AUTHOR=Sibanda Innocent , Nitschke Geoff 
  
TITLE=Bioengineering hybrid artificial life
  
JOURNAL=Frontiers in Bioinformatics
  
VOLUME=Volume 5 - 2025
  
YEAR=2025
  
URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1676359
  
DOI=10.3389/fbinf.2025.1676359
  
ISSN=2673-7647
  
ABSTRACT=The goal of bioengineering in synthetic biology is to redesign, reprogram, and rewire biological systems for specific applications using standardized parts such as promoters and ribosomes. For example, bioengineered micro-organisms capable of cleaning up environmental pollution or producing antibodies de novo to defend against viral pandemics have been predicted. Artificial Life (ALife) facilitates the design and understanding of living systems, not just those found in nature, but life as it could be, while synthetic biology provides the means to realize life as it can be engineered. Despite significant advances, the synthesis of evolving, adaptable, and bioengineered problem-solving ALife has yet to achieve practical feasibility. This is primarily due to limitations in directed evolution, fitness landscape mapping, and fitness approximation. Thus, currently synthetic (biological) ALife does not continue to evolve and adapt to changing tasks and environments. This is in stark contrast to current digital based ALife that continues to adapt and evolve in simulated environments demonstrating the dictum of life as it could be. We posit that if the bioengineering (on-demand design) of problem solving ALife is to ever become a reality then open issues pervading the directed evolution of synthetic ALife must first be addressed. This review examines open challenges in directed evolution, genetic diversity generation, fitness mapping, and fitness estimation, and outlines future directions toward a hybrid synthetic ALife design methodology. This review provides a novel perspective for a singular (hybridized) evolutionary design methodology, combining digital (in silico) and synthetic (in vitro) evolutionary design methods drawn from various bioengineering, digital and robotic ALife applications, while addressing highlighted directed evolution deficiencies.