SMITH: spatially constrained stochastic model for simulation of intra-tumour heterogeneity.

MOTIVATION: Simulations of cancer evolution are highly useful to study the effects of selection and mutation rates on cellular fitness. However, most methods are either lattice-based and cannot simulate realistically sized tumours, or they omit spatial constraints and lack the clonal dynamics of real-world tumours. RESULTS: Stochastic model of intra-tumour heterogeneity (SMITH) is an efficient and explainable model of cancer evolution that combines a branching process with a new confinement mechanism limiting clonal growth based on the size of the individual clones as well as the overall tumour population. We demonstrate how confinement is sufficient to induce the rich clonal dynamics observed in spatial models and cancer samples across tumour types, while allowing for a clear geometric interpretation and simulation of 1 billion cells within a few minutes on a desktop PC. AVAILABILITY AND IMPLEMENTATION: SMITH is implemented in C# and freely available at https://bitbucket.org/schwarzlab/smith. For visualizations, we provide the accompanying Python package PyFish at https://bitbucket.org/schwarzlab/pyfish. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PyBoolNet: a python package for the generation, analysis and visualization of boolean networks.

Motivation: The goal of this project is to provide a simple interface to working with Boolean networks. Emphasis is put on easy access to a large number of common tasks including the generation and manipulation of networks, attractor and basin computation, model checking and trap space computation, execution of established graph algorithms as well as graph drawing and layouts. Results: P y B ool N et is a Python package for working with Boolean networks that supports simple access to model checking via N u SMV, standard graph algorithms via N etwork X and visualization via dot . In addition, state of the art attractor computation exploiting P otassco ASP is implemented. The package is function-based and uses only native Python and N etwork X data types. Availability and Implementation: https://github.com/hklarner/PyBoolNet. Contact: hannes.klarner@fu-berlin.de.

Data-driven optimizations for model checking of multi-valued regulatory networks.

The model checking method has been long since established as an important tool for modelling and reverse engineering of biological systems. However, due to a high complexity of both the method and the biological systems, this approach often requires a vast amount of computational resources. In this article we show that by reducing the expressivity of the method we can gain performance while still being able to use all biologically relevant data. We utilize this approach to conduct a study of mutations in the EGFR signalling, motivated by a paper from Klinger et al. (2013). Here we aim at constructing approximated models of multiple cell-lines from sizeable sets of experimental data. Due to cancerous mutations in each cell line, there is a high degree of parameter uncertainty and the study would not be practically tractable without the performance optimizations described here.

Logical-continuous modelling of post-translationally regulated bistability of curli fiber expression in Escherichia coli.

BACKGROUND: Bacteria have developed a repertoire of signalling mechanisms that enable adaptive responses to fluctuating environmental conditions. The formation of biofilm, for example, allows persisting in times of external stresses, e.g. induced by antibiotics or a lack of nutrients. Adhesive curli fibers, the major extracellular matrix components in Escherichia coli biofilms, exhibit heterogeneous expression in isogenic cells exposed to identical external conditions. The dynamical mechanisms underlying this heterogeneity remain poorly understood. In this work, we elucidate the potential role of post-translational bistability as a source for this heterogeneity. RESULTS: We introduce a structured modelling workflow combining logical network topology analysis with time-continuous deterministic and stochastic modelling. The aim is to evaluate the topological structure of the underlying signalling network and to identify and analyse model parameterisations that satisfy observations from a set of genetic knockout experiments. Our work supports the hypothesis that the phenotypic heterogeneity of curli expression in biofilm cells is induced by bistable regulation at the post-translational level. Stochastic modelling suggests diverse noise-induced switching behaviours between the stable states, depending on the expression levels of the c-di-GMP-producing (diguanylate cyclases, DGCs) and -degrading (phosphodiesterases, PDEs) enzymes and reveals the quantitative difference in stable c-di-GMP levels between distinct phenotypes. The most dominant type of behaviour is characterised by a fast switching from curli-off to curli-on with a slow switching in the reverse direction and the second most dominant type is a long-term differentiation into curli-on or curli-off cells. This behaviour may implicate an intrinsic feature of the system allowing for a fast adaptive response (curli-on) versus a slow transition to the curli-off state, in line with experimental observations. CONCLUSION: The combination of logical and continuous modelling enables a thorough analysis of different determinants of bistable regulation, i.e. network topology and biochemical kinetics, and allows for an incorporation of experimental data from heterogeneous sources. Our approach yields a mechanistic explanation for the phenotypic heterogeneity of curli fiber expression. Furthermore, the presented work provides a detailed insight into the interactions between the multiple DGC- and PDE-type enzymes and the role of c-di-GMP in dynamical regulation of cellular decisions.

On parameter synthesis by parallel model checking.

An important problem in current computational systems biology is to analyze models of biological systems dynamics under parameter uncertainty. This paper presents a novel algorithm for parameter synthesis based on parallel model checking. The algorithm is conceptually universal with respect to the modeling approach employed. We introduce the algorithm, show its scalability, and examine its applicability on several biological models.