PNNL

Abstract

Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (“systems”) involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing and computational processing power has driven a reinvention and expansion of this field. The expansions include not only modeling of small metabolic{Ishii, 2004 #1129; Ekins, 2006 #1601; Lafaye, 2005 #1744} and signaling systems{Stevenson-Paulik, 2006 #1742; Lafaye, 2005 #1744} but also modeling of the relationships between biological components in very large systems, incluyding whole cells and organisms {Ideker, 2001 #1124; Pe'er, 2001 #1172; Pilpel, 2001 #393; Ideker, 2002 #327; Kelley, 2003 #1117; Shannon, 2003 #1116; Ideker, 2004 #1111}{Schadt, 2003 #475; Schadt, 2006 #1661}{McDermott, 2002 #878; McDermott, 2005 #1271}. Generally these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships and general features that are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (for example, the identification of an evolutionary pattern), or are interesting and valuable to researchers working on a particular problem (for example highlight a previously unknown functional pathway). Two events have occurred to bring about the field computational systems biology to the forefront. One is the advent of high throughput methods that have generated large amounts of information about particular systems in the form of genetic studies, gene expression analyses (both protein and mRNA) and metabolomics. With such tools, research to consider systems as a whole are being conceived, planned and implemented experimentally on an ever more frequent and wider scale. The other is the growth of computational processing power and tools. Methods to analyze large data sets of this kind are often computationally demanding and, as is the case in other areas, the field has benefited from continuing improvements in computational hardware and methods. The field of computational biology is very much like a telescope with two sequential lenses: one lens represents the biological data and the other represents a computational and/or mathematical model of the data. Both lenses must be properly coordinated to yield an image that reflects biological reality. This means that the design parameters for both lenses must be designed in concert to create a system that yields a model of the organism that provides both predictive and mechanistic information. The chapters in this book describe the construction of subcomponents of such a system. Computational systems biology is a rapidly evolving field and no single group of investigators has yet developed a compete system that integrates both data generation and data analysis in such a way so as to allow full and accurate modeling of any single biological organism. However, the field is rapidly moving in that direction. The chapters in this book represent a snapshot of the current methods being developed and used in the area of computational systems biology. Each method or database described within represents one or more steps on the path to a complete description of a biological system. How these tools will evolve and ultimately be integrated is an area of intense research and interest. We hope that readers of this book will be motivated by the chapters within and become involved in this exciting area of research.