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2015 | Book

Immune Response Enhancement Strategy via Hybrid Control Perspective Read chapter Read first chapter

Hybrid Systems Biology

Second International Workshop, HSB 2013, Taormina, Italy, September 2, 2013 and Third International Workshop, HSB 2014, Vienna, Austria, July 23-24, 2014, Revised Selected Papers

Editors: Oded Maler, Ádám Halász, Thao Dang, Carla Piazza

Publisher: Springer International Publishing

Book Series : Lecture Notes in Computer Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book constitutes the thoroughly refereed post-workshop proceedings of the Second International Workshop on Hybrid Systems Biology, HSB 2013, held as part of the ECAL 2013 event, in Taormina, Italy, in September 2013; and the Third International Workshop on Hybrid Systems Biology, HSB 2014, held as part of CAV 2014, in Vienna, Austria, in July 2014.

This volume presents 8 full papers together with 2 invited tutorials/surveys from 21 submissions.

The HSB 2013 workshop aims at collecting scientists working in the area of hybrid modeling applied to systems biology, in order to discuss about current achieved goals, current challenges and future possible developments.

The scope of the HSB 2014 workshop is the general area of dynamical models in biology with an emphasis on hybrid approaches, which are not restricted to a narrow class of mathematical models, and which take advantage of techniques developed separately in different sub-fields.

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Table of Contents

Frontmatter
Immune Response Enhancement Strategy via Hybrid Control Perspective
Abstract
We investigate a control method for disease dynamics, such as HIV and malaria, to boost the immune response using a model-based approach. In particular we apply the control method to select the appropriate immune response between Th1 and Th2 responses. The idea of state jump is introduced and discussed based on hybrid control systems. To implement the control idea we propose physically available methods for each biological system. The studies on malaria model and HIV model are supported by experimental data.
Hyuk-Jun Chang, Alessandro Astolfi
Fluorescent Reporter Genes and the Analysis of Bacterial Regulatory Networks
Abstract
The understanding of the regulatory networks controlling the adaptation of bacteria to changes in their environment is critically dependent on the ability to monitor the dynamics of gene expression. Here, we review the use of fluorescent reporter genes for dynamically quantifying promoter activity and other quantities characterizing gene expression. We discuss critical physical and biological parameters in the design, development, and use of fluorescent reporter strains. Moreover, we review measurement models that have been proposed to interpret primary fluorescence data and inference methods for estimating gene expression profiles from these data. As an illustration of the use of fluorescent reporter strains for analyzing bacterial regulatory networks, we consider two applications in the model bacterium Escherichia coli in some detail: the joint control of gene expression by global physiological effects and specific regulatory interactions, and the importance of protein stability for the inference and analysis of transcriptional regulatory networks. We conclude by discussing some current trends in the use of fluorescent reporter genes.
Hidde de Jong, Johannes Geiselmann
Modeling and Analysis of Qualitative Behavior of Gene Regulatory Networks
Abstract
We describe a hybrid system based framework for modeling gene regulation and other biomolecular networks and a method for analysis of the dynamic behavior of such models. A particular feature of the proposed framework is the focus on qualitative experimentally testable properties of the system. With this goal in mind we introduce the notion of the frame of a hybrid system, which allows for the discretisation of the state space of the network. We propose two different methods for the analysis of this state space. The result of the analysis is a set of attractors that characterize the underlying biological system.
Whilst in the general case the problem of finding attractors in the state space is algorithmically undecidable, we demonstrate that our methods work for comparatively complex gene regulatory network model of \(\lambda \)-phage. For this model we are able to identify attractors corresponding to two known biological behaviors of \(\lambda \)-phage: lysis and lysogeny and also to show that there are no other stable behavior regions for this model.
Alvis Brazma, Karlis Cerans, Dace Ruklisa, Thomas Schlitt, Juris Viksna
Parameter Synthesis Using Parallelotopic Enclosure and Applications to Epidemic Models
Abstract
We consider the problem of refining a parameter set to ensure that the behaviors of a dynamical system satisfy a given property. The dynamics are defined through parametric polynomial difference equations and their Bernstein representations are exploited to enclose reachable sets into parallelotopes. This allows us to achieve more accurate reachable set approximations with respect to previous works based on axis-aligned boxes. Moreover, we introduce a symbolical precomputation that leads to a significant improvement on time performances. Finally, we apply our framework to some epidemic models verifying the strength of the proposed method.
Thao Dang, Tommaso Dreossi, Carla Piazza
Optimal Observation Time Points in Stochastic Chemical Kinetics
Abstract
Wet-lab experiments, in which the dynamics within living cells are observed, are usually costly and time consuming. This is particularly true if single-cell measurements are obtained using experimental techniques such as flow-cytometry or fluorescence microscopy. It is therefore important to optimize experiments with respect to the information they provide about the system. In this paper we make a priori predictions of the amount of information that can be obtained from measurements. We focus on the case where the measurements are made to estimate parameters of a stochastic model of the underlying biochemical reactions. We propose a numerical scheme to approximate the Fisher information of future experiments at different observation time points and determine optimal observation time points. To illustrate the usefulness of our approach, we apply our method to two interesting case studies.
Charalampos Kyriakopoulos, Verena Wolf
Exploring Synthetic Mass Action Models
Abstract
In this work we propose a model that can be used to study the dynamics of mass action systems, systems consisting of a large number of individuals whose behavior is influenced by other individuals that they encounter. Our approach is rather synthetic and abstract, viewing each individual as a probabilistic automaton that can be in one of finitely many discrete states. We demonstrate the type of investigations that can be carried out on such a model using the Populus toolkit. In particular, we illustrate how sensitivity to initial spatial distribution can be observed in simulation.
Oded Maler, Ádám M. Halász, Olivier Lebeltel, Ouri Maler
Exploiting the Eigenstructure of Linear Systems to Speed up Reachability Computations
Abstract
Reachability analysis has recently proved to be a useful technique for analysing the behaviour of under-specified biological models. In this paper, we propose a method exploiting the eigenstructure of a linear continuous system to efficiently estimate a bounded interval containing the time at which the system can reach a target set from an initial set. Then this estimation can be directly integrated in an existing algorithm for hybrid systems with linear continuous dynamics, to speed up reachability computations. Furthermore, it can also be used to improve time-efficiency of the hybridization technique that is based on a piecewise-linear approximation of non-linear continuous dynamics. The proposed method is illustrated on a number of examples including a biological model.
Alexandre Rocca, Thao Dang, Eric Fanchon
RKappa: Statistical Sampling Suite for Kappa Models
Abstract
We present RKappa, a framework for the development and analysis of rule-based models within a mature, statistically empowered R environment. The infrastructure allows model editing, modification, parameter sampling, simulation, statistical analysis and visualisation without leaving the R environment. We demonstrate its effectiveness through its application to Global Sensitivity Analysis, exploring it in “parallel” and “concurrent” implementations.
The pipeline was designed for high performance computing platforms and aims to facilitate analysis of the behaviour of large-scale systems with limited knowledge of exact mechanisms and respectively sparse availability of parameter values. We illustrate it here with two biological examples. The package is available on github: https://​github.​com/​lptolik/​R4Kappa.
Anatoly Sorokin, Oksana Sorokina, J. Douglas Armstrong
Integration of Rule-Based Models and Compartmental Models of Neurons
Abstract
Synaptic plasticity depends on the interaction between electrical activity in neurons and the synaptic proteome, the collection of over 1000 proteins in the post-synaptic density (PSD) of synapses. To construct models of synaptic plasticity with realistic numbers of proteins, we aim to combine rule-based models of molecular interactions in the synaptic proteome with compartmental models of the electrical activity of neurons. Rule-based models allow interactions between the combinatorially large number of protein complexes in the postsynaptic proteome to be expressed straightforwardly. Simulations of rule-based models are stochastic and thus can deal with the small copy numbers of proteins and complexes in the PSD. Compartmental models of neurons are expressed as systems of coupled ordinary differential equations and solved deterministically. We present an algorithm which incorporates stochastic rule-based models into deterministic compartmental models and demonstrate an implementation (“KappaNEURON”) of this hybrid system using the SpatialKappa and NEURON simulators.
David C. Sterratt, Oksana Sorokina, J. Douglas Armstrong
FM-Sim: A Hybrid Protocol Simulator of Fluorescence Microscopy Neuroscience Assays with Integrated Bayesian Inference
Abstract
We present FM-Sim, a domain-specific simulator for defining and simulating fluorescence microscopy assays. Experimental protocols as performed in vitro may be defined in the simulator. The defined protocols then interact with a computational model of presynaptic behaviour in rodent central nervous system neurons, allowing simulation of fluorescent responses to varying stimuli. Rate parameters of the model may be obtained using Bayesian inference functions integrated into the simulator, given experimental fluorescence observations of the protocol performed in vitro as training data. These trained protocols allow for predictive in silico modelling of potential experimental outcomes prior to time-consuming and expensive in vitro studies.
Donal Stewart, Stephen Gilmore, Michael A. Cousin
Backmatter
Metadata
Title
Hybrid Systems Biology
Editors
Oded Maler
Ádám Halász
Thao Dang
Carla Piazza
Copyright Year
2015
Electronic ISBN
978-3-319-27656-4
Print ISBN
978-3-319-27655-7
DOI
https://doi.org/10.1007/978-3-319-27656-4

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