From the reviews: "This mosaic of reviews and research precisely reflects the dynamic field of computational and systems biology. All of the major organizational levels of life are represented ... . Each chapter has a brief introduction to a biological subject, a review of the main mathematical and statistical models for that particular topic, and a description of the main applications and conclusions. There are an abundant number of references for each chapter. ... This compilation ... is likely to inspire generations of students, researchers, and readers." (Adrian Pasculescu, ACM Computing Reviews, January, 2011)

• Analysis of Combinatorial Gene Regulation with Thermodynamic Models.- RNA Secondary Structure Prediction and Gene Regulation by Small RNAs.- Some Critical Data Quality Control Issues of Oligoarrays.- Stochastic-Process Approach to Nonequilibrium Thermodynamics and Biological Signal Transduction.- Granger Causality: Theory and Applications.- Transcription Factor Binding Site Identification by Phylogenetic Footprinting.- Learning Network from High-Dimensional Array Data.- Computational Methods for Predicting Domain–Domain Interactions.- Irreversible Stochastic Processes, Coupled Diffusions and Systems Biochemistry.- Probability Modeling and Statistical Inference in Periodic Cancer Screening.- On Construction of the Smallest One-sided Confidence Intervals and Its Application in Identifying the Minimum Effective Dose.- Group Variable Selection Methods and Their Applications in Analysis of Genomic Data.- Modeling Protein-Signaling Networks with Granger Causality Test.- DNA Copy Number Profiling in Normal and Tumor Genomes.- Spatial Disease Surveillance: Methods and Applications.- From QTL Mapping to eQTL Analysis.- An Evaluation of Gene Module Concepts in the Interpretation of Gene Expression Data.- Readout of Spike Waves in a Microcolumn.- False Positive Control for Genome-Wide ChIP-Chip Tiling Arrays.

From the reviews: "This mosaic of reviews and research precisely reflects the dynamic field of computational and systems biology. All of the major organizational levels of life are represented ... . Each chapter has a brief introduction to a biological subject, a review of the main mathematical and statistical models for that particular topic, and a description of the main applications and conclusions. There are an abundant number of references for each chapter. ... This compilation ... is likely to inspire generations of students, researchers, and readers." (Adrian Pasculescu, ACM Computing Reviews, January, 2011)

• Analysis of Combinatorial Gene Regulation with Thermodynamic Models.- RNA Secondary Structure Prediction and Gene Regulation by Small RNAs.- Some Critical Data Quality Control Issues of Oligoarrays.- Stochastic-Process Approach to Nonequilibrium Thermodynamics and Biological Signal Transduction.- Granger Causality: Theory and Applications.- Transcription Factor Binding Site Identification by Phylogenetic Footprinting.- Learning Network from High-Dimensional Array Data.- Computational Methods for Predicting Domain–Domain Interactions.- Irreversible Stochastic Processes, Coupled Diffusions and Systems Biochemistry.- Probability Modeling and Statistical Inference in Periodic Cancer Screening.- On Construction of the Smallest One-sided Confidence Intervals and Its Application in Identifying the Minimum Effective Dose.- Group Variable Selection Methods and Their Applications in Analysis of Genomic Data.- Modeling Protein-Signaling Networks with Granger Causality Test.- DNA Copy Number Profiling in Normal and Tumor Genomes.- Spatial Disease Surveillance: Methods and Applications.- From QTL Mapping to eQTL Analysis.- An Evaluation of Gene Module Concepts in the Interpretation of Gene Expression Data.- Readout of Spike Waves in a Microcolumn.- False Positive Control for Genome-Wide ChIP-Chip Tiling Arrays.

From the reviews: "This mosaic of reviews and research precisely reflects the dynamic field of computational and systems biology. All of the major organizational levels of life are represented ... . Each chapter has a brief introduction to a biological subject, a review of the main mathematical and statistical models for that particular topic, and a description of the main applications and conclusions. There are an abundant number of references for each chapter. ... This compilation ... is likely to inspire generations of students, researchers, and readers." (Adrian Pasculescu, ACM Computing Reviews, January, 2011)

• Analysis of Combinatorial Gene Regulation with Thermodynamic Models.- RNA Secondary Structure Prediction and Gene Regulation by Small RNAs.- Some Critical Data Quality Control Issues of Oligoarrays.- Stochastic-Process Approach to Nonequilibrium Thermodynamics and Biological Signal Transduction.- Granger Causality: Theory and Applications.- Transcription Factor Binding Site Identification by Phylogenetic Footprinting.- Learning Network from High-Dimensional Array Data.- Computational Methods for Predicting Domain–Domain Interactions.- Irreversible Stochastic Processes, Coupled Diffusions and Systems Biochemistry.- Probability Modeling and Statistical Inference in Periodic Cancer Screening.- On Construction of the Smallest One-sided Confidence Intervals and Its Application in Identifying the Minimum Effective Dose.- Group Variable Selection Methods and Their Applications in Analysis of Genomic Data.- Modeling Protein-Signaling Networks with Granger Causality Test.- DNA Copy Number Profiling in Normal and Tumor Genomes.- Spatial Disease Surveillance: Methods and Applications.- From QTL Mapping to eQTL Analysis.- An Evaluation of Gene Module Concepts in the Interpretation of Gene Expression Data.- Readout of Spike Waves in a Microcolumn.- False Positive Control for Genome-Wide ChIP-Chip Tiling Arrays.