Mathematical modelling of baculovirus infection process: Kinetic parameter estimation

Although there are several mathematical models present for baculovirus infection, the specific functions for insect cell growth and cell death during infection processes remain unknown. Specifically, it is challenging to identify the most suitable model from a large set of plausible models and estimate the kinetic parameters to account for the day to day variability present in the infection experiments. In this context, identification of an unstructured model that can predict the day to day variability in cell growth and cell viability can be useful in determining the optimal operating conditions in fermenters at industrial scale. The major objectives of the present work were to develop a model screening framework that can be used to select the best model and identify the growth and death mechanisms during viral infection through non-linear programming. We then constructed a series of plausible models based on system of ordinary differential equations and performed the model selection using experimental data obtained from shaker flasks. The proposed scheme was tested for selecting the model for uninfected cell growth profiles. The objective function used was the root mean square error between the predicted values and experimental data points obtained from triplicate dataset. The computational scheme was validated using two types of virus, the WT AcMNPV and stabilized AcMNPV. Additionally, we propose a numerical scheme to simulate the cell growth and cell viability during viral passaging. The kinetic parameters were estimated in case of growth of uninfected cells, cells infected with WT virus as well as stabilized AcMNPV. The result shows that Monods equation fits the best for insect cell growth without infection and infection with WT AcMNPV. Whereas, the Contois model fits the best for the stabilized virus. The simulated results also indicate that the day to day variability in cell growth and cell viability profile can be explained through the variation in the specific growth rate and the death rate. The estimated kinetic parameters indicate that the growth and death parameters undergo specific modifications during the passaging of viruses associated to infection process. Additionally, we propose an integrated model for the infection process that simulates the DNA replication, mRNA and protein expression as well as polyhedra production. Specifically, we present the comparison between the unstructured model and the structured integrated model with respect to accuracy and computation time. Current study provides a predictive framework that has a potential application for large scale production of baculovirus

Hofmeister series: An insight into its application on gelatin and alginate-based dual-drug biomaterial design

Alginate and gelatin are bio-polymers widely used in drug delivery. A range of salts can be used to achieve the required flexibility in biomaterial design. Hofmeister series gives an idea about the behaviour of salts with proteins. However, its application for the design of biomaterials and their specific effect on high-viscosity polymers and polymer mixtures has not quite been explored. Firstly, this work proposes a strategic interaction-based approach for designing a dual-drug ocular biomaterial. Secondly, the impact of different salt anions on gelatin and alginate mixture for the developed protocols is studied by a proposed method of determining shear-dependent general intrinsic viscosity. Thirdly, shear-dependent intrinsic viscosity is used to determine the interaction amongst the polymers in their mixture, which is then correlated to the release profiles and hydrodynamic radii of polymer mixtures. It is observed that Hofmeister anions behave reversely for high-viscosity negatively charged polymers and depends on the charge densities of the anions. For the polyelectrolyte/polyampholyte complex/mixture, the interactions depend on the addition sequence. It is inferred that the kosmotropes are preferred for protocols where salt is added between gelatin and alginate and chaotropes for protocols where salt is added to the gelatin and alginate complex/mixture in terms of release profiles

A steady state analysis indicates that negative feedback regulation of PTP1B by Akt elicits bistability in insulin-stimulated GLUT4 translocation

BACKGROUND: The phenomenon of switch-like response to graded input signal is the theme involved in various signaling pathways in living systems. Positive feedback loops or double negative feedback loops embedded with nonlinearity exhibit these switch-like bistable responses. Such feedback regulations exist in insulin signaling pathway as well. METHODS: In the current manuscript, a steady state analysis of the metabolic insulin-signaling pathway is presented. The threshold concentration of insulin required for glucose transporter GLUT4 translocation was studied with variation in system parameters and component concentrations. The dose response curves of GLUT4 translocation at various concentration of insulin obtained by steady state analysis were quantified in-terms of half saturation constant. RESULTS: We show that, insulin-stimulated GLUT4 translocation can operate as a bistable switch, which ensures that GLUT4 settles between two discrete, but mutually exclusive stable steady states. The threshold concentration of insulin required for GLUT4 translocation changes with variation in system parameters and component concentrations, thus providing insights into possible pathological conditions. CONCLUSION: A steady state analysis indicates that negative feedback regulation of phosphatase PTP1B by Akt elicits bistability in insulin-stimulated GLUT4 translocation. The threshold concentration of insulin required for GLUT4 translocation and the corresponding bistable response at different system parameters and component concentrations was compared with reported experimental observations on specific defects in regulation of the system

Three-Dimensional Tracking of Intracellular Calcium and Redox State during Real-Time Control in a Hypoxic Gradient in Microglia Culture: Comparison of the Channel Blocker and Reoxygenation under Ischemic Shock

Real-time three-dimensional (3-D) imaging is crucial for quantifying correlations among various molecules under acute ischemic stroke. Insights into such correlations may be decisive in selecting molecules capable of providing a protective effect within a shorter period. The major bottleneck is maintaining the cultures under severely hypoxic conditions while simultaneously 3-D imaging intracellular organelles with a microscope. Moreover, comparing the protective effect of drugs and reoxygenation remains challenging. To address this, we propose a novel workflow for the induction of gas-environment-based hypoxia in the HMC-3 cells along with 3-D imaging using laser-scanning-confocal microscopy. The imaging framework is complemented with a pipeline for quantifying time-lapse videos and cell-state classification. First, we show an imaging-based assessment of the in vitro model for hypoxia using a steep gradient in O2 with time. Second, we demonstrate the correlation between mitochondrial superoxide production and cytosolic calcium under acute hypoxia. We then test the efficacy of an L-type calcium channel blocker, compare the results with reoxygenation, and show that the blocker alleviates hypoxic conditions in terms of cytosolic calcium and viability within an acute window of one hour. Furthermore, we show that the drug reduces the expression of oxidative stress markers (HIF1A and OXR1) within the same time window. In the future, this model can also be used to investigate drug toxicity and efficacy under ischemic conditions

Delving into the Bs→ℓℓ′ B_s \to \ell \ell^{\prime}, B(s)→(K(∗),ϕ,f2′,K2∗)ℓℓ′B_{(s)} \to (K^{(*)}, \phi, f_2^{\prime}, K_2^*) \ell \ell ^{\prime} processes

To shed light on the indirect search for new physics beyond the standard model, the long standing discrepancies between the theory and experiment mediated by FCNC $b\to s \ell \ell$ quark level transitions set an ideal testing ground. Though the very recent measurements of $R_K$ and $R_{K^*}$ are consistent with the standard model, still the excitements remain on the measurements of LHCb experiment with the observables $\mathcal{B} (B_s \to \phi \mu ^+ \mu ^-)$ which has deviations at the level of $3.6 \sigma$. Additionally, standard deviation of $\sim 3.3 \sigma$ and $1.2 \sigma$, respectively for $P_5^{\prime}$ in $B \to K^* \mu ^+ \mu ^-$ and the branching ratio in $B_s \to \mu^+ \mu^-$ processes are observed. Inspired by these discrepancies, we work out the constraints on the new physics coupling parameters in the presence of a non-universal $Z'$ model. We then probe the exclusive leptonic decay channels $B_s \to \ell \ell^{\prime}$, $B_{(s)} \to (K^{(*)}, \phi, f_2^{\prime}, K_2^*) \ell \ell ^{\prime}$ induced by the neutral current transition $b\to s \ell \ell^{\prime}$. We find that the $q^2$ variation of the observables, such as, branching ratio, forward-backward asymmetry, lepton polarization asymmetry, and the very sensible observable, so called non-universality observables for LFV decays display the sensitivity of new physics. In this analysis. we estimate above mentioned observables that could shed light on the window of new physics in the near future.Comment: I have updated the title of the paper. Also I have added few references and changed the title of the image

Heterogeneity in Neuronal Calcium Spike Trains based on Empirical Distance

Statistical similarities between neuronal spike trains could reveal significant information on complex underlying processing. In general, the similarity between synchronous spike trains is somewhat easy to identify. However, the similar patterns also potentially appear in an asynchronous manner. However, existing methods for their identification tend to converge slowly, and cannot be applied to short sequences. In response, we propose Hellinger distance measure based on empirical probabilities, which we show to be as accurate as existing techniques, yet faster to converge for synthetic as well as experimental spike trains. Further, we cluster pairs of neuronal spike trains based on statistical similarities and found two non-overlapping classes, which could indicate functional similarities in neurons. Significantly, our technique detected functional heterogeneity in pairs of neuronal responses with the same performance as existing techniques, while exhibiting faster convergence. We expect the proposed method to facilitate large-scale studies of functional clustering, especially involving short sequences, which would in turn identify signatures of various diseases in terms of clustering patterns

Toward Performance Improvement of a Baculovirus–Insect Cell System under Uncertain Environment: A Robust Multiobjective Dynamic Optimization Approach for Semibatch Suspension Culture

The baculovirus expression vector system (BEVS) is one of the well-known versatile platforms for the recombinant protein/vaccine production. Mathematical modeling and optimization of a baculovirus-insect cell system can have significant industrial relevance as this reduces the number of expensive experiments and time involved in the experiment-based optimization. However, modeling and control of such a nonlinear system remains challenging due to the presence of uncertainties in the model. In this context, we propose a novel computational framework combining the principles of systems biology and dynamic optimization under uncertainty for optimizing a semibatch baculovirus-insect cell system. Toward this, first, a mathematical model replicating the dynamic experimental data on cell and virus growth was identified. Next, the proposed model was used for deterministic multiobjective dynamic optimization of the control variables, substrate, and multiplicity of infection (MOI) to achieve the conflicting objectives of productivity maximization and substrate minimization, simultaneously. Finally, based on the sensitivity analysis, six of the most influential parameters depicting model uncertainties have been considered for the robust multiobjective optimal control of the system. A comprehensive comparison displays up to 114% and 76% increases in the cell densities for the deterministic and stochastic semibatch processes, respectively, compared to the batch process. Semibatch operation also favors a minimum 40% reduction in MOI required to achieve the same level of infected cell density compared to the batch operation. This study provides a generic methodology for exhibiting a proof of concept that a semibatch suspension culture considering uncertainty in model parameters can give better productivity compared to a batch suspension culture for a BEVS

Quantitative Confocal Microscopy for Grouping of Dose–Response Data: Deciphering Calcium Sequestration and Subsequent Cell Death in the Presence of Excess Norepinephrine

Fluorescent calcium (Ca2+) imaging is one of the preferred methods to record cellular activity during in vitro preclinical studies, high-content drug screening, and toxicity analysis. Visualization and analysis for dose–response data obtained using high-resolution imaging remain challenging, due to the inherent heterogeneity present in the Ca2+ spiking. To address this challenge, we propose measurement of cytosolic Ca2+ ions using spinning-disk confocal microscopy and machine learning–based analytics that is scalable. First, we implemented uniform manifold approximation and projection (UMAP) for visualizing the multivariate time-series dataset in the two-dimensional (2D) plane using Python. The dataset was obtained through live imaging experiments with norepinephrine-induced Ca2+ oscillation in HeLa cells for a large range of doses. Second, we demonstrate that the proposed framework can be used to depict the grouping of the spiking pattern for lower and higher drug doses. To the best of our knowledge, this is the first attempt at UMAP visualization of the time-series dose response and identification of the Ca2+ signature during lytic death. Such quantitative microscopy can be used as a component of a high-throughput data analysis workflow for toxicity analysis. © Society for Laboratory Automation and Screening 2021

Heterogeneity in Neuronal Calcium Spike Trains based on Empirical Distance

Statistical similarities between neuronal spike trains could reveal significant information on complex underlying processing. In general, the similarity between synchronous spike trains is somewhat easy to identify. However, the similar patterns also potentially appear in an asynchronous manner. However, existing methods for their identification tend to converge slowly, and cannot be applied to short sequences. In response, we propose Hellinger distance measure based on empirical probabilities, which we show to be as accurate as existing techniques, yet faster to converge for synthetic as well as experimental spike trains. Further, we cluster pairs of neuronal spike trains based on statistical similarities and found two non-overlapping classes, which could indicate functional similarities in neurons. Significantly, our technique detected functional heterogeneity in pairs of neuronal responses with the same performance as existing techniques, while exhibiting faster convergence. We expect the proposed method to facilitate large-scale studies of functional clustering, especially involving short sequences, which would in turn identify signatures of various diseases in terms of clustering patterns

Abnormal Complement Activation and Inflammation in the Pathogenesis of Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a neurovascular complication in preterm babies, leading to severe visual impairment, but the underlying mechanisms are yet unclear. The present study aimed at unraveling the molecular mechanisms underlying the pathogenesis of ROP. A comprehensive screening of candidate genes in preterms with ROP (n = 189) and no-ROP (n = 167) was undertaken to identify variants conferring disease susceptibility. Allele and genotype frequencies, linkage disequilibrium and haplotypes were analyzed to identify the ROP-associated variants. Variants in CFH (p = 2.94 x 10(-7)), CFB (p = 1.71 x 10(-5)), FBLN5 (p = 9.2 x 10(-4)), CETP (p = 2.99 x 10(-5)), and CXCR4 (p = 1.32 x 10(-8)) genes exhibited significant associations with ROP. Further, a quantitative assessment of 27 candidate proteins and cytokines in the vitreous and tear samples of babies with severe ROP (n = 30) and congenital cataract (n = 30) was undertaken by multiplex bead arrays and further validated by western blotting and zymography. Significant elevation and activation of MMP9 (p = 0.038), CFH (p = 2.24 x 10(-5)), C3 (p = 0.05), C4 (p = 0.001), IL-1ra (p = 0.0019), vascular endothelial growth factor (VEGF) (p = 0.0027), and G-CSF (p = 0.0099) proteins were observed in the vitreous of ROP babies suggesting an increased inflammation under hypoxic condition. Along with inflammatory markers, activated macrophage/microglia were also detected in the vitreous of ROP babies that secreted complement component C3, VEGF, IL-1ra, and MMP-9 under hypoxic stress in a cell culture model. Increased expression of the inflammatory markers like the IL-1ra (p = 0.014), MMP2 (p = 0.0085), and MMP-9 (p = 0.03) in the tears of babies at different stages of ROP further demonstrated their potential role in disease progression. Based on these findings, we conclude that increased complement activation in the retina/vitreous in turn activated microglia leading to increased inflammation. A quantitative assessment of inflammatory markers in tears could help in early prediction of ROP progression and facilitate effective management of the disease, thereby preventing visual impairment