adaptive Laboratory Evolution

Context & background:Engineering biology addresses global challenges in healthcare,sustainable biomanufacturing,waste management,and agriculture often through microbial engineering.Adaptive Laboratory Evolution (ALE) is a powerful yet simple method to achieve these goals.By culturing microbes under selection pressures and naturally selecting those that adapt, ALE provides an elegant solution for developing organisms with enhanced functionalities. ALE also offers valuable insights into evolutionary trajectories, particularly in microbial strain engineering and antimicrobial resistance (AMR).For example, mimicking antibiotic exposure can reveal key mutations leading to resistance against multiple antibiotics.Meanwhile, adapting cells using ALE can overcome severe growth defects induced by metabolic engineering, or specialise bacterial strains for growth in harsh conditions such as complex waste streams.

Adaptive Laboratory Evolution (ALE) has traditionally relied on serial batch cultivation, which introduces bottlenecks that reduce genetic diversity and create complex, fluctuating selective pressures due to changing environmental conditions. Continuous growth systems like chemostats and turbidostats offer stable conditions, but face challenges such as biofilm formation and high operational costs, limiting their scalability and long-term use.

Our work develops automated platforms developed to mitigate these issues, which this project will extend to new applications.

Aims & Objectives: 1.Biotechnology applications of ALE: Method: Build on current ALE platform that already achieves control of selection pressure and population density to explore a broader range of stresses and environmental conditions.Application:Engineer organisms with industrial significance, such as bacteria with high-temperature tolerance or waste-degrading capabilities.Potential to learn key lessons in learning genes leading to antimicrobial resistance (AMR) and gain insights into the effectiveness of current clinical antibiotic administration strategies.

2.Engineering of novel ALE instrumentation and technologies: -Machine Development:Iteratively improve the physical components of the ALE platform to enhance performance, reliability, and scalability. Such as the introduction of differing pumps and reservoirs for nutrient sources or alternative bacterial populations.-Software Enhancement:Refine software, user experience and algorithms controlling the ALE platform.

Build and test algorithms that learns against behaviours of population against differing selection pressures and design appropriate responses. Novelty of the Research Methodology:

The novelty of this research lies in building machine learning algorithms and computational control to continuously improve technologies developed based on EPSRC funding over the past four years. By dynamically optimizing growth conditions and precisely managing differing selective pressures, the proposed strategy enhances precision control in experimental design and accelerates evolution experiments in ALE.Additionally, the high scalability and automation of the platform enable high-throughput evolution experiments.

By facilitating parallel processing of multiple strains or conditions, the research significantly accelerates discovery and optimization,and (as above) this will be explored for diverse biotechnological applications in academic and industrial research.

Alignment to EPSRC's Strategies and Research Areas:This project falls within the EPSRC Engineering Biology research area. It aligns with EPSRC's strategic priorities by fostering innovation in synthetic biology and advancing the precision and predictability of engineered biological systems. The potential impact on understanding and mitigating antimicrobial resistance also aligns with EPSRC's focus on Healthcare Technologies and tackling global health issues.

The interdisciplinary nature of the project exemplifies the approach encouraged by EPSRC's strategies.