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

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

4 An ordinary differential equation (ODE) model further suppo

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

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

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

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

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

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

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

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

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

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

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

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

17 In our Ordinary Differential Equation examples the crossing of

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

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

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

21 The ordinary differential equation model also included blood

22 This ordinary differential equation model could be fit to bot

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

49 An ordinary differential equation-based mathematical model

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

65 The ordinary differential equations (ODEs) that describe the

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

67 Ordinary differential equations (ODEs) with polynomial d

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

83 These ordinary differential equations are numerically solved b

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

110 The ordinary differential equations that define this model w

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

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

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

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

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

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

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

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

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

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

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

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

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

124 Here, using simple ordinary differential equations to represent phosphoryla

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

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

127 Ordinary differential equations were applied to describe

128 Mechanistic and semimechanistic ordinary differential equations were developed to descri

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

149 models of biochemical systems defined using ordinary differential equations.

150 teractions cannot be directly implemented as ordinary differential equations.

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

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

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

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

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

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

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

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

159 using statistical approaches and systems of ordinary differential equations.

160 dicted pathways successfully without solving ordinary differential equations.

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

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

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

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

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

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

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

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

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

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

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

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

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