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Titlebook: Analysis of Single-Cell Data; ODE Constrained Mixt Carolin Loos Book 2016 Springer Fachmedien Wiesbaden 2016 Parameter Estimation.Heterogen

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11#
發(fā)表于 2025-3-23 12:48:08 | 只看該作者
Directed Quantities in Electrodynamicsmics and structures of subpopulations. In Section?3.1, we introduce the underlying method and formulate the problems that are subsequently addressed in the following sections. In Section?3.2, we apply ODE-MMs to novel single-cell snapshot data for NGF-induced Erk signaling.
12#
發(fā)表于 2025-3-23 16:11:09 | 只看該作者
Directed Quantities in Electrodynamicsly considers the mean behavior of the cell population, but also the second order moments. In this chapter we go one step further in the direction of treating cells as individuals by modeling single-cell time-lapse data with continuous time Markov chains (CTMCs) (Gillespie, 2007).
13#
發(fā)表于 2025-3-23 19:35:29 | 只看該作者
14#
發(fā)表于 2025-3-23 22:47:27 | 只看該作者
15#
發(fā)表于 2025-3-24 04:59:19 | 只看該作者
16#
發(fā)表于 2025-3-24 10:27:28 | 只看該作者
Thermodynamics and Phase Transition,This chapter introduces the key concepts that are needed to understand this thesis. First, we describe the different types of experimental data that are analyzed. Afterwards, the principles of modeling of chemical kinetics are introduced with a focus on the chemical master equation (CME) and its approximations.
17#
發(fā)表于 2025-3-24 12:33:21 | 只看該作者
Background,This chapter introduces the key concepts that are needed to understand this thesis. First, we describe the different types of experimental data that are analyzed. Afterwards, the principles of modeling of chemical kinetics are introduced with a focus on the chemical master equation (CME) and its approximations.
18#
發(fā)表于 2025-3-24 17:09:08 | 只看該作者
19#
發(fā)表于 2025-3-24 22:03:23 | 只看該作者
20#
發(fā)表于 2025-3-24 23:44:15 | 只看該作者
Approximate Bayesian Computation for Single-Cell Time-Lapse Data Using Multivariate Statistics,ly considers the mean behavior of the cell population, but also the second order moments. In this chapter we go one step further in the direction of treating cells as individuals by modeling single-cell time-lapse data with continuous time Markov chains (CTMCs) (Gillespie, 2007).
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