🧬Systems Biology Unit 10 – Gene Regulatory Networks and Epigenetics

Key Concepts and Definitions

• Gene regulatory networks (GRNs) complex systems that control gene expression and cellular processes through interactions between genes, proteins, and other molecules
• Transcription factors (TFs) proteins that bind to specific DNA sequences and regulate the transcription of genes
• Promoters regions of DNA upstream of genes that contain binding sites for TFs and initiate transcription
• Enhancers distal regulatory elements that can increase the transcription of genes through interactions with promoters
• Epigenetics study of heritable changes in gene expression that do not involve alterations to the DNA sequence itself
• Chromatin structure organization of DNA and associated proteins (histones) that can affect gene expression
• DNA methylation addition of methyl groups to cytosine bases in DNA, often associated with gene silencing
• Histone modifications post-translational changes (acetylation, methylation) to histone proteins that can affect chromatin accessibility and gene expression

Molecular Mechanisms of Gene Regulation

• Transcriptional regulation control of gene expression at the level of RNA synthesis, mediated by TFs and regulatory elements (promoters, enhancers)
• Post-transcriptional regulation control of gene expression after RNA synthesis, including splicing, stability, and translation
• Translational regulation control of protein synthesis from mRNA, influenced by factors such as RNA-binding proteins and microRNAs
• Feedback loops regulatory motifs in which the product of a gene regulates its own expression, either positively or negatively
• Signal transduction pathways cascades of molecular interactions that transmit signals from the cell surface to the nucleus, often leading to changes in gene expression
• Combinatorial control regulation of gene expression by multiple TFs acting together, allowing for complex and precise control of cellular processes
• Noise in gene expression stochastic fluctuations in gene expression levels due to inherent randomness in molecular interactions
  • Can lead to cell-to-cell variability and influence cellular decision-making processes

Epigenetic Modifications and Their Effects

• CpG islands regions of DNA with a high frequency of CpG dinucleotides, often found in promoters and associated with gene regulation
• Histone acetylation addition of acetyl groups to lysine residues in histone tails, generally associated with increased gene expression
• Histone deacetylation removal of acetyl groups from histones, often leading to decreased gene expression and chromatin condensation
• Histone methylation addition of methyl groups to lysine or arginine residues in histones, can have activating or repressive effects depending on the specific residue and degree of methylation
• Chromatin remodeling dynamic changes in chromatin structure that can affect gene accessibility and expression, mediated by specialized protein complexes
• X-chromosome inactivation epigenetic silencing of one of the two X chromosomes in female mammals to achieve dosage compensation
• Genomic imprinting differential expression of genes depending on whether they are inherited from the maternal or paternal allele, regulated by epigenetic mechanisms
• Transgenerational epigenetic inheritance transmission of epigenetic marks across multiple generations, potentially influencing offspring phenotypes

Network Structures and Dynamics

• Network motifs recurring patterns of gene interactions found in GRNs, such as feed-forward loops and bi-fans
• Modularity organization of GRNs into functionally related subnetworks or modules that can be regulated independently
• Robustness ability of GRNs to maintain stable gene expression patterns despite perturbations or noise
• Evolvability capacity of GRNs to generate new gene expression patterns and phenotypes through mutations and natural selection
• Attractors stable gene expression states in GRNs that correspond to distinct cellular phenotypes (cell types, disease states)
• Bistability existence of two stable gene expression states in a GRN, allowing for switch-like transitions between states
• Oscillations periodic changes in gene expression levels over time, often driven by negative feedback loops (circadian rhythms)
• Stochasticity random fluctuations in gene expression that can lead to cell-to-cell variability and influence cell fate decisions

Experimental Techniques and Data Analysis

• Chromatin immunoprecipitation (ChIP) method to identify DNA-protein interactions, such as TF binding sites, by cross-linking proteins to DNA and immunoprecipitating specific proteins
• RNA sequencing (RNA-seq) high-throughput sequencing of RNA to quantify gene expression levels and identify alternative splicing events
• Single-cell RNA-seq (scRNA-seq) RNA-seq applied to individual cells, allowing for the analysis of cell-to-cell variability and the identification of rare cell types
• Assay for Transposase-Accessible Chromatin (ATAC-seq) method to identify open chromatin regions by using a transposase to insert sequencing adapters into accessible DNA
• Bisulfite sequencing technique to map DNA methylation patterns by converting unmethylated cytosines to uracil while leaving methylated cytosines unchanged
• Network inference computational methods to infer GRN structures from gene expression data, such as correlation-based approaches and Bayesian networks
• Gene set enrichment analysis (GSEA) statistical method to identify overrepresented biological pathways or functions in a set of differentially expressed genes
• Dimensionality reduction techniques (PCA, t-SNE) methods to visualize and interpret high-dimensional gene expression data by projecting it onto a lower-dimensional space

Computational Modeling of Gene Networks

• Boolean networks modeling approach that represents genes as binary variables (on or off) and uses logical rules to describe their interactions
• Ordinary differential equations (ODEs) mathematical models that describe the continuous dynamics of gene expression using differential equations
• Stochastic models modeling approaches that incorporate random fluctuations in gene expression, such as the Gillespie algorithm
• Agent-based models computational models that simulate the behavior of individual cells or molecules, allowing for the emergence of complex patterns
• Parameter estimation techniques methods to infer model parameters (reaction rates, binding affinities) from experimental data, such as maximum likelihood estimation
• Model selection approaches to compare and select among alternative models based on their ability to explain the data (Akaike information criterion)
• Sensitivity analysis methods to assess the impact of model parameters on the system's behavior and identify critical components
• Model validation techniques to test the predictions of computational models against independent experimental data

Applications in Health and Disease

• Cancer systems biology application of systems biology approaches to understand the complex molecular networks underlying cancer initiation, progression, and treatment response
• Drug discovery use of GRN models to identify novel drug targets and predict the effects of pharmacological interventions
• Personalized medicine integration of patient-specific omics data (genome, epigenome, transcriptome) to tailor diagnosis and treatment strategies
• Stem cell differentiation modeling of the GRNs that control stem cell fate decisions and guide the development of cell-based therapies
• Metabolic disorders analysis of the interplay between gene regulation and metabolic pathways in diseases such as diabetes and obesity
• Neurodegenerative diseases investigation of the gene regulatory mechanisms underlying disorders like Alzheimer's and Parkinson's disease
• Infectious diseases study of host-pathogen interactions and the role of gene regulation in the immune response to infections
• Synthetic biology engineering of novel gene circuits and cellular functions using the principles of GRNs and epigenetic regulation

Future Directions and Challenges

• Single-cell multi-omics integration of single-cell data from multiple omics layers (transcriptome, epigenome, proteome) to gain a comprehensive understanding of cellular states and transitions
• Spatial transcriptomics development of techniques to measure gene expression while preserving spatial information, enabling the study of GRNs in the context of tissue architecture
• Live-cell imaging advances in imaging technologies to visualize the dynamics of gene expression and epigenetic modifications in living cells
• CRISPR-based functional screens use of CRISPR-Cas9 technology to systematically perturb genes and regulatory elements, allowing for the high-throughput dissection of GRNs
• Machine learning applications application of deep learning and other machine learning techniques to predict gene expression patterns, infer GRN structures, and identify disease-associated regulatory variants
• Integrating multi-scale data incorporation of data from different biological scales (molecular, cellular, tissue, organ) to build comprehensive models of gene regulation and its impact on organismal phenotypes
• Addressing cellular heterogeneity development of computational methods to account for cell-to-cell variability and identify rare cell types or transient states in GRN analysis
• Translating findings to the clinic overcoming the challenges in translating insights from systems biology research into clinical applications, such as drug development and personalized medicine