ライフサイエンスコーパス: stochastic model

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

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

3 yses of these dependencies by establishing a stochastic model.

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

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

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

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

8 is unknown and generally ignored by current stochastic models.

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

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

11 c models and Gillespie's algorithm (SSA) for stochastic models.

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

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

14 proposed experiments with the use of spatial stochastic modeling.

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

16 The proposed stochastic model accounts for the spatial distribution o

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

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

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

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

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

22 Stochastic modeling and simulation provide powerful pred

23 Stochastic modelling and experimental data demonstrated

24 In this review, we aim to raise awareness of stochastic models and how to combine them with existing

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 Density functional theory, stochastic models, and experimental characterizations de

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

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

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

31 Hybrid stochastic modeling approach can be used to provide quan

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

33 One such case is the stochastic modeling approach, which can be important whe

34 lts using Monte Carlo simulation in a tiered stochastic modeling approach: exposures were the highest

35 To this aim, we developed a unified stochastic modelling approach that, starting from radiat

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

37 in progression through cell cycle, detailed stochastic models are required.

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

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 The developed hybrid stochastic model can successfully capture several key fe

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 Spatially structured stochastic models can capture these important features o

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

56 ake an information theoretic analysis of two stochastic models concerning glioma differentiation ther

57 A stochastic model confirms the presence of an autocatalyt

58 Here, by developing a system-level stochastic model constrained by a large set of single-ce

59 ally leads to the coarsest deterministic and stochastic models containing only four molecular species

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

61 A stochastic model demonstrated the separation of TF input

62 Alignment-free, stochastic models derived from k-mer distributions repre

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

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

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

66 Importantly, stochastic modeling established this cost for sequential

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

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

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

70 We developed a stochastic model for cholera importation and transmissio

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

72 the NLME model were applied to optimize the stochastic model for each patient.

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

74 We introduce a stochastic model for network communication that combines

75 Here I report on the development of a stochastic model for protein translation that is capable

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

77 We present a stochastic model for the formation of tandem repeats via

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

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

80 larization model based on coupling between a stochastic model for the segregation of signaling molecu

81 Our stochastic model for the transcription events reproduced

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

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

84 We present a stochastic model for whole chromosome replication where

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

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

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

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

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

90 We propose a stochastic modeling framework to disentangle expected de

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

92 A stochastic modeling framework was developed to depict th

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

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 tease out the role of cooperative binding in stochastic models in comparison to deterministic models,

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

98 Finally, a stochastic model including nucleoid exclusion at midcell

99 A stochastic model incorporated chronological surface cont

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

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

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 In this paper, a new hybrid stochastic model is proposed to study the effect of mole

106 boundary layer, but we found that Lagrangian stochastic modelling is effective at predicting flight m

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

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

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

110 ory and simulations that use of the standard stochastic models leads to drastically incorrect predict

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

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

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

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

115 nalysis of disease progression is based on a stochastic model of a population of infectious agents in

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

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

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

119 We study a stochastic model of cancer evolution and derive a formul

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

121 Using the stochastic model of chromatography, we showed quantitati

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

123 idate such mechanisms, we herein introduce a stochastic model of combined epigenetic regulation (ER)-

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

125 es that these outcomes are compatible with a stochastic model of cortical neurogenesis in which proge

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

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

128 ath receptor activation, we developed a semi-stochastic model of DISC/RIPoptosome formation.

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

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

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

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

133 Using a parameterized stochastic model of expansion, we find that this slowdow

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

135 extension of our previously published hybrid-stochastic model of FOCM by including the 5fTHF futile-c

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

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

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

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

140 We present a stochastic model of LINE-1 (L1) transposition in human c

141 erty line can be derived from an agent-based stochastic model of market exchange, combining all expen

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

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

144 To test this, we developed a stochastic model of neurofilament transport that tracks

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

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

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

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

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

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

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

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

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

154 Using a stochastic model of repetitive activity in afferents, we

155 We construct an accurate stochastic model of rodent feeding at the bout level in

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

157 Here we develop a stochastic model of severe acute respiratory syndrome co

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

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

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

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

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

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

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

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

166 We then apply our framework to a stochastic model of the rocky intertidal food web, parti

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

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

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

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

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

172 Here we develop a stochastic model of tSC and vacancy mediated synapse eli

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

174 A recent stochastic model of tumour-induced angiogenesis includin

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

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

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

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

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

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

181 HUVECs exposed to different flow conditions, stochastic modelling of flow induced NF-kB activation an

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

183 results open the door to the development of stochastic models of beach, dune, and barrier dynamics,

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

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

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

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

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

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

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

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

192 used to model transcriptional regulation in stochastic models of gene expression.

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

194 Our results confirm that commonly used stochastic models of gene regulatory networks are only a

195 We used stochastic models of gHAT transmission fitted to DRC cas

196 In this study, we analyze stochastic models of phenotypic switching to provide a f

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

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

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

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

201 r experimental results with predictions from stochastic models of transcription, which indicated that

202 By extending the stochastic model performance evaluation process algebra

203 The stochastic model predicted fadeout and within-herd preva

204 The stochastic model predicts that pH fluctuations decrease

205 The stochastic model presented here demonstrates the importa

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

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

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

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

210 This stochastic model recapitulates phyllotactic patterns, bo

211 formulate a multiscale asymptotic method for stochastic model reduction, from which we derive an effi

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

213 The stochastic model results in better projection of the cyc

214 The stochastic model seems irreconcilable with an ordered ti

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

216 A stochastic model simulation of translation predicted com

217 Analysis of stochastic model simulations illustrates how these pleio

218 nd replicated experimental system and in our stochastic model simulations points to potential fundame

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

220 Stochastic modeling suggested that the double-negative f

221 Prior stochastic modeling suggests that decrease in glutamate

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

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

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

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

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

227 uce a detailed, yet efficient sequence-based stochastic model that generates realistic synthetic data

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

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

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

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

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

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

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

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

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

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

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

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

240 Indeed, two-state stochastic models that seek to describe microtubule dyna

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

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

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

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

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

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

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

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

249 To fit the resulting stochastic model to data from FRAP measurements and to e

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

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

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

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

254 Here, we developed a stochastic model to quantify the variability in the gree

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

256 By fitting a stochastic model to the observed mRNA distributions, we

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

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

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

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

261 Here, we use stochastic modelling to derive general results for the i

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

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

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

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

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

270 This stochastic model unifies the physical concepts of linear

271 Stochastic modeling using reasonable biophysical paramet

272 nce and model selection for deterministic or stochastic models using (i) standard rejection ABC or se

273 The stochastic model was fitted with parameters drawn from d

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

275 Using a stochastic model, we demonstrate how neglecting environm

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 Using stochastic modeling, we reproduce in silico the response

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

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

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

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

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

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

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

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

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

291 gical implement for control is a large scale stochastic model with countless parameters lacking robus

292 We also developed a stochastic model with physically meaningful parameters t

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

294 lls fails or becomes established by coupling stochastic modeling with laboratory experiments.

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

296 ers is different than that given by standard stochastic models with Hill-type propensities.

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.