Morphological Evolution: Bioinspired Methods for Analyzing Bioinspired Robots.

To fully understand the evolution of complex morphologies, analyses cannot stop at selection: It is essential to investigate the roles and interactions of multiple processes that drive evolutionary outcomes. The challenges of undertaking such analyses have affected both evolutionary biologists and evolutionary roboticists, with their common interests in complex morphologies. In this paper, we present analytical techniques from evolutionary biology, and , and we demonstrate their applicability to robot morphologies in analyses of three evolutionary mechanisms: randomness (genetic mutation), development (an explicitly implemented genotype-to-phenotype map), and selection.

Embodied Computational Evolution: Feedback Between Development and Evolution in Simulated Biorobots.

Given that selection removes genetic variance from evolving populations, thereby reducing exploration opportunities, it is important to find mechanisms that create genetic variation without the disruption of adapted genes and genomes caused by random mutation. Just such an alternative is offered by random epigenetic error, a developmental process that acts on materials and parts expressed by the genome. In this system of embodied computational evolution, simulated within a physics engine, epigenetic error was instantiated in an explicit genotype-to-phenotype map as transcription error at the initiation of gene expression.

Herbivore diet breadth mediates the cascading effects of carnivores in food webs.

Predicting the impact of carnivores on plants has challenged community and food web ecologists for decades. At the same time, the role of predators in the evolution of herbivore dietary specialization has been an unresolved issue in evolutionary ecology. Here, we integrate these perspectives by testing the role of herbivore diet breadth as a predictor of top-down effects of avian predators on herbivores and plants in a forest food web.

A multiple time-scale computational model of a tumor and its micro environment.

Experimental evidence suggests that a tumor's environment may be critical to designing successful therapeutic protocols: Modeling interactions between a tumor and its environment could improve our understanding of tumor growth and inform approaches to treatment. This paper describes an efficient, flexible, hybrid cellular automaton-based implementation of numerical solutions to multiple time-scale reaction-diffusion equations, applied to a model of tumor proliferation. The growth and maintenance of cells in our simulation depend on the rate of cellular energy (ATP) metabolized from nearby nutrients such as glucose and oxygen.