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

PEPA'd Oysters: Converting Dynamic Energy Budget Models to Bio-PEPA, illustrated by a Pacific oyster case study

We present a Bio-PEPA (Biochemical-Performance Evaluation Process Algebra) computational model for the Pacific oyster, derived from a DEB (Dynamic Energy Budget) mathematical model. Experience with this specific model allows us to propose a generic scheme for translation between the widely-used DEB theory and Bio-PEPA. The benefits of translation are that a range of novel analysis tools become available, therefore improving the potential to understand complex biological phenomena at a systems level. This work also provides a link between biology, mathematics and computer science: such interlinking of disciplines is the core of the systems approach to biology

Stochastic properties of the plant circadian clock

Circadian clocks are gene regulatory networks whose role is to help the organisms to cope with variations in environmental conditions such as the day/night cycle. In this work, we explored the effects of molecular noise in single cells on the behaviour of the circadian clock in the plant model species Arabidopsis thaliana. The computational modelling language Bio-PEPA enabled us to give a stochastic interpretation of an existing deterministic model of the clock, and to easily compare the results obtained via stochastic simulation and via numerical solution of the deterministic model. First, the introduction of stochasticity in the model allowed us to estimate the unknown size of the system. Moreover, stochasticity improved the description of the available experimental data in several light conditions: noise-induced fluctuations yield a faster entrainment of the plant clock under certain photoperiods and are able to explain the experimentally observed dampening of the oscillations in plants under constant light conditions. The model predicts that the desynchronization between noisy oscillations in single cells contributes to the observed damped oscillations at the level of the cell population. Analysis of the phase, period and amplitude distributions under various light conditions demonstrated robust entrainment of the plant clock to light/dark cycles which closely matched the available experimental data

A High-Level Language for Rule-Based Modelling

Rule-based languages such as Kappa excel in their support for handling the combinatorial complexities prevalent in many biological systems, including signalling pathways. But Kappa provides little structure for organising rules, and large models can therefore be hard to read and maintain. This paper introduces a high-level, modular extension of Kappa called LBS-κ. We demonstrate the constructs of the language through examples and three case studies: a chemotaxis switch ring, a MAPK cascade, and an insulin signalling pathway. We then provide a formal definition of LBS-κ through an abstract syntax and a translation to plain Kappa. The translation is implemented in a compiler tool which is available as a web application. We finally demonstrate how to increase the expressivity of LBS-κ through embedded scripts in a general-purpose programming language, a technique which we view as generally applicable to other domain specific languages

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Defining, enforcing and checking privacy policies in data-intensive applications

The rise of Big Data is leading to an increasing demand for large-scale data-intensive applications (DIAs), which have to analyse massive amounts of personal data (e.g. customers' location, cars' speed, people heartbeat, etc.), some of which can be sensitive, meaning that its confidentiality has to be protected. In this context, DIA providers are responsible for enforcing privacy policies that account for the privacy preferences of data subjects as well as for general privacy regulations. This is the case, for instance, of data brokers, i.e. companies that continuously collect and analyse data in order to provide useful analytics to their clients. Unfortunately, the enforcement of privacy policies in modern DIAs tends to become cumbersome because (i) the number of policies can easily explode, depending on the number of data subjects, (ii) policy enforcement has to autonomously adapt to the application context, thus, requiring some non-trivial runtime reasoning, and (iii) designing and developing modern DIAs is complex per se. For the above reasons, we need specific design and runtime methods enabling so called privacy-by-design in a Big Data context. In this article we propose an approach for specifying, enforcing and checking privacy policies on DIAs designed according to the Google Dataflow model and we show that the enforcement approach behaves correctly in the considered cases and introduces a performance overhead that is acceptable given the requirements of a typical DIA

Defining, enforcing and checking privacy policies in data-intensive applications

The rise of Big Data is leading to an increasing demand for large-scale data-intensive applications (DIAs), which have to analyse massive amounts of personal data (e.g. customers' location, cars' speed, people heartbeat, etc.), some of which can be sensitive, meaning that its confidentiality has to be protected. In this context, DIA providers are responsible for enforcing privacy policies that account for the privacy preferences of data subjects as well as for general privacy regulations. This is the case, for instance, of data brokers, i.e. companies that continuously collect and analyse data in order to provide useful analytics to their clients. Unfortunately, the enforcement of privacy policies in modern DIAs tends to become cumbersome because (i) the number of policies can easily explode, depending on the number of data subjects, (ii) policy enforcement has to autonomously adapt to the application context, thus, requiring some non-trivial runtime reasoning, and (iii) designing and developing modern DIAs is complex per se. For the above reasons, we need specific design and runtime methods enabling so called privacy-by-design in a Big Data context. In this article we propose an approach for specifying, enforcing and checking privacy policies on DIAs designed according to the Google Dataflow model and we show that the enforcement approach behaves correctly in the considered cases and introduces a performance overhead that is acceptable given the requirements of a typical DIA.</p