A detailed model and Monte Carlo simulation for predicting DIP genome length distribution in baculovirus infection of insect cells

Das, Ashok; Dutta, Soumajit; Sen, Moumita; Saxena, Abha; Kumar, Jitendra; Giri, Lopamudra; Murhammer, David W.; Chakraborty, Jayanta

doi:10.1002/bit.27566

Cited by 5 publications

(5 citation statements)

References 31 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We hypothesized the large deletions observed in our approach represent these DVGs, which have been observed in a wide variety of viruses ( 63 , 64 ). The diversity of defective genomes in viral populations has been examined using methods ranging from sequencing and transmission electron microscopy ( 67 ) to theoretical modeling using Monte Carlo simulations ( 68 ) and conventional NGS ( 69 , 70 ). Because our approach allows us to examine the diversity of deletions in greater resolution than ever before, we sought to examine the dynamics of these large deletions in more detail.…”

Section: Resultsmentioning

confidence: 99%

“…RNA-Seq datasets of poliovirus replicating in mice (all analyzed tissues) and CirSeq datasets of poliovirus [wild-type (WT) and mutants] were published in ref. 68 , and were retrieved from the NCBI BioProject database using the accession code PRJNA383905. Ribosome profiling data of dengue virus replicating in Huh7 cells correspond to the data published in ref.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

High-resolution mapping reveals the mechanism and contribution of genome insertions and deletions to RNA virus evolution

Rangel

Dolan

Taguwa

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

RNA viruses rapidly adapt to selective conditions due to the high intrinsic mutation rates of their RNA-dependent RNA polymerases (RdRps). Insertions and deletions (indels) in viral genomes are major contributors to both deleterious mutational load and evolutionary novelty, but remain understudied. To characterize the mechanistic details of their formation and evolutionary dynamics during infection, we developed a hybrid experimental-bioinformatic approach. This approach, called MultiMatch, extracts insertions and deletions from ultradeep sequencing experiments, including those occurring at extremely low frequencies, allowing us to map their genomic distribution and quantify the rates at which they occur. Mapping indel mutations in adapting poliovirus and dengue virus populations, we determine the rates of indel generation and identify mechanistic and functional constraints shaping indel diversity. Using poliovirus RdRp variants of distinct fidelity and genome recombination rates, we demonstrate tradeoffs between fidelity and Indel generation. Additionally, we show that maintaining translation frame and viral RNA structures constrain the Indel landscape and that, due to these significant fitness effects, Indels exert a significant deleterious load on adapting viral populations. Conversely, we uncover positively selected Indels that modulate RNA structure, generate protein variants, and produce defective interfering genomes in viral populations. Together, our analyses establish the kinetic and mechanistic tradeoffs between misincorporation, recombination, and Indel rates and reveal functional principles defining the central role of Indels in virus evolution, emergence, and the regulation of viral infection.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

High-resolution mapping reveals the mechanism and contribution of genome insertions and deletions to RNA virus evolution

Rangel

Dolan

Taguwa

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…In the MC method, the time between two events is considered a random variable assumed to follow the Poisson distribution. A detailed discussion of the MC method can be found elsewhere [7,52–55]. The inputs that were provided to simulate the aggregation process using the MC algorithm are the values of fc, β∗, ψ and the initial PSD.…”

Section: Resultsmentioning

confidence: 99%

An efficient multiscale bi-directional PBM-DEM coupling framework to simulate one-dimensional aggregation mechanisms

Das

Kaur

et al. 2022

Proc. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

The mesoscale population balance modelling (PBM) technique is widely used in predicting aggregation processes. The accuracy and efficiency of PBM depend on the formulation of its kernels. A model of the volume- and time-dependent one-dimensional aggregation kernel is developed for predicting the temporal evolution of the considered particulate system. To make the developed model physically relevant, the PBM model needs three unknown parameters as input: volume-dependency in collisions, collision frequency per particle and aggregation probability. For this, the microscale discrete element model (DEM) is used. The system’s collision frequency is extracted periodically using a novel collision detection algorithm that detects and ignores duplicate collisions. Finally, a multiscale bi-directional PBM–DEM coupling framework is presented to simulate the aggregation mechanism. PBM and DEM simulations take place periodically to update the particle size distribution (PSD) and extract the collision-frequency, respectively. The coupling framework successfully explains the dependence between the PSD and the collision frequency. Additionally, computational cost of the algorithm is optimized while maintaining the accuracy of the results. Lastly, the accuracy and efficiency of the developed framework are verified using two different test cases. In one of the examples, a simple aggregation is simulated directly inside the DEM for the first time.

show abstract

“…(Costela-Ruiz et al, 2020;García, 2020;Ni et al, 2021;Ong et al, 2021;Thwaites et al, 2021) (Farshbafnadi et al, 2021;Tian et al, 2020) such as presence of various comorbidities, CT findings from both the lungs and other inflammatory laboratory markers. Hence, model needs to be further updated by including the empirical correlation as shown by (Voutouri et al, 2021) be employed for the solution of the equations (Das et al, 2020;Taghizadeh et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, our model is deterministic, and thus it does not incorporate uncertainties in the values of the model parameters, or the initial values employed. Uncertainty can be incorporated into the model by choosing the initial values of the model variables or the model parameters through a distribution, where the distribution is sampled a number of times, and a Monte Carlo sampling method can be employed for the solution of the equations (Das et al, 2020; Taghizadeh et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Computational framework to understand the clinical stages of COVID‐19 and visualization of time course for various treatment strategies

Sharma

Sarkar

Mitra

et al. 2023

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

Coronavirus disease 2019 is known to be regulated by multiple factors such as delayed immune response, impaired T cell activation, and elevated levels of proinflammatory cytokines. Clinical management of the disease remains challenging due to interplay of various factors as drug candidates may elicit different responses depending on the staging of the disease. In this context, we propose a computational framework which provides insights into the interaction between viral infection and immune response in lung epithelial cells, with an aim of predicting optimal treatment strategies based on infection severity. First, we formulate the model for visualizing the nonlinear dynamics during the disease progression considering the role of T cells, macrophages and proinflammatory cytokines. Here, we show that the model is capable of emulating the dynamic and static data trends of viral load, T cell, macrophage levels, interleukin (IL)-6 and TNF-α levels. Second, we demonstrate the ability of the framework to capture the dynamics corresponding to mild, moderate, severe, and critical condition. Our result shows that, at late phase (>15 days), severity of disease is directly proportional to pro-inflammatory cytokine IL6 and tumor necrosis factor (TNF)-α levels and inversely proportional to the number of T cells. Finally, the simulation framework was used to assess the effect of drug administration time as well as efficacy of single or multiple drugs on patients. The major contribution of the proposed framework is to utilize the infection progression model for clinical management and administration of drugs inhibiting virus replication and cytokine levels as well as immunosuppressant drugs at various stages of the disease.

show abstract

A detailed model and Monte Carlo simulation for predicting DIP genome length distribution in baculovirus infection of insect cells

Cited by 5 publications

References 31 publications

High-resolution mapping reveals the mechanism and contribution of genome insertions and deletions to RNA virus evolution

High-resolution mapping reveals the mechanism and contribution of genome insertions and deletions to RNA virus evolution

An efficient multiscale bi-directional PBM-DEM coupling framework to simulate one-dimensional aggregation mechanisms

Computational framework to understand the clinical stages of COVID‐19 and visualization of time course for various treatment strategies

Contact Info

Product

Resources

About