scholarly journals Catalytic promiscuity in the RNA World may have aided the evolution of prebiotic metabolism

2021 ◽  
Vol 17 (1) ◽  
pp. e1008634
Author(s):  
Dániel Vörös ◽  
Balázs Könnyű ◽  
Tamás Czárán

The Metabolically Coupled Replicator System (MCRS) model of early chemical evolution offers a plausible and efficient mechanism for the self-assembly and the maintenance of prebiotic RNA replicator communities, the likely predecessors of all life forms on Earth. The MCRS can keep different replicator species together due to their mandatory metabolic cooperation and limited mobility on mineral surfaces, catalysing reaction steps of a coherent reaction network that produces their own monomers from externally supplied compounds. The complexity of the MCRS chemical engine can be increased by assuming that each replicator species may catalyse more than a single reaction of metabolism, with different catalytic activities of the same RNA sequence being in a trade-off relation: one catalytic activity of a promiscuous ribozyme can increase only at the expense of the others on the same RNA strand. Using extensive spatially explicit computer simulations we have studied the possibility and the conditions of evolving ribozyme promiscuity in an initial community of single-activity replicators attached to a 2D surface, assuming an additional trade-off between replicability and catalytic activity. We conclude that our promiscuous replicators evolve under weak catalytic trade-off, relatively strong activity/replicability trade-off and low surface mobility of the replicators and the metabolites they produce, whereas catalytic specialists benefit from very strong catalytic trade-off, weak activity/replicability trade-off and high mobility. We argue that the combination of conditions for evolving promiscuity are more probable to occur for surface-bound RNA replicators, suggesting that catalytic promiscuity may have been a significant factor in the diversification of prebiotic metabolic reaction networks.

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.


Gene ◽  
2007 ◽  
Vol 389 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Elisa Biondi ◽  
Sergio Branciamore ◽  
Luca Fusi ◽  
Selma Gago ◽  
Enzo Gallori

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771905 ◽  
Author(s):  
Pengpeng Chen ◽  
Ying Chen ◽  
Shouwan Gao ◽  
Qiang Niu ◽  
Jun Gu

Due to the combination of constrained power, low duty cycle, and high mobility, neighbor discovery is one of the most challenging problems in wireless sensor networks. Existing discovery designs can be divided into two types: pairwise-based and group-based. The former schemes suffer from high discovery delay, while the latter ones accelerate the discovery process but incur too much energy overhead, far from practical. In this article, we propose a novel efficient group-based discovery method based on relative distance, which makes a delicate trade-off between discovery delay and energy consumption. Instead of directly referring to the wake-up schedules of a whole group of nodes, efficient group-based discovery selectively recommends nodes that are most likely to be neighbors, in which the probability is calculated based on the nodes’ relative distance. Moreover, the sequence of received signal strengths are employed to estimate the relative distance for avoiding the effect of the node distribution. Extensive simulations are conducted to verify the effectiveness of the design. The results indicate that efficient group-based discovery statistically achieves a good trade-off between energy cost and discovery latency. Efficient group-based discovery also shows one order of magnitude reduction in discovery delay with a maximum of 6.5% increase in energy consumption compared with typical discovery methods.


2021 ◽  
Vol 12 (1) ◽  
pp. 8-16
Author(s):  
Talita Leite dos Santos Moraes ◽  
Joana Monteiro Fraga de Farias ◽  
Brunielly Santana Rezende ◽  
Fernanda Oliveira de Carvalho ◽  
Michael Silveira Santiago ◽  
...  

Background: Progressive mobility in the ICU has been recommended; however, the definitions of low, moderate, and high mobility in the ICU still diverge between studies. Therefore, our objective was to classify the mobility of the sample from verticalization and active withdrawal from the bed, and from that, to analyze the chances of discharge, death, and readmission to the ICU. Materials and methods: This is an observational and retrospective study that consults the medical records of individuals admitted to the ICU of the University Hospital of Sergipe (HU/SE) between August 2017 and August 2018. Mobility level was classified based on the Intensive Care Unit Mobility Scale (IMS). Results: A total of 121 individuals were included. The mean age was 61.45 ± 16.45, being 53.7% female. Of these, 28 (23.1%) had low mobility, 33 (27.3%) had moderate mobility, and 60 (49.6%) had high mobility. Individuals with low mobility were 45 times more likely to die (OR = 45.3; 95% CI = 3.23–636.3) and 88 times less likely to be discharged from the ICU (OR = 0.22; 95% CI = 0.002–0.30). Conclusion: Those who evolved with low mobility had a higher chance of death and a lower chance of discharge from the ICU. Moderate and high mobility were not associated with the investigated outcomes.


2019 ◽  
Vol 30 (6) ◽  
pp. 1522-1529 ◽  
Author(s):  
Kentarou Matsumura ◽  
C Ruth Archer ◽  
David J Hosken ◽  
Takahisa Miyatake

AbstractSecuring matings is a key determinant of fitness, and in many species, males are the sex that engages in mate searching. Searching for mates is often associated with increased mobility. This elevated investment in movement is predicted to trade-off with sperm competitiveness, but few studies have directly tested whether this trade-off occurs. Here, we assessed whether artificial selection on mobility affected sperm competitiveness and mating behavior, and if increased mobility was due to increased leg length in red flour beetles (Tribolium castaneum). We found that, in general, males selected for decreased mobility copulated for longer, stimulated females more during mating, and tended to be better sperm competitors. Surprisingly, they also had longer legs. However, how well males performed in sperm competition depended on females. Males with reduced mobility always copulated for longer than males with high mobility, but this only translated into greater fertilization success in females from control populations and not the selection populations (i.e. treatment females). These results are consistent with a mate-searching/mating-duration trade-off and broadly support a trade-off between mobility and sperm competitiveness.


2007 ◽  
Vol 362 (1486) ◽  
pp. 1831-1839 ◽  
Author(s):  
Christoph Flamm ◽  
Lukas Endler ◽  
Stefan Müller ◽  
Stefanie Widder ◽  
Peter Schuster

A self-consistent minimal cell model with a physically motivated schema for molecular interaction is introduced and described. The genetic and metabolic reaction network of the cell is modelled by multidimensional nonlinear ordinary differential equations, which are derived from biochemical kinetics. The strategy behind this modelling approach is to keep the model sufficiently simple in order to be able to perform studies on evolutionary optimization in populations of cells. At the same time, the model should be complex enough to handle the basic features of genetic control of metabolism and coupling to environmental factors. Thereby, the model system will provide insight into the mechanisms leading to important biological phenomena, such as homeostasis, (circadian) rhythms, robustness and adaptation to a changing environment. One example of modelling a molecular regulatory mechanism, cooperative binding of transcription factors, is discussed in detail.


2018 ◽  
Author(s):  
Xiaotao Shen ◽  
Xin Xiong ◽  
Ruohong Wang ◽  
Yandong Yin ◽  
Yuping Cai ◽  
...  

Metabolite identification is a long-standing challenge in untargeted metabolomics and a major hurdle for functional metabolomics studies. Here, we developed a metabolic reaction network-based recursive algorithm and webserver called MetDNA for the large-scale and unambiguous identification of metabolites (available at http://metdna.zhulab.cn). We showcased the versatility of our workflow using different instrument platforms, data acquisition methods, and biological sample types and demonstrated that over 2,000 metabolites could be identified from one experiment.


1999 ◽  
Vol 1 (4) ◽  
pp. 299-308 ◽  
Author(s):  
Hiroshi Shimizu ◽  
Noboru Takiguchi ◽  
Hisaya Tanaka ◽  
Suteaki Shioya

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