This publication serves as a general introduction to Computational Systems Biology, as well as an introduction to the SBPKPD platform, more about which can be found at this website: http://www.sbpkpd.org/
One of the notable features of this platform is its statistic capabilities:
An automated statistical analysis provides parameter estimates with their standard errors, covariance matrix, residual plots and goodness-of-fit measures, such as the Akaike Information and Schwartz criteria.On the technical foundations of the platform:
To avoid typical problems of accessibility (owing to restriction to one platform or browser type), we based our SBPKPD on the platform-free Java-based Google Widget Toolkit technology. All models are implemented and run in R, a programming language for statistical computing (http://www.r-project.org/). [...] To our knowledge, no tool in this area has been designed so far for execution on an R-based cluster, and we would like to use this exciting possibility for computationally expensive tasks.Further development will focus on the following things:
With its solid conceptual base and its mathematical background, our SBPKPD platform is suitable for further development into more specialized facilities. In a (semi)automatic in vitro–in vivo correlation system, existing models and approaches such as PK fitting supported by new processes like numerical deconvolution, could establish mathematical relations between the in vitro drug dissolution and its in vivo behaviour. Such an ‘IVIVC’ system could be quite useful for clinical and pharmaceutical research in the process of new drug admission, for which few tools exist, and which are all commercial: it is our goal to stimulate crossinstitutional cooperation in this area by providing an open-source simulation and modelling platform, the development of which will also be guided by clinical users informed best about current needs in daily medical practice.Here are some more quotations from the paper, giving an introduction to Computational Systems Biology in general. First, a quote about the history of systems biology.
Genomics started from biochemistry and then molecular biology. It was paralleled by a development in physics and mathematics, which led to applications of non-equilibrium thermodynamics in biology, mathematical biology and ultimately to metabolic control analysis, flux balance analysis and dynamic network modelling. These two upward movements have since been combined into a scientific discipline called Systems Biology. Systems Biology (SB) aims at understanding how biological function emerges in the interactions between components of biological systems. Ultimately, SB should enable one to understand how improper networking of the macromolecules of living organisms leads to their diseases and how molecular interference may redirect those networks to their proper functioning. SB has progressed to new understanding of the organization and functioning of metabolic and signal transduction pathways in ways that had been impossible with molecular and cell biology, and indeed with functional genomics, alone. Moreover, not even SB has delivered yet the understanding of the functioning of entire organisms, such as in an understanding of disease or in actual drug discovery.What approach to systems biology is realistic?
[T]he extreme bottom-up approach to whole organism SB that would describe the activity of every individual macromolecule, is not within the reach of the present computation methodologies, and, even worse, not within the reach of the necessary experimentation facilities.How about pharmacokinetics?
The equations used in pharmacokinetics (PK) [...] use abstractions of physiological processes to fit equations to observed dynamics of the concentrations of drugs in the patient. Parameters again refer to abstractions of real components of the systems; they include ‘distribution volumes’, which often much exceed realistic volumes, as they comprise the effects of partition coefficients. This is fine for quasisteady states, but may not work well in dynamic situations, or when saturable kinetics determines distributions. Indeed, mechanistic PK is probably the most neglected field in the area of medically relevant biosimulations.Why models are often unrealistic:
The lack of quantitative and standardized in vivo measurement techniques at the molecular level forces one to obtain in vitro data in artificial or cell line-derived constructs (e.g. Caco-2) or to interrogate animal models barely resembling the human. The accompanying hurdle is the in vitro-to-in vivo and/or inter-species extrapolation (often based on phenomenological and disputable allometric ‘laws’). Each of these steps is full of simplifications distorting the reality one thinks to observe.Further, the paper lists tools for model creation:
There exist of course a number of excellent tools for physiologically based whole-body models like SIMCYP, GASTROPLUS and PKSIM or ADAPT II, WINNONLIN, NONMEM and KINETICA for compartmental (population) PK analysis. The tools in the first category suffer from their closed architecture making open source collaboration impossible. On the other hand, tools in the latter category are accessible as standalone applications, running to a large extent under Windows only. Their user-friendliness varies between very sophisticated but expensive, and disputable (e.g. Fortran syntax in NONMEM) but free or inexpensive. [...] In this paper we shall introduce SBPKPD, a platform for [...] an open-source collaboration.
A number of opensource model repositories exist, with a broad spectrum of models and simulation facilities (Java Web Simulation - JWS and Biomodels.net, or more specialized (e.g. CCDB, which contains cell cyclerelated models only)). Together, JWS online and Biomodels store hundreds of kinetic models for metabolic, signal transduction and gene-expression pathways.Regarding Java Web Simulation, the authors further explain:
JWS online also offers the possibility to run simulations and multiple analysis options (e.g. steady-state and metabolic-control analysis) for any of its models online, i.e. without downloading of software tools. This is what defines it as a ‘live’-model repository, i.e. the models are alive through the web. Through the web, one can change parameter values in any of the models and calculate the implications for model behaviour. One can also determine which steps in a modelled pathway most determine a specific flux or concentration. The view is to make mathematical models produced by SB useful to scientists who are ignorant of mathematics. The use of JWS online is close to experimentation. It may be important that quality control of models is disentangled from the application or validation of the models. If these important activities are mixed, internal inconsistency of modelling may cover up for lack of experimental validation.
JWS online also has the perspective of the silicon organism, also called the virtual biochemical organism (human) (http://vbhuman.org/). This means that it hopes that its models can be linked up with each other such that they grow, ultimately to cover significant parts of entire organisms. This may seem less efficient than the approach of genome-wide kinetic models for entire organisms, but it may not be. The automobile industry is using modular production lines to improve the robustness of the overall production flow to fluctuations in the activities in individual steps. Modularity also makes the quality control manageable. Checking the quality of a genome-wide model is impossible for any individual because of the great complexity. Scientific experts may still be able to check the quality of pathway models.
An important deliverable of the JWS online and Biomodels facilities will become the connecting of adjacent models into larger models of part of the whole cell. Such an activity could greatly reduce the total complexity of the modelling of whole organisms. Success is not guaranteed however; it will depend on whether the biological function is indeed modular and on advances in multi-scale modelling approaches. The organization of whole organisms into tissues, of tissues into cells, and of cells into organelles, as well as the separation between transcription, translation and metabolism, suggests that biology is indeed modular, perhaps because of the same robustness requirements as the automobile industry. At the same time, where such obvious modules are absent this may signal a functional reason, and the approach might not work.