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Modeling protein-DNA binding time in Stochastic Discrete Event Simulation of Biological Processes

P. Ghosh, S. Ghosh, K. Basu, S. Das
2007 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology  
This paper presents a parametric model to determine the execution time of one biological function (i.e. simulation event): protein-DNA binding by abstracting the function as a stochastic process of microlevel  ...  This probability is coarse grained to estimate the stochastic behavior of the biological function.  ...  CONCLUSION We have presented a simplified model to estimate the DNAprotein binding time by transforming the biological function as a stochastic process of a number of biological micro events and use the  ... 
doi:10.1109/cibcb.2007.4221254 fatcat:auukvya56zhfpii3rwmlbnxsze

Double-Chain Unscented Expectation Propagation for Inference in Stochastic Dynamical Models of Biological Processes

Hao Wu, Stefan Bernhard
2013 International Work-Conference on Bioinformatics and Biomedical Engineering  
But there is a fundamental and not satisfactorily solved problem in Bayesian inference of biological processes, that is how to approximate the posterior distributions fast and accurately in real world  ...  In many fields of biology and medicine we are confronted with the task of analyzing and estimating a complex process based on partial observations.  ...  Introduction In many fields of biology and medicine, we can model a complex process by the following equation: x t = f (x t−1 , θ, v t ) y t = g (x t , θ, w t ) (1) where x t is the system state at time  ... 
dblp:conf/iwbbio/WuB13 fatcat:m3x43kqf6jadbl72lvanl25dh4

Modeling of stochastic biological processes with non-polynomial propensities using non-central conditional moment equation

Atefeh Kazeroonian, Fabian J. Theis, Jan Hasenauer
2014 IFAC Proceedings Volumes  
Biological processes exhibiting stochastic fluctuations are mainly modeled using the Chemical Master Equation (CME).  ...  The properties of the non-central MCM are analyzed using a model for the regulation of pili formation on the surface of bacteria, which possesses rational propensity functions.  ...  The authors acknowledge financial support from the German Federal Ministry of Education and Research (BMBF) within the Virtual Liver project (Grant No. 0315766) and LungSys II (Grant No. 0316042G), and  ... 
doi:10.3182/20140824-6-za-1003.02298 fatcat:wekbixtnufh6biaycvelmvupq4

Steady state probability approximation applied to stochastic model of biological network

Md. Shahriar Karim, David M. Umulis, Gregery T. Buzzard
2011 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)  
Acknowledgements Based on "Steady state probability approximation applied to stochastic model of biological network", by Shahriar Karim  ...  As more biological data is available, stochastic modeling is becoming increasingly popular to estimate properties in networks where the time evolution of the system is unpredictable and dependent on unavoidable  ...  Introduction Many biological networks exhibit stochasticity due to a combinatorial effect of low molecular concentrations and slow system dynamics.  ... 
doi:10.1109/gensips.2011.6169442 dblp:conf/gensips/KarimUB11 fatcat:fydhyrwwubdmvdtyxnbdtmn3fe

Discrete stochastic processes, replicator and Fokker-Planck equations of coevolutionary dynamics in finite and infinite populations

Jens Christian Claussen
2008 Stochastic Models in Biological Sciences   unpublished
Finite-size fluctuations in coevolutionary dynamics arise in models of biological as well as of social and economic systems.  ...  This brief tutorial review surveys a systematic approach starting from a stochastic process discrete both in time and state.  ...  In this direction, the Bak-Sneppen model [Bak93] is a pioneering minimal model for the extinction of species, which however is difficult to relate to the biological scenario.  ... 
doi:10.4064/bc80-0-1 fatcat:dvxw2hgq5reehmz4zkr4dn2tqu

Stochastic simulation of biological cellular processes using VHDL-AMS

G.D. Peterson, J.M. Lancaster
Proceedings of the 2002 IEEE International Workshop on Behavioral Modeling and Simulation, 2002. BMAS 2002.  
Accurate, predictive models of biological cellular processes are a key component of the quest to transform the engineering of genetic controls within organisms.  ...  , microfluidic, optical, and thermal systems, similar benefits can result from the use of AMS HDLs for biological systems modeling.  ...  Roberts, and Paul Frymier in understanding the biological systems of interest and the current state of the art in biological system modeling.  ... 
doi:10.1109/bmas.2002.1291069 fatcat:dyuswp53jrh3laykjyzz7kvybu

Editorial: Special Issue on Stochastic Modelling of Reaction–Diffusion Processes in Biology

Radek Erban, Hans G. Othmer
2014 Bulletin of Mathematical Biology  
Reaction-diffusion equations are used to model many biological processes, ranging from intracellular signaling, metabolic processes and gene control at the cellular level, to birth-death processes and  ...  or for densities of individuals, and stochastic models in which individual events of reaction or diffusion are followed.  ...  Reaction-diffusion equations are used to model many biological processes, ranging from intracellular signaling, metabolic processes and gene control at the cellular level, to birth-death processes and  ... 
doi:10.1007/s11538-013-9929-z pmid:24402472 pmcid:PMC3997611 fatcat:gwvp34yr45hr3jgej7s6saqzbe

Stochastic physics, complex systems and biology

Hong Qian
2013 Quantitative Biology  
Phenotypic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a perspective.  ...  Monod's necessity and chance, gives rise to an evolutionary process in Darwinian sense, in terms of discrete jumps among attractors, with punctuated equilibrium, spontaneous random "mutations" and "adaptations  ...  In applied mathematics, statistics is associated with data-driven modeling and stochastic process is associated with population distribution based mechanistic modeling.  ... 
doi:10.1007/s40484-013-0002-6 fatcat:uf7h2yhil5eknodmttuq2ussfu

Modelling and analysis of biological systems

Gabriel Ciobanu, Maciej Koutny
2012 Theoretical Computer Science  
The modelling and analysis of biological systems has also attracted considerable interest from both process calculi and Petri net research communities.  ...  The main aim of the MeCBIC (Membrane Computing and Biologically Inspired Process Calculi) series of workshops is to bring together researchers working on membrane computing and other biologically inspired  ...  Phillips, investigates issues involved in the simulation of many stochastic process calculi developed for biological modelling.  ... 
doi:10.1016/j.tcs.2011.12.064 fatcat:745rtf2ehnbrnbt5yh4mw5i4pi

Markov Stochastic Processes in Biology and Mathematics -- the Same, and yet Different

Miłosława Sokół
2018 JOURNAL OF ADVANCES IN MATHEMATICS  
Virtually every biological model utilising a random number generator is a Markov stochastic process.  ...  Similar principle applies to intensity matrices, stochastic and intensity kernels resulting from considering many biological models as Markov stochastic processes.  ...  Biological models include dynamical models showing a change of some biological quantity in time or the so-called models of artificial life [9] , [11] , [12] .  ... 
doi:10.24297/jam.v14i1.7202 fatcat:jhihpgl26vgtxe6z3lts4g4joa

On Stochastic Processes in Biology

A. T. Reid
1953 Biometrics  
formulating mathematical models of various biological phenomena.  ...  ON STOCHASTIC PROCESSES IN BIOLOGY A. T.  ... 
doi:10.2307/3001703 fatcat:dijgsqu3yjerjo5rf4wfofgbqe

Modelling Network Performance with a Spatial Stochastic Process Algebra

Vashti Galpin
2009 2009 International Conference on Advanced Information Networking and Applications  
model piecewise deterministic Markov processes transition-driven stochastic hybrid automata delay-tolerant networks packets modelled as a continuous flow periods of connectivity modelled stochastically  ...  Hillston and Luca Bortolussi process algebra to model discrete, stochastic and continuous behaviour semantic model piecewise deterministic Markov processes transition-driven stochastic hybrid  ... 
doi:10.1109/aina.2009.75 dblp:conf/aina/Galpin09 fatcat:qmpfkunz75h75odczrzvm4zodq

Stochastic Behavior and Explicit Discrete Time in Concurrent Constraint Programming [chapter]

Jesús Aranda, Jorge A. Pérez, Camilo Rueda, Frank D. Valencia
2008 Lecture Notes in Computer Science  
We address the inclusion of stochastic information into an explicitly timed concurrent constraint process language. An operational semantics is proposed as a preliminary result.  ...  In the light of stochastic models for quantitative information, the explicit time in tcc poses a legitimate question, that of determining to what extent the notions of stochastic duration and of discrete  ...  Also, although it is not evident that every sCCP process can be translated into our language (the tell operator in sCCP has continuation), we are confident we can model most of the biological systems described  ... 
doi:10.1007/978-3-540-89982-2_57 fatcat:a3apgv4dp5celcku2zjnnh4ynu

By way of introduction: Modelling living systems, their diversity and their complexity: some methodological and theoretical problems

Alain Pavé
2006 Comptes rendus. Biologies  
Modelling of biological systems by taking into account deterministic and stochastic components.  ...  It is not a formal demonstration, but it suggests that deterministic and mechanistic models and then biological and ecological processes can be represented by such models, can be precisely 'biological  ... 
doi:10.1016/j.crvi.2005.09.011 pmid:16399638 fatcat:jmwzc4tk5vgefjf75tf2bppx6e

Training the Stochastic Kinetic Model of Neuron for Calculation of an Object's Position in Space

Aleksandra Świetlicka, Krzysztof Kolanowski, Rafał Kapela
2019 Journal of Intelligent and Robotic Systems  
In this paper we focus on the stochastic kinetic extension of the well-known Hodgkin-Huxley model of a biological neuron. We show the gradient descent algorithm for training of the neuron model.  ...  The trained stochastic kinetic model of neuron is tested in solving the problem of approximation, where for the approximated function the membrane potential obtained using different models of a biological  ...  The procedure of the training of the stochastic kinetic model of a biological neural network and application of the trained model in an image processing task are shown in [23] .  ... 
doi:10.1007/s10846-019-01068-0 fatcat:owgriyq2jvddfdjda5z4dby47e
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