12 papers · ranked by Valyu relevance
Alexander Lalejini, Emily Dolson, Anya E. Vostinar, Luis Zaman
Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a…
André L. L. Moreira, César Rennó-Costa
Evolution optimizes cellular behavior throughout sequential generations by selecting the successful individual cells in a given context. As gene regulatory networks (GRNs) determine the behavior of single cells by ruling the activation of different processes - such as cell differentiation and death - how GRNs change…
Lillian T. Tatka, Lucian P. Smith, Herbert M. Sauro
Evolutionary algorithms, a class of optimization techniques inspired by biological evolution, have emerged as powerful tools for the optimization of complex systems, including the evolution of mass-action chemical reaction networks. This work explores the application of evolutionary algorithms in this domain…
Kira Villiers, Kai P. Voss-Fels, Eric Dinglasan, Bertus Jacobs + 2 more
Loss of genetic diversity in elite crop breeding pools can severely limit long-term genetic gains, and limits gain for new traits that are becoming important as the climate changes, such as heat tolerance. Introgression of specific traits and pre-breeding germplasm is an alternative to introduce new diversity, however…
Artem Kaznatcheev
Valiant [1] proposed to treat Darwinian evolution as a special kind of computational learning from statistical queries. The statistical queries represent a genotype’s fitness over a distribution of challenges. And this distribution of challenges along with the best response to them specify a given abiotic environment…
Stefano Nolfi, Paolo Pagliuca
We investigate the use of competitive co-evolution for synthesizing progressively better solutions. Specifically, we introduce a set of methods to measure historical and global progress. We discuss the factors that facilitate genuine progress. Finally, we compare the efficacy of four qualitatively different algorithms.…
Martín Gutiérrez, Yerko Ortiz, Javier Carrión
Metaheuristic procedures (MH) have been a trend driving Artificial Intelligence (AI) researchers for the past 50 years. A variety of tools and applications (not only in Computer Science) stem from these techniques. Also, MH frequently rely on evolution, a trademark process involved in cell colony growth. Generally, MH…
Kangbien Park
Directed evolution of asexual populations is expected to offer a wide range of benefits to humanity. Achieving efficient directed evolution (DE) requires a quantitative formulation of the experimental methodologies—or logics—that can address potential challenges throughout the evolutionary process. In this article, I…
João Sartori, Eduardo Krempser, Ana Carolina Ramos Guimarães, Lucas de Almeida Machado
The optimization of protein sequences for enhanced binding and stability remains a formidable challenge in bioengineering due to the vastness of sequence space. Existing state-of-the-art methods, including traditional structure-based design and protein language models, use fitness estimators as objective functions to…
Oliver Attie, Brian Sulkow, Chong Di, Wei-Gang Qiu
Learning algorithms have been proposed as a non-selective mechanism capable of creating complex adaptive systems in life. Evolutionary learning however has not been demonstrated to be a plausible cause for the origin of a specific molecular system. Here we show that genetic codes as optimal as the Standard Genetic Code…
Juliette Luiselli, David P. Parsons, Romain Gallé, Paul Banse + 2 more
Aevol is a forward-in-time simulator of genome architecture. It simulates a population of individuals, each with an explicit genome whose sequence and architecture can be modified by various mutational operators, including substitutions, indels, and large-scale chromosomal rearrangements. This enables performing in…
Kangbien Park
Humans have long employed directed evolution (DE) to engineer desired biological traits. In this paper, I introduce an algebraic framework that provides a quantitative representation of the general phenotypic traits of asexual populations, enabling the systematic modeling of DE processes. Within this framework, key…