Note that Kepler postulated other less-enduring mathematical models of planetary dynamics. His Mysterium Cosmographicum showed that if you nest spheres and Platonic polyhedra in the right order (sphere-octahedron-sphere-icosahedron-sphere-dodecahedron-sphere-tetrahedron-sphere-cube-sphere), the sizes of the spheres correspond to the relative sizes of the first six planets' orbits. This simple, abstract way of accounting for empirical data was probably just a happy coincidence. Happy coincidences are a potential danger in systems biology as well.
However, systems biology stands to gain a lot from reductionism, and in this sense systems biology is anything but the antithesis of reductionism. Just as you can build up to an understanding of complex digital circuits by studying individual electronic components, then modular logic gates, and then higher-order combinations of gates, one may well be able to achieve an understanding of complex biological systems by studying proteins and genes, then motifs (see below), and then higher-order combinations of motifs.
Infrastructure for Systems Biology Europe (ISBE) is a project to establish essential, centralized services for systems biology researchers throughout the systems biology lifecycle. A key component of ISBE is to support the management, integration and exchange of data, models, results and protocols. To inform further ISBE development, we surveyed the community to evaluate the uptake of available standards, and current practices of researchers in data and model management.
The survey was sent to major mailing lists targeting the systems biology and computational biology communities and advertised at relevant consortia meetings. It elicited 153 responses, from 17 countries across 6 continents, with a cross section of the systems biology community represented (Appendix Fig S1). Lessons from the survey are being implemented as part of an ISBE supporting project, FAIRDOM (www.fair-dom.org).
It comes with student-friendly reading lists and a companion website featuring a short exam prep version of the book and educational modeling programs. The text is written in an easily accessible style and includes numerous worked examples and study questions in each chapter. For this edition, a section on medical systems biology has been included.
This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface.
SBML-SAT for Windows, Mac, and Linux can be freely downloaded from its website -SAT. The manual documentation file including a detailed tutorial for the usage of SBML-SAT is also available in the website. The future updates of SBML-SAT will be released on the website as well. Like most other SBML supported software systems, SBML-SAT requires a link to libSBML and utilizes SBMLToolbox , which allows us to import SBML into MATLAB . Once the SBML model is imported into SBML-SAT, a MATLAB file will be automatically generated, which includes the ODEs of the model. This is very helpful for the user, who wants to code in MATLAB for other purposes. To speed up the process of solving the ODEs, we employed the CVODE module of SUNDIALS (Suite of Nonlinear and Differential/Algebraic Equation Solvers) as the ODE Solver . An interface to setting the options of CVODE solver is also available in SBML-SAT. Both SBMLToolbox and SUNDIALS  can be freely downloaded.
Currently, a SBML model editor module is not available in SBML-SAT. Fortunately, many existing free software packages such as CellDesigner, SBMLeditor and COPASI, share a common functionality for constructing and editing SBML models. The users can easily generate their models with these free software packages and then run a variety of analyses in SBML-SAT by importing the model in SBML format. Although SBML-SAT doesn't provide a SBML editor for model construction, it provides a convenient track for modifying the initial conditions of the state variables and parameter values in the model. Moreover, delay differential equation models are not supported in SBML-SAT, as in most existing software systems. In practice, delay differential equations can be solved in approximation by converting to ordinary differential equations using the linear chain transformation . Therefore, users can still apply SBML-SAT to their delay differential equation models.
There are more than 120 SBML-supporting software packages for kinetic analysis of biological models and this number continues to grow. However, a powerful, flexible and broadly applicable software package for global sensitivity analysis and robustness analysis has been lacking. In reality, it is difficult and time consuming to implement different sensitivity analysis algorithms especially the global sensitivity analysis methods. Here we introduced, a free Matlab-based software tool, SBML-SAT, for both local and global sensitivity analysis of SBML models. With a user-friendly graphic interface, this tool allows the user to run sensitivity analysis, steady state analysis and robustness analysis for a variety of model outputs. Models involving events are also supported in SBML-SAT. Furthermore, created in Matlab, the most popular software used in the community of systems biology , SBML-SAT has a good cross-compatibility with different platforms. Taken all together, we can expect that SBML-SAT will have a broad applicability among systems biologists.
Systems biology aims at the integrative analysis of large-scale biological systems up to whole cells. To realise this goal, we integrate knowledge into executable or computational models.1 This process has been developed the furthest in the field of metabolic modelling, where the community routinely works with genome-scale models. These models are defined at the level of biochemical reactions, cover the entire metabolic network of even complex cells, and can be simulated to predict system-level functionality.2,3 The methodology is well established and supported by rich toolboxes for network reconstruction, validation and simulation,4 and it constitutes the paradigm for bottom-up modelling. However, these tools cannot be used for signal transduction networks, due to the difference between mass and information transfer networks.5
MIRIAM is a project of the international initiative BioModels.net , which aims are multiple: define agreed-upon standards for model curation, define agreed-upon vocabularies for annotating models with connections to biological data resources and provide a free access to published, peer-reviewed, annotated, computational models. Others projects of this initiative includes BioModels Database , a free, centralised database of curated, published, quantitative kinetic models of biochemical and cellular systems; and the Systems Biology Ontology (SBO) . All these projects together support the exchange and reuse of quantitative models. MIRIAM originates from the specific requirement to facilitate the exchange of kinetic models between databases, standards and software, as witnessed by the original authors, involved in BioModels Database, CellML, COPASI, DOCQS, JWS Online, MathSBML, RegulonDB, SBML, SBMLmerge, SBW and SigPath. The support of MIRIAM in the community has been growing steadily since its release, as witnessed by the growing number of citations, the recognition in community surveys  and the incorporation of MIRIAM annotations in widely used standard formats such as SBML . Because quantitative modelling is only one facet of modern integrative biology, MIRIAM has now joined the Minimum Information for Biological and Biomedical Investigations (MIBBI), a broader effort to enhance cooperation between guidelines in life science .
The developer of a software to be used in computational systems biology will have to import models already encoded. If an interface to display them is to be created (Web-based or rich client), one needs to convert all the MIRIAM URIs for instance into physical addresses, which can be used to recover the knowledge stored in the entities pointed to by the annotations. The conversion from MIRIAM URIs to physical addresses can be done using the getDataEntries() method of MIRIAM Web Services.
Dupuytren's disease (DD) is an ill-defined fibroproliferative disorder of the palm of the hands leading to digital contracture. DD commonly occurs in individuals of northern European extraction. Cellular components and processes associated with DD pathogenesis include altered gene and protein expression of cytokines, growth factors, adhesion molecules, and extracellular matrix components. Histology has shown increased but varying levels of particular types of collagen, myofibroblasts and myoglobin proteins in DD tissue. Free radicals and localised ischaemia have been suggested to trigger the proliferation of DD tissue. Although the existing available biological information on DD may contain potentially valuable (though largely uninterpreted) information, the precise aetiology of DD remains unknown. Systems biology combines mechanistic modelling with quantitative experimentation in studies of networks and better understanding of the interaction of multiple components in disease processes. Adopting systems biology may be the ideal approach for future research in order to improve understanding of complex diseases of multifactorial origin. In this review, we propose that DD is a disease of several networks rather than of a single gene, and show that this accounts for the experimental observations obtained to date from a variety of sources. We outline how DD may be investigated more effectively by employing a systems biology approach that considers the disease network as a whole rather than focusing on any specific single molecule. 2b1af7f3a8