A Roomful of Robovacs: How to Think About Genetic Programs

The notion of a genetic program has been widely criticized by both biologists and philosophers. But the debate has revolved around a narrow conception of what programs are and how they work, and many criticisms are linked to this same conception. To remedy this, I outline a modern and more apt idea of a program that possesses many of the features critics thought missing from programs. Moving away from over-simplistic conceptions of programs opens the way to a more fruitful interplay of ideas between the complexity of biology and our most complex engineering discipline

Further Clarification on Permissive and Instructive Causes

I respond to recent criticism of my analysis of the permissive-instructive distinction and outline problems with the alternative analysis on offer. Amongst other problems, I argue that the use of formal measures is unclear and unmotivated, that the distinction is conflated with others that are not equivalent, and that no good reasons are provided for thinking the alternative model or formal measure tracks what biologists are interested in. I also clarify my own analysis where it has been misunderstood or ignored

Further Clarification on Permissive and Instructive Causes

I respond to recent criticism of my analysis of the permissive-instructive distinction and outline problems with the alternative analysis on offer. Amongst other problems, I argue that the use of formal measures is unclear and unmotivated, that the distinction is conflated with others that are not equivalent, and that no good reasons are provided for thinking the alternative model or formal measure tracks what biologists are interested in. I also clarify my own analysis where it has been misunderstood or ignored

Signals that make a Difference

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks—from the way monkeys in a troop communicate, to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this paper, we argue there is a tension between how Skyrms talks of signaling networks and his formal measure of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts; they must be difference-makers for acts

A Publish-Subscribe Model of Genetic Networks

We present a simple model of genetic regulatory networks in which regulatory connections among genes are mediated by a limited number of signaling molecules. Each gene in our model produces (publishes) a single gene product, which regulates the expression of other genes by binding to regulatory regions that correspond (subscribe) to that product. We explore the consequences of this publish-subscribe model of regulation for the properties of single networks and for the evolution of populations of networks. Degree distributions of randomly constructed networks, particularly multimodal in-degree distributions, which depend on the length of the regulatory sequences and the number of possible gene products, differed from simpler Boolean NK models. In simulated evolution of populations of networks, single mutations in regulatory or coding regions resulted in multiple changes in regulatory connections among genes, or alternatively in neutral change that had no effect on phenotype. This resulted in remarkable evolvability in both number and length of attractors, leading to evolved networks far beyond the expectation of these measures based on random distributions. Surprisingly, this rapid evolution was not accompanied by changes in degree distribution; degree distribution in the evolved networks was not substantially different from that of randomly generated networks. The publish-subscribe model also allows exogenous gene products to create an environment, which may be noisy or stable, in which dynamic behavior occurs. In simulations, networks were able to evolve moderate levels of both mutational and environmental robustness

Selecting optimal partitioning schemes for phylogenomic datasets

BACKGROUND Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics. METHODS We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere. RESULTS We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores. CONCLUSIONS These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets.RL would like to acknowledge support from a National Evolutionary Synthesis Centre (NESCent) short-term visitor grant. We would also like to acknowledge support from NESCent to pay for open-access publishing

PartitionFinder 2: New Methods for Selecting Partitioned Models of Evolution for Molecular and Morphological Phylogenetic Analyses

PartitionFinder 2 is a program for automatically selecting best-fit partitioning schemes and models of evolution for phylogenetic analyses. PartitionFinder 2 is substantially faster and more efficient than version 1, and incorporates many new methods and features. These include the ability to analyze morphological datasets, new methods to analyze genome-scale datasets, new output formats to facilitate interoperability with downstream software, and many new models of molecular evolution. PartitionFinder 2 is freely available under an open source license and works on Windows, OSX, and Linux operating systems. It can be downloaded from www.robertlanfear.com/partitionfinder. The source code is available at https://github.com/brettc/partitionfinder.RML was supported by the Australian Research Council. AMW was supported by NSF DEB-1256993. This work was supported by the Macquarie University Genes to Geoscience center

The Other Cooperation Problem: Generating Benefit

Understanding how cooperation evolves is central to explaining some core features of our biological world. Many important evolutionary events, such as the arrival of multicellularity or the origins of eusociality, are cooperative ventures between formerly solitary individuals. Explanations of the evolution of cooperation have primarily involved showing how cooperation can be maintained in the face of free-riding individuals whose success gradually undermines cooperation. In this paper I argue that there is a second, distinct, and less well explored, problem of cooperation that I call the generation of benefit. Focusing on how benefit is generated within a group poses a different problem: how is it that individuals in a group can (at least in principle) do better than those who remain solitary? I present several different ways that benefit may be generated, each with different implications for how cooperation might be initiated, how it might further evolve, and how it might interact with different ways of maintaining cooperation. I argue that in some cases of cooperation, the most important underlying 'problem' of cooperation may be how to generate benefit, rather than how to reduce conflict or prevent free-riding