A novel telecommunications-based approach to HIV modeling and simulation

Sharp, Aaron; Pannier, Angela K.; Wysocki, Beata J.; Wysocki, Tadeusz A.

doi:10.1016/j.nancom.2012.01.003

Cited by 8 publications

(7 citation statements)

References 30 publications

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“…If we consider a unit distance between the two neurons through the synapse, the PDF of neurotransmitter diffusion given in (11) can give us a service time distribution of neurotransmitter service for unit distance case. It should also be mentioned here that a normalization constant according to (10) should be multiplied so that the PDF does not exceed the unit area 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 under the curve condition. Thus, the distribution of service time for a single M/G/1 neurotransmitter service is given as…”

Section: Response Timementioning

confidence: 99%

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A queueing-theoretical delay analysis for intra-body nervous nanonetwork

Abbasi

Akan

2015

Nano Communication Networks

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Please cite this article as: N.A. Abbasi, O.B. Akan, A queueing-theoretical delay analysis for intra-body nervous nanonetwork, Nano Communication Networks (2015), http://dx. AbstractNanonetworks is an emerging field of study where nanomachines communicate to work beyond their individual limited processing capabilities and perform complicated tasks. The human body is an example of a very large nanoscale communication network, where individual constituents communicate by means of molecular nanonetworks. Amongst the various intra-body networks, the nervous system forms the largest and the most complex network. In this paper, we introduce a queueing theory based delay analysis model for neuro-spike communication between two neurons. Using standard queueing model blocks such as servers, queues and fork-join networks, impulse reception and processing through the nervous system is modeled as arrival and service processes in queues. Simulations show that the response time characteristics of the model are comparable to those of the biological neurons.

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Section: Response Timementioning

confidence: 99%

“…These studies result in the formulation of various models of synaptic channels under different scenarios. On the other hand, [10,11,12] introduce models for biological systems based on layered queueing networks.…”

Section: Introductionmentioning

confidence: 99%

A queueing-theoretical delay analysis for intra-body nervous nanonetwork

Abbasi

Akan

2015

Nano Communication Networks

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“…Queuing theory has previously been used to describe data communication networks [45], servicing of patients at hospitals [41], and the HIV infection process [46]. In our model, nonviral gene delivery was modeled as a digital process because events, such as the number of internalized pDNA packets or pDNAs, occur as integer numbers.…”

Section: Theoretical Aspects Of Modelmentioning

confidence: 99%

Identifying Intracellular pDNA Losses From a Model of Nonviral Gene Delivery

Martin

Wysocki

et al. 2015

IEEE Trans.on Nanobioscience

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Abstract-Nonviral gene delivery systems are a type of nanocommunication system that transmit plasmid packets (i.e., pDNA packets) that are programmed at the nanoscale to biological systems at the microscopic cellular level. This engineered nanocommunication system suffers large pDNA losses during transmission of the genetically encoded information, preventing its use in biotechnological and medical applications. The pDNA losses largely remain uncharacterized, and the ramifications of reducing pDNA loss from newly designed gene delivery systems remain difficult to predict. Here, the pDNA losses during primary and secondary transmission chains were identified utilizing a MATLAB model employing queuing theory simulating delivery of pEGFPLuc transgene to HeLa cells carried by Lipofectamine 2000 nonviral DNA carrier. Minimizing pDNA loss during endosomal escape of the primary transmission process results in increased number of pDNA in the nucleus with increased transfection, but with increased probability of cell death. The number of pDNA copies in the nucleus and the amount of time the pDNAs are in the nucleus directly correlates to improved transfection efficiency. During secondary transmission, pDNAs are degraded during distribution to daughter cells. Reducing pDNA losses improves transfection, but a balance in quantity of nuclear pDNA, mitosis, and toxicity must be considered in order to achieve therapeutically relevant transfection levels.

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“…Having some similarities to the Gillespie algorithm, queueing networks can be represented as a Markov chain, being more convenient to use than directly implementing the Gillespie algorithm [14]. Queueing networks have previously been used to describe data communication networks [15], servicing of patients at hospitals [16], the HIV infection process [17], pharmacokinetic modeling [18], and non-viral gene delivery [19]. Moreover, the implementation as described by Martin et al [19], has accounted for other cell processes, like mitosis or cell necrosis, which are not easy to implement in the ODE approach.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling and Stochastic Simulation of Metabolic Networks

Soliman

et al. 2018

Preprint

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17Increased technological methods have enabled the investigation of biology at nanoscale levels. 18 Nevertheless, such systems necessitate the use of computational methods to comprehend the complex 19 interactions occurring. Traditionally, dynamics of metabolic systems are described by ordinary differential 20 equations producing a deterministic result which neglects the intrinsic heterogeneity of biological systems. 21 More recently, stochastic modeling approaches have gained popularity with the capacity to provide more 22 realistic outcomes. Yet, solving stochastic algorithms tend to be computationally intensive processes. 23 Employing the queueing theory, an approach commonly used to evaluate telecommunication networks, 24 reduces the computational power required to generate simulated results, while simultaneously reducing 25 expansion of errors inherent to classical deterministic approaches. Herein, we present the application of 26 queueing theory to efficiently simulate stochastic metabolic networks. For the current model, we utilize 27 glycolysis to demonstrate the power of the proposed modeling methods, and we describe simulation and 28 pharmacological inhibition in glycolysis to further exemplify modeling capabilities. 30 Author Summary 31Computational biology is increasingly used to understand biological occurances and complex 32 dynamics. Biological modeling, in general, aims to represent a biological system with computational 33 approaches, as realistically and accurate as current methods allow. Metabolomics and metabolic systems 34 have emerged as an important aspect of cellular biology, allowing a more sentive view for understanding 35 the complex interactions occurring intracellularly as a result of normal or perturbed (or diseased) states. To 36 understand metabolic changes, many researchers have commonly used Ordianary Differential Equations to 37 produce in silico models of the in vitro system of interest. While these have been beneficial to date, 38 continuing to advance computational methods of analyzing such systems is of interest. Stochastic models 39 that include randomness have been known to produce more reaslistic results, yet the difficulty and intesive 40 time component urges additional methods and techniques to be developed. In the present research, we 3 41 propose using queueing networks as a technique to model complex metabolic systems, doing such with a 42 model of glycolysis, a core metabolic pathway. 43 44 58 are a representation of reality, aiming to accurately represent the system of study. Inclusion of all cellular 59 components indirectly or directly involved are considered far too complex to model. Consequently, 60 simplifications and assumptions must be made and often the perceived non-pivotal details, such as 61 stochasticity, omitted. Nevertheless, the accuracy and competence of the model is dependent on these 62 assumptions and simplifications. 63 Many approaches may be taken to model the dynamics of metabolic systems; importantly, the 64 categoriza...

show abstract

A novel telecommunications-based approach to HIV modeling and simulation

Cited by 8 publications

References 30 publications

A queueing-theoretical delay analysis for intra-body nervous nanonetwork

A queueing-theoretical delay analysis for intra-body nervous nanonetwork

Identifying Intracellular pDNA Losses From a Model of Nonviral Gene Delivery

Dynamic Modeling and Stochastic Simulation of Metabolic Networks

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