Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions

Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions

Pairwise models are commonly used to describe many-species communities. In these models, an individual receives additive fitness effects from pairwise interactions with each species in the community ('additivity assumption'). All pairwise interactions are typically represented by a single equation where parameters reflect signs and strengths of fitness effects ('universality assumption'). Here,...

Texture Modeling by Multiple Pairwise Pixel Interactions

A Markov random eld model with a Gibbs prob ability distribution GPD is proposed for describing particular classes of grayscale images which can be called spatially uniform stochastic textures The model takes into account only multiple short and long range pairwise interactions between the gray lev els in the pixels An e ective learning scheme is introduced to recover a structure and strength o...

Modeling Population Dynamics with Volterra-Lotka Equations

The purpose of this project is to model multi-species interactions using Volterra-Lotka equations in both two and three dimensions. Changes in population dynamics that arise as a result of modifying parameters are examined. The population dynamics of the resulting systems are analyzed in terms of stability around equilibrium points and within invariant surfaces. Of particular interest is period...

The Importance of Pairwise Interactions

Maximally informative pairwise interactions in networks.

Several types of biological networks have recently been shown to be accurately described by a maximum entropy model with pairwise interactions, also known as the Ising model. Here we present an approach for finding the optimal mappings between input signals and network states that allow the network to convey the maximal information about input signals drawn from a given distribution. This mappi...