Computational Modeling of Signaling Networks by Dagmar Iber, Georgios Fengos (auth.), Xuedong Liu, Meredith

By Dagmar Iber, Georgios Fengos (auth.), Xuedong Liu, Meredith D. Betterton (eds.)

Signaling networks are composed of diverse signaling pathways and every has its personal difficult part components. Signaling outputs are dynamic, terribly advanced and but hugely particular. In, Computational Modeling of Signaling Networks: equipment 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 comprise the type of certain description and implementation suggestion that's the most important for buying optimum ends up in the laboratory.

Thorough and intuitive, Computational Modeling of Signaling Networks: equipment and Protocols aids scientists in carrying on with examine of the way signaling networks behave in area and time to generate particular organic responses and the way these responses impression biology and medicine.

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Extra info for Computational Modeling of Signaling Networks

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Geier et al. Table 1 General workflow of gradient-based minimization procedures. In each parameter update step, the system of ODEs and the sensitivity equations are integrated Initialize model system and parameters LOOP Integrate ODE (Eq. 2) and sensitivity equations (Eq. 9) based on current parameter vector Calculate residuals defined in Eq. 7 and Jacobian of residuals based on sensitivities and Eq. , if some parameters are nonidentifiable. The third commonly used update scheme approximates the optimized function (Eq.

Additionally, all 2 Analyzing and Constraining Signaling Networks. . 37 parameters related to the I-Smad expression are highly correlated. This is in part expected as the I-Smad mRNA states are not observed and therefore parameters related to the mRNA dynamics cannot be inferred from the data. Note that a strong correlation does not necessarily imply a bad identification as the coefficient of variation for the single parameters can still be very small. We will focus on these intrinsic variations in the next paragraph.

Column 4: average computation time in minutes. 83 GHz. Column 5: optimal x2 value (bold font) and GOF probability (regular font). The x2 distribution has 264 degrees of freedom. Column 6: percent of identifiable parameters. Parameters are called identifiable if their coefficient of variation is smallerà than 1. Column 7: norm of the relative parameter deviation defined as Àp 2 ║ ptrue ptrue ║ . The norm is given for all (bold font) and only for the identifiable (regular font) parameter Deviation of % parameter Identifiable estimates: all, parameter identifiable Optimizer Jacobian % True (Eq.

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