Below are research proposals that members of the Adami lab have written and submitted for funding. Please contact Chris Adami to discuss these projects in more detail.
The goal of the DARPA-funded SyNAPSE project is to build a microprocessor-based machine that mimics many of the characteristics of biological neural networks (i.e., high connectivity, synapse plastic- ity, and scale). The proposed device is termed a neuromorphic machine, and recent simulations have indeed approached biological scale. However, while SyNAPSE has been demonstrated on a variety of tasks, from optical character recognition and classification to control of quadrotor helicopters, it has done so at a cost of $42 million. In contrast, the BEACON-funded DvD (“Darwin vs. DARPA”) project has demonstrated the evolution of logic circuits that outperform SyNAPSE on optical character recognition for 1/1000th of the cost. In this phase of the DvD project, we now turn our attention to a more complex task: Evolving logic circuits that play the computer game “Tetris.”
Gene regulation in animals is arguably at least as important as the genes that are being regulated. Animal body plans, their structures and in particular the functions that the animal morphology provides, are the consequence over time and space of successive regulatory and developmental processes. Gene regulation in animals is a highly complex process, and can be likened to a computation that the regulatory machinery performs. Often, single genes are regulated by a complex network of genes with activators, repressors, attenuators and the like, and the elucidation of these networks has taken molecular and develop- mental biologists decades. But while we know a tremendous amount about how genes and their associated proteins evolve, much less is known about how regulatory systems evolve. We know the basic building blocks: multiple transcription factor binding sites that regulate the expression of other transcription factors that ultimately lead to the expression of the regulated gene. Each transcription factor has a specific affinity to its binding site, and binding sites can interact either synergistically or antagonistically. If we compare the regulatory regions for the same gene across species in the same family, we can see sometimes significant differences in the regulatory sequence. Are these differences adaptive? How do regulatory networks change in response to changes, either in the environment or in response to a change in body size? In the proposed work, we will analyze the gene regulatory network or “cis-regulatory module” (CRM) that regulates the patterning of a fly embryo in the dorsal-ventral axis. This is a well-studied system for which expression and sequence data is available from the Arnosti lab, and aligned homologous CRMs have been collected. Yet, we do not know in detail how evolution affects such systems. What are the “operators” that evolution uses to change these networks? A computational analysis of the regulatory region of 12 Drosophila species will help us move towards a better understanding of how regulatory systems evolve.
The Adami Lab studies evolution in many different computational environments, including the popular game development platform Unity3D, which supports our research in 3D virtual physics environ- ments. A group of students in our lab (R. Olson, J. Schossau, and D. Phillips) started a game project with the preliminary title Evolve and Conquer (E&C), a real time strategy game which incorporates the core principles of evolution: inheritance, variation, and natural selection. E&C immerses the player in a world governed by these evolutionary principles, allowing the player to experience evolution in action first-hand as a core aspect of the game mechanics. The player must understand the core principles of evolution in order to prevail and either overcome or learn to coexist with an evolving computer opponent.
We propose an integration of our digital swarm evolution platform with a biological system to study the evolution of simulated prey behavior in response to predation. Particularly, we are looking to establish whether the predator confusion effect exists, and whether it can select for swarming behavior in groups of evolving prey that initially move randomly.