Evolutionary trajectories are constrained by trade-offs when mutations that benefit one life history trait incur fitness costs in other traits. As resistance to tetracycline antibiotics by increased efflux can be associated with an increase in length of the Escherichia coli chromosome of 10% or more, we sought costs of resistance associated with doxycycline. However, it was difficult to identify any because the growth rate (r), carrying capacity (K) and drug efflux rate of E. coli increased during evolutionary experiments where the species was exposed to doxycycline. Moreover, these improvements remained following drug withdrawal. We sought mechanisms for this seemingly unconstrained adaptation, particularly as these traits ought to trade-off according to rK selection theory. Using prokaryote and eukaryote microorganisms, including clinical pathogens, we show that r and K can trade-off, but need not, because of 'rK trade-ups'. r and K trade-off only in sufficiently carbon-rich environments where growth is inefficient. We then used E. coli ribosomal RNA (rRNA) knockouts to determine specific mutations, namely changes in rRNA operon (rrn) copy number, than can simultaneously maximize r and K. The optimal genome has fewer operons, and therefore fewer functional ribosomes, than the ancestral strain. It is, therefore, unsurprising for r-adaptation in the presence of a ribosome-inhibiting antibiotic, doxycycline, to also increase population size. We found two costs for this improvement: an elongated lag phase and the loss of stress protection genes.
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http://dx.doi.org/10.1038/s41559-016-0050 | DOI Listing |
Phys Rev E
October 2024
School of Mathematics and Statistics, and Computational Sciences Hubei Key Laboratory, Wuhan University, Wuhan 430072, China.
We introduce a deep least action method (DLAM) rooted in the principle of least action to solve the trajectory of an evolution problem. DLAM offers an efficient unsupervised solution and can be applied once the action or Lagrangian of the concerned physical system is clear, totally avoiding the differential equations. As required by the least action principle, we incorporate a normalized deep neural network to exactly satisfy the initial-terminal value conditions; thus the evolution problem is transformed into an unconstrained optimization problem.
View Article and Find Full Text PDFArtif Life
October 2024
University of Tübingen, Department of Computer Science, Max Planck Institute for Biological Cybernetics, Bernstein Center for Computational Neuroscience Tübingen.
Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Yet, how the environmental factors and structural constraints on the learning network influence the optimal plasticity mechanisms remains obscure even for simple settings. To elucidate these dependencies, we study meta-learning via evolutionary optimization of simple reward-modulated plasticity rules in embodied agents solving a foraging task.
View Article and Find Full Text PDFOpen Res Eur
June 2024
Institute of Theoretical Astrophysics, University of Oslo, Oslo, Oslo, Norway.
Our knowledge of galaxy formation and evolution has incredibly progressed through multi-wavelength observational constraints of the interstellar medium (ISM) of galaxies at all cosmic epochs. However, little is known about the physical properties of the more diffuse and lower surface brightness reservoir of gas and dust that extends beyond ISM scales and fills dark matter haloes of galaxies up to their virial radii, the circumgalactic medium (CGM). New theoretical studies increasingly stress the relevance of the latter for understanding the feedback and feeding mechanisms that shape galaxies across cosmic times, whose cumulative effects leave clear imprints into the CGM.
View Article and Find Full Text PDFSci Rep
September 2024
Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, Alexandria, 21934, Egypt.
Financial Portfolio Optimization Problem (FPOP) is a cornerstone in quantitative investing and financial engineering, focusing on optimizing assets allocation to balance risk and expected return, a concept evolving since Harry Markowitz's 1952 Mean-Variance model. This paper introduces a novel meta-heuristic approach based on the Black Widow Algorithm for Portfolio Optimization (BWAPO) to solve the FPOP. The new method addresses three versions of the portfolio optimization problems: the unconstrained version, the equality cardinality-constrained version, and the inequality cardinality-constrained version.
View Article and Find Full Text PDFBiomimetics (Basel)
September 2024
AI Group, Department of Informatics, University of Sussex, Brighton BN1 9RH, UK.
This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants, namely biased eavesdropping PSO (BEPSO) and altruistic heterogeneous PSO (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited in search algorithms. The primary search behaviour of the BEPSO algorithm is inspired by eavesdropping behaviour observed in nature coupled with a cognitive bias mechanism that enables particles to make decisions on cooperation.
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