ALife XV provides a venue for a number of tutorials, which are run and administered independently from the main conference. If you are interested in attending one of the tutorials, please visit the relevant website below. For tutorials that do not currently have a website, please contact the tutorial organiser for more details.
Markov Brains have proven to be a powerful tool for studying theoretical evolution and for modeling biological systems. In this workshop you will be introduced to Markov Brains and MABE. MABE is an evolution system used to generate and evolve populations of digital organisms. Organisms contain genomes which define brains, which in turn control the actions of the organisms. Once created, organisms are placed into a digital world. Worlds can be designed to generate scores which can be used to determine which organisms will sire the next generation or worlds can be designed as open ended systems with implicate mating and death. In either case, both asexual and sexual reproduction are supported, and various forms of mutation and crossover can be applied during reproduction. MABE supports multi-chromosome multi-ploidy genomes. More info.
Avida-ED is a simplified version of the Avida Digital Evolution Software Platform, designed for educational purposes. Digital organisms exist within the program as self-replicating computer programs. Users can define the probability that an instruction will be copied incorrectly, resulting in a heritable mutation. The world imposes selective pressures both from a competition for space, and in most setups, rewards for performing bitwise logical tasks. Because populations of organisms have a source of variation, inheritance, and selection, over the course of generations they will evolve by natural selection – this is an example of evolution, rather than a simulation of it. Within the framework of Avida-ED, users can control factors such as the size of the world, the mutation rate, which logical tasks are rewarded, and which organism(s) is/are used as the ancestor. We have developed a series of introductory exercises aimed at teaching students key concepts in evolutionary biology, including what it means that mutations are random and that selection acts upon pre-existing variation rather than generating variation itself. We’ve also had success using this software as a platform for independent student research, where students need to come up with a question, generate the data, choose what to analyze, and then test for meaningful differences. In this tutorial, we will guide participants through the workings of the software, what options are available, and how it’s been used in a variety of classrooms ranging from high school through upper division undergraduate courses. We will also distribute a lab manual which contains these exercises, additional ones which may be of interest, and relevant readings for students. More info.
Aevol is a digital genetics model in which populations of digital organisms undergo variation and selection, creating a Darwinian dynamics. By modifying the characteristics of selection (e.g. population size, type of environment, environmental variations) or genetic variation (e.g. mutation rates, chromosomal rearrangement rates, horizontal transfer), one can study experimentally the impact of these parameters on the evolved organisms. In particular, since Aevol integrates a realistic model of the genome, it allows for the study of various structural properties of the genome including gene number, syntheny, and proportion of coding sequences. The Aevol simulation platform also includes tools to analyze phylogenies and measure characteristics of the organisms and populations during their evolution. More info.
It is becoming widely accepted that experimental results without proper statistical analysis must be considered anecdotal, and may even be wholly inaccurate. Increasingly, if your paper has inadequate statistical analysis, it will be rejected. This introductory tutorial aims at refreshing your memory on the basic statistical techniques used when analyzing experiments. Introductory topics are covered, starting with the T-test, confidence intervals, and non-parametric statistics, and progressing to statistical model building through regression (linear and polynomial), and Analysis of Variance (ANOVA). All ideas and techniques are intuitively presented using Excel and R examples and through the extensive use of graphics and animation … concentrating on concepts, not equations. Contact info: firstname.lastname@example.org
In this hands-on tutorial, participants will explore models and build their own models in NetLogo. NetLogo is a multi-agent modeling (ABM) environment widely used in a wide variety of disciplines. NetLogo is designed to be easy to use, but computationally powerful and is widely used both for research and for education.
In this tutorial, we will introduce NetLogo as an ABM language, and assist participants in building their own models. Additionally we will discuss the process of constructing, validating and verifying models. Finally we will showcase interesting ALife models, and introduce two important research features recently added to the NetLogo programming environment: dynamic network manipulation and analysis, and multi-level agent-based modeling. More info.
Organizers: Arthur Hjorth, Bryan Guo, Bryan Head, and Uri Wilensky.