ライフサイエンスコーパス: ordinary differential equation

1 been almost exclusively modelled by using an ordinary differential equation.

2 ng back to the lungs is calculated from this ordinary differential equation.

3 species or model them with patchy models by ordinary differential equations.

4 assumed perfectly mixed, and represented by ordinary differential equations.

5 o represent the FIM in terms of solutions of ordinary differential equations.

6 c models implemented as systems of nonlinear ordinary differential equations.

7 xtracellular (multicellular) events by using ordinary differential equations.

8 states using 15 interacting reactions and 26 ordinary differential equations.

9 isher information matrix to solving a set of ordinary differential equations.

10 ons between genes as a system of first-order ordinary differential equations.

11 ining FBA with regulatory Boolean logic, and ordinary differential equations.

12 models of biochemical systems defined using ordinary differential equations.

13 teractions cannot be directly implemented as ordinary differential equations.

14 ed using analytical solutions to a system of ordinary differential equations.

15 of the model are described by a system of 50 ordinary differential equations.

16 n transfigures the demonstrated problem into ordinary differential equations.

17 tions that are translated by Cellerator into ordinary differential equations.

18 hich are typically represented as systems of ordinary differential equations.

19 on throughout the tumor volume via a pair of ordinary differential equations.

20 no longer satisfy the uniqueness theorem for ordinary differential equations.

21 odel and systemic cytokine concentrations by ordinary differential equations.

22 ithm for dissipative quadratic n-dimensional ordinary differential equations.

23 the model as a system of coupled stochastic ordinary differential equations.

24 hronODE, an interpretable framework based on ordinary differential equations.

25 both non-stiff and stiff systems of coupled Ordinary Differential Equations.

26 ion model, expressed in terms of a system of ordinary differential equations.

27 l equations, as well as classical systems of ordinary differential equations.

28 terms of their representation as systems of ordinary differential equations.

29 nomenological dynamic growth models based on ordinary differential equations.

30 cal model of Shh aggregation using nonlinear ordinary differential equations.

31 ommonly represented as systems of autonomous ordinary differential equations.

32 ential equations is converted into nonlinear ordinary differential equations.

33 We model the HDX with a system of ordinary differential equations.

34 urrent alternatives consisting of up to 1000 ordinary differential equations.

35 cations, which are typically studied through ordinary differential equations.

36 using statistical approaches and systems of ordinary differential equations.

37 dicted pathways successfully without solving ordinary differential equations.

38 also to any system that can be described by ordinary differential equations.

39 affects the CYP1A biomarker, the model uses ordinary differential equations.

40 are inevitably modelled by stiff systems of ordinary differential equations.

41 h curves are classically modeled by means of ordinary differential equations.

42 l biology literature and defined as a set of ordinary differential equations.

43 e pathway and built a kinetic model based on ordinary differential equations.

44 ry rate ([Formula: see text])-in a system of ordinary differential equations analogous to the Suscept

45 VCell provides a number of ordinary differential equation and stochastic numerical

46 uations of the system can be approximated by ordinary differential equations and a Ornstein-Uhlenbeck

47 igands based on the law of mass action using ordinary differential equations and agent-based modellin

48 e, and consists of a large system of coupled ordinary differential equations and algebraic equations.

49 Numerical simulations based on ordinary differential equations and analytical modeling

50 se theoretical models are generally based on ordinary differential equations and become intractable w

51 In CDSM, interactions are represented by ordinary differential equations and compared across cond

52 odeled the cortisol dynamics using nonlinear ordinary differential equations and estimated the kineti

53 ifferential equations, including subcellular ordinary differential equations and extracellular reacti

54 squares formulation that handles systems of ordinary differential equations and is implemented in Ma

55 valuated using data simulated with nonlinear ordinary differential equations and known cyclic network

56 complex partial differential equations into ordinary differential equations and solves them using th

57 ed a hybrid computational model comprised of ordinary differential equations and stochastic simulatio

58 Computations are presented using ordinary differential equations and stochastic spatial s

59 oximate simulators of these systems, such as ordinary differential equations and t-Leaping approximat

60 molecular mechanisms into sets of nonlinear ordinary differential equations and use standard analyti

61 suming (i) equilibrium of a linear system of ordinary differential equations, and (ii) deterministic

62 computing machine) that, when its non-linear ordinary differential equations are integrated numerical

63 These ordinary differential equations are numerically solved b

64 The simultaneous first-order ordinary-differential equations are solved numerically f

65 The simultaneous first-order ordinary-differential equations are solved numerically f

66 ive assumptions and hypotheses formulated as ordinary differential equations) are separated from the

67 thematical model that is used to derive this ordinary differential equation assumes that the partial

68 odel their competition using a system of two ordinary differential equations based on the Lotka-Volte

69 In this work, we present an ODE (Ordinary Differential Equation)-based model of the expre

70 Ordinary Differential Equation-based (ODE) models are us

71 An ordinary differential equation-based mathematical model

72 re studied using numerical simulations of an ordinary differential equation-based multi-compartment m

73 In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models tha

74 We employed a nonlinear ordinary differential equations-based model to simulate

75 An ordinary-differential-equations-based kinetic model was

76 Deterministic models based on ordinary differential equations can capture essential re

77 r simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL

78 rowth models, each consisting of a system of ordinary differential equations, derived from the bi-exp

79 ions and compare them to the solution of the ordinary differential equations described above.

80 ion kinetics have been limited to systems of ordinary differential equations describing spatially ave

81 , it is found that equilibrium properties of ordinary differential equations describing the dynamics

82 ycolytic metabolism with a system of coupled ordinary differential equations describing the individua

83 thematical model, in the form of a system of ordinary differential equations, describing dynamics of

84 transduction pathways traditionally employs ordinary differential equations, deterministic models ba

85 In our Ordinary Differential Equation examples the crossing of

86 When the ordinary differential equation for the [Ca(2+)] in a res

87 essible to analysis by reduction to a set of ordinary differential equations for the amplitudes of sh

88 When these equations are coupled to ordinary differential equations for the bulk cytosolic a

89 sulting probability densities are coupled to ordinary differential equations for the bulk myoplasmic

90 This projection yields a system of ordinary differential equations for the spatio-temporal

91 The model is described by twelve ordinary differential equations for the time rate of cha

92 Instead, we derive and solve the systems of ordinary differential equations for the two lower-order

93 formulated in terms of tractable systems of ordinary differential equations for which we provide an

94 Our ordinary differential equation formulation and associate

95 entifying governing equations in the form of ordinary differential equations from noisy experimental

96 isting of low-dimensional systems of coupled ordinary differential equations, from these more complex

97 three proof-of-concept applications: solving ordinary differential equations, generating ultra-wideba

98 odule to reduce the generated mechanisms, an ordinary differential equations generator and solver to

99 ystems modeling, particularly via systems of ordinary differential equations, has been used to effect

100 Dynamical models in the form of systems of ordinary differential equations have become a standard t

101 we modeled the integrin signaling network as ordinary differential equations in multiple compartments

102 ters of people and their vaccination status, Ordinary Differential Equation integration between fixed

103 f-magnitude speedups relative to a CPU-based ordinary differential equation integrator.

104 parameter space of a parameterized system of ordinary differential equations into regions for which t

105 ng algorithm that incorporates the system of ordinary differential equations into the neural networks

106 s/deterministic model, expressed as a set of ordinary differential equations, into a discrete/stochas

107 It is found that the solution of the ordinary differential equation is very different from th

108 tabolic pathways through mechanistic sets of ordinary differential equations is a piece of the genoty

109 Modeling of dynamical systems using ordinary differential equations is a popular approach in

110 Modeling of dynamical systems using ordinary differential equations is a popular approach in

111 Because our model comprises only 17 ordinary differential equations, its computational cost

112 and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based

113 The ordinary differential equation model also included blood

114 holded fashion, and a simple two-compartment ordinary differential equation model correctly predicts

115 This ordinary differential equation model could be fit to bot

116 affecting responses to ICIs, we construct an ordinary differential equation model describing in vivo

117 Here, we used an ordinary differential equation model for CML, which expl

118 In this paper we present a simple ordinary differential equation model for wound healing i

119 An ordinary differential equation model is developed and th

120 e single-cell level, a mechanistic nonlinear ordinary differential equation model is used to calculat

121 s in combination with a previously validated ordinary differential equation model of apoptosis to sim

122 We employed a logic-based ordinary differential equation model of fibroblast mecha

123 We consider a three-dimensional ordinary differential equation model of inflammation con

124 We constructed an ordinary differential equation model of SHR and SCR in t

125 We developed an ordinary differential equation model of the infectious p

126 We here present a simple ordinary differential equation model of the intrahost im

127 We formulate a deterministic nonlinear ordinary differential equation model of the sterol regul

128 developed an agent-based model (ABM) and an ordinary differential equation model of tumor regression

129 matory phase in more detail, we developed an ordinary differential equation model that accounts for t

130 In doing so, we derive an ordinary differential equation model that explores how t

131 tions of one or more cytokines to develop an ordinary differential equation model that includes the e

132 lus involved in efficacy, here we develop an ordinary differential equation model that predicts bacte

133 We developed an ordinary differential equation model to describe this be

134 We developed a within-host ordinary differential equation model to track the dynami

135 in part on principal component analysis, an ordinary differential equation model was constructed, co

136 A disease SEIRS ordinary differential equation model was created, and an

137 To do this, a previously published ordinary differential equation model was developed with

138 We also developed an ordinary differential equation model which is the Kolmog

139 tion dynamic trends more effectively than an ordinary differential equation model with generalized ma

140 sed on the evolution of CML according to our ordinary differential equation model.

141 We developed a personalisable ordinary differential equations model of human epidermis

142 mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and

143 enz equations, a system of three-dimensional ordinary differential equations modeling atmospheric con

144 We use stochastic and ordinary-differential-equation modeling frameworks to ex

145 The underlying system of ordinary differential equations, modelling the host-para

146 ethods based on specific parameterization of ordinary differential equation models and demonstrate a

147 Ordinary differential equation models are nowadays widel

148 Ordinary differential equation models are widespread; un

149 is based on the notion that all mechanistic ordinary differential equation models can be coupled wit

150 of rapid rebinding and show that well-mixed ordinary differential equation models can use this proba

151 Ordinary differential equation models facilitate the und

152 this work we developed a series of nonlinear ordinary differential equation models that are direct re

153 First, we calibrate ordinary differential equation models to time-resolved p

154 sed on parameter inference of stochastic and ordinary differential equation models using Approximate

155 article, a new hybrid algorithm integrating ordinary differential equation models with dynamic Bayes

156 ro bioluminescence experiments and in silico ordinary differential equation models, and will lead to

157 urs may be well characterised by homogeneous ordinary differential equation models.

158 rtial differential equation model and not by ordinary differential equation models.

159 autonomous oscillations in yeast, we analyze ordinary differential equations models of large populati

160 ction networks): it builds dynamic (based on ordinary differential equation) models, which can be use

161 structed computationally by use of a coupled ordinary differential equation network (CODE) in a 2D la

162 Further reducing a 106-node ordinary differential equations network encompassing the

163 n (ASR) that identifies links among nodes of ordinary differential equation networks, given a small s

164 HOENIX, a modeling framework based on neural ordinary differential equations (NeuralODEs) and Hill-La

165 Resurrection of Smoluchowski's 1918 full Ordinary Differential Equation (ODE) approach to the PBM

166 We model the gene expression dynamics by an ordinary differential equation (ODE) based formalism.

167 nd sensitivity analysis of large and complex ordinary differential equation (ODE) based models.

168 his paper is that change of variables in the ordinary differential equation (ODE) for the competition

169 city while retaining the algorithmic ease of ordinary differential equation (ODE) inference.

170 We built an ordinary differential equation (ODE) model describing pa

171 An ordinary differential equation (ODE) model further suppo

172 onte Carlo (MCMC) method for the sampling of ordinary differential equation (ode) model parameters.

173 Therefore, we generated an ordinary differential equation (ODE) model powered by ex

174 Using a nonlinear ordinary differential equation (ODE) model that accounts

175 ts are negligible and we modify the standard ordinary differential equation (ODE) model to accommodat

176 We previously developed a nonlinear ordinary differential equation (ODE) model to explain th

177 s presented leveraging a fully deterministic ordinary differential equation (ODE) model.

178 Ordinary differential equation (ODE) models are widely u

179 In fact, commonly used ordinary differential equation (ODE) models of genetic c

180 cluding parameter reduction versus canonical ordinary differential equation (ODE) models, analytical

181 edge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other mo

182 Finally, in the special case of linear ordinary differential equation (ODE) models, we explore

183 pecific tool for building compartmentalized, ordinary differential equation (ODE) models.

184 differential equation models, and especially ordinary differential equation (ODE) models.

185 with cancer were developed with the help of ordinary differential equation (ODE) models.

186 r the identification of links among nodes of ordinary differential equation (ODE) networks, given a s

187 For the inst-MFA case, the ordinary differential equation (ODE) system describing t

188 C signal is cast explicitly as a first-order ordinary differential equation (ODE) with total titrant

189 port JUMPt, a software package using a novel ordinary differential equation (ODE)-based mathematical

190 Using ordinary differential equation (ODE)-based modeling, we

191 Two mathematical models, a system of ordinary differential equations (ODE) and a continuous-t

192 s reactions deterministically as a system of ordinary differential equations (ODE) and uses a Monte C

193 ost existing methods of dynamic modeling use ordinary differential equations (ODE) for individual gen

194 it remains challenging to parameterize these Ordinary Differential Equations (ODE) for large scale ki

195 inty upon the estimation of parameters in an ordinary differential equations (ODE) model of a cell si

196 Most models of viral infections use ordinary differential equations (ODE) that reproduce the

197 mic model, expressed in terms of a system of ordinary differential equations (ODE), developed by Stil

198 these results to solutions from a continuum, ordinary differential equations (ODE)-based model.

199 sentimental dynamics described by a group of ordinary differential equations (ODE).

200 Here, an ordinary-differential-equations (ODE) based kinetic mode

201 ral Networks (RhINNs) for solving systems of Ordinary Differential Equations (ODEs) adopted for compl

202 i) Boolean logic, (ii) deterministic kinetic ordinary differential equations (ODEs) and (iii) stochas

203 erical solutions of renovated boundary layer ordinary differential equations (ODEs) are attained by a

204 Networks of coupled ordinary differential equations (ODEs) are the natural l

205 nomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent an

206 esent Cellbox, a recently proposed system of ordinary differential equations (ODEs) based model that

207 network (GRN) models that are formulated as ordinary differential equations (ODEs) can accurately ex

208 nd commonly described by Lotka-Volterra-type ordinary differential equations (ODEs) for continuous po

209 When coupled to ordinary differential equations (ODEs) for the bulk myop

210 Next, we derived ordinary differential equations (ODEs) from the data rel

211 In particular, the use of sets of nonlinear ordinary differential equations (ODEs) has been proposed

212 deaths) and corresponds to the standard SIR ordinary differential equations (ODEs) in the infinite p

213 fusion, we transform the governing PDEs into ordinary differential equations (ODEs) representing the

214 stochastic differential equations (SDEs) and ordinary differential equations (ODEs) that addresses th

215 o numerically solve the system of non-linear ordinary differential equations (ODEs) that are created

216 The ordinary differential equations (ODEs) that describe the

217 We used ordinary differential equations (ODEs) to describe the t

218 based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively

219 Ordinary differential equations (ODEs) were used to simu

220 ystem of coupled self-similar and non-linear ordinary differential equations (ODEs) with boundary res

221 Ordinary differential equations (ODEs) with polynomial d

222 ethods of modelling biochemical pathways are ordinary differential equations (ODEs), and logical/grap

223 thematical model, in the form of a system of ordinary differential equations (ODEs), governing cancer

224 try, characterized by highly coupled sets of ordinary differential equations (ODEs), is dynamically s

225 ower-dimensional, in the form of a system of ordinary differential equations (ODEs), solves the contr

226 linear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and

227 rical approximations to solve the underlying ordinary differential equations (ODEs), which can compro

228 a non-autonomous nonlinear system (NANLS) of ordinary differential equations (ODEs), with coefficient

229 ess this, we have used an integrated coupled ordinary differential equations (ODEs)-based framework d

230 ese approaches with detailed models based on ordinary differential equations (ODEs).

231 an-field behaviors of which are described by ordinary differential equations (ODEs).

232 for creating and simulating models that use ordinary differential equations (ODEs).

233 represented by systems of coupled non-linear ordinary differential equations (ODEs).

234 iii) solving the non-linear stiff systems of ordinary differential equations (ODEs); (iv) bifurcation

235 tical model, in the form of a system of five ordinary differential equations, of the core of this con

236 of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm

237 Unlike previous models that are based on ordinary differential equations, our mathematical model

238 s, including forward and inverse problems of ordinary differential equations, partial differential eq

239 cient than population-based methods based on ordinary differential equations, partial differential eq

240 A local projection onto a system of ordinary differential equations predicts the consequence

241 od is successfully implemented to solve ODE (ordinary differential equation) problems with various co

242 Therefore, fitted ordinary differential equations provide a basis for sing

243 Mechanistic models based on ordinary differential equations provide powerful and acc

244 stem, we have integrated a set of structured ordinary differential equations quantifying T7 replicati

245 teristic extensively for dynamic networks of ordinary differential equations ranging up to 30 interac

246 ntial equation), and reaction rate equation (ordinary differential equation) representations for CRNs

247 e model was formulated as a set of nonlinear ordinary differential equations represented with power-l

248 ractions, we have constructed a system of 29 ordinary differential equations representing different p

249 he partial differential equation, and so the ordinary differential equation should not be used if an

250 e new parameters embedded within a system of ordinary differential equations, similar to the well-kno

251 Our approach uses ordinary differential equations, solved implicitly and n

252 arying transmission rate over a selection of ordinary differential equation solvers and tuning parame

253 ral modeling frameworks: agent-based models, ordinary differential equations, stochastic reaction sys

254 omplex bio-models and supports deterministic Ordinary Differential Equations; Stochastic Differential

255 A nonlinear ordinary differential equation suffices to describe the

256 nheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-

257 MANTIS wraps a C/C++ ordinary-differential equations system and Runge-Kutta s

258 Our method uses ordinary differential equation systems to represent cyto

259 we present DeepVelo, a neural network-based ordinary differential equation that can model complex tr

260 ion, and we derive its continuum limit as an ordinary differential equation that generalizes the repl

261 tions that are translated by Cellerator into ordinary differential equations that are numerically sol

262 versatile control framework based on neural ordinary differential equations that automatically learn

263 The model is composed of ordinary differential equations that connect the molecul

264 The ordinary differential equations that define this model w

265 nts can be calculated by solving a system of ordinary differential equations that depend only on the

266 system are characterized by four non-linear, ordinary differential equations that describe rates of c

267 model takes the form of a set of nonlinear, ordinary differential equations that describe the change

268 developed that solves a system of algebraic-ordinary differential equations that describe the phenom

269 el of the infection described by six coupled ordinary differential equations that describe the time c

270 We cast the master equation in terms of ordinary differential equations that describe the time e

271 e pathogenesis of periodontitis by employing ordinary differential equations that described the dynam

272 is a four-dimensional, non-linear system of ordinary differential equations that describes the dynam

273 the co-culture's behavior using a system of ordinary differential equations that enable us to predic

274 s a result, techniques that are based on the ordinary differential equation to calculate the mixed-ve

275 We have used a system of coupled ordinary differential equations to analyze the regulator

276 mpartmentalized model of RVF and the related ordinary differential equations to assess disease spread

277 a and formulated a compartmental model using ordinary differential equations to investigate how the c

278 We use a system of ordinary differential equations to investigate the separ

279 ed a varying coefficient model with multiple ordinary differential equations to learn a series of net

280 he human gut microbiota, we used a system of ordinary differential equations to model mathematically

281 Here, we use a nonlinear system of ordinary differential equations to model oocyte selectio

282 this protein, we introduced a new system of ordinary differential equations to model regulatory netw

283 We develop a system of ordinary differential equations to model the dynamics of

284 Here, using simple ordinary differential equations to represent phosphoryla

285 In this paper, we construct a model of ordinary differential equations to study the dynamics of

286 eveloped a set of models using compartmental ordinary differential equations to systematically invest

287 dy applied an age-structured model, based on ordinary differential equations, to describe an oyster p

288 A mathematical model with one ordinary differential equation was used to estimate tran

289 A system of ordinary differential equations was used to calculate pr

290 Ordinary differential equations were applied to describe

291 Mechanistic and semimechanistic ordinary differential equations were developed to descri

292 cancer cells in the body, using a system of ordinary differential equations which gives rates of cha

293 on the space of solutions to the associated ordinary differential equations which no longer satisfy

294 terms of coupled non-homogeneous first-order ordinary differential equations, which have a dynamic re

295 dynamic biological phenomena as solutions to ordinary differential equations, which, when parameters

296 , the model is constructed as a system of 10 ordinary differential equations with 27 parameters chara

297 Using a system of ordinary differential equations with a pair approximatio

298 actions at the system-level lead to a set of ordinary differential equations with many unknown parame

299 a simplified mechano-chemical model based on ordinary differential equations with three major protein

300 Biological processes are often modeled by ordinary differential equations with unknown parameters.