Mapping an expanding territory: computer simulations in evolutionary biology

Towards evolutionary predictions: Current promises and challenges

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

Model-as-replica, model-as-instrument: Representational power and contextual versatility in animal models

Existing scholarship on animal models tends to foreground either of the two major roles research organisms play in different epistemic contexts, treating their representational and instrumental roles separately. Based on an empirical case study, this article explores the changing relationship between the two epistemic roles of a research organism over the span of a decade, while the organism was used to achieve various knowledge ends. This rat model was originally intended as a replica of human susceptibility to cardiac arrest. In a fortunate stroke of serendipity, however, the experimenters detected the way mother-infant interactions regulated the pups' resting cardiac rate. This intriguing outcome thus became the model's new representational target and began driving the development of an experimental system. Henceforth, the model acquired an instrumental function, serving to detect and measure system-specific differences. Its subsequent development involved creating stimulus-response measures to explain and theorize those differences. It was this instrumental use of the model that pushed the experimenters into unchartered territory and conferred to the model an ability to adapt to varied epistemic contexts. Despite the prominence of this instrumental role, however, the model's representational power continued to guide research. The model's representational target was widened beyond heart rate to reflect other functional phenomena, such as behavioral activity and sleep/wake rhythm. The rat model was thus transformed from an experimental organism designed to instantiate cardiac regulation to a model organism taken to represent the development of a whole, intact animal under the regulatory influence of maternal care. This article examines this multifaceted transformation within the context of the salient shifts in modeling practice and variations in the model's representational power. It thus explores how the relationship between the representational and instrumental uses of the model changed with respect to the varying exigencies of the investigative context, foregrounding its contextual versatility.

Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations

Evolutionary inferences require reliable phylogenies. Morphological data have traditionally been analyzed using maximum parsimony, but recent simulation studies have suggested that Bayesian analyses yield more accurate trees. This debate is ongoing, in part, because of ambiguity over modes of morphological evolution and a lack of appropriate models. Here, we investigate phylogenetic methods using two novel simulation models-one in which morphological characters evolve stochastically along lineages and another in which individuals undergo selection. Both models generate character data and lineage splitting simultaneously: the resulting trees are an emergent property, rather than a fixed parameter. Standard consensus methods for Bayesian searches (Mki) yield fewer incorrect nodes and quartets than the standard consensus trees recovered using equal weighting and implied weighting parsimony searches. Distances between the pool of derived trees (most parsimonious or posterior distribution) and the true trees-measured using Robinson-Foulds (RF), subtree prune and regraft (SPR), and tree bisection reconnection (TBR) metrics-demonstrate that this is related to the search strategy and consensus method of each technique. The amount and structure of homoplasy in character data differ between models. Morphological coherence, which has previously not been considered in this context, proves to be a more important factor for phylogenetic accuracy than homoplasy. Selection-based models exhibit relatively lower homoplasy, lower morphological coherence, and higher inaccuracy in inferred trees. Selection is a dominant driver of morphological evolution, but we demonstrate that it has a confounding effect on numerous character properties which are fundamental to phylogenetic inference. We suggest that the current debate should move beyond considerations of parsimony versus Bayesian, toward identifying modes of morphological evolution and using these to build models for probabilistic search methods. [Bayesian; evolution; morphology; parsimony; phylogenetics; selection; simulation.].

Robot life: simulation and participation in the study of evolution and social behavior

This paper explores the case of using robots to simulate evolution, in particular the case of Hamilton's Law. The uses of robots raises several questions that this paper seeks to address. The first concerns the role of the robots in biological research: do they simulate something (life, evolution, sociality) or do they participate in something? The second question concerns the physicality of the robots: what difference does embodiment make to the role of the robot in these experiments. Thirdly, how do life, embodiment and social behavior relate in contemporary biology and why is it possible for robots to illuminate this relation? These questions are provoked by a strange similarity that has not been noted before: between the problem of simulation in philosophy of science, and Deleuze's reading of Plato on the relationship of ideas, copies and simulacra.