Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry.

Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases, we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

Reproducible and flexible simulation experiments with ML-Rules and SESSL.

Summary: The modeling language ML-Rules allows specifying and simulating complex systems biology models at multiple levels of organization. The development of such simulation models involves a wide variety of simulation experiments and the replicability of generated simulation results requires suitable means for documenting simulation experiments. Embedded domain-specific languages, such as SESSL, cater both requirements. With SESSL, the user can integrate diverse simulation experimentation methods and third-party software components into an executable, readable simulation experiment specification. A newly developed SESSL binding for ML-Rules exploits these features of SESSL, opening up new possibilities for executing and documenting simulation experiments with ML-Rules models. Availability and Implementation: ML-Rules is implemented in Java, SESSL and its bindings are implemented in Scala. The source code is available under open-source licenses: ML-Rulesgit.informatik.uni-rostock.de/mosi/mlrules2ML-Rules Quickstart (Graphical Editor)git.informatik.uni-rostock.de/mosi/mlrules2-quickstartSESSLgit.informatik.uni-rostock.de/mosi/sessl and sessl.orgSESSL Quickstart (Experiment Template)git.informatik.uni-rostock.de/mosi/sessl-quickstart Furthermore, Maven-compatible compiled packages of ML-Rules, SESSL, and the SESSL bindings are available from the Maven Central Repository at maven.org (org.sessl:* and org.jamesii:mlrules). Supplementary Material: The supplementary material contains a more complex case study that exemplifies the usage of the SESSL binding for ML-Rules. Contact: tom.warnke@uni-rostock.de.

Case-based prediction in experimental medical studies.

Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be utilized for a case-based ranking and adaptation of similar cases. We introduce the system OASES and its application to medical experimental studies to demonstrate this approach.