ライフサイエンスコーパス: mathematical model

1 computational fluid dynamics and analytical mathematical models).

2 nderstand this heterogeneity, we developed a mathematical model.

3 eriments, including treatments unseen by the mathematical model.

4 oint of data saturation was estimated with a mathematical model.

5 ons from the literature to formulate a novel mathematical model.

6 lementing the strategy in a US state using a mathematical model.

7 to estimate key HIV epidemic indicators from mathematical models.

8 ions are best addressed with systems biology mathematical models.

9 -numerical, qualitative data to parameterize mathematical models.

10 sets, these techniques are strictly based on mathematical models.

11 ess of PrEP has relied on clinical trials or mathematical models.

12 nto finite-sized target structures employing mathematical modeling.

13 icated by biofilm thickness measurements and mathematical modeling.

14 rcial and institutional IR databases without mathematical modeling.

15 perturbation, bioinformatic predictions, and mathematical modeling.

16 ociated with sexual receptivity length using mathematical modeling.

17 ct under a range of supply constraints using mathematical modelling.

18 ed environment, it is necessary to integrate mathematical models across biological scales.

19 Mathematical models allow us to predict the phase respon

20 Mathematical modelling also suggests that the binding dy

21 Mathematical models also suggest a substantial overlap b

22 Experimental and mathematical modeling analyses suggest that active cargo

23 In addition, assumptions to make the mathematical models analytically tractable limit the sea

24 We built a mathematical model and analysed it using numerically sim

25 We developed a transmission-dynamic mathematical model and calibrated it to data from two hi

26 predicted the probability of rebound using a mathematical model and inference approach.

27 We used a mathematical model and Monte Carlo algorithm to estimate

28 e learning model is applied to calibrate the mathematical model and to fit it to the overall survival

29 In this study, mathematical modeling and 3D printing were used to analy

30 We used mathematical modeling and analysis of observational data

31 types of hybrid error correction methods by mathematical modeling and analysis on both simulated and

32 Mathematical modeling and corresponding in vivo experime

33 Our mathematical modeling and genetic tests validate this me

34 Here, using mathematical modeling and statistical analyses of T cell

35 Here, we combine mathematical modelling and data from eleven bipartite pl

36 icroscopy, transmission electron microscopy, mathematical modelling and genetic manipulation to visua

37 Mathematical models and computer simulations demonstrate

38 paid to the comprehensive description of the mathematical models and parameters of the active and pas

39 terpreting clinical diagnostics, and for the mathematical models and serological surveys that underpi

40 Our combined statistical/mathematical modeling approach expands the utility of ge

41 This mathematical modeling approach provides strong evidence

42 Using a mathematical modeling approach, we found that simply int

43 Taking a mathematical modelling approach, we investigate how tumo

44 ristics of EMT dynamics, with a focus on the mathematical modeling approaches that have been instrume

45 tion to key experimental findings, we review mathematical modeling approaches that help researchers i

46 Our imaging assay and mathematical model are easy to implement and provide a s

47 Population genetics mathematical models are developed here to demonstrate th

48 Fortunately, mathematical models are uniquely positioned to provide a

49 tions for behavioral modifications, based on mathematical models, are most likely to be followed in t

50 burden-is sufficiently high, an effect that mathematical models attribute to increased competition.

51 I show that a mathematical model based on environmental stochasticity,

52 Mathematical modeling based on our data provides estimat

53 We then present a mathematical model, based on inferred functional interac

54 he local hydrodynamics and, as revealed by a mathematical model benchmarked on the observations, on c

55 rve as the basis for developing a variety of mathematical models, but they ensure that any mathematic

56 o, and in vitro studies, in combination with mathematical modeling can help optimize and guide the de

57 We tested plausibility for CPP by combined mathematical modeling, comparison of predictions from th

58 of experiments, large-scale omics data, and mathematical modeling, complemented by the use of synthe

59 We present a mathematical model comprising a Polycomb/Trithorax respo

60 Our mathematical model consists of seven ODEs describing the

61 Here, we create a simple mathematical model coupling T-cell recognition with an e

62 Mathematical models demonstrate that a serological testi

63 Herein, a mathematical model demonstrates that the large amount of

64 Mathematical modeling demonstrates that the interface de

65 Mathematical modeling demonstrates that the NAC-induced

66 We developed a minimal mathematical model demonstrating growth factor signaling

67 The mathematical model derived in this work can be easily ad

68 A mathematical model describes tumor growth in animal mode

69 A mathematical model describing the viral infection dynami

70 We further evaluated how well bio-mathematical models designed to predict performance chan

71 Previous mathematical modeling efforts have focused on bundles' r

72 This approach, in conjunction with mathematical modeling, enabled us to quantify the nuclea

73 The derivation of a mathematical model enables the quantitative prediction o

74 llow the optimal schedules generated through mathematical modeling entirely, but travelers who better

75 s greatly benefited from a variety of simple mathematical models focusing on the conditions and patte

76 A mathematical model for D(eff) in the extracelluar space

77 We develop a mathematical model for fitness landscapes generated by s

78 A mathematical model for RNAPI elongation confirmed the im

79 In this paper, we present an integrated mathematical model for surface coverage bead-based assay

80 We propose a mathematical model for the dynamics of c-di-GMP and (p)p

81 ormation processing capabilities, we built a mathematical model for the eS6 phosphorylation (eS6-P) c

82 ng this pattern formation is elucidated by a mathematical model for the frictional motion of the colo

83 Using a mathematical model for transmission and control of VL, w

84 The mathematical model for tumour spheroid growth is paramet

85 In this work we study mathematical models for a basic chemical signal, the arr

86 s increasing importance placed on the use of mathematical models for the effective design and managem

87 In this study, we developed a mathematical modeling framework to quantify the contribu

88 To that end, we developed a mathematical modeling framework to simulate the AMP tria

89 n in microbiology and molecular biology, the mathematical models generally used to describe the popul

90 Mathematical modeling has been at the forefront of this

91 Conversely, recent work in mathematical modeling has demonstrated the relevance of

92 Although mathematical modeling has provided many insights into th

93 In plants, mathematical modelling has been comprehensively integrat

94 Mathematical modelling has successfully been used to pro

95 Yet, few mathematical models have aimed to identify optimal treat

96 While several mathematical models have been explored in the context of

97 Mathematical models have predicted long-term persistent

98 Mathematical models imply that a prolonged time (decades

99 rticle, we describe the assembly of a hybrid mathematical model in which the spatial spread of inflam

100 ization (smFISH), time-lapse microscopy, and mathematical modeling in single fission yeast cells to u

101 The core of the platform is the mathematical model, in the form of a system of ordinary

102 To test this, we present an individual-based mathematical model incorporating all the important cell-

103 Mathematical models incorporating variability successful

104 As predicted by a simple mathematical model, increasing sigma(B) expression shift

105 High-complexity DNA barcoding and mathematical modeling indicate a high rate of de novo ac

106 Our mathematical model indicates that local nutrient depleti

107 Mathematical modeling indicates that the Plk4 oscillatio

108 Furthermore, no existing mathematical model integrates the impact of diurnal neur

109 for researchers interested in incorporating mathematical modeling into their scientific process.

110 The mathematical model is computationally implemented for a

111 Here, a mathematical model is developed to quantify the dynamics

112 A mathematical model is used to rapidly evaluate dosing st

113 Rationale: Mathematical modeling is used to understand disease dyna

114 Whilst mathematical modelling is well established in informing

115 However, developing useful mathematical models is challenging because of the often

116 Unfortunately, the construction of complete mathematical models is often hindered by a pervasive pro

117 The dynamic nature of our mathematical model makes it a powerful tool both for und

118 Bio-mathematical models may be useful for aiding crews in sc

119 In this study, we implemented a mathematical modelling method (catalytic model) combined

120 Current mathematical modeling methods for assessing cytopenia ri

121 While many mathematical models (MMs) have described the course of i

122 Our initial mathematical model obtained a correlation coefficient of

123 ial infection to create a fully parametrised mathematical model of a systemic Vibrio infection.

124 Although these properties fit a recent mathematical model of automatic gradient scaling, that m

125 ased on these data, we constructed a dynamic mathematical model of bacterial interactions in the lake

126 Here we develop a mathematical model of colorectal cancer initiation throu

127 f colorectal cancer can be recovered using a mathematical model of colorectal cancer initiation toget

128 OR activity fitted a mathematical model of competitive receptor binding and s

129 of Europe's largest countries, and develop a mathematical model of COVID-19 dynamics, using latest da

130 Here, we present a mathematical model of early stage angiogenesis that perm

131 In this study, we develop a mathematical model of germinal center dynamics and use i

132 We developed and calibrated a mathematical model of gonorrhea transmission among men w

133 ariation in infection outcomes, we devised a mathematical model of malaria infection that allowed hos

134 aging of MCTS systems to drive a biophysical mathematical model of MCTS growth and mechanical interac

135 tigens in a growing cancer by constructing a mathematical model of neoantigen evolution.

136 We have developed a mathematical model of nuclear protein transport within a

137 Using a mathematical model of P. falciparum malaria transmission

138 to urinary protein excretion, we developed a mathematical model of protein reabsorption in the human

139 erences are based only on a well-established mathematical model of recombination and make no assumpti

140 and empirical understanding by developing a mathematical model of sexual conflict that incorporates

141 Using a mathematical model of such molecular signalling mechanis

142 We developed a mathematical model of TB transmission to project the imp

143 Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based o

144 Focusing on mutational events, we provide a mathematical model of the full process of tumor evolutio

145 We use a mathematical model of the HIV epidemic in South Africa t

146 ce methods to fully identify parameters in a mathematical model of the infection.

147 In this work, we developed a comprehensive mathematical model of the network that describes the tem

148 The principle is based on a mathematical model of the sporozoite rate (the proportio

149 Further, a mathematical model of the system for each agent demonstr

150 A mathematical model of the wild boar ASF system is develo

151 new fully-coupled, agent-based, multi-scale mathematical model of tumor growth, angiogenesis and met

152 biophysical experiments in combination with mathematical modeling of aggregation kinetics and discov

153 This study demonstrates how mathematical modeling of cancer evolution can be used to

154 a response-guided therapy approach, based on mathematical modeling of early viral kinetics, to reduce

155 Importantly, our work, including mathematical modeling of forces and material stiffness d

156 Mathematical modeling of healthcare-associated infection

157 Mathematical modeling of plant metabolism enables the pl

158 Lastly, mathematical modeling of tGD spread within populations r

159 Mathematical modeling of the stochastic process of cance

160 By combining our sequencing data and mathematical modeling of transcription, we found that Xr

161 some profiling data using recent advances in mathematical modelling of mRNA translation.

162 Mathematical modelling of real complex networks aims to

163 Mathematical modelling of transmission dynamics predicte

164 destructive monitoring of cancer clones, and mathematical modelling of tumour evolution.

165 Mathematical models of biological reactions at the syste

166 vel therapeutic approaches and build further mathematical models of combination therapies to treat pr

167 n 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathway

168 Mathematical models of gene flow in populations, which i

169 e clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamic

170 Mathematical models of mosquito-barrier interactions ide

171 d yield to intervention techniques guided by mathematical models of neuronal ensemble dynamics.

172 Here, we developed novel mathematical models of Process S based on cortical activ

173 We compared experimental data to mathematical models of spreading mechanisms to determine

174 .Methods: We estimated outcomes using linked mathematical models of TB epidemiology in the United Sta

175 r using these geometries for biophysical and mathematical modeling once these data can be represented

176 is based on the combination of two existing mathematical models: one representing the dynamics of ph

177 Supported by mathematical modelling, our results establish that multi

178 eview selected experimental and theoretical (mathematical) models pertaining to the hypothesis that l

179 Here, we introduce a mathematical modeling platform, PopAlign, that automatic

180 nderstanding of disease dynamics, and robust mathematical models play an important role in forecastin

181 100% and transmission rates as high as 94%, mathematical models predict that these systems could spr

182 The mathematical model predicted preferential CPP into cells

183 Mathematical modeling provides a means to investigate th

184 Stochastic mathematical modeling provides insight to the biological

185 ty classifications were compared to previous mathematical model publications.

186 However, mathematical models require in vitro data prior to predi

187 Mathematical modeling revealed that SOC reduces mechanic

188 Mathematical modeling reveals that small values of the r

189 In support of such an approach, mathematical models serve as a connection between the si

190 The mathematical model shows how homophily as a friend-choic

191 A mathematical model shows how the shape of early multicel

192 Mathematical modelling shows that amplifications also tu

193 +)] from 5.0 to 2.7 mmol/L) and supported by mathematical modeling studies.

194 In this mathematical modelling study of perinatal hepatitis B tr

195 ther MSCs exert a pro- or anti-tumor action, mathematical models such as this one help to quantitativ

196 Mathematical models suggest that endocytic organelle tet

197 Mathematical modeling suggests that the differential aff

198 Mathematical modelling suggests that the probability of

199 Deep learning (DL) is a widely applied mathematical modeling technique.

200 tors) are parameterized and used to inform a mathematical model that can predict circuit performance,

201 ic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor

202 nisms driving these dynamics by developing a mathematical model that captures consortia response as s

203 We developed a mathematical model that combines biochemical signaling w

204 egulation of flagella quantity, we propose a mathematical model that connects the flagellar gene regu

205 In this study we develop a novel mathematical model that describes HIV-1 infection in the

206 A phenomenological mathematical model that forms an essential basis for opt

207 rmine the effect of such noise by studying a mathematical model that includes the realistic noisy ope

208 disease and normal individuals, we devised a mathematical model that incorporates two PT transport pr

209 tand this relationship, we used a multiscale mathematical model that integrates data from biology and

210 We have developed a mathematical model that may be used to identify trial de

211 In this study, we develop a mechanistic, mathematical model that permits both direct (host-to-hos

212 We also develop a mathematical model that provides mechanistic insights in

213 siologically relevant biochemicals, a robust mathematical model that quantifies the contributions of

214 There is hence an urgent need for mathematical modeling that can quantitatively describe t

215 In mathematical models that are frequently used to guide su

216 mplex processes into simpler subsystems; and mathematical models that can accelerate the design-build

217 iour can make it difficult to create concise mathematical models that can be easily extended or modif

218 This can be achieved with mathematical models that properly account for risk heter

219 We demonstrate, using a mathematical model, that dissolution of calcium that has

220 Here we show, through mathematical modelling, that the patterns of dominant Sp

221 together with the mechanistic framework of a mathematical model, there can be a considerably enhanced

222 Behavioral strategies and abstract mathematical models thereof have been described in detai

223 Here we use a mathematical model to assess if isolation and contact tr

224 We developed a serocatalytic mathematical model to capture the change in seroprevalen

225 By using a mathematical model to compare the cumulative number of c

226 We generate a simple mathematical model to describe the transmembrane inserti

227 Further, we developed and validated a mathematical model to determine that fast, small-volume

228 e possibilities by fitting an age-structured mathematical model to epidemic data from China, Italy, J

229 s (2011-12, 2015-16, and 2017-18), we used a mathematical model to estimate the number of prevented i

230 Here we established a descriptive mathematical model to estimate the probability of self-r

231 including machine learning and a mechanistic mathematical model to find the optimal protocol for admi

232 The experimental data were integrated in a mathematical model to gain new insights into the inhibit

233 We developed a mathematical model to investigate the effect of asymptom

234 We applied a previously published mathematical model to investigate the effect of interven

235 study we created and validated a multiscale mathematical model to investigate the impact of cross-ta

236 his article, we study a general, but simple, mathematical model to investigate whether the presence o

237 We used a mathematical model to predict the efficacy and effective

238 f relevant pathogen-specific RDTs, we used a mathematical model to predict the probability of correct

239 esent the results of utilizing a biophysical mathematical model to predict tumor response for two HER

240 Here, we fitted a mathematical model to seroprevalence livestock and human

241 Here we develop a mathematical model to show, that in highly variable envi

242 Finally, we used a mathematical model to simulate HRV under decreased "coup

243 rmed an allocative efficiency study, using a mathematical model to simulate the progression of HCV in

244 Previously, we developed a mathematical model to understand factors governing the e

245 combine statistical analysis and stochastic mathematical modeling to analyze Repli-BS data from huma

246 ngle-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell

247 rial results about vaccination strategy with mathematical modeling to assess HPV eradication.

248 Here, we combine experiments and mathematical modeling to elaborate the minimal autonomou

249 We used mathematical modeling to estimate HDV-HBsAg-host paramet

250 Here, we use mathematical modeling to examine the epidemiological imp

251 ere, we combine an experimental approach and mathematical modeling to explore how phages and their mo

252 Here, we utilize mathematical modeling to investigate how small outward c

253 ents in computer-controlled bioreactors with mathematical modeling to investigate whether containment

254 ouse system that utilizes clonal tracing and mathematical modeling to monitor the growth of each canc

255 analysis of the RAD51-ssDNA interaction with mathematical modeling to show that the flexibility of DN

256 notypic slice cultures, clonal analysis, and mathematical modeling to show the two-step process of in

257 In this study we use mathematical modeling to understand better how these con

258 transcriptomics, infection-based assays and mathematical modelling to reassess the relationship betw

259 We applied existing and developed new mathematical models to calculate the health and climate

260 hat moved across its sonar field and applied mathematical models to differentiate between nonpredicti

261 t synthetic Lactococcus lactis consortia and mathematical models to elucidate the role of interaction

262 We use mathematical models to explore how the efficacy of LAIV

263 Here we use mathematical models to explore size-control strategies t

264 We use mathematical models to illustrate their possible modes o

265 experiments, extensive field data and novel mathematical models to indirectly estimate the magnitude

266 dying prion aggregation to show the power of mathematical models to synergistically interact with exp

267 ciation constants were determined by fitting mathematical models to the experimental data.

268 ia and combining single-cell RNA sequencing, mathematical modelling, transplantation assays and intra

269 A recently-developed population-level T2DM mathematical model was adapted and applied to Jordan.

270 re characterized and a multiscale stochastic mathematical model was proposed to link the in vitro and

271 A mathematical model was set up and validated to predict t

272 An individual-based mathematical model was used to simulate annual HIV incid

273 Supported by a quantitative mathematical model, we characterized PULSE in protoplast

274 Using a mathematical model, we estimate that a large fraction of

275 Guided by a resource-aware mathematical model, we identify and engineer natural and

276 Using a mathematical model, we show that the temporal asynchrony

277 analysis of ESCRT-III-depleted cells with a mathematical model, we show that upstream ESCRT-induced

278 Using a detailed mathematical model, we simulated realistic NFkB signalin

279 Through experimental perturbation and mathematical modeling, we demonstrate that the propertie

280 Using genetics and mathematical modeling, we develop an alternative model o

281 single-cell imaging, genomic approaches, and mathematical modeling, we find that hESCs commit to exit

282 Using mathematical modeling, we found that ratio-sensing is a

283 ysiology, optogenetics, behavioral tasks and mathematical modeling, we found that subthalamic stimula

284 Based on mathematical modeling, we here demonstrate that clusters

285 Guided by mathematical modeling, we identified the primary compone

286 fixed cell analysis, live cell imaging, and mathematical modeling, we show that populations of newly

287 Together with mathematical modeling, we unambiguously demonstrate no c

288 Combining uncaging experiments with mathematical modeling, we were able to determine binding

289 Using mathematical modelling, we propose that the BotC network

290 Using statistical and mathematical models, we predicted prevalence of both inf

291 Using mechanistic mathematical models, we show how simple manipulations to

292 Using 3 well-established mathematical models, we show that low-level prevalence c

293 We found that bio-mathematical models were able to predict average changes

294 C and five different semi-theoretical drying mathematical models were examined to characterize the dr

295 ual patient level, and (3) computational and mathematical modeling, which blends in silico simulation

296 ostate cancer and CRPC.Significance: Merging mathematical modeling with experimental data, this study

297 In this study, we integrate mathematical modeling with microenvironmental perturbati

298 We further combined mathematical modeling with quantitative trait and expres

299 Recent work, which combines mathematical modelling with dynamic clamp experiments, s

300 in periodically-paced excitable media using mathematical models with different levels of complexity: