Transactions on Computational Systems Biology I

In recent years there has been an incredible explosion of computational studies of molecular biology systems, particularly those related to the analysis of the structure and organization of molecular networks, as the initial steps toward the possible simulation of the behavior of simple cellular systems. Needless to say, this task will not be possible without the availability of a new class of data derived from experimental proteomics. Large-scale application of the yeast two-hybrid system, affinity purification (TAPs-MS), and other methodologies are for the first time providing overviews of complete protein interaction networks. Interestingly a number of computational methods are also contributing substantially to the identification of protein interactions, by comparing genome organization and evolution. Other disciplines, such as structural biology and computational structural biology, are complementing the information on interaction networks by providing detailed molecular descriptions of the corresponding complexes, which will become essential for the direct manipulation of the networks using theoretical or experimental methods. The storage, manipulation and visualization of the huge volumes of information about protein interactions and networks pose similar problems, irrespective of the source of the information: experimental or computational. In this sense, a number of competing systems and emerging standards have appeared in parallel with the publication of the data. In this review, we will provide an overview of the main experimental, high-throughput methods for the study of protein interactions, the parallel developments of computational methods for the prediction of protein interactions based on genome and sequence information, and the development of databases and standards that facilitate the analysis of all this information.

Systems Biology constitutes tools and approaches aimed at deciphering complex biological entities. It is assumed that such complexity arose gradually, beginning from a few relatively simple molecules at life’s inception, and culminating with the emergence of composite multicellular organisms billions of years later. The main point of the present paper is that very early in the evolution of life, molecular ensembles with high complexity may have arisen, which are best described and analyzed by the tools of Systems Biology. We show that modeled prebiotic mutually catalytic pathways have network attributes similar to those of present-day living cells. This includes network motifs and robustness attributes. We point out that early networks are weighted (graded), but that using a cutoff formalism one may probe their degree distribution and show that it approximate that of a random network. A question is then posed regarding the potential evolutionary mechanisms that may have led to the emergence of scale-free networks in modern cells.

We examined the process of virtualization to deal with data intensive problems. Since data integration is a first-order priority in systems biology, we started developing a new method to manipulate data models through ordinary metadata transactions, i.e. by preserving the original data format stored in resources. After discussing why metamodels are made for, and the interplay of modeling languages in metamodel design, we presented a systemic metamodel-driven strategy to integrate semantically heterogeneous data.

Calculations of potential numbers of interactions between gene products to generate physiological functions show that we can expect a highly non-linear relation between genome size and functional complexity. Moreover, very small differences in gene numbers or sequence can translate into very large differences in functionality.

As a preliminary step in testing the expressiveness of Beta-binders against realistic case studies, we comment on a number of operational properties of the formalism and present a set of derived patterns that can be useful when modeling complex biosystems.

Diverse modeling and simulation methods are being applied in the area of Systems Biology. Most models in Systems Biology can easily be located within the space that is spanned by three dimensions of modeling: continuous and discrete; quantitative and qualitative; stochastic and deterministic. These dimensions are not entirely independent nor are they exclusive. Many modeling approaches are hybrid as they combine continuous and discrete, quantitative and qualitative, stochastic and deterministic aspects. Another important aspect for the distinction of modeling approaches is at which level a model describes a system: is it at the “macro” level, at the “micro” level, or at multiple levels of organization. Although multi-level models can be located anywhere in the space spanned by the three dimensions of modeling and simulation, clustering tendencies can be observed whose implications are discussed and illustrated by moving from a continuous, deterministic quantitative macro model to a stochastic discrete-event semi-quantitative multi-level model.

Systems biology aims at an understanding of increasingly large and complex cellular systems making use of computational approaches, e.g. numerical simulations. The size and complexity of the underlying biochemical reaction networks call for methods to speed up simulations and/or dissect the biochemical network into smaller subsystems which can be studied independently. Both goals can be achieved by so-called complexity reduction algorithms. However, existing complexity reduction approaches for biochemical reaction networks are mostly based on studying the steady state behavior of a system and/or are based on heuristics. Given the fact that many complex biochemical systems display highly nonlinear dynamics and that this dynamics plays a crucial role in the functioning of the organism, a new methodology has to be developed. Therefore, we present a new complexity reduction method which is time-dependent and suited not only for steady states, but for all possible dynamics of a biochemical system. It makes use of the evolution of the different time–scales in the system, allowing to reduce the number of equations necessary to describe the system which is speeding up the computation time. In addition, it is possible to study the way different variables/metabolites contribute to the reduced equation system which indicates how strongly they interact and couple. In the extreme case of variables decoupling in a specific state, the method allows the complete dissection of the system resulting in subsystems that can be studied in isolation. The whole method provides a systematic tool for an automated complexity reduction of arbitrary biochemical reaction networks. With the aid of a specific example, the oscillatory peroxidase-oxidase system, we show that coupling of time–scales depends heavily on the specific dynamics of the system. Therefore, neither computational improvement nor systematic understanding can be achieved by studying these aspects solely under steady state conditions.