Parallel BioScape: A Stochastic and Parallel Language for Mobile and Spatial Interactions

BioScape is a concurrent language motivated by the biological landscapes found at the interface of biology and biomaterials. It has been motivated by the need to model antibacterial surfaces, biofilm formation, and the effect of DNAse in treating and preventing biofilm infections. As its predecessor, SPiM, BioScape has a sequential semantics based on Gillespie's algorithm, and its implementation does not scale beyond 1000 agents. However, in order to model larger and more realistic systems, a semantics that may take advantage of the new multi-core and GPU architectures is needed. This motivates the introduction of parallel semantics, which is the contribution of this paper: Parallel BioScape, an extension with fully parallel semantics.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347

An Intuitive Automated Modelling Interface for Systems Biology

We introduce a natural language interface for building stochastic pi calculus models of biological systems. In this language, complex constructs describing biochemical events are built from basic primitives of association, dissociation and transformation. This language thus allows us to model biochemical systems modularly by describing their dynamics in a narrative-style language, while making amendments, refinements and extensions on the models easy. We demonstrate the language on a model of Fc-gamma receptor phosphorylation during phagocytosis. We provide a tool implementation of the translation into a stochastic pi calculus language, Microsoft Research's SPiM

Computational Modeling for the Activation Cycle of G-proteins by G-protein-coupled Receptors

In this paper, we survey five different computational modeling methods. For comparison, we use the activation cycle of G-proteins that regulate cellular signaling events downstream of G-protein-coupled receptors (GPCRs) as a driving example. Starting from an existing Ordinary Differential Equations (ODEs) model, we implement the G-protein cycle in the stochastic Pi-calculus using SPiM, as Petri-nets using Cell Illustrator, in the Kappa Language using Cellucidate, and in Bio-PEPA using the Bio-PEPA eclipse plug in. We also provide a high-level notation to abstract away from communication primitives that may be unfamiliar to the average biologist, and we show how to translate high-level programs into stochastic Pi-calculus processes and chemical reactions.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005

Spatial extension of stochastic Pi calculus

We introduce a spatial extension of stochastic pi-calculus that provides a formalism to model systems of discrete, connected locations. We define the extended stochastic semantics and also give deterministic semantics in terms of a system of ordinary differential equations. We describe two simple examples, one based on a standard epidemic model and one modelling resistance in plant tissues

Flux Analysis in Process Models via Causality

We present an approach for flux analysis in process algebra models of biological systems. We perceive flux as the flow of resources in stochastic simulations. We resort to an established correspondence between event structures, a broadly recognised model of concurrency, and state transitions of process models, seen as Petri nets. We show that we can this way extract the causal resource dependencies in simulations between individual state transitions as partial orders of events. We propose transformations on the partial orders that provide means for further analysis, and introduce a software tool, which implements these ideas. By means of an example of a published model of the Rho GTP-binding proteins, we argue that this approach can provide the substitute for flux analysis techniques on ordinary differential equation models within the stochastic setting of process algebras

On Quantitative Comparison of Chemical Reaction Network Models

Chemical reaction networks (CRNs) provide a convenient language for modelling a broad variety of biological systems. These models are commonly studied with respect to the time series they generate in deterministic or stochastic simulations. Their dynamic behaviours are then analysed, often by using deterministic methods based on differential equations with a focus on the steady states. Here, we propose a method for comparing CRNs with respect to their behaviour in stochastic simulations. Our method is based on using the flux graphs that are delivered by stochastic simulations as abstract representations of their dynamic behaviour. This allows us to compare the behaviour of any two CRNs for any time interval, and define a notion of equivalence on them that overlaps with graph isomorphism at the lowest level of representation. The similarity between the compared CRNs can be quantified in terms of their distance. The results can then be used to refine the models or to replace a larger model with a smaller one that produces the same behaviour or vice versa.Comment: In Proceedings HCVS/PERR 2019, arXiv:1907.0352

Quantitative approaches in support of the early development of T-cell redirecting therapies

T-cell redirecting therapies such as CD3-bispecific antibodies and CAR-T cells are promising assets in our fight against cancer. By redirecting T-cells towards tumour cells, these therapies induce efficient eradication of tumours. Many questions remain regarding their efficacy and safety in patients. The success of a drug candidate starts with nonclinical investigations before going into patients. This work focused on developing tools to improve the translatability of nonclinical research of T-cell redirecting therapies. In this work, a mechanistic in silico model was developed that integrates an in vitro dataset of the pharmacology of cibisatamab, a CD3-bispecific antibody. The model may serve as a tool in early development to explore and quantify the impact of target expression densities on the pharmacology of CD3-bispecifics. Also, this work proposed the collection of data over multiple time points and designed a new experimental setup and analysis that allows assessing the pharmacology in an unbiased and time-independent manner. As such, the kinetics of experimental readouts can be considered to make informed decisions about the development of the compound and assist in dose selection. Lastly, the work presents a fresh look on cytokine release syndrome and identifies drug-target disease related factors and individual risk factors as the root cause of CRS. It postulates a combination of mechanistic modelling with real world data to enable individualized risk assessment.Gerichtete T-Zell-Therapien sind ein vielversprechendes Mittel im Kampf gegen Krebs. Bei dieser Therapie werden T-Zellen auf Tumorzellen gerichtet, was zu einer hocheffizienten Abtötung des Tumors führt. Es bleiben viele Fragen bezüglich ihrer Wirksamkeit und Sicherheit offen. Der Erfolg eines Arzneimittels beginnt mit nichtklinischen Untersuchungen. Diese Dissertation konzentrierte sich auf die Entwicklung Instrumente zur Verbesserung der nichtklinischen Forschung von gerichtete T-Zell-Therapien. In dieser Dissertation wurde ein mechanistisches In-silico-Modell entwickelt, das einen in-vitro-Datensatz zur Pharmakologie von cibisatamab integriert. Das Modell kann als Werkzeug in der Entwicklung dienen, um die Auswirkungen der Targetdichten auf die Pharmakologie von CD3-bispezifischen Antikörpern zu quantifizieren. In dieser Dissertation wurde auch ein neuer Versuchsaufbau und Analyse entwickelt, die eine unverzerrte und zeitunabhängige Bewertung der pharmakologischen Aktivität ermöglicht. Auf diese Weise kann die Kinetik der Messwerte berücksichtigt werden. Dies ist von Bedeutung, um fundierte Entscheidungen über die Entwicklung der Wirkstoffe und die Dosisauswahl zu treffen. Schließlich wirft die Arbeit einen Blick auf das Cytokine Release Syndrome und identifiziert Risikofaktoren als Ursache für CRS und empfiehlt eine Kombination von Modellierung und real-world Daten zur Ermöglichung einer individuellen CRS Risikobewertung bei der Behandlung mit gerichteten T-Zell-Therapien