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6th International Workshop on Hybrid Systems Biology (HSB)

作者： Grossmann, Gerrit Wolf, Verena Saarland Univ D-66123 Saarbrucken Germany

ISBN: (纸本)9783030280420;9783030280413

Stochastic processes can model many emerging phenomena on networks, like the spread of computer viruses, rumors, or infectious diseases. Understanding the dynamics of such stochastic spreading processes is therefore of fundamental interest. In this work we consider the wide-spread compartment model where each node is in one of several states (or compartments). Nodes change their state randomly after an exponentially distributed waiting time and according to a given set of rules. For networks of realistic size, even the generation of only a single stochastic trajectory of a spreading process is computationally very expensive. Here, we propose a novel simulation approach, which combines the advantages of event-based simulation and rejection sampling. Our method outperforms state-of-the-art methods in terms of absolute run-time and scales significantly better while being statistically equivalent.

关键词： Spreading process SIR Epidemic modeling Monte-Carlo simulation gillespie algorithm

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Continuous-Time Simulation of Epidemic Processes on Dynamic Interaction Networks 1

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12th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP) / Conference of the Behavioral-Representation-in-Modeling-and-Simulation- Society (BRiMS)

作者： Ahmad, Rehan Xu, Kevin S. Univ Toledo EECS Dept 2801 W Bancroft St Toledo OH 43606 USA

ISBN: (数字)9783030217419

ISBN: (纸本)9783030217419;9783030217402

Contagious processes on networks, such as spread of disease through physical proximity or information diffusion over social media, are continuous-time processes that depend upon the pattern of interactions between the individuals in the network. Continuous-time stochastic epidemic models are a natural fit for modeling the dynamics of such processes. However, prior work on such continuous-time models doesn't consider the dynamics of the underlying interaction network which involves addition and removal of edges over time. Instead, researchers have typically simulated these processes using discrete-time approximations, in which one has to trade off between high simulation accuracy and short computation time. In this paper, we incorporate continuous-time network dynamics (addition and removal of edges) into continuous-time epidemic simulations. We propose a rejection-sampling based approach coupled with the well-known gillespie algorithm that enables exact simulation of the continuous-time epidemic process. Our proposed approach gives exact results, and the computation time required for simulation is reduced as compared to discrete-time approximations of comparable accuracy.

关键词： Stochastic epidemic model SIR model Continuous-time network Dynamic network Rejection sampling gillespie algorithm

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Agent-based Modelling of Bitcoin Consensus without Block Rewards 5

Agent-based Modelling of Bitcoin Consensus without Block Rew...

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5th IEEE International Conference on Blockchain (Blockchain)

作者： Kraner, Benjamin Li, Sheng-Nan Teixeira, Andreia Sofia Tessone, Claudio J. Univ Zurich Blockchain & Distributed Ledger Technol Grp Zurich Switzerland Univ Zurich UZH Blockchain Ctr Blockchain & Distributed Ledger Technol Grp Zurich Switzerland Univ Lisbon LASIGE Lisbon Portugal Univ Lisbon Fac Sci Lisbon Portugal

ISBN: (数字)9781665461047

ISBN: (纸本)9781665461047

Trust is key to the efficient functioning of any fiat or crypto-currency and so is for the consensus algorithm behind the functioning of blockchain systems. By an arbitrary design choice, Bitcoin and most Proof-of-Work (PoW) blockchains have a limited supply. Once block rewards vanish, only transaction fees will remain as an incentive for miners to partake in the verification process. In this paper, we analyse the impact that miners bargaining over block composition has on consensus in the absence of block rewards: in this situation, competing blocks at the same height may be more attractive to peers by including less transactions (i.e. sharing the mempool). The mining and acceptance of blocks can be modelled as an Ultimatum Game, where miners' strategies represent their fairness sentiment. Extending previous Literature, our study focuses on the effect of the transaction arrival rate on global consensus in the system and whether local consensus is formed under certain assumptions about the strategies of miners. We find that consensus is threatened when the supply of transactions is low and stable consensus only emerges when the amount of unconfirmed transactions remains sufficient. In addition, when miners are set with randomised strategies, it is more difficult for the system to achieve consensus. Our research suggests that transitioning from a block reward incentive to a transaction fee incentive may weaken and even destroy the consensus of PoW-based systems.

关键词： Blockchain consensus Bitcoin Agent-based Model Ultimatum Game gillespie algorithm

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Modelling of translation of human protein disulfide isomerase in Escherichia coli -: A case study of gene optimisation

Modelling of translation of human protein disulfide isomeras...

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Conference on Targets and Background XI - Characterization and Representation

作者： Niemitalo, O Neubauer, A Liebal, U Myllyharju, J Juffer, AH Neubauer, P Univ Oulu Dept Proc & Environm Engn Bioproc Engn Lab Oulu Finland Univ Oulu Collagen Res Unit Dept Med Biochem & Mol Biol Oulu Finland Triacle Biocomp Oulu Finland Bioctr Oulu Oulu Finland

Recombinant human protein disulfide isomerase (PDI) was expressed in vivo in Escherichia coli using a non-optimised gene sequence and an optimised sequence with four 5' codons substituted by synonymous codons that take less time to translate. The optimisation resulted in a 2-fold increase of total PDI concentration and by successive optimisation with expression at low temperature in a 10-fold increase of the amount of soluble PDI in comparison with the original wild-type construct. The improvement can be due to a faster clearing of the ribosome binding site on the mRNA, elevating the translation initiation rate and resulting in higher ribosome loading and better ribosome protection of the PDI mRNA against endonucleolytic cleavage by RNase. This hypothesis was supported by a novel computer simulation model of E. coli translational ribosome traffic based upon the stochastic gillespie algorithm. The study indicates the applicability of such models in optimisation of recombinant protein sequences. (c) 2005 Elsevier B.V All rights reserved.

关键词： codon usage gillespie algorithm translation PDI recombinant protein Escherichia coli computer simulation

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Mathematical modeling shows the frequency of Ca²⁺ sparks in cells depends on the ryanodine receptor's arrangement 6th

Mathematical modeling shows the frequency of Ca<SUP>2+</SUP>...

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6th Annual International Young Scientists Conference on HPC and Computational Science (YSC)

作者： Iaparov, B. I. Kharnzin, S. Yu. Moskvin, A. S. Solovyova, O. E. Ural Fed Univ 19 Mira Str Ekaterinburg 620002 Russia UB RAS Inst Immunol & Physiol 106 Pervomayskaya Str Ekaterinburg 620219 Russia

Calcium dynamics plays key role in many intracellular processes. Studying of calcium sparks, that is events of calcium release via groups of ryanodine receptors, or RyR channels, is of a great importance for live sciences. Recent experimental studies show, that RyRs have a non-uniform arrangement in calcium release units (CRU), however, to our knowledge, there is yet no published Ca' spark model which takes into account this fact. In the paper we made use of a genetic simulated annealing (GSA) algorithm for the RyR cluster generation and gillespie algorithm for modeling calcium spark. Computer simulation of the model CRU taking into account a specific homotetrameric structure of the RyR channel shows that experimental nearest neighbor distances (NND) distributions of RyRs in CRU do not provide exact information about calcium spark probability. Having the same NND distribution, the CRUs can have different inverse distance matrices and hence different calcium spark properties. Our model approach opens novel perspectives for studying the calcium release process. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： Ryanodine receptor Calcium sparks gillespie algorithm Global optimization genetic algorithm simulated annealing

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On the coupling of noisy processes in biology to produce functional phenotypic variability

On the coupling of noisy processes in biology to produce fun...

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作者： Patange, Om University of Cambridge

学位级别：博士

Noise is ubiquitous in biology. Recent studies have demonstrated that both gene expression and physiological processes, such as growth, can be noisy. The cell-to-cell variation resulting from this stochasticity has been implicated in survival strategies for bacterial populations. However, it remains unclear how single cells couple gene expression with growth to implement these strategies. In this thesis we show how noisy expression of a key stress response regulator, RpoS, allows $\textit{E. coli}$ to modulate its noisy growth dynamics to survive future adverse environments. We first demonstrate that single cells in bulk, exponential phase cultures have heterogeneous $\textit{rpoS}$ expression. Combining microfluidics and time-lapse microscopy we reveal multi-generation RpoS activity pulses are responsible for this heterogeneity. We next show that RpoS and growth have stochastic dynamics and are anti-correlated. With a stochastic simulation of chemical reactions coupled to a deterministic cell growth model we show that a mutual inhibition loop between RpoS activity and growth rate is sufficient to capture the observed dynamics. We test our model by performing experimental perturbations and find good agreement between theory and experiment. Next, we demonstrate the functionality of this phenotypic variability by using the microfluidic platform to apply a short, intense period of oxidative stress. By tracking cells prior to the stress and testing for survival after the stress we reveal that $\textit{E. coli}$ prepare for sudden stressful events by entering prolonged periods of slow growth mediated by RpoS. This dynamic phenotype is captured by the RpoS-growth feedback model. Our synthesis of noisy gene expression, growth, and survival paves the way for further exploration of functional phenotypic variability.

关键词： noise rpos growth stress response e. coli phenotypic heterogeneity single-cell time-lapse microscopy quantitative biology stochastic simulation gillespie algorithm Mother Machine microfluidic bet-hedging stochastic gene expression

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MODELLING APPROACHES FOR EPIDEMIOLOGICAL MODELS

MODELLING APPROACHES FOR EPIDEMIOLOGICAL MODELS

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作者： Malakhovskaia, Veronika LAPPEENRANTA UNIVERSITY OF TECHNOLOGY

学位级别：硕士

Predicting the spread of diseases remains an important aspect for epidemiological problems. For these purposes, such areas as statistics, sociology and biology are used. Nowadays, there is a large number of approaches for modelling epidemiological problems using compartmen- tal models. The aim of this paper is to obtain solutions for a mathematical model of dynamics of an influenza epidemic using various methods and compare them to identify the optimal method for solving this problem. A number of the algorithms such as Markov Chain Monte Carlo method (MCMC) for the deterministic modelling and gillespie algorithm and a discrete approach for the agent-based modelling (ABM) were implemented with Matlab, a numerical computing environment. The data for implementing the algorithms is taken from the case of influenza epidemic in British Boarding School in 1978.

关键词： MCMC Monte Carlo epidemiology Markov chain SIR-model agent-based modeling gillespie algorithm

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A gillespie-based Computational Model for Integrating Event-driven and Multi-Agent Based Simulation 15

A Gillespie-based Computational Model for Integrating Event-...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Sara Montagna Andrea Omicini Danilo Pianini University di Bologna

ISBN: (纸本)9781450337717

Based on two intuitions - (i) event-driven systems and multi-agent systems are two computational paradigms that are amenable of a coherent interpretation within a unique conceptual framework; (ii) integrating the two simulation approaches can lead to a more expressive and powerful simulation framework - we propose a computational model integrating Discrete-Event Simulation (DES) and Multi-Agent Based Simulation (MABS), based on an extension of the gillespie's stochastic simulation algorithm.

关键词： Multi-agent based simulation Event-driven simulation Stochastic simulation gillespie algorithm

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A Stochastic Model for The Transmission Dynamics of Toxoplasma Gondii

A Stochastic Model for The Transmission Dynamics of Toxoplas...

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作者： Gao, Guangyue Virginia Tech | University

Toxoplasma gondii (T. gondii) is an intracellular protozoan parasite. The parasite can infect all warm-blooded vertebrates. Up to 30% of the world’s human population carry a Toxoplasma infection. However, the transmission dynamics of T. gondii has not been well understood, although a lot of mathematical models have been built. In this thesis, we adopt a complex life cycle model developed by Turner et al. and extend their work to include diffusion of hosts. Most of researches focus on the deterministic models. However, some scientists have reported that deterministic models sometimes are inaccurate or even inapplicable to describe reaction-diffusion systems, such as gene expression. In this case stochastic models might have qualitatively different properties than its deterministic limit. Consequently, the transmission pathways of T. gondii and potential control mechanisms are investigated by both deterministic and stochastic model by us. A stochastic algorithm due to gillespie, based on the chemical master equation, is introduced. A compartment-based model and a Smoluchowski equation model are described to simulate the diffusion of hosts. The parameter analyses are conducted based on the reproduction number. The analyses based on the deterministic model are verified by stochastic simulation near the thresholds of the parameters.

关键词： gillespie algorithm Toxoplasma Gondii Finite Difference Method Transmission Dynamics Compartment-Based Model Stochastic Simulation Thesis

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PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment

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BMC BIOINFORMATICS 2011年第1期12卷 1-6页

作者： Sipos, Botond Massingham, Tim Jordan, Gregory E. Goldman, Nick EMBL European Bioinformat Inst Hinxton England Biol Res Ctr Inst Genet Lab Mol Biodivers H-6701 Szeged Hungary

Background: The Monte Carlo simulation of sequence evolution is routinely used to assess the performance of phylogenetic inference methods and sequence alignment algorithms. Progress in the field of molecular evolution fuels the need for more realistic and hence more complex simulations, adapted to particular situations, yet current software makes unreasonable assumptions such as homogeneous substitution dynamics or a uniform distribution of indels across the simulated sequences. This calls for an extensible simulation framework written in a high-level functional language, offering new functionality and making it easy to incorporate further complexity. Results: PhyloSim is an extensible framework for the Monte Carlo simulation of sequence evolution, written in R, using the gillespie algorithm to integrate the actions of many concurrent processes such as substitutions, insertions and deletions. Uniquely among sequence simulation tools, PhyloSim can simulate arbitrarily complex patterns of rate variation and multiple indel processes, and allows for the incorporation of selective constraints on indel events. User-defined complex patterns of mutation and selection can be easily integrated into simulations, allowing PhyloSim to be adapted to specific needs. Conclusions: Close integration with R and the wide range of features implemented offer unmatched flexibility, making it possible to simulate sequence evolution under a wide range of realistic settings. We believe that PhyloSim will be useful to future studies involving simulated alignments.

关键词： Monte Carlo Simulation Sequence Evolution Selective Constraint gillespie algorithm Sequence Alignment algorithm

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