Dynamics of Mathematical Models in Biology

Abstract

This volume focuses on contributions from both the mathematics and life science community surrounding the concepts of time and dynamicity of nature, two significant elements which are often overlooked in modeling process to avoid exponential computations. The book is divided into three distinct parts: dynamics of genomes and genetic variation, dynamics of motifs, and dynamics of biological networks. Chapters included in dynamics of genomes and genetic variation analyze the molecular mechanisms and evolutionary processes that shape the structure and function of genomes and those that govern genome dynamics. The dynamics of motifs portion of the volume provides an overview of current methods for motif searching in DNA, RNA and proteins, a key process to discover emergent properties of cells, tissues, and organisms. The part devoted to the dynamics of biological networks covers networks aptly discusses networks in complex biological functions and activities that interpret processes in cells. Moreover, chapters in this section examine several mathematical models and algorithms available for integration, analysis, and characterization. Once life scientists began to produce experimental data at an unprecedented pace, it become clear that mathematical models were necessary to interpret data, to structure information with the aim to unveil biological mechanisms, discover results, and make predictions. The second annual “Bringing Maths to Life” workshop held in Naples, Italy October 2015, enabled a bi-directional flow of ideas from and international group of mathematicians and biologists. The venue allowed mathematicians to introduce novel algorithms, methods, and software that may be useful to model aspects of life science, and life scientists posed new challenges for mathematicians. Preface; Contents; Contributors; Transcriptional Regulation: When 1+1=2; 1 Introduction; 2 When 1+1=2: The Prediction of Genomic Transcription Factor Binding Sites Based on DNA Sequence; 3 When 1+1=2: The Prediction of Regulatory Activity Based on Genomic TF-DNA-Binding; 4 When 1+1=2: The Prediction of Target Genes by Incorporating the Three-Dimensional Genome Organization; 5 When 1+1=2: The Prediction of Gene Expression Level Based on Transcription Factor Binding Strength; 6 Conclusions and Future Directions; References. Differential Network Analysis and Graph Classification: A Glocal Approach1 Introduction; 2 The HIM Distance and Kernel; 3 Application to -omic Studies; 3.1 The UKBEC Dataset; 3.2 The D. melanogaster Development Dataset; 4 Conclusion; References; Structural vs Practical Identifiability of Nonlinear Differential Equation Models in Systems Biology; 1 Introduction; 2 Structural vs Practical Identifiability; 2.1 Structural Identifiability Analysis; 2.2 Practical Identifiability Analysis; 3 A Model of JAK-STAT Signaling Pathway; 3.1 Identifiability Analysis of the JAK-STAT Model; 4 Conclusions. 1 Introduction2 Results and Discussion; 2.1 Gene Orthology; 2.2 Differential Expression Analysis; 2.3 Evolutionary Conserved Elements in S. purpuratusand P. miniata Gut GRNs; 3 Conclusion; 4 Methods; 4.1 Animal Handling and Microinjection Procedures; 4.2 Embryos Collection, RNA Extraction, and Sequencing; 4.3 Quality Control, Mapping, and Differential Expression; 4.4 Identification and Clustering of Orthologs; 4.5 Transcription Factor Identification; References; Reconstructing a Genetic Network from Gene Perturbations in Secretory Pathway of Cancer Cell Lines; 1 Introduction. 2 Material and Methods2.1 Data Retrieval; 2.2 Reconstruction of Regulatory Interactions; 2.3 Networks Reconstructions and Enrichment Analysis; 3 Results; 3.1 Networks Reconstructions; 3.2 Role of Secretion Regulators in Different Cellular Processes; 4 Discussions and Conclusions; References; Dissecting the Functions of the Secretory Pathway by Transcriptional Profiling; 1 Introduction; 2 Materials and Methods; 2.1 Strategy; 2.2 Data Collection and Processing; 2.3 Gene Set Enrichment Analysis; 2.4 Transcription Factor Prediction; 3 Result and Discussion; 3.1 Conclusion; References.