MOTIVATION: Defective viral genomes (DVGs) are variants of the wild-type (wt) virus that lack the ability to complete autonomously an infectious cycle. However, in the presence of their parental (helper) wt virus, DVGs can interfere with the replication, encapsidation, and spread of functional genomes, acting as a significant selective force in viral evolution. DVGs also affect the host's immune responses and are linked to chronic infections and milder symptoms. Thus, identifying and characterizing DVGs is crucial for understanding infection prognosis. Quantifying DVGs is challenging due to their inability to sustain themselves, which makes it difficult to distinguish them from the helper virus, especially using high-throughput RNA sequencing. An accurate quantification is essential for understanding their very dynamical interactions with the helper virus. RESULTS: We present a method to simultaneously estimate the abundances of DVGs and wt genomes within a sample by identifying genomic regions with significant deviations from the expected sequencing depth. Our approach involves reconstructing the depth profile through a linear system of equations, which provides an estimate of the number of wt and DVG genomes of each type. Until now, in silico methods have only estimated the DVG-to-wt ratio for localized genomic regions. This is the first method that simultaneously estimates the proportions of wt and DVGs genome wide from short-reads RNA sequencing. AVAILABILITY AND IMPLEMENTATION: The Matlab code and the synthetic datasets are freely available at https://github.com/jmusan/wtDVGquantific.

If they undergo new mutations at each replication cycle, why are RNA viral genomes so fragile, with most mutations being either strongly deleterious or lethal? Here we provide theoretical and numerical evidence for the hypothesis that genetic fragility is partly an evolutionary response to the multiple population bottlenecks experienced by viral populations at various stages of their life cycles. Modelling within-host viral populations as multi-type branching processes, we show that mutational fragility lowers the rate at which Muller's ratchet clicks and increases the survival probability through multiple bottlenecks. In the context of a susceptible-exposed-infectious-recovered epidemiological model, we find that the attack rate of fragile viral strains can exceed that of more robust strains, particularly at low infectivities and high mutation rates. Our findings highlight the importance of demographic events such as transmission bottlenecks in shaping the genetic architecture of viral pathogens.

The EvoEvo project was a 2013–2017 FP7 European project aiming at developing new evolutionary approaches in information science and producing novel algorithms based on the current understanding of molecular and evolutionary biology, with the ultimate goals of addressing open-ended problems in which the specifications are either unknown or too complicated to express, and of producing software able to operate even in unpredictable, varying conditions. Here we present the main rationals of the EvoEvo project and propose a set of design rules to evolve adaptive software systems. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

How does complexity evolve in artificial and natural systems? A central concept within genetic systems is epistasis, namely the modulation of the effects of a given gene by one or several other genes. Epistasis is known to have an impact on many features of organisms, from recombination and sex to the ruggedness of the underlying fitness landscapes. However, the multi-scale nature of evolution and organisms makes often difficult to properly characterize epistatic interactions. Here we study the hierarchical organization of epistatic interactions between machine instructions in evolved digital organisms. We present a new quantitative approach to discover epistatic interactions that is able to capture the presence and role of groups of epistatic modules. Therefore, it thus takes into account the intrinsic nested nature of individual complexity. We found evidences of modular epistasis in avidians, with some modules having a tendency toward antagonistic epistasis while others show the opposite epistatic sign. We also found that this modular organization was positively correlated to organismal robustness.

Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites.

The rate of mutation is central to evolution.Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious.As a consequence, the mutation rate that maximizes adaptation will be some intermediate value.Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates.We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation.Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum.Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings.We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes.To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates.In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost.We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation.This finding has important implications for applied evolutionary research in both biological and computational realms.