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A Glossary for Systems Biology Contents Index - Glossary - Modularity A Closer System Submit - Comment Model Etymology The word stems from the Latin 'modus', meaning 'measure' or 'type'. Historically, the term first meant scale models of the original, i.e. a model of something. It is still used that way in terms like 'model airplane'. Later it designated analogies that were used to explain complex concepts by comparison with well-known objects, like the atomic model explained by analogy to a planetary system. These kinds of models enable us to postulate, extrapolate and propose hypotheses which can then be tested against reality. In these cases the term is used in the meaning of a model for something [27]. The kind of model we talk about in systems biology is still a model for something, but at an even more abstract level. Models in Biology Classical biologists use model as a short form of 'model organism'. By that they mean an organism that is sufficiently similar in all respects important for a certain experiment that it can be used instead of the organism that the results of that experiment are ultimately meant for. A typical example is the use of laboratory animals for experiments, for example guinea pigs (real or metaphorical), mice, pigs, flies, worms, yeast, etc., to make predictions about human diseases and their possible treatment.   Molecular biologists use the term model in a way similar to that of systems theorists; they understand it as a formal description of connections and dependencies between distinct parts of a metabolic pathway. The difference is that they usually use verbal descriptions, not mathematical equations and that their models usually are limited to qualitative descriptions of connections, without quantitative influences. Models in Systems Theory For systems theorists, model means a system of equations, with initial and boundary conditions, that (ideally) describe the qualitative and quantitative behavior of a system, including its dynamics, over the full range of values. Dynamics The fundamental difference between mathematical models of molecular biologists and systems theorists lies in the range of dynamical behavior that they expect the model to be able to represent. Most models of biological systems still only cover steady-state properties of a system (static), or are valid for a linearization of the system around a certain steady state (local dynamic). This is due to the nature of measurements taken in biology: samples have to be taken and analyzed for the compounds of interest. This sampling process has a limited resolution in time - the maximum frequency at which samples can be taken and analyzed. This time constant often does not allow to get series of measurements that represent the (sometimes quite fast) dynamic behavior of intra-cellular processes, but rather are limited to states a system stays in for a sufficiently long (i.e. measurable) time. Biological systems are typically non-linear, resulting in complex dynamic behaviors. The stable states of a system are those easiest to identify by simple observation. They are also the states best suited to take measurements, since values do not change quickly, accommodating traditional (slow) measurement techniques. Transitions between stable states are often quite fast, defying measurement with traditional methods. Consequently, models were built which consider transitions to be simple switching between stable states. They are often called 'connectionist' models because they only consider the fact that a connection exists between two states, but not the dynamics of the transition. These dynamics contain additional information about a system, though, so there is an interest to obtain it and incorporate it into models. A first step can be to consider local dynamics, i.e. the dynamics within a small area around a steady state. Local dynamics can often be extracted from measurement data, giving a first indication of transition dynamics. Such information could be the direction the states begin to change in, or whether the changes start slowly and accelerate or are fast from the beginning. Such knowledge can be represented in linear models, which are valid around that specific steady state. Such models would be a linear approximation of the real non-linear dynamics of the original system. Recent improvements to traditional measurement techniques and newly developed technologies enable measurement of the fast dynamic changes during transitions. They provide a wealth of empirical data which can be used to improve existing models. Those improved models will usually be non-linear, making analysis more difficult. They will be able to represent a systems global dynamics, though. Specificity Another problem that is closely connected to the dependence on empirical data described above is the specificity of the resulting models. Due to their empirical nature they are in most cases limited to represent a special cell line's behavior. Some might show qualitative connections and dependencies of proteins, but cannot explain the quantitative behavior of these connections. Others might show the qualitative behavior of a metabolic pathway for a selected cell line, might even allow ball-park estimations of quantitative dependencies, but are unable to cross the border to even closely related cell types. This is due to the fact that different cell lines might act identically in a qualitative way but in a completely different quantitative dimension; or even change their qualitative behavior completely along with concentration dimensions while working along the exact same interconnection pathways of proteins (modularity) [58]. Perspectives As already mentioned above, advances in measurement techniques and connected fields bring the hope that empirical data might soon be available that will allow more complex models to be developed. But this is not necessarily a good development in all respects. These new models will be very detailed, as they will be based on a wealth of data, and that might just as well hinder effective research in some areas. Depending on the goals of that research, ``it is important to carefully consider the purpose of model building: Whether it is to obtain an in-depth understanding of system behavior or to predict complex behaviors in response to complex stimuli, we must first define the scope and abstraction level of the model'' [34]. If, for example, the aim of a series of simulations is to investigate the long-term changes that a certain stimulus induces (e.g. in a case of adaptation), then the short-term fluctuations that occur in between the stimulus and the adaptation might be of little or no interest for the research aim [54]. Nevertheless, they will still have to be simulated and will take up a lot of computing power and time, even though they could be irrelevant. A model with the right abstraction level would leave out the short term fluctuations and only show effects in the relevant time scale. The problem with this idea is that it is not all that easy to decide which effects can safely be left out of a simulation for a specific purpose without compromising the integrity of the simulation result. Functional dependencies of the different parts of a pathway are the critical issue here [54]. A modular concept (modularity), based on realistic functional modules in the biological pathways, could be an important tool in classifying which elements are important for a certain research goal and which are not. ``The problems of applying systems theory in biology'' include ``the difficulty of building precise and yet general models'' [62]. Therefore, it will be equally important to make progress not only in obtaining base data for developing new and better models but also in ``studies on classifications and comparison of circuits'' within biological systems, to get a feel for the ``richness of design patterns used'' and their evolutionary connections. The hope behind this kind of research would be to find ``a possible evolutionary family of circuits'' or ``a periodic table for functional regulatory circuits'' [34]. Such a 'family of circuits' might form the basis for a family of modules and modular models that will allow selective simplification of models (or rather adaptation of a model's level of abstraction) to fit their level of abstraction to the purpose of the research they are used for (modularity).   Contents   Index Glossary Submit Comment

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