scholarly journals Genetic Drift and Selection in Many-Allele Range Expansions

2017 ◽  
Author(s):  
Bryan T. Weinstein ◽  
Maxim O. Lavrentovich ◽  
Wolfram Möbius ◽  
Andrew W. Murray ◽  
David R. Nelson
Keyword(s):  
Random Walk ◽  
Spatial Structure ◽  
Genetic Drift ◽  
Evolutionary Dynamics ◽  
Single Gene ◽  
Agar Plate ◽  
Random Walk Model ◽  
Predictive Tool ◽  
E Coli ◽  
A New Technique

AbstractWe experimentally and numerically investigate the evolutionary dynamics of four competing strains of E. coli with differing expansion velocities in radially expanding colonies. We compare experimental measurements of the average fraction, correlation functions between strains, and the relative rates of genetic domain wall annihilations and coalescences to simulations modeling the population as a one-dimensional ring of annihilating and coalescing random walkers with deterministic biases due to selection. The simulations reveal that the evolutionary dynamics can be collapsed onto master curves governed by three essential parameters: (1) an expansion length beyond which selection dominates over genetic drift; (2) a characteristic angular correlation describing the size of genetic domains; and (3) a dimensionless constant quantifying the interplay between a colony’s curvature at the frontier and its selection length scale. We measure these parameters with a new technique that precisely measures small selective differences between spatially competing strains and show that our simulations accurately predict the dynamics with no additional fitting. Our results suggest that that the random walk model can act as a useful predictive tool when describing the evolutionary dynamics of range expansions composed of an arbitrary number of competing alleles with different fitnesses.Author summaryPopulation expansions occur naturally during the spread of invasive species and have played a role in our evolutionary history when many of our ancestors expanded out of Africa. We use a colony of bacteria expanding into unoccupied, nutrient-rich territory on an agar plate as a model system to explore how an expanding population’s spatial structure impacts its evolutionary dynamics. Spatial structure is present in expanding microbial colonies because daughter cells migrate only a small distance away from their mothers each generation. Generally, the constituents of expansions occurring in nature and in the lab have different genetic compositions (genotypes, or alleles if a single gene differs), each instilling different fitnesses, which compete to proliferate at the frontier. Here, we show that a random-walk model can accurately predict the dynamics of four expanding strains of E. coli with different fitnesses; each strain represents a competing allele. Our results can be extended to describe any number of competing genotypes with different fitnesses in a naturally occurring expansions. Our model can also be used to precisely measure small selective differences between spatially competing genotypes in controlled laboratory settings.

The Order in Which Antibiotics are Administered Affects the Fitness Costs of Adherent-Invasive E. coli (AIEC) Strains

2019 ◽  
Author(s):  
Jordan B Gregg
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Combination Therapy ◽  
Agar Plate ◽  
Sequential Therapy ◽  
Negative Control ◽  
Clinical Settings ◽  
Fitness Costs ◽  
Inflammatory Gene Expression ◽  
E Coli

AIEC-LF82 is a strain of bacteria that is surmised to have a role in causing IBD and Crohn’s disease by activating pro-inflammatory gene expression in organisms. Using antibiotics via combination therapy has been a technique used in clinical settings in an attempt to treat the strains, however, the attempts have not been that effective nor efficient in terms of completely halting the growth and colonization of AIEC to treat IBD and Crohn's disease patients. Research has shown that regarding hindering or preventing the colonization bacterial colonies, sequential therapy tends to be more effective and time-efficient than combination therapy, with fewer adverse effects. To test if this is also the case with the AIEC-LF82 strain of bacteria, I first tested AIEC’s response to combination therapy using the Penicillin-Streptomycin, Kanamycin-Chloramphenicol, antimicrobial peptide (AMP), Kanamycin, SPE phase and LB agar plates, all of which were experimental plates other than the LB agar plate that acted as the negative control. I then tested AIEC-LF82’s response to sequential therapy using the LB+ Kan + Spe, LB + AMP + Spe, LB+ Kan/Cam + Spe, LB + P/S + Spe, LB + P/S + Kan and LB + P/S + AMP and one LB agar plate acting as the negative control. The only differences between sets a and b were the order in which antibiotics were administered in the six aforementioned treatment sets. Ultimately, I found that set b of sequential therapy, strong-weak antibiotic treatments, was the most effective treatment but that set a regarding sequential therapy was actually the least effective of all of the treatments. In conclusion, using strong-weak sequential antibiotic therapy treatments appears to be a potentially promising option to treat patients suffering from Crohn's disease and IBD.

A Multi-Label Classification Algorithm Based on Random Walk Model

2010 ◽  
Vol 33 (8) ◽  
pp. 1418-1426 ◽  
Author(s):  
Wei ZHENG ◽  
Chao-Kun WANG ◽  
Zhang LIU ◽  
Jian-Min WANG
Keyword(s):  
Random Walk ◽  
Classification Algorithm ◽  
Random Walk Model

Quartile histogram assessment of glioma malignancy using high b ‐value diffusion MRI with a continuous‐time random‐walk model

2021 ◽  
Vol 34 (4) ◽  
Author(s):  
M. Muge Karaman ◽  
Jiaxuan Zhang ◽  
Karen L. Xie ◽  
Wenzhen Zhu ◽  
Xiaohong Joe Zhou
Keyword(s):  
Random Walk ◽  
Continuous Time ◽  
Diffusion Mri ◽  
B Value ◽  
Continuous Time Random Walk ◽  
Random Walk Model ◽  
High B Value

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

scholarly journals Genome-Wide Screening Identifies Six Genes That Are Associated with Susceptibility to Escherichia coli Microcin PDI

2015 ◽  
Vol 81 (20) ◽  
pp. 6953-6963 ◽  
Author(s):  
Zhe Zhao ◽  
Lauren J. Eberhart ◽  
Lisa H. Orfe ◽  
Shao-Yeh Lu ◽  
Thomas E. Besser ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Atp Synthase ◽  
Disulfide Bonds ◽  
Gene Knockout ◽  
Single Gene ◽  
Site Directed Mutagenesis ◽  
Content Type ◽  
E Coli ◽  
Synthase Inhibitor ◽  
Loss Of Susceptibility

ABSTRACTThe microcin PDI inhibits a diverse group of pathogenicEscherichia colistrains. Coculture of a single-gene knockout library (BW25113;n= 3,985 mutants) against a microcin PDI-producing strain (E. coli25) identified six mutants that were not susceptible (ΔatpA, ΔatpF, ΔdsbA, ΔdsbB, ΔompF, and ΔompR). Complementation of these genes restored susceptibility in all cases, and the loss of susceptibility was confirmed through independent gene knockouts inE. coliO157:H7 Sakai. Heterologous expression ofE. coliompFconferred susceptibility toSalmonella entericaandYersinia enterocoliticastrains that are normally unaffected by microcin PDI. The expression of chimeric OmpF and site-directed mutagenesis revealed that the K47G48N49region within the first extracellular loop ofE. coliOmpF is a putative binding site for microcin PDI. OmpR is a transcriptional regulator forompF, and consequently loss of susceptibility by the ΔompRstrain most likely is related to this function. Deletion of AtpA and AtpF, as well as AtpE and AtpH (missed in the original library screen), resulted in the loss of susceptibility to microcin PDI and the loss of ATP synthase function. Coculture of a susceptible strain in the presence of an ATP synthase inhibitor resulted in a loss of susceptibility, confirming that a functional ATP synthase complex is required for microcin PDI activity. Intransexpression ofompFin the ΔdsbAand ΔdsbBstrains did not restore a susceptible phenotype, indicating that these proteins are probably involved with the formation of disulfide bonds for OmpF or microcin PDI.

Tracking method to random walk model based on orientation code matching

2008 ◽  
Author(s):  
Kazuhiro Kagoike ◽  
Satoru Takahashi ◽  
Hidenori Takauji ◽  
Shun'ichi Kaneko
Keyword(s):  
Random Walk ◽  
Random Walk Model ◽  
Tracking Method ◽  
Model Based
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