The Effects of Statistical Multiplicity of Infection on Virus Quantification and Infectivity Assays

Many biological assays are employed in virology to quantify parameters of interest. Two such classes of assays, virus quantification assays (VQA) and infectivity assays (IA), aim to estimate the number of viruses present in a solution, and the ability of a viral strain to successfully infect a host cell, respectively. VQAs operate at extremely dilute concentrations and results can be subject to stochastic variability in virus-cell interactions. At the other extreme, high viral particle concentrations are used in IAs, resulting in large numbers of viruses infecting each cell, enough for measurable change in total transcription activity. Furthermore, host cells can be infected at any concentration regime by multiple particles, resulting in a statistical multiplicity of infection (SMOI) and yielding potentially significant variability in the assay signal and parameter estimates. We develop probabilistic models for SMOI at low and high viral particle concentration limits and apply them to the plaque (VQA), endpoint dilution (VQA), and luciferase reporter (IA) assays. A web-based tool implementing our models and analysis is also developed and presented. We test our proposed new methods for inferring experimental parameters from data using numerical simulations and show improvement on existing procedures in all limits.Comment: 19 pages, 11 figures, 1 tabl

Stochastic Physics of Biological Assays and Improved Inference

Biological assays typically employ large numbers of constituent agents (virus particles, fluorescing antibodies, oligonucleotides, etc) to quantitatively measure the effects of various natural processes such as virus infectivity, cell membrane receptor concentrations, and the binding affinity of biomarkers. For the simplicity of analysis, these assays often intentionally eliminate most random variability by factoring only expected values of experimental quantities. However, ignoring the prevalent stochastic effects on the individual agent level may risk depriving this analysis of important and even confounding results. In this work, we model the kinetics, combinatorics, and statistical effects of various biological assays involving virology, cell identification, and molecular evolution in order to argue the importance of stochasticity in experimental results. First, concerning the variability in viral entry pathways, specifically for human immunodeficiency virus (HIV), we model the kinetics of receptor/coreceptor binding and membrane fusion, presenting a more accurate functional expression for infection in the presence of inhibiting drugs. Second, we create probabilistic models of virological assays including the plaque, endpoint dilution, and luciferase reporter assays, showing how parameters relating the statistical multiplicity of infection (SMOI) and particle to PFU ratios directly effect the distribution of infected cells. We use these stochastic models to formulate updated analytic techniques to estimate unknown and desired quantities such as the initial viral particle count in solution or the infectivity of a strain. Third, we investigate the effects of non-specific binding of fluorescing antibodies in flow cytometry and how a modified Langmuir adsorption model can inform optimal protocol design. Furthermore, we create a full probabilistic model of equilibrium binding dynamics in order to develop an automatic-gating procedure for improved cell identification and sorting. Finally, an attractive alternative to the use of antibodies in many biological assays and medical procedures is the use of short, genetic sequences called aptamers with strong binding affinity to mark specific target epitopes. The enrichment of target aptamers employs the SELEX (Systematic Evolution of Ligands by EXponential enrichment) protocol, a method that employs molecular evolution to filter out unwanted gene sequences. We create a probabilistic model of the enrichment procedure using concepts of statistical mechanics in order to present optimal improvements to the protocol

Stochastic Physics of Biological Assays and Improved Inference

Biological assays typically employ large numbers of constituent agents (virus particles, fluorescing antibodies, oligonucleotides, etc) to quantitatively measure the effects of various natural processes such as virus infectivity, cell membrane receptor concentrations, and the binding affinity of biomarkers. For the simplicity of analysis, these assays often intentionally eliminate most random variability by factoring only expected values of experimental quantities. However, ignoring the prevalent stochastic effects on the individual agent level may risk depriving this analysis of important and even confounding results. In this work, we model the kinetics, combinatorics, and statistical effects of various biological assays involving virology, cell identification, and molecular evolution in order to argue the importance of stochasticity in experimental results. First, concerning the variability in viral entry pathways, specifically for human immunodeficiency virus (HIV), we model the kinetics of receptor/coreceptor binding and membrane fusion, presenting a more accurate functional expression for infection in the presence of inhibiting drugs. Second, we create probabilistic models of virological assays including the plaque, endpoint dilution, and luciferase reporter assays, showing how parameters relating the statistical multiplicity of infection (SMOI) and particle to PFU ratios directly effect the distribution of infected cells. We use these stochastic models to formulate updated analytic techniques to estimate unknown and desired quantities such as the initial viral particle count in solution or the infectivity of a strain. Third, we investigate the effects of non-specific binding of fluorescing antibodies in flow cytometry and how a modified Langmuir adsorption model can inform optimal protocol design. Furthermore, we create a full probabilistic model of equilibrium binding dynamics in order to develop an automatic-gating procedure for improved cell identification and sorting. Finally, an attractive alternative to the use of antibodies in many biological assays and medical procedures is the use of short, genetic sequences called aptamers with strong binding affinity to mark specific target epitopes. The enrichment of target aptamers employs the SELEX (Systematic Evolution of Ligands by EXponential enrichment) protocol, a method that employs molecular evolution to filter out unwanted gene sequences. We create a probabilistic model of the enrichment procedure using concepts of statistical mechanics in order to present optimal improvements to the protocol

Hasil utama penelitian kacang-kacangan dan umbi-umbian tahun 2005

iv, 22 hal. : Ilus. ; 28 c

Xist nucleates local protein gradients to propagate silencing across the X chromosome

The lncRNA Xist forms ∼50 diffraction-limited foci to transcriptionally silence one X chromosome. How this small number of RNA foci and interacting proteins regulate a much larger number of X-linked genes is unknown. We show that Xist foci are locally confined, contain ∼2 RNA molecules, and nucleate supramolecular complexes (SMACs) that include many copies of the critical silencing protein SPEN. Aggregation and exchange of SMAC proteins generate local protein gradients that regulate broad, proximal chromatin regions. Partitioning of numerous SPEN molecules into SMACs is mediated by their intrinsically disordered regions and essential for transcriptional repression. Polycomb deposition via SMACs induces chromatin compaction and the increase in SMACs density around genes, which propagates silencing across the X chromosome. Our findings introduce a mechanism for functional nuclear compartmentalization whereby crowding of transcriptional and architectural regulators enables the silencing of many target genes by few RNA molecules