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Fundamentals of systems biology : from synthetic circuits to whole-cell models /
For decades biology has focused on decoding cellular processes one gene at a time, but many of the most pressing biological questions, as well as diseases such as cancer and heart disease, are related to complex systems involving the interaction of hundreds, or even thousands of gene products and ot...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press,
2015.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Section I Building intuition
- Chapter 1 Variations on a theme of control. Learning objections ; Variations ; Autoregulation ; Our theme: A typical negative autoregulatory circuit ; Chapter summary ; Recommended reading
- Chapter 2 Variation : Boolean representations. Learning objectives ; Boolean logic and rules ; State metrices ; State transitions ; Dynamics ; Timescales ; Advantages and disadvantages of boolean analysis ; Chapter summary ; Recommended reading ; Problems
- Chapter 3 Variation : analytical solutions of ordinary differential equations. Learning objectives ; Synthetic biological circuits ; From compartment models to odes ; Specifying and simplifying odes with assumptions ; The steady-state assumption ; Solving the system without feedback: Removal of activator ; Key properties of the system dynamics ; Solving the system without feedback: Addition of activator ; Comparison of modeling to experimental measurements ; Addition of autoregulatory feedback ; Comparison of the regulated and unregulated systems ; Chapter summary ; Recommended reading ; Problems
- Chapter 4 Variation : graphical analysis. Learning objectives ; Revisiting the protein synthesis odes ; Plotting x versus dx/dt ; Fixed points and vector fields ; From vector fields to time-course plots ; Nonlinearity ; Bifurcation analysis ; Adding feedback ; Two-equation systems ; Chapter summary ; Recommended reading ; Problems
- Chapter 5 Variation : numerical integration. Learning objectives ; The Euler method ; Accuracy and error ; The midpoint method ; The Runge-Kutta method ; Chapter summary ; Recommended reading ; Problems
- Chapter 6 Variation : stochastic simulation. Learning objectives ; Single cells and low molecule numbers ; Stochastic simulations ; The probablility that two molecules interact and react in a given time interval ; The probability of a given molecular reaction occurring over time ; The relationship between kinetic and stochastic constants ; Gillespie's stochastic simulation algorithm ; Stochastic simulation of unregulated gene expression ; Stochastic simulations versus other modeling approaches ; Chapter summary ; Recommended reading ; Problems
- Section II From circuits to networks
- Chapter 7 Transcriptional regulation. Learning objectives ; Transcriptional regulation and complexity ; More complex transcriptional circuits ; The transcriptional regulatory feed-forward motif ; Boolean analysis of the most common internally consistent feed-forward motif identified in E. Coli ; An ode-based approach to analyzing the coherent feed-forward loop ; Robustness of the coherent feed-forward loop ; Experimental interrogation of the coherent feed-forward loop ; Changing the interaction from an and to an or relationship ; The single0input module ; Just-in-time gene expression ; Generalization of the feed-forward loop: Flagellar biosynthesis in E. Coli ; Other regulatory motifs ; Chapter summary ; Recommended reading ; Problems
- Chapter 8 Signal transduction. Learning objectives ; Receptor-Ligand binding to form a complex ; Application to real receptor-Ligand pairs ; Formation of larger complexes ; Protein localization ; The NF-KB activity ; Alternative representations for the same process ; Specifying parameter values from data ; Bounding parameter values ; Model sensitivity to parameter values ; Reducing complexity by eliminating parameters ; Parameter interactions ; Chapter summary ; Recommended reading ; Problems
- Chapter 9 Metabolism. Learning objeectives ; Cellular metabolism ; Metabolic reactions ; Compartment models of metabolite concentration ; The Michaelis-Menten equation for enzyme kinetics ; Determining kinetic parameters for the Michaelis-Menten system ; Incorporating enzyme inhibitory effects ; Flux balance analysis ; Steady-state assumption and exchange fluxes ; Solution spaces ; The objective function ; Defining the optimization problem ; Solving FBA problems using MATLAB ; Applications of FBA to large-scale metabolic models ; Using FBA for metabolic engineering ; Chapter summary ; Recommended reading ; Problems
- Chapter 10 Integrated models. Learning objectives ; Dynamic FBA: External versus internal concentrations ; Environmental constraints ; Integration of FBA simulations over time ; Comparing dynamic FBA to experimental data ; FBA and transcriptional regulation ; Transcriptional regulatory constraints ; Regulatory FBA: Method ; Regulatory FBA: Application ; Toward whole-cell modeling ; Chapter summary ; Recommended reading ; Problems.