ライフサイエンスコーパス: ordinary differential

1 upled set of nonlinear partial differential, ordinary differential and algebraic equations with an ou

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

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

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

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

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

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

8 An ordinary differential equation (ODE) model further suppo

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

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

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

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

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

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

15 Ordinary differential equation (ODE) models are widely u

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

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

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

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

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

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

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

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

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

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

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

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

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

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

30 In our Ordinary Differential Equation examples the crossing of

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

32 Our ordinary differential equation formulation and associate

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

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

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

36 The ordinary differential equation model also included blood

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

38 This ordinary differential equation model could be fit to bot

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

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

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

42 An ordinary differential equation model is developed and th

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

66 Ordinary differential equation models are nowadays widel

67 Ordinary differential equation models are widespread; un

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

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

70 Ordinary differential equation models facilitate the und

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

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

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

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

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

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

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

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

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

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

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

82 A nonlinear ordinary differential equation suffices to describe the

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

84 Our method uses ordinary differential equation systems to represent cyto

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

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

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

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

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

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

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

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

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

94 An ordinary differential equation-based mathematical model

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

126 The ordinary differential equations (ODEs) that describe the

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

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

129 Ordinary differential equations (ODEs) were used to simu

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

131 Ordinary differential equations (ODEs) with polynomial d

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

149 Numerical simulations based on ordinary differential equations and analytical modeling

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

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

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

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

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

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

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

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

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

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

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

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

162 These ordinary differential equations are numerically solved b

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

201 The ordinary differential equations that define this model w

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

219 Here, using simple ordinary differential equations to represent phosphoryla

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

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

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

223 Ordinary differential equations were applied to describe

224 Mechanistic and semimechanistic ordinary differential equations were developed to descri

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

263 models of biochemical systems defined using ordinary differential equations.

264 teractions cannot be directly implemented as ordinary differential equations.

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

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

267 n transfigures the demonstrated problem into ordinary differential equations.

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

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

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

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

272 odel and systemic cytokine concentrations by ordinary differential equations.

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

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

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

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

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

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

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

280 nomenological dynamic growth models based on ordinary differential equations.

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

282 ommonly represented as systems of autonomous ordinary differential equations.

283 ential equations is converted into nonlinear ordinary differential equations.

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

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

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

287 using statistical approaches and systems of ordinary differential equations.

288 dicted pathways successfully without solving ordinary differential equations.

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

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

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

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

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

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

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

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

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

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

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

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