Asia Pacific University Library catalogue

Includes bibliographical references (pages 111-117).

1. Introduction -- 1.1 Motivation -- 1.2 Problem statement -- 1.2.1 Game design problem -- 1.2.2 Biochemistry discovery problem -- 1.3 Outline --

2. Related literature -- 2.1 Volunteer computing and human computation -- 2.2 Serious games and gamification -- 2.3 Computational biochemistry --

3. Framework -- 3.1 Introduction -- 3.2 Biochemistry background -- 3.3 Framework description -- 3.3.1 Architecture -- 3.3.2 Coevolution strategy -- 3.3.3 Categorization as a game -- 3.4 Game design challenges -- 3.4.1 Visualizations -- 3.4.2 Interactions -- 3.4.3 Scoring -- 3.4.4 Introductory levels -- 3.5 Rewards and social interaction -- 3.5.1 Rewards and ranking types -- 3.6 Conclusion --

4. Protein structure prediction -- 4.1 Introduction -- 4.2 Quest to the natives -- 4.3 CASP8 experiments -- 4.4 Evaluation -- 4.5 Rebuild and refine comparison -- 4.5.1 First strand swap example -- 4.5.2 Player contribution and expertise -- 4.6 Alignment tool and CASP9 -- 4.7 Solution of crystal structure -- 4.8 Conclusion --

5. Protein design -- 5.1 Introduction -- 5.2 Framework extension -- 5.2.1 Foldit -- 5.2.2 Iteration strategy -- 5.3 Science transfer -- 5.3.1 Visualizations -- 5.3.2 Tools -- 5.3.3 Conditions -- 5.4 Introductory levels -- 5.5 Examples -- 5.5.1 Fibronectin -- 5.5.2 Diels-Alder -- 5.6 Conclusion --

6. Protein structure refinement algorithms -- 6.1 Introduction -- 6.2 Related work -- 6.3 Overview -- 6.3.1 Cookbook -- 6.4 CASP9 analysis -- 6.4.1 Recipe sharing -- 6.4.2 Inheritance relationships -- 6.4.3 Ratings -- 6.5 Script recipe adoption analysis -- 6.6 Algorithm categories -- 6.7 Context dependence -- 6.8 Recipe evolution -- 6.9 Performance comparison -- 6.10 Conclusion --

7. Conclusion -- 7.1 Contributions -- 7.2 Future work -- Bibliography -- Author's biography.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

When we first set out to create Foldit over six years ago, it wasn't clear that a game-based approach to scientific discovery would work. So we planned from the start for the game to be continually adapting and changing, in order to keep improving based on the lessons we'd learn. It took several years of design, development, and continued iteration from a team of computer scientists and biochemists until the game was at a point where we made our first exciting discovery. The nature of the challenging problems we were facing required this time and refinement to solve. We are now seeing a number of other games that allow players to contribute to scientific research. Much of this growth has been in fields related to biology and biochemistry: EteRNA for designing RNA shapes, EyeWire for mapping neurons, and Phylo for aligning genetic sequences. Each of these games has had exciting scientific results produced by game play. Games are being applied in other areas as well, such as in the Algoraph suite of games for solving graph theory problems. I have been involved in the development of two more science games: Nanocrafter, which aims to push the frontiers of DNA-based synthetic biology, and Flow Jam, which allows players to help formally verify software.

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