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In this paper, we were devoted to quantitatively analyzing the dimensional change of 2024Al alloy during the aging process. Types of precipitates have been defined and the corresponding volume fraction has been measured with Transmission Electron Microscopy (TEM) and Three-Dimensional Atom Probe Tomography (3D-APT). Guinier-Preston-Bagaryatsky (GPB) zone (0.86 vol.%) was found after aging for 8 h. GPB zone (0.17 vol.%) and S' phase (1.02 vol.%) was found after aging for 24 h. The reduced dimensional change was attributed to the reduction of matrix lattice parameter. Relative to the initial supersaturated solid solution state, the lattice strain of -2.79 × 10−5 and -5.16 × 10−5 was produced after 8 h and 24 h aging respectively. The difference of the lattice parameters between the precipitates and matrix was also considered. A model was established to quantitatively describe the relationship between the precipitation process and dimensional change of 2024Al alloy.

Islatravir (MK-8591) is a high-potency reverse transcriptase translocation inhibitor in development for the treatment of HIV-1 infection. Data from preclinical and clinical studies suggest that ~30% to 60% of islatravir is excreted renally and that islatravir is not a substrate of renal transporters. To assess the impact of renal impairment on the pharmacokinetics of islatravir, an open-label phase 1 trial was conducted with individuals with severe renal insufficiency (RI). A single dose of islatravir 60 mg was administered orally to individuals with severe RI (estimated glomerular filtration rate [eGFR]30 mL/min/1.73 m

Coronavirus disease 2019 (COVID‐19) global pandemic is caused by severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) viral infection, which can lead to pneumonia, lung injury, and death in susceptible populations. Understanding viral dynamics of SARS‐CoV‐2 is critical for development of effective treatments. An Immune‐Viral Dynamics Model (IVDM) is developed to describe SARS‐CoV‐2 viral dynamics and COVID‐19 disease progression. A dataset of 60 individual patients with COVID‐19 with clinical viral load (VL) and reported disease severity were assembled from literature. Viral infection and replication mechanisms of SARS‐CoV‐2, viral‐induced cell death, and time‐dependent immune response are incorporated in the model to describe the dynamics of viruses and immune response. Disease severity are tested as a covariate to model parameters. The IVDM was fitted to the data and parameters were estimated using the nonlinear mixed‐effect model. The model can adequately describe individual viral dynamics profiles, with disease severity identified as a covariate on infected cell death rate. The modeling suggested that it takes about 32.6 days to reach 50% of maximum cell‐based immunity. Simulations based on virtual populations suggested a typical mild case reaches VL limit of detection (LOD) by 13 days with no treatment, a moderate case by 17 days, and a severe case by 41 days. Simulations were used to explore hypothetical treatments with different initiation time, disease severity, and drug effects to demonstrate the usefulness of such modeling in informing decisions. Overall, the IVDM modeling and simulation platform enables simulations for viral dynamics and treatment efficacy and can be used to aid in clinical pharmacokinetic/pharmacodynamic (PK/PD) and dose‐efficacy response analysis for COVID‐19 drug development.

Evolution of graphene nanosheets (GNSs) during mechanical milling process was investigated to fabricate GNSs/Al nanocomposite by routes combined of pressure infiltration and hot extrusion. The structural integrity and dispersion homogeneity of GNSs together with the degree of interfacial reaction were systematically discussed. It was revealed that a moderate milling time existed for well dispersion and small quantities of Al4C3 (partial reaction bonding) via mechanical milling technique. A highest tensile strength of 276 MPa was obtained only with 0.3 wt% GNSs, which was 22.5% enhancement over the unreinforced Al matrix with the same preparation process and no decrease in elongation. Meanwhile, compared with reference Al, a great enhancement of electrical conductivity was achieved about 17.4%, which had not been reported previously. This study could provide new insights into the control factors of mechanical milling process for GNSs/Al composite and improve the interfacial bonding condition in situ during ball milling and subsequent densification processes to take full advantage of the extraordinary performance of graphene in metal matrix composites.

Principal component analysis (PCA) and partial least squares (PLS) regression were applied to investigate the effect of glutathione-enriched inactive dry yeast (g-IDY) on the amino acids and volatile components of kiwi wine. Results indicated that the addition of g-IDY had positive effect on most amino acids of kiwi wine, especially glutamine and glycine. In case of pure juice fermentation, the concentrations of ethyl decanoate, 2-methylbutyric acid, trans-2-nonenal and hexyl butyrate had notably positive correlation with the addition of g-IDY. PLS regression indicated that the amino acids were highly interrelated to the volatile compositions, and glycine had the strongest positive impact on the concentrations of esters and total volatile components. This might explain the similar effect of g-IDY on the amino acids and volatile components of kiwi wine. Besides, PLS regression showed that E-nose was a good method to predict volatile compositions of kiwi wine, especially esters.

Accuracy and stability have always been the eternal pursuit of inertial navigation. Among them, the dimensional stability of inertial instrument materials is a key factor in determining the accuracy level of equipment. The dimensional changing(DC) behavior is a material problem extracted from the common accuracy drift problem in engineering technology. The DC behavior of 45vol% SiC P /2024Al composite under no load at constant temperature was systematically studied in-depth using in-situ heating XRD and double Cs-corrected transmission electron microscopy. The trend and magnitude of lattice constant and strain mapping of matrix near SiC/Al interface were both tracked and examined. A high-resolution thermal dilatometer was used to measure the macroscopic DC. Thermal mismatch residual stress has a decisive effect on the DC of SiC P /2024Al composite. The effect of exsolution on the DC of SiC P /2024Al composite was also discussed. Different dominant mechanism of DC between aluminum alloy and composites leads to the difference in the magnitude, rate, and stabilization of the dimensional change. In order to verify the validity of the mechanism, the DC results of different matrix and different heat treatment states were compared and explained. The estimation method of DC based on the volume change of unit cell could accurately predict the trend of DC and the magnitude of SiC P /2024Al composite. This provides instructive guidance for deeply understanding the DC behavior of aluminum matrix composites, and enriches the research work of micro-plastic deformation.

Fresh “Xuxiang” kiwifruit juice with or without pomace was used for fermentation with different levels of glutathione‐enriched inactive dry yeast (g‐IDY). After alcohol fermentation, the glutathione (GSH) content, color parameters, phenolic composition, and antioxidant activities of kiwi wine were determined. The results suggested that the addition of g‐IDY to kiwi juice increased GSH, glutathione disulfide, total GSH concentrations, and the total phenolic content of kiwi wine. No obvious effect on individual phenolic content was found in wine with g‐IDY. The color parameters of wines fermented with pomace were remarkably improved with addition of 100 mg/L and 300 mg/L g‐IDY. The supply of g‐IDY improved the antioxidant activities of kiwi wine fermented with pomace. Principal component analysis suggested that with the same supplies of g‐IDY, wine fermented with pomace was better than wine fermented through pure juice in all measurements. PRACTICAL APPLICATIONS: The improvements in color and antioxidant capacity of kiwi wine have always been the focus of kiwi wine brewing. Constant improvement in methods which contribute to high quality of kiwi wine provides crucial support for wine industry and even agriculture development. The aim of this study is to investigate the impacts of glutathione‐enriched inactive dry yeast (g‐IDY) on quality of kiwi wine. These results could provide feasible advices about g‐IDY application to kiwi wine for improving the sensory and nutritional properties.

Persimmon tannin (PT) exhibits antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA) isolated from retail pork. The involved molecular mechanisms were investigated for the first time using transcriptome and metabolome in this study. Results showed that subinhibitory concentration of PT (0.5 mg/ml) induced significant changes in MRSA at both transcriptional and metabolic levels, as 370 genes and 19 metabolites were differentially expressed. Bioinformatic analysis revealed that the varying genes and metabolites were mainly involved in pathways of membrane transport, amino acids, carbohydrate, and energy metabolism. The highlighted changes were those related to osmotic regulation, intracellular pH regulation, amino acid synthesis and metabolism, glycolysis, TCA cycle and iron metabolism, suggesting the multifaceted effects including cell membrane damage, amino acids limitation, energy metabolism disorder and iron deprivation induced by PT. The results provided insight into the anti-MRSA mechanism of PT, which is useful for PT’s development and application in food safety.

The microstructural evolution process of face-centered cubic Cu-30 wt%Zn, which has very low stacking fault (SF) energy of only 14 mJ/m 2 , processed by high-pressure torsion, was investigated using transmission electron microscopy. Results reveal that deformation SFs/twin boundaries and cell blocks play the key role in the grain refinement process from ultrafine grains to nano grains. Equiaxed coarse grains with grain sizes of several microns were refined to ultrafine grains through the formation of high density of SFs, twins and cell blocks. With the accumulation of high density of dislocations at SF/twin boundaries, the emission of secondary SFs/twins further refined grains and transformed ultrafine grains into equiaxed grains with grain size of several tens nanometers. The observed grain refinement mechanism is significantly different from those of materials with medium to high SF energies in which full dislocation activities play a key role for grain refinement.

CD8+ lymphocytes play an important role in suppressing in vivo viral replication in HIV infection. However, both the extent to which and the mechanisms by which CD8+ lymphocytes contribute to viral control are not completely understood. A recent experiment depleted CD8+ lymphocytes in simian immunodeficiency virus (SIV)-infected rhesus macaques (RMs) on antiretroviral treatment (ART) to study the role of CD8+ lymphocytes. CD8+ lymphocytes depletion resulted in temporary plasma viremia in all studied RMs. Viral control was restored when CD8+ lymphocytes repopulated. We developed a viral dynamic model to fit the viral load (VL) data from the CD8 depletion experiment. We explicitly modeled the dynamics of the latent reservoir and the SIV-specific effector cell population including their exhaustion and their potential cytolytic and noncytolytic functions. We found that the latent reservoir significantly contributes to the size of the peak VL after CD8 depletion, while drug efficacy plays a lesser role. Our model suggests that the overall CD8+ lymphocyte cytolytic killing rate is dynamically changing depending on the levels of antigen-induced effector cell activation and exhaustion. Based on estimated parameters, our model suggests that before ART or without ART the overall CD8 cytolytic killing rate is small due to exhaustion. However, after the start of ART, the overall CD8 cytolytic killing rate increases due to an expansion of SIV-specific CD8 effector cells. Further, we estimate that the cytolytic killing rate can be significantly larger than the cytopathic death rate in some animals during the second phase of ART-induced viral decay. Lastly, our model provides a new explanation for the puzzling findings by Klatt et al. and Wong et al. that CD8 depletion done immediately before ART has no noticeable effect on the first phase viral decay slope seen after ART initiation Overall, by incorporating effector cells and their exhaustion, our model can explain the effects of CD8 depletion on VL during ART, reveals a detailed dynamic role of CD8+ lymphocytes in controlling viral infection, and provides a unified explanation for CD8 depletion experimental data., Author summary CD8+ lymphocytes play an important role in suppressing in vivo viral replication in HIV infection. However, both the extent to which and the mechanisms by which CD8+ lymphocytes contribute to viral control are not completely understood. By mathematically modeling data from a recent CD8 depletion experiment done in antiretroviral (ART) treated animals, our results suggest that the overall CD8+ lymphocyte cytolytic killing rate is dynamically changing depending on the levels of antigen-induced effector cell activation and exhaustion, i.e. before ART or without ART the overall CD8 cytolytic killing rate is small due to exhaustion. However, after the start of ART, the overall CD8 cytolytic killing rate increases due to an expansion of SIV-specific CD8 effector cells. By incorporating effector cells and their exhaustion, our model explains the effects on viral load of CD8 depletion done before ART or during ART, reveals a detailed dynamic role of CD8+ lymphocytes in controlling viral infection, and provides a unified explanation for CD8 depletion experimental data.

Copyright © 2020 National Academy of Sciences., The reservoir of HIV latently infected cells is the major obstacle for eradication of HIV infection. The “shock-and-kill” strategy proposed earlier aims to reduce the reservoir by activating cells out of latency. While the intracellular HIV Tat gene circuit is known to play important roles in controlling latency and its transactivation in HIV-infected cells, the detailed control mechanisms are not well understood. Here we study the mechanism of probabilistic control of the latent and the transactivated cell phenotypes of HIV-infected cells. We reconstructed the probability landscape, which is the probability distribution of the Tat gene circuit states, by directly computing the exact solution of the underlying chemical master equation. Results show that the Tat circuit exhibits a clear bimodal probability landscape (i.e., there are two distinct probability peaks, one associated with the latent cell phenotype and the other with the transactivated cell phenotype). We explore potential modifications to reactions in the Tat gene circuit for more effective transactivation of latent cells (i.e., the shock-and-kill strategy). Our results suggest that enhancing Tat acetylation can dramatically increase Tat and viral production, while increasing the Tat–transactivation response binding affinity can transactivate latent cells more rapidly than other manipulations. Our results further explored the “block and lock” strategy toward a functional cure for HIV. Overall, our study demonstrates a general approach toward discovery of effective therapeutic strategies and druggable targets by examining control mechanisms of cell phenotype switching via exactly computed probability landscapes of reaction networks.

The discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multi-scale nature of many networks where reaction rates have large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the Accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multi-finite buffers for reducing the state space by O(n!), exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes, and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be pre-computed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multi-scale networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks., 44 pages, 13 figures

The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEG), we truncate the state space by limiting the total molecular copy numbers in each MEG. We further describe a theoretical framework for analysis of the truncation error in the steady state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we introduce an a priori method to estimate the upper bound of its truncation error, which can be rapidly computed from reaction rates, without costly trial solutions of the dCME. We show results of applying our methods to four stochastic networks. We demonstrate how truncation errors and steady state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate out theories. Overall, the novel state space truncation and error analysis methods developed here can be used to ensure accurate direct solutions to the dCME for a large class of stochastic networks., 41 pages, 6 figures

Fundamental to biological decision-making is the ability to generate bimodal expression patterns where two alternate expression states simultaneously exist. Here, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIV’s fate decision between active replication and viral latency. We find that the HIV Tat protein manipulates the intrinsic toggling of HIV’s promoter, the LTR, to generate bimodal ON-OFF expression, and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-length virus. Strikingly, computational analysis indicates that the Tat circuit’s non-cooperative ‘non-latching’ feedback architecture is optimized to slow the promoter’s toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that non-latching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean-expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIV’s decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels.

The micro-creep behavior of metal matrix composite for inertial instrumentation was studied in this work. The interface, defect state and precipitation behavior during micro-creep were focused. The strain-time and strain rate-time relationships for SiCp/2024Al were investigated in-depth. The stress exponent and activation energy were measured 3 and 34.0 kJ/mol, respectively. It was found that the dislocation viscous slip dominates micro-creep mechanism. Moreover, the influence of load on precipitation behavior and the evolution of micro-creep microstructure were also discussed.

Despite years of fully suppressive antiretroviral therapy (ART), HIV persists in the host and is never eradicated. One major barrier to eradication is that multiple different cell types are infected that may individually contribute to HIV persistence. Tissue macrophages are critical contributors to HIV disease (1–3); however, their specific role in HIV persistence during long-term suppressive ART has not been established (4–6). Using humanized myeloid-only mice (MoM), we demonstrate that HIV infection of tissue macrophages is rapidly suppressed by ART, as determined by a rapid drop in plasma viral load and a dramatic drop in the levels of cell-associated viral RNA and DNA. No virus rebound was observed in the plasma of 67% of the ART treated animals at seven weeks post-ART interruption, and no replication competent virus was rescued from the tissue macrophages obtained from these animals. In contrast, in a subset of animals (~33%), a significantly delayed viral rebound was observed that is consistent with the establishment of persistent infection in tissue macrophages. These observations represent the first direct evidence of HIV persistence in tissue macrophages in vivo.

The supplementary text, figures and tables.

As a major nutraceutical component of a typical Mediterranean vegetable chicory, chicoric acid (CA) has been well-documented due to its excellent antioxidant and antiobesity bioactivities. In the current study, the effects of CA on lipopolysaccharide (LPS)-stimulated oxidative stress in BV-2 microglia and C57BL/6J mice and the underlying molecular mechanisms were investigated. Results demonstrated that CA significantly reversed LPS-elicited cell viability decrease, mitochondrial dysfunction, activation of NFκB and MAPK stress pathways, and inflammation responses via balancing cellular redox status. Furthermore, molecular modeling study demonstrated that CA could insert into the pocket of Keap1 and up-regulated Nrf2 signaling and, thus, transcriptionally regulate downstream expressions of antioxidant enzymes including HO-1 and NQO-1 in both microglial cells and ip injection of LPS-treated mouse brain. These results suggested that CA attenuated LPS-induced oxidative stress via mediating Keap1/Nrf2 transcriptional pathways and downstream enzyme expressions, which indicated that CA has great potential as a nutritional preventive strategy in oxidative stress-related neuroinflammation.

Computational modelling of cells can reveal insight into the mechanisms of the important processes of tissue development. However, current cell models have limitations and are challenged to model detailed changes in cellular shapes and physical mechanics when thousands of migrating and interacting cells need to be modelled. Here we describe a novel dynamic cellular finite-element model (DyCelFEM), which accounts for changes in cellular shapes and mechanics. It also models the full range of cell motion, from movements of individual cells to collective cell migrations. The transmission of mechanical forces regulated by intercellular adhesions and their ruptures are also accounted for. Intra-cellular protein signalling networks controlling cell behaviours are embedded in individual cells. We employ DyCelFEM to examine specific effects of biochemical and mechanical cues in regulating cell migration and proliferation, and in controlling tissue patterning using a simplified re-epithelialization model of wound tissue. Our results suggest that biochemical cues are better at guiding cell migration with improved directionality and persistence, while mechanical cues are better at coordinating collective cell migration. Overall, DyCelFEM can be used to study developmental processes when a large population of migrating cells under mechanical and biochemical controls experience complex changes in cell shapes and mechanics.

Fundamental to biological decision-making is the ability to generate bimodal expression patterns where 2 alternate expression states simultaneously exist. Here, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIV’s fate decision between active replication and viral latency. We find that the HIV transactivator of transcription (Tat) protein manipulates the intrinsic toggling of HIV’s promoter, the long terminal repeat (LTR), to generate bimodal ON-OFF expression and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-length virus. Strikingly, computational analysis indicates that the Tat circuit’s noncooperative “nonlatching” feedback architecture is optimized to slow the promoter’s toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that nonlatching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIV’s decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels., Author summary A central and recurring feature of cell-fate regulating circuits is their ability to generate bimodal expression—2 alternate expression states that exist simultaneously—with each state corresponding to a different cell fate. To understand the mechanisms enabling bimodality in a natural decision-making circuit, we examined HIV’s fate-selection circuit, the Tat circuit. This bimodal circuit is sufficient and necessary to generate a bet-hedging decision between 2 alternate HIV fates: active viral replication and long-lived dormancy (proviral latency). The dormant state, which is resistant to most antiviral drugs, is the primary clinical barrier to curing an HIV infection. While the canonical role of positive feedback is to amplify a signal, surprisingly, we find that the HIV transactivator of transcription (Tat) positive-feedback architecture is instead optimized to expand the regime of HIV expression bimodality. From an evolutionary perspective, the results suggest that positive-feedback circuits may have evolved to robustly generate bimodality in certain contexts, and, given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression patterns appears to support the hypothesis that HIV’s active-versus-latent decision confers viral fitness.

Gradient-directed cell migration (chemotaxis) and growth (chemotropism) are processes that are essential to the development and life cycles of all species. Cells use surface receptors to sense the shallow chemical gradients that elicit chemotaxis and chemotropism. Slight asymmetries in receptor activation are amplified by downstream signaling systems, which ultimately induce dynamic reorganization of the cytoskeleton. During the mating response of budding yeast, a model chemotropic system, the pheromone receptors on the plasma membrane polarize to the side of the cell closest to the stimulus. Although receptor polarization occurs before and independently of actin cable-dependent delivery of vesicles to the plasma membrane (directed secretion), it requires receptor internalization. Phosphorylation of pheromone receptors by yeast casein kinase 1 or 2 (Yck1/2) stimulates their internalization. We showed that the pheromone-responsive Gβγ dimer promotes the polarization of the pheromone receptor by interacting with Yck1/2 and locally inhibiting receptor phosphorylation. We also found that receptor phosphorylation is essential for chemotropism, independently of its role in inducing receptor internalization. A mathematical model supports the idea that the interaction between Gβγ and Yck1/2 results in differential phosphorylation and internalization of the pheromone receptor and accounts for its polarization before the initiation of directed secretion.

Genome sequences provide the overall genetic blueprint of cells, but cells possessing the same genome can exhibit diverse phenotypes. There is a multitude of mechanisms controlling cellular epigenetic states and that dictate the behavior of cells. Among these, networks of interacting molecules, often under stochastic control, depending on the specific wirings of molecular components and the physiological conditions, can have a different landscape of cellular states. In addition, chromosome folding in three-dimensional space provides another important control mechanism for selective activation and repression of gene expression. Fully differentiated cells with different properties grow, divide, and interact through mechanical forces and communicate through signal transduction, resulting in the formation of complex tissue patterns. Developing quantitative models to study these multi-scale phenomena and to identify opportunities for improving human health requires development of theoretical models, algorithms, and computational tools. Here we review recent progress made in these important directions.

Biological reaction networks are stochastic due to random thermal fluctuations. Stochasticity plays important roles in regulation of biochemical reaction networks when the copy numbers of molecular species are small and often results in unexpected outcomes. For example, it has been shown that a basic enzymatic reaction system can display stochastic focusing (SF) by increasing the sensitivity of the network as a result of the increasing signal noise [1]. Although stochastic simulation algorithm has been widely used to study such systems, it is ineffective in examining rare events and this becomes a significant issue when the tails of probability distributions are relevant as is the case of SF. Here we use the ACME method for the exact solution of the discrete Chemical Master Equation and study the probability landscape of product molecules in the basic enzymatic reaction system used in the original SF study [1]. Examinations of the effects of signal molecules under different stochastic processes show that SF is at play as stochastic changes enhance the system sensitivity. However, we also observed that the noise in signaling under certain stochastic processes in the same reaction network lead to a decrease in the system sensitivities, thus the network experiences stochastic defocusing. We further show that signal molecules following certain stochastic processes in the same reaction network can give rise to noise-induced bistability in the distribution of product molecules. These results highlight the fundamental role of stochasticity in biological reaction networks and the need for exact computation of probability landscape of the molecules in the system. It also points to possible importance of positive and negative feedback loops in such networks for control of the intrinsic noise.[1] Paulsson et. al, 2000, PNAS.

Efforts for curing HIV infection have been largely unsuccessful. A major obstacle is that the virus forms latent reservoirs in resting CD4+ T cells, which cannot be eradicated by current antiretroviral therapies. However, latently infected cells can be activated at later times to enter the transactivation state of viral production. The selection between latency and transactivation are likely stochastically controlled. However, detailed control mechanisms are unknown. To address this issue, we have developed a stochastic model for the intracellular genetic circuit controlling HIV latency and transactivation, and computed directly the steady state and time evolving probability landscape using the multi-finite buffer discrete Chemical Master Equation (mb-dCME) method. The computed stochastic steady state landscape of the HIV Tat circuit exhibits bistable behavior, with a highly stable latent state with zero Tat protein and a much wider but less stable transactivation state of nonzero Tat protein. Our calculation results agree with experimental observations of latency and transactivation dynamics. To further examine how the rate of intracellular HIV-1 transactivation is affected by the positive feedback of Tat circuit, we construct a mono-stable circuit by removing this feedback. We show that the transactivation rate in the mono-stable circuit is significantly lower than the wild-type bistable circuit. To study how the HIV-1 provirus can be transactivated more effectively, we systematically explored different biochemical properties of Tat binding, reaction rates, and transportation rates between nucleus and cytoplasm. Our results suggest novel approaches for eradicating HIV-1. Our results show that the simple positive feedback loop of Tat self-activation is sufficient to generate bistability, without the need for cooperativity and negative feedbacks. Our approach is general and can be applied to study broad issues in genetic circuits modeling in systems biology.