As the international genomic research community moves from the tool-making efforts of the Human Genome Project into biomedical applications of those tools, new metaphors are being suggested as useful to understanding how our genes work – and for understanding who we are as biological organisms. In this essay we focus on the Human Microbiome Project as one such translational initiative. The HMP is a new ‘metagenomic’ research effort to sequence the genomes of human microbiological flora, in order to pursue the (...) interesting hypothesis that our ‘microbiome’ plays a vital and interactive role with our human genome in normal human physiology. Rather than describing the human genome as the ‘blueprint’ for human nature, the promoters of the HMP stress the ways in which our primate lineage DNA is interdependent with the genomes of our microbiological flora. They argue that the human body should be understood as an ecosystem with multiple ecological niches and habitats in which a variety of cellular species collaborate and compete, and that human beings should be understood as ‘superorganisms’ that incorporate multiple symbiotic cell species into a single individual with very blurry boundaries. These metaphors carry interesting philosophical messages, but their inspiration is not entirely ideological. Instead, part of their cachet within genome science stems from the ways in which they are rooted in genomic research techniques, in what philosophers of science have called a ‘tools-to-theory’ heuristic. Their emergence within genome science illustrates the complexity of conceptual change in translational research, by showing how it reflects both aspirational and methodological influences. (shrink)

Rolling Stones guitarist Keith Richards has argued that rock and roll happens from the neck down. In this contribution to The Rolling Stones and Philosophy, edited by Luke Dick and George Reisch, I draw on neuroscience to argue that, in the parlance of John Stuart Mill, rock and roll is both a higher and a lower pleasure.

The introduction of computer simulation to paleobiology ushered in a new, experimental style of reasoning. Rather than starting with observed fossil patterns and hypothesizing causal processes that may have produced them, it became possible to start with a process model, and from it to simulate a range of possible patterns. ;The MBL Model is a stochastic model of phylogenetic evolution . Computer simulations conducted with the MBL Model served as thought experiments in stochastic evolution. In the MBL work, similarities between (...) empirical and stochastically simulated clades mounted a visual argument that stochastic processes potentially explained large fluctuations in diversity. Stanley and others countered that the similarities were an artifact of scaling. The scaling problem may have been obscured by visual bias: measures of clade shape are scale-invariant, but diversification itself is highly scale-dependent. ;Null and neutral models are frequently conflated. Null models generate hypothetical data distributions under conditions that exclude some process of interest to test whether patterns in an actual data distribution provide statistical evidence for that process. A neutral model assumes selective equivalence among all units at a specified hierarchical level in the evolving system. Neutral models are often inappropriate as null models. Pattern recognition presupposes a null model, but tacit null models are subject to persistent cognitive biases, necessitating explicit formulation of null models. Nonetheless, failure to reject the null model should not preclude further investigation of a pattern. ;Often, models are not candidates for falsification, but serve to simulate data for testing concepts or methods. Sepkoski and Kendrick , and Robeck, Maley, and Donoghue used the MBL Model to test whether mass extinction periodicity is an artifact of paraphyly in taxonomic data. Such numerical experiments, which make all assumptions explicit, can serve as templates for localizing points of disagreement. Models may fall into lineages, each generation providing lessons for the next, but often sharing operational assumptions. Artifacts of shared operational assumptions are often a threat to the validity of inferences. Results holding across models should not be accepted unless they withstand rigorous robustness analysis. (shrink)