### Abstract

Evolutionary graph theory studies the evolutionary dynamics of populations structured on graphs. A central problem is determining the probability that a small number of mutants overtake a population. Currently, Monte Carlo simulations are used for estimating such fixation probabilities on general directed graphs, since no good analytical methods exist. In this paper, we introduce a novel deterministic framework for computing fixation probabilities for strongly connected, directed, weighted evolutionary graphs under neutral drift. We show how this framework can also be used to calculate the expected number of mutants at a given time step (even if we relax the assumption that the graph is strongly connected), how it can extend to other related models (e.g. voter model), how our framework can provide non-trivial bounds for fixation probability in the case of an advantageous mutant, and how it can be used to find a non-trivial lower bound on the mean time to fixation. We provide various experimental results determining fixation probabilities and expected number of mutants on different graphs. Among these, we show that our method consistently outperforms Monte Carlo simulations in speed by several orders of magnitude. Finally we show how our approach can provide insight into synaptic competition in neurology.

Original language | English (US) |
---|---|

Pages (from-to) | 136-144 |

Number of pages | 9 |

Journal | BioSystems |

Volume | 111 |

Issue number | 2 |

DOIs | |

State | Published - Feb 2013 |

Externally published | Yes |

### Fingerprint

### Keywords

- Complex networks
- Evolutionary dynamics
- Moran process

### ASJC Scopus subject areas

- Biochemistry, Genetics and Molecular Biology(all)
- Applied Mathematics
- Modeling and Simulation
- Statistics and Probability

### Cite this

*BioSystems*,

*111*(2), 136-144. https://doi.org/10.1016/j.biosystems.2013.01.006

**A novel analytical method for evolutionary graph theory problems.** / Shakarian, Paulo; Roos, Patrick; Moores, Geoffrey.

Research output: Contribution to journal › Article

*BioSystems*, vol. 111, no. 2, pp. 136-144. https://doi.org/10.1016/j.biosystems.2013.01.006

}

TY - JOUR

T1 - A novel analytical method for evolutionary graph theory problems

AU - Shakarian, Paulo

AU - Roos, Patrick

AU - Moores, Geoffrey

PY - 2013/2

Y1 - 2013/2

N2 - Evolutionary graph theory studies the evolutionary dynamics of populations structured on graphs. A central problem is determining the probability that a small number of mutants overtake a population. Currently, Monte Carlo simulations are used for estimating such fixation probabilities on general directed graphs, since no good analytical methods exist. In this paper, we introduce a novel deterministic framework for computing fixation probabilities for strongly connected, directed, weighted evolutionary graphs under neutral drift. We show how this framework can also be used to calculate the expected number of mutants at a given time step (even if we relax the assumption that the graph is strongly connected), how it can extend to other related models (e.g. voter model), how our framework can provide non-trivial bounds for fixation probability in the case of an advantageous mutant, and how it can be used to find a non-trivial lower bound on the mean time to fixation. We provide various experimental results determining fixation probabilities and expected number of mutants on different graphs. Among these, we show that our method consistently outperforms Monte Carlo simulations in speed by several orders of magnitude. Finally we show how our approach can provide insight into synaptic competition in neurology.

AB - Evolutionary graph theory studies the evolutionary dynamics of populations structured on graphs. A central problem is determining the probability that a small number of mutants overtake a population. Currently, Monte Carlo simulations are used for estimating such fixation probabilities on general directed graphs, since no good analytical methods exist. In this paper, we introduce a novel deterministic framework for computing fixation probabilities for strongly connected, directed, weighted evolutionary graphs under neutral drift. We show how this framework can also be used to calculate the expected number of mutants at a given time step (even if we relax the assumption that the graph is strongly connected), how it can extend to other related models (e.g. voter model), how our framework can provide non-trivial bounds for fixation probability in the case of an advantageous mutant, and how it can be used to find a non-trivial lower bound on the mean time to fixation. We provide various experimental results determining fixation probabilities and expected number of mutants on different graphs. Among these, we show that our method consistently outperforms Monte Carlo simulations in speed by several orders of magnitude. Finally we show how our approach can provide insight into synaptic competition in neurology.

KW - Complex networks

KW - Evolutionary dynamics

KW - Moran process

UR - http://www.scopus.com/inward/record.url?scp=84873682650&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84873682650&partnerID=8YFLogxK

U2 - 10.1016/j.biosystems.2013.01.006

DO - 10.1016/j.biosystems.2013.01.006

M3 - Article

VL - 111

SP - 136

EP - 144

JO - BioSystems

JF - BioSystems

SN - 0303-2647

IS - 2

ER -