ライフサイエンスコーパス: stochastic model

1 ities were well described by a two-parameter stochastic model.

2 ate during use) nanoRelease is designed as a stochastic model.

3 s, and cell spreading as a three-dimensional stochastic model.

4 ophic expenditure due to surgery, we built a stochastic model.

5 he same level of predictive capability as do stochastic models.

6 is unknown and generally ignored by current stochastic models.

7 oncogenesis) can be difficult to observe in stochastic models.

8 ministic rather than random, as suggested by stochastic models.

9 estimated by incorporating such factors into stochastic models.

10 ric correlation matrices for these nonlinear stochastic models.

11 on in boundary sharpening using multi-scale, stochastic models.

12 ghting the need for analysis of more general stochastic models.

13 me must be considered, which is achieved via stochastic modeling.

14 proposed experiments with the use of spatial stochastic modeling.

15 rtitioning at cell division; and c), a fully stochastic model accommodating both sources of populatio

16 A simple stochastic model accounting for the essential steps of c

17 The proposed stochastic model accounts for the spatial distribution o

18 d on single-motor parameters, we developed a stochastic model and a mean-field theoretical descriptio

19 By observing a connection between a stochastic model and a multiclass queue, we obtain a clo

20 We then introduce a dynamic stochastic model and show that prediction of dynamic dis

21 ave incorporated the torque mechanism into a stochastic model and simulated transcription both with a

22 V = 10(-2) to 100 m/s by mapping on a simple stochastic model and turns out to be of the order of gam

23 Here, using stochastic modeling and fluorescence microscopy, we show

24 Stochastic modeling and simulation provide powerful pred

25 cribe the APD signal using an autoregressive stochastic model, and we establish the interrelations be

26 onstrated this approach mathematically using stochastic modeling, and applied it to experimental time

27 nstrate this principle mathematically, using stochastic modeling, and experimentally, using simple sy

28 Density functional theory, stochastic models, and experimental characterizations de

29 vity analysis method that is appropriate for stochastic models, and we demonstrate how this analysis

30 tes were calculated with a tension-dependent stochastic model applied to FnIII modules in each molecu

31 To conclude, the present multiscale stochastic modeling approach allows studying cellular ev

32 In this study, we adopt a stochastic modeling approach to address multiple pathway

33 A deterministic and a stochastic model are both developed and give the same re

34 Both deterministic and stochastic models are constructed to describe the transm

35 ty of our approach lies in specifying latent stochastic models at the single-cell level, and then agg

36 observed distribution, we explored a simple stochastic model based on geometric Brownian motion.

37 l division events are evaluated, including a stochastic model based on the probability of cell divisi

38 We propose a simple stochastic model based on the two successive mutations h

39 We then develop a 3D stochastic model based on these individual behaviors to

40 This stochastic model, based upon a diffusion approximation,

41 noise control analysis can be applied to any stochastic model belonging to continuous time Markovian

42 nd that anomalous speeds are observed in the stochastic model, but only when the carrying capacity of

43 atistical verification and model checking of stochastic models by providing an effective means for ex

44 h, we quantify cell division control using a stochastic model, by inferring the division rate as a fu

45 Here we construct an agent-based stochastic model calibrated by experimental measurements

46 Moreover, we illustrate how the identified stochastic model can be used to determine light inductio

47 We find that the stochastic model can more robustly reproduce two fundame

48 In summary, we demonstrate that a stochastic model can recapitulate experimental observati

49 Moreover, we find that only the stochastic model can simultaneously reproduce these char

50 Furthermore, we show that only with the full stochastic model can the relative importance of environm

51 Deterministic and stochastic models can be analyzed in ABC-SysBio.

52 ters, especially for small populations where stochastic models can be expected to differ most from th

53 echnique for integrating dynamic features in stochastic models can be extended to any subduction zone

54 However, a recent stochastic model combining the main elements of niche th

55 We tested this method on a stochastic model, containing 18 parameters, of the cardi

56 not fully capture the observed behavior, our stochastic model correctly predicts the experimental dyn

57 We exactly solve a stochastic model describing a ubiquitous motif in membra

58 a of ribosomal density on mRNAs with a novel stochastic model describing ribosome traffic dynamics du

59 We developed a stochastic model describing the dynamic interactions amo

60 Here, we used stochastic models describing focus expansion as a means

61 subjected to microstructural analyses using stochastic models describing the relative contributions

62 hical model for cancerous stem cells and the stochastic model, driven by the observation of chromosom

63 Importantly, stochastic modeling established this cost for sequential

64 Using a stochastic model fit to seasonal flu surveillance data f

65 ase incidence for the next 6 months, using a stochastic model fitted to data from Sierra Leone.

66 To explain our results we propose a spatial stochastic model (following a philosophy of the Widom-Ro

67 We developed a stochastic model for cholera importation and transmissio

68 Properties of the stochastic model for CMT are developed below in a test c

69 We analyze a stochastic model for coupled degradation of mRNAs and sR

70 nderstand this finding, we propose a general stochastic model for mutually interacting complex system

71 In this paper, we present a stochastic model for studying such longitudinal data in

72 We develop a coarse-grained stochastic model for the influence of signal relay on th

73 We examine a nonlinear stochastic model for the parasite load of a single host

74 Our stochastic model for the transcription events reproduced

75 We present a semi-stochastic model for the transmission of a microparasite

76 First, we derive a stochastic model for thermally-activated motion of dislo

77 Here, we investigate a stochastic model for this phenomenon, in which gene tran

78 We develop a stochastic model for viral entry that incorporates a com

79 We present a stochastic model for whole chromosome replication where

80 The finding is cast into a stochastic model for Z1Z2 interdomain elasticity that is

81 nd assessed the appropriateness of different stochastic models for describing HCV focus expansion.

82 ovides a possible behavioral explanation for stochastic models for financial systems in general and p

83 core of the computer climate models, reduced stochastic models for low-frequency variability, and mod

84 erativity, is analyzed in this paper through stochastic models for molecular interactions.

85 /2N/4N ploidy series, consistent with simple stochastic models for molecular noise.

86 One of the earliest stochastic models for the growth of stem cell population

87 es were recorded on a categorical scale, and stochastic models for year-to-year changes in abundance

88 porating these phenomena into our multiscale stochastic modeling framework significantly changes the

89 a quality than prior studies, (2) advances a stochastic modeling framework to include microbial inact

90 systematic strategies for the estimation of stochastic models from climate data.

91 population, although recently some important stochastic models have been developed.

92 As an alternative to parsimony analyses, stochastic models have been proposed for morphological c

93 Recently, stochastic models have been used to predict distribution

94 ach-scale tracer experiments, and multiscale stochastic modeling improves assessment of microbial tra

95 stic model dependent on fixed lineages and a stochastic model in which choices of division modes and

96 he exact analytical solution of a simplified stochastic model, in which the numbers of competing mRNA

97 Finally, a stochastic model including nucleoid exclusion at midcell

98 ractions in excellent agreement with a local stochastic model, indicating that long-range correlation

99 We developed a stochastic model, integrating results of single-molecule

100 We find that the stochastic model is able to generate the full spectrum o

101 This stochastic model is based on an influenza epidemic model

102 An analytical stochastic model is developed and compared with the meas

103 A spatially-explicit, stochastic model is developed for Bahia bark scaling, a

104 This filopodia stochastic model is integrated into migratory dynamics o

105 ct, the likelihood of the data under complex stochastic models is often analytically or numerically i

106 D(3)E is based on an analytically tractable stochastic model, it provides additional biological insi

107 odels written in a variant of the rule-based stochastic modelling language Kappa, with spatial extens

108 Deterministic and stochastic models led us to focus on basal transcription

109 Stochastic models may therefore have an important role t

110 When integrated with discrete stochastic models, measurements of cell-to-cell variabil

111 Here, however, I present a stochastic model of a CaV2.1/BKCa(alpha-only) complex, a

112 Here, by analysing a stochastic model of a minimum feedback network underlyin

113 We use a spatially explicit stochastic model of an Ae. aegypti population in Iquitos

114 We revisit the one-dimensional stochastic model of an earlier study by D. K. Lubensky a

115 A stochastic model of bc(1) turnover was used to confirm t

116 Our stochastic model of bipartite cooperation uses simple sp

117 Here we present a stochastic model of cellular transitions that allows und

118 Using the stochastic model of chromatography, we showed quantitati

119 lar trends are predicted by a discrete state stochastic model of collective motor dynamics, these ana

120 We construct and implement a stochastic model of convergent extension, using a minima

121 Here we demonstrate using a stochastic model of cooperative cluster formation that s

122 ether human cancer follows a hierarchical or stochastic model of differentiation is controversial.

123 rintuitive result by applying a quantitative stochastic model of diffusion.

124 In this study, we have created an integrated stochastic model of DNA damage repair by non-homologous

125 In this work we study a simple stochastic model of domain growth.

126 Here, we study a stochastic model of drug resistance emergence and consec

127 ss of containment strategies, we developed a stochastic model of Ebola transmission between and withi

128 Here we introduce a stochastic model of evolution that involves residue subs

129 By combining TIRF microscopy and a stochastic model of exocytosis, we found that vesicle ex

130 Using a stochastic model of fiber repair, it is assumed that mec

131 Using a stochastic model of gene expression at the nucleotide an

132 This trend is consistent with a stochastic model of gene expression where mRNA copy numb

133 Here we use a stochastic model of infection dynamics to estimate the e

134 probability of fixation is used to develop a stochastic model of joint male and female phenotypic evo

135 This study integrated a dynamic stochastic model of measles transmission in Uganda (2010

136 We propose a minimalist stochastic model of multilevel (or group) selection.

137 Here we present the first stochastic model of multiple mating cells whose morpholo

138 presents the first, to our knowledge, fully stochastic model of neutrophil activation, which, though

139 A novel stochastic model of nucleic acid chemistry was developed

140 he kinetics of nucleosome organization, in a stochastic model of nucleosome positioning and dynamics.

141 We present a reanalysis of the stochastic model of organelle production and show that t

142 We present a reanalysis of the stochastic model of organelle production and show that t

143 Finally, we developed a simple stochastic model of our positive-feedback system and sho

144 Here we build a stochastic model of p53 induced apoptosis comprised of c

145 by incorporating copy number variance with a stochastic model of plasmid replication.

146 lts with those from a simple one-dimensional stochastic model of population dynamics at the base of t

147 Here we develop a general stochastic model of predator-prey spatial dynamics to pr

148 We developed a stochastic model of primordia initiation at the shoot ap

149 lls of the embryonic retina and fit the same stochastic model of proliferation.

150 In this article, we present a stochastic model of protein receptor trafficking at the

151 nal resolution by a suitable one-dimensional stochastic model of random-direction plane waves.

152 hm of the total probability of a MSA under a stochastic model of sequence evolution along a time axis

153 Previously, a stochastic model of single-stranded RNA virus assembly w

154 he solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polari

155 ibution, we compare live-cell imaging with a stochastic model of telomere dynamics that we developed.

156 derstand stiffness sensing, we constructed a stochastic model of the "motor-clutch" force transmissio

157 In this paper we provide a stochastic model of the budding yeast cell cycle that ac

158 in vitro "mini gut" studies, we use a hybrid stochastic model of the crypt to investigate how exogeno

159 ctors interact, we built a three-dimensional stochastic model of the experimentally observed isotropi

160 By incorporating these observations into a stochastic model of the flagellar bundle, we demonstrate

161 As a case study, we consider a stochastic model of the Hes1 system expressed in terms o

162 Here, we develop a stochastic model of the multiple antibiotic resistance n

163 The first is a stochastic model of the progression of cell cycle states

164 avior and statistics of long trajectories, a stochastic model of their nonequilibrium motion is requi

165 We developed a stochastic model of three-dimensional dynamics in canopi

166 Here, we developed a spatial stochastic model of tobacco-related HNSCC at the tissue

167 We present a stochastic model of transcription that considers these c

168 Here, we develop a stochastic model of transcriptional regulation that allo

169 Here we use a simple stochastic model of translation to characterize the effe

170 We developed an individual-based, stochastic model of tuberculosis disease in a hypothetic

171 A recent stochastic model of tumour-induced angiogenesis includin

172 As an alternative to a stochastic model of viral transmission, we hypothesize t

173 ccounted for by a newly-developed Lagrangian stochastic model of weakly-flying insect movements in th

174 Here, we present the results of stochastic modeling of hematopoietic stem cell (HSC) clo

175 mast cell, which then served as a basis for stochastic modeling of inositol-trisphosphate-mediated c

176 Using measurements and stochastic modeling of mycobacterial cell size and cell-

177 le-cell time-lapse luminescence imaging with stochastic modeling of the time traces, we quantified th

178 a proof of concept, this approach shows that stochastic modelling of a specific immune networks rende

179 Smoldyn is a software package for stochastic modelling of spatial biochemical networks and

180 The incorporation of domain growth into stochastic models of biological processes is of increasi

181 in computational biology to build and solve stochastic models of biological processes.

182 imating crowding effects with coarse-grained stochastic models of capsid assembly, using the crowding

183 Here, building on previous stochastic models of consumer-resource interactions betw

184 Ecosystem thresholds can be combined with stochastic models of disturbance to identify targets for

185 the noise contributions predicted by correct stochastic models of either intrinsic or extrinsic mecha

186 roaches for calibration and prediction using stochastic models of epidemics.

187 statistical methods of estimation applied to stochastic models of evolution.

188 xtension, and we compared this behavior with stochastic models of Fn fibers with different molecular

189 significant interest in efforts to calibrate stochastic models of gene expression and obtain informat

190 s issue, we invoke a mapping between general stochastic models of gene expression and systems studied

191 d for the first-passage time distribution in stochastic models of gene expression.

192 To address this problem, we apply stochastic models of signal integration by T cells to da

193 olutionary processes depend fundamentally on stochastic models of speciation and mutation.

194 of the process algebra approach is to allow stochastic models of the population (parasite and immune

195 se and spread, often in ways consistent with stochastic models of transcription and translation.

196 The effects of CMT from the stochastic model on a large-scale convectively coupled w

197 By extending the stochastic model performance evaluation process algebra

198 The stochastic model predicted fadeout and within-herd preva

199 he clinical data was observed in case of the stochastic model projections as compared to their determ

200 more than 70% for two patients by using the stochastic model projections.

201 re behind the observed noise reduction and a stochastic model provides quantitative support to the pr

202 y of this simple principle by reconstructing stochastic models (reaction structure plus propensities)

203 This stochastic model recapitulates phyllotactic patterns, bo

204 estimation and prediction for these types of stochastic models remain limited.

205 Mutant phenotypes in the stochastic model reproduce the experimental observations

206 The stochastic model results in better projection of the cyc

207 The stochastic model seems irreconcilable with an ordered ti

208 Our simple stochastic model shows how the regulation of ant colony

209 A stochastic model simulation of translation predicted com

210 Analysis of stochastic model simulations illustrates how these pleio

211 Data from a chemotaxis mutant and stochastic modeling suggest that fluctuations of the reg

212 Stochastic modeling suggested that the double-negative f

213 Prior stochastic modeling suggests that decrease in glutamate

214 ic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-

215 We present a stochastic model that accurately describes simultaneousl

216 Here we report the development of a spatial stochastic model that addresses the dynamics of ErbB3 ho

217 babilistic and equation-free analyses of the stochastic model that calculate stationary states for th

218 Here, we present a stochastic model that can generate bistability of the Hi

219 We develop a stochastic model that describes the entry process at the

220 We treat both a stochastic model that grows an explicit three-dimensiona

221 stability of microtubules, we propose here a stochastic model that includes all relevant biochemical

222 We have formulated a simple stochastic model that includes purse-string contractilit

223 T cell repertoire diversity maintenance by a stochastic model that incorporates the concept of compet

224 ccount for these observations with a minimal stochastic model that is based on an autocatalytic cycle

225 Here, we propose a stochastic model that naturally combines these two evolu

226 We develop a stochastic model that quantitatively captures the means

227 aments is investigated theoretically using a stochastic model that takes into account the hydrolysis

228 It is based on a discrete-state stochastic model that takes into account the most releva

229 random-impact rule allows us to formulate a stochastic model that uncouples the effects of productiv

230 However, accessing this source requires stochastic models that are usually difficult to analyze.

231 to be far noisier than predicted by standard stochastic models that assume homogeneous mixing.

232 issect this data requires the development of stochastic models that can both deconvolve the stages of

233 By analyzing this large dataset, we identify stochastic models that can explain evolutionary patterns

234 ysical reasoning is utilized to build simple stochastic models that capture the significant intermitt

235 istic worlds, leading to spatially explicit, stochastic models that encompass speciation, extinction,

236 We have developed a test suite of stochastic models that have been solved either analytica

237 Mechanistic stochastic models that include all of these factors have

238 h and derive corresponding deterministic and stochastic models that incorporate biological details.

239 ference hinges critically both on developing stochastic models that provide a reasonable description

240 pes were known, we developed two alternative stochastic models that relate p16(INK4a) expression to a

241 In theoretical ecology, simple stochastic models that satisfy two basic conditions abou

242 mbination of experimental data and a general stochastic model, that the degree of phenotypic variatio

243 n, we calibrated a dynamic, individual-based stochastic model, the HIV Synthesis Model, to multiple d

244 r addressing this problem for discrete-state stochastic models, the analysis of SDE and other continu

245 In contrast to previous one-dimensional stochastic models, the presented simulation approach can

246 ower the maximal absolute eigenvalues of the stochastic model, thereby contributing to increased stab

247 In this contribution, we develop a stochastic model to analytically account for this distri

248 thod allows the steady-state behavior of the stochastic model to be easily computed, facilitates the

249 albopictus; the former was assessed using a stochastic model to calculate R0 and the latter was asse

250 ddress this gap by developing a mechanistic, stochastic model to characterize phosphorus, nitrogen, b

251 We developed a stochastic model to describe these microbial transport a

252 f cytochrome c and ubiquinone pool using the stochastic model to evaluate the DeltaG of the bc(1) com

253 We have extended the stochastic model to include de-differentiation and show

254 We developed a stochastic model to investigate the underlying mechanism

255 In this work we extend the stochastic model to more realistic BKCa-CaV complexes wi

256 We introduce a spatial stochastic model to provide insight into this process.

257 To investigate this, we used a stochastic model to study regulation of downstream targe

258 and range size of species arising under our stochastic model to those observed across 1,269 species

259 Here we used stochastic modeling to analyze and quantify the ability

260 We utilize multi-scale, stochastic modeling to investigate the design principles

261 Here, we use spatial stochastic modeling to show that tradeoffs arise between

262 Here, we develop stochastic models to analyze the loss probabilities for

263 Here, we develop simple deterministic and stochastic models to compare the confinement properties

264 rticular, the challenging problem of fitting stochastic models to data.

265 When fitting continuous-time stochastic models to discretely observed time series the

266 We use simple deterministic and stochastic models to gain insight into residual viremia

267 We illustrate the use of spatially explicit stochastic models to optimize targeting of surveillance

268 To test this, first we used stochastic models to predict that variability in the num

269 mportance of building biologically realistic stochastic models to test biological models more stringe

270 tive insights into these results and a novel stochastic model tracking cell-volume and cell-cycle pre

271 This stochastic model unifies the physical concepts of linear

272 The stochastic model was fitted with parameters drawn from d

273 A stochastic model was used to estimate the number of huma

274 Using a stochastic model, we demonstrate how neglecting environm

275 Using a unified stochastic model, we demonstrate that this coexistence i

276 Extending our behavioral model to a stochastic model, we derive and explain a set of quantit

277 the mean search time for the discrete-state stochastic model, we derived analytical forms of the app

278 However, using a stochastic model, we show that the appearance of trends

279 Combining experiments and stochastic modeling, we find that increasing the ATP sti

280 g candidate filament turnover pathways using stochastic modeling, we found that exponential polymer m

281 riments, Bayesian statistical inference, and stochastic modeling, we introduce and illustrate a strat

282 Using stochastic modeling, we reproduce in silico the response

283 Using deterministic and stochastic modeling, we reproduced in silico the differe

284 prints and fingerprints generated by several stochastic models, we derive accurate approximations for

285 riginal deterministic models approximate the stochastic model well in most situations, but that the n

286 Two combined stochastic models were developed: patient and epidemic m

287 n essential tool for the analysis of complex stochastic models when the likelihood function is numeri

288 ection of images is estimated by first using stochastic modelling where the locations of clusters in

289 an increasing appetite for individual-based stochastic models which can capture the fine details of

290 tudying transient and long-term behaviour of stochastic models which have periodic phases-several dif

291 nal response in budding yeast to calibrate a stochastic model, which is then used as a basis for pred

292 tate transitions can be described by using a stochastic model, which predicts that ICE fitness is opt

293 intensive time-series datasets and improved stochastic modelling will help to explore their importan

294 MPL results could be reproduced by a simple stochastic model with a single adjustable parameter.

295 atistically exactly solvable one-dimensional stochastic model with relevance for low frequency variab

296 ta analysis method that combines mechanistic stochastic modelling with the powerful methods of non-pa

297 Linear stochastic models with multiple spatiotemporal scales ar

298 and deterministic models as well as between stochastic models with time-series and time-point measur

299 We use a stochastic model, with data from viral competition exper

300 evin dynamics can be embedded in an extended stochastic model without explicit memory.