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A Glossary for Systems Biology Contents Index - Glossary - System A Closer A Closer Submit - Comment Systems Biology As already shown in the Chapter Introduction, systems biology has been around - under different names - for quite some time. Idea There are basically two approaches to systems biology. One has its roots in biology, the other in systems theory. The former sees it as a way to integrate data from a variety of sources [25]. For the latter, the main idea is that the methods developed in those fields might also have a useful application in biology, since engineering sciences have a tradition of borrowing from natures design principles. Only recently, the prospect of 'designing' biological systems has become feasible. Currently this is mostly done by 'improving' plants or animals by adding genes from other organisms, but first simple from-scratch designs of biological functional modules are starting to appear [45]. Examples are designed cells as thermometers [17] and oscillators which are independent of the cell cycle [40]. Even before all this became possible, though, the possibility of using engineering methods to assist in 'reverse engineering nature' had a certain appeal. Developments This is not the first attempt at systems-level analysis of biological systems; there have been several efforts in the past, the most notable of which is cybernetics, or biological cybernetics, proposed by NORBERT WIENER. As shown in the historical review (see Chapter History), those earlier attempts did provide solutions for special problems, but were bound to fail as a 'real' systems biology because of the lack of understanding of molecular biology at the time and insufficient data due to deficiencies in measurement techniques [31]. Today's advances in measurement, data acquisition and handling technologies provide a wealth of new data which can be used to improve existing models. That data can be divided into four categories or key properties: system structures, system dynamics, control methods, and design methods [34]. Progress in these areas requires ``breakthroughs in our understanding of computational sciences, genomics, and measurement technologies, and integration of such discoveries with existing knowledge'' [34]. (see Fig. 2.1) Most authors agree that the scientific approach used until recently had some serious shortcomings, especially the attempt of ``much of twentieth-century biology [...] to reduce biological phenomena to the behavior of molecules'' [21] and to try ``to explain observable phenomena by reducing them to an interplay of elementary units investigatable independently of each other'' [60]. KITANO compares this to making a list of all parts of a plane, which eventually produces a complete catalogue but does not help understand the plane's functional complexity. Another picture he uses is that of a road map: Like connectionist models of biological pathways, it is a static map of all connections that does not show the dynamical ``traffic patterns, why such traffic patterns emerge, and how we can control them'' [34]. There are two proposals of how to improve biological research. One group wants to use a new system-oriented approach. Another wants to continue the successful work along proven lines and make progress by ``integrating the different levels of information pertaining to genes, mRNAs, proteins, and pathways'' [25], which have up to date been used individually. Both call their new way 'systems biology'. These approaches can coexist without hurting each other. Quite the contrary, they can profit from each other's discoveries. They might grow into one some day, but that need not be, either. One thing that is definitely needed, though, is a decision on what to call the respective approaches. Two different approaches under the same name would be contrary to this paper's intentions and pose too many dangers for misunderstanding.2.1 People adhering to the systems-oriented approach gradually replace the old connectionist2.2 viewpoint with a new systematic one, without forgetting the cases of successful research it has made possible. That may be because these successes at the same time pointed out its limitations [21]. There seems to be consent in this group that the old approach was needed to lay the groundwork. To make the next big step in understanding biological processes possible, though, they believe that there has to be a change in attitude towards what has to be researched and where to look for it, that is, to turn to systematic properties produced by systems of components: ``The emphasis in biology is now shifting from identifying individual components and molecules to the study of the vast networks that biological molecules create, which regulate and control life.'' [DOYLE in [50]] Partners In order to describe the new kind of functions and properties they are now looking for, biologists need a new vocabulary. Many of the properties and terms they start to use for this are the same ones systems theory uses [21,34]. This coincidence of terms makes systems theory the logical choice for biology's partner for the new approach, even though there are differences in the exact meaning that they assign those terms. In addition, systems theory has always been looking for systematic properties and has even made attempts in the past to apply their methods to biology. It was hard going and their efforts had only limited success. The same would probably happen to biologists, were they to try this new approach alone. There are other reasons for this besides the technological deficiencies already mentioned. The main one is that they each lack the methods and understanding the other has developed over a long time. Probably each group could eventually come to a working approach all by themselves, but that would mean duplicating a lot of work that has already been done by the other, wasting time and resources. It is reasonable to believe that the cooperative way will be faster, even if the partners take some time in which both mostly explain their respective knowledge to each other before starting to think about how the combined knowledge can be used to make progress. Currently, systems biology is in some sort of orientation phase, familiarizing itself with the new vocabulary and working on building the relations between the new range of systematic properties and the old body of knowledge about the basic constituting elements. The next step is going to be an integration of both fields' knowledge and an attempt to fill the gaps between the two that become apparent during the integration process. Then will come a time when the 'new' properties will be researched in terms of how they arise within a cell [21]. To accomplish this, systems biology ``must examine the structure and dynamic of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism'' [34]. Most researchers seem to agree, though, that this will have to continue in parallel to the new approach, in order to provide and extend the basis for it. The new outlook is characterized by the basic idea of ``wholeness'' [11], i.e. it will consider ``problems of organization, phenomena not resolvable into local events, dynamic interactions manifest in the difference of behavior of parts when isolated or in higher configuration, etc.; in short, systems of various orders not understandable by investigation of their respective parts in isolation'' [11]. One thing that cannot be stressed enough is that this means a fundamental change ``in our notion of what to look for in biology. While an understanding of genes and proteins continues to be important, the focus is on understanding a system's structure and dynamics''[34]. At the same time this is ``a golden opportunity for systems-level analysis to be grounded in molecular-level understanding, resulting in a continuous spectrum of knowledge'' [34]. Modularity The implications of thinking in terms of systems are starting to take hold in current research. This is evident in a number of new ideas that have been brought into the discussion. The concept of modularity for example, that has served engineers and systems theorists well for some time, has been rediscovered for biology. As shown in the examples for modularity, classical biology already had this concept on a rather macroscopic scale, without explicitly calling it by this name. Now researchers see a ``modular framework'' for biology, ``treating subsystems of complex molecular networks as functional units that perform identifiable tasks perhaps even able to be characterized in familiar engineering terms'' [39]. This would also coincide nicely with the concept of systems in systems theory (system, modularity), where scientists think in terms of classes of systems, defined by a certain set of common characteristics, which can be handled by a common set of methods. It would also be the ideal basis for future developments to even more complex models, once the cellular and sub-cellular levels can be described in sufficient detail. A longterm goal is to produce fully qualified models of first cells, then organs, then even complete organisms [31]. This could be seen as a macro-scale extension to the modular concepts discussed in the section modularity and as an application of long-standing technical-engineering practice to biological engineering. What is it good for? One major goal of these efforts clearly is a better understanding of how cells work. Model-building is a tool to that end as well as a standardized form of representation for knowledge about a system. This is different from the way biologist defined models in the past, using prose descriptions of concepts and ideas. But once the knowledge exists, what can be done with it? ``The most feasible application of systems biology research is to create a detailed model of cell regulation, focused on particular signal-transduction cascades and molecules to provide system-level insights into mechanism-based drug discovery. Such models may help to identify feedback mechanisms that offset the effects of drugs and predict systemic side effects.'' [34] Application possibilities are endless: easier drug design; 'personalized' drugs, i.e. built for purpose, side effect free medicines, developed for (or at least adapted to) individual patients; directed, reliable manipulation of gene information (e.g. treatment of tumors or hereditary diseases); and more. ``It may even be possible to use a multiple drug system to guide the state of malfunctioning cells to the desired state with minimal side effects. Such a systemic response cannot be rationally predicted without a model of intracellular biochemical and genetic interactions.'' [34] With such models another transfer from engineering practice would become possible: Newly designed drugs could be tested in simulations before going into clinical testing. This would reduce risks to test subjects and patients and could eventually eliminate the need for animal testing.   For these applications to be realistic, though, apart from vastly increased computing power it will be absolutely necessary to be able to tune the level of detail to the aim of research as described in Chapters Model and Modularity. First of all, though, before any of these visions can become reality, has to come a fundamental understanding of the processes in cells at the smallest level (i.e. level of smallest systems). The basis for macro-level insights is still micro-level knowledge, a basis that has been built continuously up till now and will increase as technologies improve. This is needed not only to understand the mechanisms to be used and manipulated. The ability to assess the risks that are inherent in manipulating so complex and intricately balanced a machinery as molecular processes in or between cells will be possibly even more important. Here simulations could considerably reduce the risk of creating potentially dangerous mutations and help clarify genetic mechanisms of inheritance and gene transfer and their consequences. This would help to understand the complexity of biological systems and make it more manageable than it is today. Complexity One of the more recent discoveries is that the complexity, which is unarguably present in biological systems, is often not a complexity of function. It is rather a complexity of regulation that is necessary to ensure that a relatively simple function can be maintained robustly in spite of serious fluctuations in the environment (robustness). In other words, ``the objective of this complexity is to guarantee that the core function will generate reliable output. In a nutshell, the system complexity is built in to provide for simple behavior'' [39]. This is in sharp contrast to the popular chaos and complexity theories, which associate complexity with fractals and edge-of-chaos, originating in simple systems [15,16]; see also Chapter 'Recommended Readings'. This distribution of complexity can also be observed on a level even lower than that of cell functions. As the various genome projects [49,53,48,10] were able to show, there are more regulatory sections to a genome than there are for metabolic functions and a lot of sections have no essential function at all (see Figure 2.2) [28,39,16]. If this information proves to be general, it could be speculated that the compositional complexity of cells is designed chiefly to enable cells to maintain simple functions reliably in uncertain and variable environments [39] (robustness, sensitivity).   Another aspect of complexity at the genetic level is contained in the realization that there is no strict demarcation between information storage and functional units. Contrary to general public perception, genes are not just passive elements of information storage. Instead, gene regulation is an important part of a lot of basic processes in cells. Metabolites interact with gene regulators and influence gene expression, thereby modulating behavior of the metabolic pathways. Conclusion As shown at the beginning of this chapter, two different concepts are pursued under the name of 'systems biology'. They are joined in their vision to increase knowledge in (molecular and cell) biology. They differ in their approaches: continue along successful lines by integrating data from all available sources, or ``to understand biology at the system level'' [34]. Within the latter group, there are different opinions on the best way to reach this goal and on the amount of influence each field of research should have on this decision. One group sees systems biology as an extension of biology, a ``biology for systems-level studies, not physics, systems science, or informatics, which try to apply certain dogmatic principles to biology'' [32]. These researchers want biology to have the leading role, with all other sciences in supporting positions. Others take the opposing view and urge biologists to adopt systems theory's concepts and reshape biology accordingly. Only then, in their opinion, will biologists ask really new questions based on system-theoretic concepts ``rather than using these concepts to represent in still another way the phenomena which are already explained in terms of biophysical or biochemical principles'' [47]. Some even see this as the natural conclusion of the merging of the two fields [62].   Contents   Index Glossary Submit Comment

Universität Magdeburg      University of Liège		last modified by sysbio 29 Jun. 2011