MIT: Computer model reveals cells' inner workings

October 17, 2008

New approach could help tailor chemotherapy treatments

CAMBRIDGE, Mass. -- After spending years developing a computational model to help illuminate cell signaling pathways, a team of MIT researchers decided to see what would happen if they "broke" the model.

The results, reported in the Oct. 17 issue of the journal Cell, reveal new ways in which cells process chemical information and could indicate how to maximize the effectiveness of disease treatments such as chemotherapy.

A couple of years ago, MIT faculty member Michael Yaffe and colleagues reported a data-driven computational model that allows them to simultaneously investigate the relationships between several cell signaling pathways, which control the cell's response to inflammation, growth factors, DNA damage and other events.

Such a model can help researchers figure out how cells will respond to growth factors and treatments like chemotherapy, allowing them to potentially tailor treatments to individual patients.

In the Cell paper, the team took a new approach to wring more information out of their model: They looked at what happens in cells under conditions where the model fails catastrophically. This approach, which the authors call "model breakpoint analysis," is an extension of more traditional failure analysis methods commonly used by engineers to figure out what design changes would make a bridge fall down or an engine fail.

"In traditional engineering sciences, you frequently use computational methods to predict what effect a faulty strut or a broken wire will have on a plane or an electronic circuit - will it fail, and if so, when and how?" said Yaffe, associate professor of biology and biological engineering and senior author of the new paper.

Doing the same thing in cellular models can reveal important and previously unexplored aspects of the signaling pathways.

To "break" the computer model, the researchers entered increasingly implausible inputs to the model until it no longer correctly predicted cell fate. To their surprise, they found that the model remained accurate for a long time.

"As the data we used to build the model got progressively worse and worse - more and more biologically inaccurate - the model would work fine, and then when you got to a certain threshold - the breakpoint - the model suddenly wouldn't predict anymore," said Yaffe, who is affiliated with MIT's David H. Koch Institute for Integrative Cancer Research, the Broad Institute of MIT and Harvard, and Beth Israel Deaconess Medical Center.

"By going back and looking at what happened in the model when the predictions failed, we discovered a surprising amount of new biology that was actually happening in the living cell," Yaffe said.

One significant, unexpected finding is the observation that both overactive and underactive mutations within a particular gene, such as those found in cancer, reduce cell death compared to the normal gene. This suggests that normal cells are poised to die whenever there is trouble, but perhaps not tumor cells.

That means the dynamic range of cell signaling - the spread between the highest and lowest levels of a biological signal, like the range of volumes you can hear on your stereo - may be a greater determinant of what cells do than the absolute level of a particular signal, said Yaffe.

The computer modeling approach offers the chance to learn about biological phenomena that might take thousands of hours in the laboratory to uncover, according to Yaffe.

"In addition, rather than looking at one pathway in the cell in isolation, we could look at five pathways or eight pathways simultaneously," he said. "It also reinforces how engineering ideas can really illuminate biological mechanisms."

Massachusetts Institute of Technology

---

---

	Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity) by John H. Miller (Author), Scott E. Page (Author) This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be...
	Essentials of Computational Chemistry: Theories and Models by Christopher J. Cramer (Author) Essentials of Computational Chemistry provides a balanced introduction to this dynamic subject.  Suitable for both experimentalists and theorists, a wide range of samples and applications are included drawn from all key areas.  The book carefully leads the reader thorough the necessary equations providing information explanations and reasoning where necessary and firmly placing each equation in context.
	Simulating Organizations: Computational Models of Institutions and Groups by Michael Prietula (Editor), Kathleen Carley (Editor), Les Gasser (Editor) The globalization of the economy, increasing number of transnational organizations, and rapid changes in robotics, information, and telecommunication technologies are just a few of the factors significantly altering organizational time scales, forms, complexity, and environments. Time scales have shrunk, new organizational forms are emerging, and organizational environments are expanding and mutating at unprecedented rates. Computational modeling affords opportunities to both understand and respond to these complex changes.Paralleling developments in the physical sciences, computational modeling is emerging in the social and organizational sciences....
	Computational Solution of Large-Scale Macroeconometric Models (Advances in Computational Economics) by Giorgio Pauletto (Author) The purpose of this book is to present the available methodologies for the solution of large-scale macroeconometric models. This work reviews classical solution methods and introduces more recent techniques, such as parallel computing and nonstationary iterative algorithms. The development of new and more efficient computational techniques has significantly influenced research and practice in macroeconometric modeling. This volume supplies practitioners and researchers with both a general presentation of numerical solution methods and specific discussions about particular problems encountered in the field. The work is organized into five chapters. Solution techniques for linear and nonlinear systems are reviewed and a graph-theoretical approach to perform this analysis is...
	A Comparison of Computational Cognitive Models: Agent-Based Systems versus Rule-Based Architectures Increased operational costs and reductions in force size are two of the major factors driving the need for improved computer simulations within the military community. Human performance models are used in various aspects of simulation, including controlling computer generated forces, tactical decision aides, intelligent tutoring systems and new system design. This research makes a comparison between two categories of human performance models, multi-agent systems and rule-based architectures. Each type of model has its own strengths and weaknesses, and is therefore better suited for certain applications. Complex military simulations need human performance models that take advantage of the strengths of more than one type of model. The purpose of this research is to compare the...
	Computational Granular Dynamics: Models and Algorithms by Thorsten Pöschel (Author), T. Schwager (Author) Computer simulations not only belong to the most important methods for the theoretical investigation of granular materials, but provide the tools that have enabled much of the expanding research by physicists and engineers. The present book is intended to serve as an introduction to the application of numerical methods to systems of granular particles. Accordingly emphasis is on a general understanding of the subject rather than on the presentation of latest advances in numerical algorithms. Although a basic knowledge of C++ is needed for the understanding of the numerical methods and algorithms in the book, it avoids usage of elegant but complicated algorithms to remain accessible for those who prefer to use a different programming language. While the book focuses more on models than on...
	The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in Statistics) by Christian P. Robert (Author) This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.
	Contemporary Computational Geodynamics in China: Data, Models and Simulation by Huai Zhang (Editor), David Yuen (Editor) Computational methods for studies in Geophysics and Geodynamics are becoming increasingly more sophisticated. This book presents the latest developments in computational environments, grid computing technologies and data management. The organisation of geological data and their ontologies are focused upon with respect to integrating geological data into the state-of-the-art cyberinfrastructure for studies of modern geodynamics. This includes the presentation of fundamental theory, modeling and numerical simulation results, and the latest developments in geodynamics and seismic research, with a number of case studies from China.
	Fuzzy Computational Ontologies in Contexts: Formal Models of Knowledge Representation with Membership Degree and Typicality of Objects, and Their Applications by Yi Cai (Author), Ching-man Au Yeung (Author), Ho-fung Leung (Author) Knowledge Representation plays an essential role in Semantic Web, in particular in automated information processing and communications among software agents.  This book, entitled "Fuzzy Computational Ontologies in Contexts: Formal Models of Knowledge Representation with Membership Degree and Typicality, and Their Applications", discusses knowledge representation in Semantic Web.  It introduces the relevant background knowledge, models of fuzzy ontologies, importance and priority of properties in concepts, and object typicality in fuzzy ontologies and context-aware ontologies. The book is intended for graduate students, engineers, and researchers in the field of artificial intelligence and computer science. Yi Cai is an Assistant Professor in School of Software Engineering, South China...
	Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges: Second International Workshop, STACOM 2011, Held in ... Vision, Pattern Recognition, and Graphics) by Oscar Camara (Editor), E. Konukoglu (Editor), Mihaela Pop (Editor), Kawal Rhode (Editor), Maxime Sermesant (Editor), Alistair Young (Editor)