GPU-powered Simulation Methodologies for Biological Systems

The study of biological systems witnessed a pervasive cross-fertilization between experimental investigation and computational methods. This gave rise to the development of new methodologies, able to tackle the complexity of biological systems in a quantitative manner. Computer algorithms allow to faithfully reproduce the dynamics of the corresponding biological system, and, at the price of a large number of simulations, it is possible to extensively investigate the system functioning across a wide spectrum of natural conditions. To enable multiple analysis in parallel, using cheap, diffused and highly efficient multi-core devices we developed GPU-powered simulation algorithms for stochastic, deterministic and hybrid modeling approaches, so that also users with no knowledge of GPUs hardware and programming can easily access the computing power of graphics engines.Comment: In Proceedings Wivace 2013, arXiv:1309.712

Dynamical Probabilistic P Systems: Definitions and Applications

We introduce dynamical probabilistic P systems, a variant where probabilities associated to the rules change during the evolution of the system, as a new approach to the analysis and simulation of the behavior of complex systems. We define the notions for the analysis of the dynamics and we show some applications for the investigation of the properties of the Brusselator (a simple scheme for the Belousov-Zabothinskii reaction), the Lotka-Volterra system and the decay process

Stochastic Approaches in P Systems for Simulating Biological Systems

Different stochastic strategies for modeling biological systems with P systems are reviewed in this paper, such as the multi-compartmental approach and dynamical probabilistic P systems. The respective results obtained from the simulations of a test case study (the quorum sensing phenomena in Vibrio Fischeri colonies) are shown, compared and discussed

Towards a P Systems Pseudomonas Quorum Sensing Model

Pseudomonas aeruginosa is an opportunistic bacterium that exploits quorum sensing communication to synchronize individuals in a colony and this leads to an increase in the effectiveness of its virulence. In this paper we derived a mechanistic P systems model to describe the behavior of a single bacterium and we discuss a possible approach, based on an evolutionary algorithm, to tune its parameters that will allow a quantitative simulation of the system.Kingdom's Engineering and Physical Sciences Research Council EP/D021847/

Reaction Cycles in Membrane Systems and Molecular Dynamics

We are considering molecular dynamics and (sequential) membrane systems from the viewpoint of Markov chain theory. The first step is to understand the structure of the configuration space, with respect to communicating classes. Instead of a reachability analysis by traditional methods, we use the explicit monoidal structure of this space with respect to rule applications. This leads to the notion of precycle, which is an element of the integer kernel of the stoichiometric matrix. The generators of the set of precycles can be effectively computed by an incremental algorithm due to Contejean and Devie. To arrive at a characterization of cycles, we introduce the notion of defect, which is a set of geometric constraints on a configuration to allow a precycle to be enabled, that is, be a cycle. An important open problem is the effcient calculation of the defects. We also discuss aspects of asymptotic behavior and connectivity, as well as give a biological example, showing the usefulness of the method for model checking

Real-world assessment of healthcare provided by the National Health Service: The network of regional Beaver research platforms

Real-world evidence can provide answers on healthcare utilization and appropriateness, post-marketing drugs safety and comparative effectiveness, and cost-effectiveness profiles of healthcare pathways. Healthcare utilization databases, possibly integrated with drug and disease registries, electronic medical records, survey and cohort data (i.e. real-world data), allow to trace healthcare ‘footprints’ left from beneficiaries of National Health Service. Beaver is a research platform available on demand to Italian regions which we developed for computing indicators of healthcare utilization and clinical outcomes, as well as for generating evidence on effectiveness and cost-effectiveness profile. Two distinct solutions may be adopted. One, the so-called Beaver Light front-end allows to automatically compute health indicators of adherence to official guidelines. Two, the so-called Beaver Full front-end involves an eight-step procedure entirely driven by the study protocol. In order to fulfil the directives recently issued by the European Parliament and Council and the Italian Authority for the protection of individual data, the platform resides in each region’s infrastructure, so limiting the free movement of electronic health data. Indeed, regional authorities should be responsible for data safety and for allowing data accessibility. The use of standardized and validated algorithms enables to obtain regional estimates that, being obtained by employing regional platforms containing data extracted with standardized procedure, may be compared and possibly summarized by using common meta-analytic techniques. In conclusion, the Beaver regional platform is a promising tool which may contribute to stimulate healthcare research in Italy

Emerging ensembles of kinetic parameters to identify experimentally observed phenotypes

Background: Determining the value of kinetic constants for a metabolic system in the exact physiological conditions is an extremely hard task. However, this kind of information is of pivotal relevance to effectively simulate a biological phenomenon as complex as metabolism. Results: To overcome this issue, we propose to investigate emerging properties of ensembles of sets of kinetic constants leading to the biological readout observed in different experimental conditions. To this aim, we exploit information retrievable from constraint-based analyses (i.e. metabolic flux distributions at steady state) with the goal to generate feasible values for kinetic constants exploiting the mass action law. The sets retrieved from the previous step will be used to parametrize a mechanistic model whose simulation will be performed to reconstruct the dynamics of the system (until reaching the metabolic steady state) for each experimental condition. Every parametrization that is in accordance with the expected metabolic phenotype is collected in an ensemble whose features are analyzed to determine the emergence of properties of a phenotype. In this work we apply the proposed approach to identify ensembles of kinetic parameters for five metabolic phenotypes of E. Coli, by analyzing five different experimental conditions associated with the ECC2comp model recently published by Hädicke and collaborators. Conclusions: Our results suggest that the parameter values of just few reactions are responsible for the emergence of a metabolic phenotype. Notably, in contrast with constraint-based approaches such as Flux Balance Analysis, the methodology used in this paper does not require to assume that metabolism is optimizing towards a specific goal

Dynamical Probabilistic P Systems: Definitions and Application

Summary. We introduce dynamical probabilistic P systems, a variant where probabilities associated to the rules change during the evolution of the system, as a new approach to the analysis and simulation of the behavior of complex systems. We define the notions for the analysis of the dynamics and we show some applications for the investigation of the properties of the Brusselator (a simple scheme for the Belousov-Zabothinskii reaction), the Lotka-Volterra system and the decay process.

Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models

Background: Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) can be strongly affected by the choice of flux boundaries, with particular regard to the flux of reactions that sink nutrients into the system. To mitigate possible errors introduced by a poor selection of such boundaries, a rational approach suggests to focus the modeling efforts on the pivotal ones. Methods: In this work, we present a methodology for the automatic identification of the key fluxes in genome-wide constraint-based models, by means of variance-based sensitivity analysis. The goal is to identify the parameters for which a small perturbation entails a large variation of the model outcomes, also referred to as sensitive parameters. Due to the high number of FBA simulations that are necessary to assess sensitivity coefficients on genome-wide models, our method exploits a master-slave methodology that distributes the computation on massively multi-core architectures. We performed the following steps: (1) we determined the putative parameterizations of the genome-wide metabolic constraint-based model, using Saltelli’s method; (2) we applied FBA to each parameterized model, distributing the massive amount of calculations over multiple nodes by means of MPI; (3) we then recollected and exploited the results of all FBA runs to assess a global sensitivity analysis. Results: We show a proof-of-concept of our approach on latest genome-wide reconstructions of human metabolism Recon2.2 and Recon3D. We report that most sensitive parameters are mainly associated with the intake of essential amino acids in Recon2.2, whereas in Recon 3D they are associated largely with phospholipids. We also illustrate that in most cases there is a significant contribution of higher order effects. Conclusion: Our results indicate that interaction effects between different model parameters exist, which should be taken into account especially at the stage of calibration of genome-wide models, supporting the importance of a global strategy of sensitivity analysis