By Dagmar Iber, Georgios Fengos (auth.), Xuedong Liu, Meredith D. Betterton (eds.)
ISBN-10: 1617798320
ISBN-13: 9781617798320
ISBN-10: 1617798339
ISBN-13: 9781617798337
Signaling networks are composed of diverse signaling pathways and every has its personal complicated part components. Signaling outputs are dynamic, terribly advanced and but hugely particular. In, Computational Modeling of Signaling Networks: tools and Protocols, specialist researchers within the box offer key concepts to check signaling networks. targeting structures of ODEs, parameterization of signaling types, signaling pathways, mass-action kinetics and ODEs, and the way to exploit modeling to plot experiments. Written within the hugely profitable Methods in Molecular Biology™ sequence layout, the chapters contain the type of exact description and implementation suggestion that's the most important for purchasing optimum ends up in the laboratory.
Thorough and intuitive, Computational Modeling of Signaling Networks: tools and Protocols aids scientists in carrying on with research of ways signaling networks behave in house and time to generate particular organic responses and the way these responses effect biology and medicine.
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Extra info for Computational Modeling of Signaling Networks
Example text
Geier et al. Intuitively, the curvature determines how well the minimum is confined and therefore how well parameter estimates can be defined. The curvature of w2dof (p ∗ ) at the minimum is determined by the dof ÂP matrix of second derivatives, the so-called Fisher information matrix (FIM). FIM ¼ ð@ 2 Rij =@p2l Þ % J T J : (11) The approximation in Eq. 11 neglects the second derivative p terms but is computationally inexpensive as J ijl ¼ ð@Rij =@pl Þ is the calculated gradient matrix of the residuals during minimization.
However, if we perform model simulation for this system, we will find that the number of the oscillations during the investigated time might be 7, rather than 3 (Fig. 1c). Inspired by the model prediction, we could design a new experiment to verify the actual number of oscillations by taking more samplings at proper time points. 2. Materials The material requirement for mathematical modeling of cellular signaling pathways is not demanding. However, getting high quality of quantitative experimental data sets is still one of the bottlenecks for the development of the mathematical models.
Parameter limits used for the estimation are indicated as light gray bars. Some parameter estimates have a small variance and nicely fit the true parameter values indicated as dark gray bars. , k7 and k13. However, most parameter estimates have a large variance and do not match the true parameter on average. , by means of (9)) reveals a strong functional dependence with other parameters, meaning that they cannot be identified under the given experimental setup. Figure 3d, e shows two representative scatter plots of pairs of parameter estimates which are functionally related.
Computational Modeling of Signaling Networks by Dagmar Iber, Georgios Fengos (auth.), Xuedong Liu, Meredith D. Betterton (eds.)
by Donald
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