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1. Unifying Phylogenetic Birth–Death Models in Epidemiology and Macroevolution

   https://doi.org/10.1093/sysbio/syab049  

   MacPherson, Ailene; Louca, Stilianos; McLaughlin, Angela; Joy, Jeffrey B; Pennell, Matthew W (June 2021, Systematic Biology)

   Albert, James (Ed.)

   Abstract Birth–death stochastic processes are the foundations of many phylogenetic models and are widely used to make inferences about epidemiological and macroevolutionary dynamics. There are a large number of birth–death model variants that have been developed; these impose different assumptions about the temporal dynamics of the parameters and about the sampling process. As each of these variants was individually derived, it has been difficult to understand the relationships between them as well as their precise biological and mathematical assumptions. Without a common mathematical foundation, deriving new models is nontrivial. Here, we unify these models into a single framework, prove that many previously developed epidemiological and macroevolutionary models are all special cases of a more general model, and illustrate the connections between these variants. This unification includes both models where the process is the same for all lineages and those in which it varies across types. We also outline a straightforward procedure for deriving likelihood functions for arbitrarily complex birth–death(-sampling) models that will hopefully allow researchers to explore a wider array of scenarios than was previously possible. By rederiving existing single-type birth–death sampling models, we clarify and synthesize the range of explicit and implicit assumptions made by these models. [Birth–death processes; epidemiology; macroevolution; phylogenetics; statistical inference.] 

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2. Robust Estimation of Tree Structured Gaussian Graphical Models

   Katiyar, Ashish; Hoffmann, Jessica; Caramanis, Constantine (June 2019, Proceedings of Machine Learning Research)

   Consider jointly Gaussian random variables whose conditional independence structure is specified by a graphical model. If we observe realizations of the variables, we can compute the covariance matrix, and it is well known that the support of the inverse covariance matrix corresponds to the edges of the graphical model. Instead, suppose we only have noisy observations. If the noise at each node is independent, we can compute the sum of the covariance matrix and an unknown diagonal. The inverse of this sum is (in general) dense. We ask: can the original independence structure be recovered? We address this question for tree structured graphical models. We prove that this problem is unidentifiable, but show that this unidentifiability is limited to a small class of candidate trees. We further present additional constraints under which the problem is identifiable. Finally, we provide an O(n^3) algorithm to find this equivalence class of trees. 

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3. Exact Steady-State Distributions of Multispecies Birth–Death–Immigration Processes: Effects of Mutations and Carrying Capacity on Diversity

   Renaud Dessalles, Maria D’Orsogna (October 2018, Journal of statistical physics)

   Stochastic models that incorporate birth, death and immigration (also called birth–death and innovation models) are ubiquitous and applicable to many problems such as quantifying species sizes in ecological populations, describing gene family sizes, modeling lymphocyte evolution in the body. Many of these applications involve the immigration of new species into the system. We consider the full high-dimensional stochastic process associated with multispecies birth–death–immigration and present a number of exact and asymptotic results at steady state.We further include random mutations or interactions through a carrying capacity and find the statistics of the total number of individuals, the total number of species, the species size distribution, and various diversity indices. Our results include a rigorous analysis of the behavior of these systems in the fast immigration limit which shows that of the different diversity indices, the species richness is best able to distinguish different types of birth–death–immigration models. We also find that detailed balance is preserved in the simple noninteracting birth–death–immigration model and the birth–death–immigration model with carrying capacity implemented through death. Surprisingly, when carrying capacity is implemented through the birth rate, detailed balance is violated. 

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4. Exact Steady-State Distributions of Multispecies Birth–Death–Immigration Processes: Effects of Mutations and Carrying Capacity on Diversity

   Renaud Dessalles, Maria D'Orsogna (October 2018, Journal of statistical physics)

   Stochastic models that incorporate birth, death and immigration (also called birth–death and innovation models) are ubiquitous and applicable to many problems such as quantifying species sizes in ecological populations, describing gene family sizes, modeling lymphocyte evolution in the body. Many of these applications involve the immigration of new species into the system. We consider the full high-dimensional stochastic process associated with multispecies birth–death–immigration and present a number of exact and asymptotic results at steady state.We further include random mutations or interactions through a carrying capacity and find the statistics of the total number of individuals, the total number of species, the species size distribution, and various diversity indices. Our results include a rigorous analysis of the behavior of these systems in the fast immigration limit which shows that of the different diversity indices, the species richness is best able to distinguish different types of birth–death–immigration models. We also find that detailed balance is preserved in the simple noninteracting birth–death–immigration model and the birth–death–immigration model with carrying capacity implemented through death. Surprisingly, when carrying capacity is implemented through the birth rate, detailed balance is violated. 

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5. Evolutionary games on isothermal graphs

   https://doi.org/10.1038/s41467-019-13006-7  

   Allen, Benjamin; Lippner, Gabor; Nowak, Martin A. (November 2019, Nature Communications)

   Abstract Population structure affects the outcome of natural selection. These effects can be modeled using evolutionary games on graphs. Recently, conditions were derived for a trait to be favored under weak selection, on any weighted graph, in terms of coalescence times of random walks. Here we consider isothermal graphs, which have the same total edge weight at each node. The conditions for success on isothermal graphs take a simple form, in which the effects of graph structure are captured in the ‘effective degree’—a measure of the effective number of neighbors per individual. For two update rules (death-Birth and birth-Death), cooperative behavior is favored on a large isothermal graph if the benefit-to-cost ratio exceeds the effective degree. For two other update rules (Birth-death and Death-birth), cooperation is never favored. We relate the effective degree of a graph to its spectral gap, thereby linking evolutionary dynamics to the theory of expander graphs. Surprisingly, we find graphs of infinite average degree that nonetheless provide strong support for cooperation. 

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