a protein’s function once its structure is known, and because it is so challenging to observe a protein’s structure directly. Proteins—chains of smaller molecules called amino acids—are central to biochemistry because they are the primary chemical for almost all cellular processes. Understanding a protein’s structure is necessary to understand its functions, because a protein’s shape determines how it will interact with other molecules. Thus, an important problem in biochemistry is the protein structure prediction problem: given the sequence of amimo acids that make up a protein, what is its structure? It is possible to experimentally determine a protein’s structure through methods such as X-Ray Crystallography and Nuclear Magnetic Resonance spectroscopy. Experimental methods, however, can be costly, time consuming, and difficult. This makes computational methods that can accurately predict a protein’s structure an attractive solution. However, computational methods are often intractable; the vast number of possible shapes a protein can take make it difficult to find the correct structure. The spatial nature of this problem makes it a good candidate for the application of human spatial reasoning.
A related problem is that of protein design: given a desired function for a protein, what is the amino acid sequence that, when folded, will carry it out? In this case, computational methods are even more attractive. Synthesizing proteins to test every design would be prohibitively expensive, while computational methods can allow us to filter out designs that are not likely to work. Protein design has implications for drug design, in inhibitors and vaccines, for biofuel design, in enzymes, and for other areas. Human creativity can be applied to help create novel proteins that did not exist before.
1.3 Outline
In this book we will show the effectiveness of the game-based framework as an approach to scientific discovery. Chapter 2 discusses the literature related to this book. Chapter 3 gives an overview of the game-based framework used in this research, describing the dual goals of engagement and scientific relevance, and the coevolution approach we take. A discussion of using this framework for problem solving as applied to protein structure prediction is given in Chapter 4, and we show that players can predict the unknown structures of naturally occurring proteins, even where all previous methods have failed. Further discussion of applying this framework to leverage player creativity for protein design is given in Chapter 5, and we show that players can become an integral part of the design of novel and effective proteins. Chapter 6 describes an approach to allowing players to codify and automate their strategies, and we show that players can socially develop highly effective algorithms. Finally, Chapter 7 provides a summary and discusses possible future directions for research.
1. http://www.tetris.com/; last retrieved May 2014.
2. http://www.rubiks.com/; last retrieved May 2014.
3. http://brainage.com/; last retrieved May 2014.
4. http://bigbrainacademy.com/; last retrieved May 2014.
2 Related Literature
This book is related to several bodies of existing literature, including volunteer computing, human computation, serious games, computational biochemistry, and visualization and interaction.
2.1 Volunteer Computing and Human Computation
Volunteer computing is a method by which volunteers are able to donate their computer’s spare time and space to various projects. The volunteer computing model has risen in popularity recently, and has allowed scientists access to unprecedented amounts of computational power. One of the oldest and largest volunteer computing projects is SETI@home1 [Sullivan III et al. 1997 ]. This project uses a screensaver to analyze radio telescope data. There is an open source platform for developing volunteer computing projects, the Berkeley Open Infrastructure for Network Computing (BOINC),2 which allows users to manage and share their computer’s resources between the many projects using the platform [Anderson 2004 ]. BOINCprojects have a variety of goals, from climate prediction [Stainforth et al. 2005 ] to searching for pulsars [Knispel et al. 2010 ].
By using the volunteer computing model, projects not only gain access to massive computation, but also allows the public to make contributions to science. However, with this model, their contributions are mostly passive—they don’t even have to be at their computer. This work aims to use not only the power of networks of computers, but also that of networks of humans, and allow people to make active contributions to science.
There has been work recently on leveraging a human workforce for computational tasks that computers are not yet able to perform satisfactorily. A more general field of “human computation” or “distributed thinking” is emerging. On a smaller scale, augmenting automated heuristics with interactive human input can help to solve basic spatial problems [Anderson et al. 2000, Lesh et al. 2005 ]. On a larger scale, general tasks desired for humans to perform are posted online, and users can determine which tasks they would like to perform. Amazon’s Mechanical Turk3 is one example of such a system, where users are actually paid to perform tasks. Example tasks include translation of text, rating search results, and determining the tone of an article. Bossa is an open source system for managing similar user tasks.4
In this context, there has been much interest in using games as a means of motivating people to perform tasks that are currently difficult for computers. One particularly active area is in computer vision and image recognition. Humansare particularly adept at reading words in images and determining the objects in a scene, when compared with current computational methods. The difference in ability is strong enough that vision-based tests are often used as a proof of humanity with CAPTCHAs [Ahn et al. 2003 ].
Games such as the ESP game [von Ahn and Dabbish 2004 ], Peekaboom [von Ahn et al. 2006 ], and Google Image Labeler5 use human image-recognition ability to produce labeled images from gameplay. Image recognition has also been used for finding particular features of interest in scientific data, such as looking for signs of interstellar dust [Westphal et al. 2010 ], measuring and aligning features on a planet’s surface,6 and classifying galaxy shapes.7 These projects have been successful in motivating players to sift through large image sets, which would otherwise be a mundane task.
Some games have approached other types of problems. Pebble It8 is a game which studies human solutions to the graph pebbling problem, with the goal of developing better algorithms to solve it [Cusack et al. 2006 ]. Outside of games, some work has examined how to fit human problem solving into various optimization problems [Anderson et al. 2000, Lesh et al. 2005 ]. This work is different because it leverages a deeper human problem solving ability to create interesting scientific results.
2.2 Serious Games and Gamification
Recently, a field known as “serious games” has been identified. The most general definition is any game that has a purpose beyond simply entertaining the player; however, it often connotes games whose purpose is training or education. The line between game and simulation or application is also not always well defined. A game-based approach is appealing because games are meant to be engaging and motivating. Furthermore, other fields are taking advantage of the fact
