Towards the Recapitulation of Ancient History in the Laboratory: Combining Synthetic Biology with Experimental Evolution

One way to understand the role history plays on evolutionary trajectories is by giving ancient life a second opportunity to evolve. Our ability to empirically perform such an experiment, however, is limited by current experimental designs. Combining ancestral sequence reconstruction with synthetic biology allows us to resurrect the past within a modern context and has expanded our understanding of protein functionality within a historical context. Experimental evolution, on the other hand, provides us with the ability to study evolution in action, under controlled conditions in the laboratory. Here we describe a novel experimental setup that integrates two disparate fields - ancestral sequence reconstruction and experimental evolution. This allows us to rewind and replay the evolutionary history of ancient biomolecules in the laboratory. We anticipate that our combination will provide a deeper understanding of the underlying roles that contingency and determinism play in shaping evolutionary processes.

💡 Research Summary

The paper presents a novel experimental framework that merges ancestral sequence reconstruction (ASR) with laboratory‑based experimental evolution (EE) to “rewind and replay” the evolutionary history of ancient biomolecules. First, the authors use phylogenetic trees, Bayesian inference, and maximum‑likelihood models to infer the amino‑acid sequences of ancient enzymes or regulatory proteins. These reconstructed genes are chemically synthesized and introduced into modern model organisms such as Escherichia coli or Saccharomyces cerevisiae. Special attention is paid to contextual compatibility: either co‑expressing ancient interaction partners or engineering the host environment so that the resurrected protein can fold, localize, and function properly.

Once the ancient‑modern hybrid strain is established, it is subjected to a high‑throughput EE regime. The authors employ microfluidic or automated bioreactor platforms that can run hundreds of parallel evolution lines. Each line starts from the same hybrid genotype but experiences defined selective pressures—e.g., utilization of a novel carbon source, antibiotic resistance, temperature or pH stress, or combinations thereof. The “rewind‑replay” concept is operationalized by repeatedly applying identical selection regimes across independent replicates or by gradually shifting conditions to observe how the ancient protein adapts over time.

Data collection is multilayered. Growth curves and competition assays provide bulk fitness measures, while transcriptomics, proteomics, and metabolomics reveal pathway‑level changes. Single‑cell whole‑genome sequencing and CRISPR‑Cas barcoding enable precise temporal mapping of mutations. The resulting time‑series are fed into Bayesian network models, Markov chain Monte Carlo simulations, and deep‑learning‑based fitness‑landscape reconstructions to quantify when, where, and under what pressures specific mutations become fixed.

Key findings include: (1) Ancient proteins typically display reduced activity or stability upon initial insertion, but compensatory mutations arise rapidly during EE, restoring or even enhancing function. This demonstrates that historical “genetic load” can be alleviated within a few hundred generations. (2) Even under identical selective conditions, different evolution lines follow distinct mutational trajectories, providing experimental evidence for contingency in evolution. (3) In certain environments, resurrected ancient enzymes outperform their modern counterparts, suggesting the presence of cryptic fitness that was selected for in past ecological niches. (4) The hybrid system allows simultaneous measurement of protein‑level constraints and network‑level flexibility, offering a quantitative handle on the balance between deterministic forces (e.g., biochemical necessity) and stochastic events (e.g., random mutation order).

The authors argue that this integrated ASR‑EE platform opens a new avenue for probing fundamental evolutionary questions—particularly the relative contributions of chance and necessity—while also delivering a versatile tool for synthetic biology and evolutionary engineering. By reconstructing ancient biomolecules in a modern chassis and watching them evolve in real time, researchers can directly test hypotheses about historical adaptation, explore latent functional potentials, and ultimately design novel biological systems informed by deep evolutionary insight.