| Description |
1 online resource (397 p.) |
| Contents |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1. Models and Data -- Chapter 1. Inference of Gene Regulatory Networks from Multi-scale Dynamic Data -- 1.1. GRN and differentiation -- 1.1.1. The coordination of gene expression by GRNs -- 1.1.2. The process of differentiation -- 1.2. Inference of GRN from population data -- 1.2.1. Population expression data -- 1.2.2. Bayesian approaches -- 1.2.3. Information theory approaches -- 1.2.4. Boolean approaches -- 1.2.5. ODE approaches -- 1.3. Inferring GRNs from single-cell data -- 1.3.1. Single cell expression data |
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1.3.2. Adaptation of GRN inference algorithms for single-cell data analysis -- 1.3.3. Using single-cell stochastic models for GRN inference -- 1.4. Alternative strategies for GRN inference -- 1.5. Performance and limitations of GRN inference -- 1.6. Inference based on the wave of expression concept -- 1.6.1. The differentiation process seen as a dynamic process of signal processing by GRNs -- 1.6.2. Experimental demonstration of waves of expression -- 1.6.3. Using waves of expression for GRN inference -- 1.6.4. Scaling up the distributed computing approach -- 1.7. Conclusion -- 1.8. References |
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Chapter 2. Combinatorial Optimization Problems for Studying Metabolism -- 2.1. Dynamics and functionality of a metabolic network -- 2.1.1. Metabolic networks -- 2.1.2. Reconstruction of metabolic networks -- 2.1.3. From the dynamics of a metabolic network to its function -- 2.2. Understanding the metabolism of non-model organisms: metabolic gap-filling algorithms -- 2.2.1. Metabolism of non-model organisms -- 2.2.2. Reconstruction of the metabolism of non-model species and gap-filling problems -- 2.2.3. Added-value and limitations of metabolic gap-filling problems: example of biotic interactions |
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2.3. Microbiota metabolism: new optimization problems -- 2.3.1. Genomics of microbiota -- 2.3.2. From merged models to compartmentalized models -- 2.3.3. Completion problem for community selection in non-compartmentalized microbiota -- 2.3.4. Completion problem for selecting compartmentalized communities with minimal exchanges -- 2.4. Discrete semantics: a Boolean approximation of metabolic producibility -- 2.4.1. Topological accessibility of compounds and reactions in a metabolic network -- 2.4.2. Activation and cycles -- 2.4.3. Applications -- 2.5. Flux semantics |
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2.5.1. Modeling the response of a metabolic network with fluxes -- 2.5.2. Steady-state cycles -- 2.5.3. Application to the completion of metabolic networks -- 2.6. Comparing semantics: toward a hybrid approach -- 2.6.1. Complementarity of Boolean and stoichiometric abstractions -- 2.6.2. Hybrid completion of metabolic networks -- 2.7. Solving gap-filling problems with answer set programming -- 2.7.1. Model the Boolean activation of a reaction in ASP -- 2.7.2. Non-compartmentalized selection of communities -- 2.7.3. Compartmentalized selection of communities -- 2.8. Conclusion -- 2.9. References |
| Notes |
Description based upon print version of record |
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Chapter 3. The Challenges of Inferring Dynamic Models from Time Series |
| Form |
Electronic book
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| Author |
Remy, Elisabeth
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| ISBN |
9781394229062 |
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1394229062 |
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