This document discusses gene expression noise and stochasticity. It begins with an overview of the basic processes in gene expression and the differential equations that describe them. It then notes that in reality, the numbers of molecules involved are often low and subject to random fluctuations due to Brownian motion. This stochasticity means that gene expression occurs as a probabilistic process that can be modeled using the master equation and results in Poisson distributions. The document explores how mRNA and protein noise is propagated and amplified, and discusses experimental measurements of variability within and between cells in bacteria.
The document discusses the Gateway cloning system, which allows efficient transfer of genes between vectors using site-specific recombination. It describes how the system uses bacteriophage lambda integrase to catalyze recombination between att sites. Genes can be shuttled between an entry clone containing attL sites and a destination vector containing attR sites via an LR reaction. The Gateway system provides a simple and efficient way to clone genes into multiple expression vectors without restriction digestion or ligation.
Marker free transgenics: concept and approachesShilpa Malaghan
This document discusses approaches for producing marker-free transgenic plants. It describes three main strategies: 1) Co-transformation of the gene of interest and selectable marker genes, followed by segregation of the genes in subsequent generations. 2) Using site-specific recombination systems like Cre/lox and FLP/FRT to excise the selectable marker gene. 3) Using transposon-based systems to remove the marker gene. It provides examples of each method and discusses their advantages and disadvantages. The goal is to eliminate the use of selectable marker genes to address issues like food safety, gene stacking, and horizontal gene transfer.
Comparative genome mapping involves comparing genetic maps between closely related species to study genome evolution and understand relationships at the genetic level. Genomes can be compared by looking at features like gene location and order, as well as sequence similarity. Many model systems have been used for comparative mapping, including plants like rice and maize, Arabidopsis and Brassica, tomato and potato. These studies have revealed things like conserved synteny between species, rates of rearrangement, and the effects of polyploidization. Comparative mapping is a useful tool for understanding genomes and their relationships across species.
Vectors are DNA molecules that can accept foreign DNA and be replicated within a host cell. They are required for cloning genes and transferring them to bacteria. Common vectors include plasmids, bacteriophages, cosmids, and artificial chromosomes. Expression vectors are used to produce proteins from cloned DNA, and come in prokaryotic and eukaryotic varieties. Cloning vectors replicate recombinant DNA within host cells to produce multiple clones and can accommodate different sized DNA fragments depending on the vector type.
This document provides an overview of proteomics and protein-protein interactions. It begins with an introduction to proteomics, including its history and importance. It then discusses protein structure, including the primary, secondary, tertiary, and quaternary levels. The document outlines different types of proteomics, such as expression, structural, and functional proteomics. It also describes the various steps involved in proteome analysis, including sample preparation, separation, identification, and use of databases. The document discusses techniques for studying protein-protein interactions and provides examples like co-immunoprecipitation and yeast two-hybrid screening. Overall, the document provides a comprehensive overview of the key concepts and methods in the field of proteomics.
This document discusses biosafety issues related to genetically modified crops. It provides background on GM crops and their history. It then outlines several biosafety concerns including the safety of inserted genes and proteins, ecological impacts such as increased weediness and effects on biodiversity, environmental concerns like secondary pest problems and insect resistance, and socioeconomic issues. The regulatory mechanisms in place in India to evaluate GM crops are also described, including the various competent authorities. International regulations like the Cartagena Protocol are also mentioned.
PCR is a technique used to amplify DNA. There are several types of PCR including multiplex PCR, nested PCR, RT-PCR, quantitative PCR, hot-start PCR, touchdown PCR, assembly PCR, colony PCR, methylation-specific PCR, and LAMP assay. Each type has a specific application or mechanism. For example, multiplex PCR allows simultaneous analysis of multiple targets, nested PCR increases specificity, RT-PCR converts RNA to cDNA, and quantitative PCR measures the amount of target DNA or RNA.
The document discusses the Gateway cloning system, which allows efficient transfer of genes between vectors using site-specific recombination. It describes how the system uses bacteriophage lambda integrase to catalyze recombination between att sites. Genes can be shuttled between an entry clone containing attL sites and a destination vector containing attR sites via an LR reaction. The Gateway system provides a simple and efficient way to clone genes into multiple expression vectors without restriction digestion or ligation.
Marker free transgenics: concept and approachesShilpa Malaghan
This document discusses approaches for producing marker-free transgenic plants. It describes three main strategies: 1) Co-transformation of the gene of interest and selectable marker genes, followed by segregation of the genes in subsequent generations. 2) Using site-specific recombination systems like Cre/lox and FLP/FRT to excise the selectable marker gene. 3) Using transposon-based systems to remove the marker gene. It provides examples of each method and discusses their advantages and disadvantages. The goal is to eliminate the use of selectable marker genes to address issues like food safety, gene stacking, and horizontal gene transfer.
Comparative genome mapping involves comparing genetic maps between closely related species to study genome evolution and understand relationships at the genetic level. Genomes can be compared by looking at features like gene location and order, as well as sequence similarity. Many model systems have been used for comparative mapping, including plants like rice and maize, Arabidopsis and Brassica, tomato and potato. These studies have revealed things like conserved synteny between species, rates of rearrangement, and the effects of polyploidization. Comparative mapping is a useful tool for understanding genomes and their relationships across species.
Vectors are DNA molecules that can accept foreign DNA and be replicated within a host cell. They are required for cloning genes and transferring them to bacteria. Common vectors include plasmids, bacteriophages, cosmids, and artificial chromosomes. Expression vectors are used to produce proteins from cloned DNA, and come in prokaryotic and eukaryotic varieties. Cloning vectors replicate recombinant DNA within host cells to produce multiple clones and can accommodate different sized DNA fragments depending on the vector type.
This document provides an overview of proteomics and protein-protein interactions. It begins with an introduction to proteomics, including its history and importance. It then discusses protein structure, including the primary, secondary, tertiary, and quaternary levels. The document outlines different types of proteomics, such as expression, structural, and functional proteomics. It also describes the various steps involved in proteome analysis, including sample preparation, separation, identification, and use of databases. The document discusses techniques for studying protein-protein interactions and provides examples like co-immunoprecipitation and yeast two-hybrid screening. Overall, the document provides a comprehensive overview of the key concepts and methods in the field of proteomics.
This document discusses biosafety issues related to genetically modified crops. It provides background on GM crops and their history. It then outlines several biosafety concerns including the safety of inserted genes and proteins, ecological impacts such as increased weediness and effects on biodiversity, environmental concerns like secondary pest problems and insect resistance, and socioeconomic issues. The regulatory mechanisms in place in India to evaluate GM crops are also described, including the various competent authorities. International regulations like the Cartagena Protocol are also mentioned.
PCR is a technique used to amplify DNA. There are several types of PCR including multiplex PCR, nested PCR, RT-PCR, quantitative PCR, hot-start PCR, touchdown PCR, assembly PCR, colony PCR, methylation-specific PCR, and LAMP assay. Each type has a specific application or mechanism. For example, multiplex PCR allows simultaneous analysis of multiple targets, nested PCR increases specificity, RT-PCR converts RNA to cDNA, and quantitative PCR measures the amount of target DNA or RNA.
Here are the key steps in ID3's approach to selecting the "best" attribute at each node:
1. Calculate the entropy (impurity/uncertainty) of the target attribute for the examples reaching that node.
2. Calculate the information gain (reduction in entropy) from splitting on each candidate attribute.
3. Select the attribute with the highest information gain. This attribute best separates the examples according to the target class.
So in this example, ID3 would calculate the information gain from splitting on attributes A1 and A2, and select the attribute with the highest gain. The goal is to pick the attribute that produces the "purest" partitions at each step.
Viral vectors are efficient tools for gene delivery due to viruses' ability to transfer DNA into host cells. The document discusses several types of viral vectors, including adenoviral, adeno-associated, retroviral, lentiviral, and baculovirus vectors. It provides details on the structure and genome organization of different viruses used to create these vectors. The document also explains the process of generating recombinant viral vectors by removing unnecessary viral genes and inserting genes of interest. Viral vectors allow for transient or stable gene expression and are useful for both research and clinical applications such as gene therapy and vaccine development.
Comparative genomics in eukaryotes, organellesKAUSHAL SAHU
Comparative genomics involves comparing the genomic features of different organisms, such as DNA sequences, genes, and gene order. This field has revealed both similarities and differences between organisms that can provide insights into evolutionary relationships. Some of the first comparative genomic studies compared large DNA viruses. Since then, many complete genome sequences have been determined, including for yeast, fruit flies, worms, plants, mice, and humans. While humans have around 35,000 genes, complexity is not solely due to gene number. Comparative analysis of human and mouse genomes shows 40% sequence similarity and similar gene numbers, but different genome sizes. Mitochondrial genomes also yield insights when compared between domains of life. Computational tools like BLAST are used to facilitate genomic
Genomic and cDNA libraries are collections of DNA fragments used for gene discovery and analysis. cDNA libraries contain only expressed genes and are useful for eukaryotic analysis since they lack introns. Genomic libraries contain all DNA sequences from an organism's genome. Both types of libraries are constructed by fragmenting DNA, inserting fragments into cloning vectors, and transforming bacteria to generate clones containing DNA fragments. Libraries are screened using probes to identify clones containing genes of interest.
This document discusses antisense RNA and DNA technology. It explains that antisense works by introducing short DNA or RNA sequences that are complementary to target mRNA, preventing translation into protein. This can inhibit genetic disorders caused by mutated proteins. The document provides examples of using antisense to treat diseases like cancer and viruses. It describes various methods of delivering antisense sequences into cells and notes that while promising, most antisense therapies have yet to produce significant clinical results, though one was approved by the FDA to treat cytomegalovirus retinitis.
Transposable elements are segments of DNA that can move within genomes. They are present in all domains of life and have been shown to drive evolution by causing mutations through insertion, deletion, and rearrangement. Barbara McClintock discovered transposons in maize in the 1940s and was awarded a Nobel Prize for this work. Transposable elements are classified as DNA transposons or retrotransposons, and can be further divided into autonomous and non-autonomous types based on their ability to excise and transpose independently.
Transplastomics involves integrating transgenes into the chloroplast genome rather than the nuclear genome, resulting in a pure population of transformed chloroplasts. This has advantages over nuclear transformation like high levels of transgene expression and accumulation of proteins. Chloroplast transformation can be used to develop crops with resistance to pests, stresses, and the ability to produce vaccines, industrial enzymes and biomaterials. The process involves using a plasmid with the gene of interest flanked by chloroplast DNA regions, transforming via particle bombardment, selecting transformants using antibiotics, and recovering homoplasmic transplastomic plant lines. However, limitations include a narrow crop range, over-reliance on antibiotic markers, and lack of thorough evaluation of transplastomic plants
This document discusses marker-free transgenics, which aim to generate transgenic plants without selectable marker genes. It describes various strategies to produce marker-free plants, including using screenable markers, co-transformation, site-specific recombination, multi-autotransformation vectors, intrachromosomal recombination, and transposon-based methods. The document concludes that developing marker-free transgenic crops could help advance crop improvement efforts and increase public acceptance of transgenic technologies.
Basic principle of gene expression & methods ofshrikant wankhede
This document discusses gene expression and methods of gene transfer. It begins by defining gene expression as the process by which information from a gene is used to produce a functional product, often a protein. It then explains the central dogma of biology - that DNA is transcribed into RNA which is translated into protein. The document focuses on viral and non-viral methods of gene transfer, describing several types of viral vectors including retroviruses, lentiviruses, adenoviruses, and adeno-associated viruses. It also discusses non-viral methods such as electroporation, gene guns, oligonucleotides, and liposomes. Finally, it briefly mentions some applications of gene transfer technologies.
This document discusses dot plots and sequence alignments. It begins with an overview of dot plots, explaining that they are a graphical representation used to visualize similarities between two sequences. It describes how dot plots are constructed and notes that they are useful for finding repeated or inverted repeated structures. The document then discusses how to reduce noise in dot plots and provides examples of dot plots. It also discusses sequence alignments, including global vs local alignments and different algorithms for pairwise and multiple sequence alignment such as Needleman-Wunsch, Smith-Waterman, and ClustalW. It notes why multiple alignments are performed and concludes with discussing how to measure algorithm efficiency.
Functional genomics uses genome-wide experimental approaches to assess gene function on a large scale. It analyzes gene expression through techniques like transcriptomics and proteomics. Transcriptomics analyzes gene expression profiles through RNA sequencing or microarray analysis. Microarray analysis involves hybridizing fluorescently-labeled cDNA or cRNA to microarrays containing DNA probes to measure gene expression levels across thousands of genes simultaneously. Functional genomics provides a global understanding of gene function and molecular interactions through integrated omics approaches.
The document provides an overview of plant genome sequence assembly, including:
1) A brief history of sequencing technologies and their improvements over time, from Sanger sequencing to newer technologies producing longer reads.
2) Key steps in a sequencing project including read processing, filtering, and corrections before assembly into contigs and scaffolds using appropriate software.
3) Factors to consider for experimental design and assembly optimization such as sequencing depth, library types, and software choices depending on the genome and data characteristics.
Map-based cloning is a technique used to identify the genetic cause of a mutant phenotype by isolating overlapping DNA segments that progress along the chromosome toward a candidate gene. The process involves initially identifying a marker close to the gene of interest and then saturating the region with additional markers. Large populations are screened to find markers that rarely recombine with the gene. Genomic libraries are screened to find clones containing the markers, and chromosomal walking is used to obtain flanking markers on a single clone. DNA fragments between the markers are tested to rescue the wild-type phenotype and identify the candidate gene.
The document discusses various methods for studying gene expression and function, including analyzing RNA transcripts. It describes techniques like northern hybridization, DNA-mRNA hybridization, S1 nuclease mapping, primer extension, and PCR that can be used to study transcripts and locate start/stop points. The document also covers methods for studying gene regulation, such as identifying protein binding sites through gel retardation assays and footprinting, and using deletion analysis to identify control sequences.
This document discusses reporter genes, which are marker genes used to analyze gene expression. It describes the features of ideal reporter genes and several commonly used ones, including opine synthase, chloramphenicol acetyltransferase, β-glucuronidase, bacterial luciferase, firefly luciferase, and green fluorescent protein. Reporter genes allow quantification of gene expression and regulation by fusing a gene of interest to the reporter gene. The document concludes that reporter gene technology is widely used to study various cellular processes and holds promise for applications in gene therapy and drug development.
These are the first lecture slides of the BITS bioinformatics training session on the UCSC Genome Browser.
See http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203990:orange-genome-browsers-ucsc-training&catid=81:training-pages&Itemid=190
1. Gene regulatory networks have properties that enable exploratory adaptation, such as context-dependent binding and alternative splicing, that allow for a large number of expression patterns.
2. A theoretical model of a random network model with a feedback stress signal driving exploratory dynamics demonstrates how convergence to a stable phenotype is possible through a "drive reduction" principle without selection.
3. While convergence is possible, it is non-universal and depends on network properties like connection sparsity; the model shows how exploratory dynamics in gene expression can produce individual cellular adaptation within a few generations to unforeseen challenges.
The document discusses a quantitative law of reciprocity between robustness and plasticity in biology. It presents evidence that more robust circadian rhythms and oscillatory dynamics demonstrate greater plasticity in response to environmental changes. This reciprocity relationship holds across different levels of biological organization, from molecular oscillations to spatial and temporal pattern formation. The proposed mechanism involves adaptation on a limit cycle via a buffer molecule, allowing robustness of a key parameter like period while permitting plasticity in other parameters like phase.
Here are the key steps in ID3's approach to selecting the "best" attribute at each node:
1. Calculate the entropy (impurity/uncertainty) of the target attribute for the examples reaching that node.
2. Calculate the information gain (reduction in entropy) from splitting on each candidate attribute.
3. Select the attribute with the highest information gain. This attribute best separates the examples according to the target class.
So in this example, ID3 would calculate the information gain from splitting on attributes A1 and A2, and select the attribute with the highest gain. The goal is to pick the attribute that produces the "purest" partitions at each step.
Viral vectors are efficient tools for gene delivery due to viruses' ability to transfer DNA into host cells. The document discusses several types of viral vectors, including adenoviral, adeno-associated, retroviral, lentiviral, and baculovirus vectors. It provides details on the structure and genome organization of different viruses used to create these vectors. The document also explains the process of generating recombinant viral vectors by removing unnecessary viral genes and inserting genes of interest. Viral vectors allow for transient or stable gene expression and are useful for both research and clinical applications such as gene therapy and vaccine development.
Comparative genomics in eukaryotes, organellesKAUSHAL SAHU
Comparative genomics involves comparing the genomic features of different organisms, such as DNA sequences, genes, and gene order. This field has revealed both similarities and differences between organisms that can provide insights into evolutionary relationships. Some of the first comparative genomic studies compared large DNA viruses. Since then, many complete genome sequences have been determined, including for yeast, fruit flies, worms, plants, mice, and humans. While humans have around 35,000 genes, complexity is not solely due to gene number. Comparative analysis of human and mouse genomes shows 40% sequence similarity and similar gene numbers, but different genome sizes. Mitochondrial genomes also yield insights when compared between domains of life. Computational tools like BLAST are used to facilitate genomic
Genomic and cDNA libraries are collections of DNA fragments used for gene discovery and analysis. cDNA libraries contain only expressed genes and are useful for eukaryotic analysis since they lack introns. Genomic libraries contain all DNA sequences from an organism's genome. Both types of libraries are constructed by fragmenting DNA, inserting fragments into cloning vectors, and transforming bacteria to generate clones containing DNA fragments. Libraries are screened using probes to identify clones containing genes of interest.
This document discusses antisense RNA and DNA technology. It explains that antisense works by introducing short DNA or RNA sequences that are complementary to target mRNA, preventing translation into protein. This can inhibit genetic disorders caused by mutated proteins. The document provides examples of using antisense to treat diseases like cancer and viruses. It describes various methods of delivering antisense sequences into cells and notes that while promising, most antisense therapies have yet to produce significant clinical results, though one was approved by the FDA to treat cytomegalovirus retinitis.
Transposable elements are segments of DNA that can move within genomes. They are present in all domains of life and have been shown to drive evolution by causing mutations through insertion, deletion, and rearrangement. Barbara McClintock discovered transposons in maize in the 1940s and was awarded a Nobel Prize for this work. Transposable elements are classified as DNA transposons or retrotransposons, and can be further divided into autonomous and non-autonomous types based on their ability to excise and transpose independently.
Transplastomics involves integrating transgenes into the chloroplast genome rather than the nuclear genome, resulting in a pure population of transformed chloroplasts. This has advantages over nuclear transformation like high levels of transgene expression and accumulation of proteins. Chloroplast transformation can be used to develop crops with resistance to pests, stresses, and the ability to produce vaccines, industrial enzymes and biomaterials. The process involves using a plasmid with the gene of interest flanked by chloroplast DNA regions, transforming via particle bombardment, selecting transformants using antibiotics, and recovering homoplasmic transplastomic plant lines. However, limitations include a narrow crop range, over-reliance on antibiotic markers, and lack of thorough evaluation of transplastomic plants
This document discusses marker-free transgenics, which aim to generate transgenic plants without selectable marker genes. It describes various strategies to produce marker-free plants, including using screenable markers, co-transformation, site-specific recombination, multi-autotransformation vectors, intrachromosomal recombination, and transposon-based methods. The document concludes that developing marker-free transgenic crops could help advance crop improvement efforts and increase public acceptance of transgenic technologies.
Basic principle of gene expression & methods ofshrikant wankhede
This document discusses gene expression and methods of gene transfer. It begins by defining gene expression as the process by which information from a gene is used to produce a functional product, often a protein. It then explains the central dogma of biology - that DNA is transcribed into RNA which is translated into protein. The document focuses on viral and non-viral methods of gene transfer, describing several types of viral vectors including retroviruses, lentiviruses, adenoviruses, and adeno-associated viruses. It also discusses non-viral methods such as electroporation, gene guns, oligonucleotides, and liposomes. Finally, it briefly mentions some applications of gene transfer technologies.
This document discusses dot plots and sequence alignments. It begins with an overview of dot plots, explaining that they are a graphical representation used to visualize similarities between two sequences. It describes how dot plots are constructed and notes that they are useful for finding repeated or inverted repeated structures. The document then discusses how to reduce noise in dot plots and provides examples of dot plots. It also discusses sequence alignments, including global vs local alignments and different algorithms for pairwise and multiple sequence alignment such as Needleman-Wunsch, Smith-Waterman, and ClustalW. It notes why multiple alignments are performed and concludes with discussing how to measure algorithm efficiency.
Functional genomics uses genome-wide experimental approaches to assess gene function on a large scale. It analyzes gene expression through techniques like transcriptomics and proteomics. Transcriptomics analyzes gene expression profiles through RNA sequencing or microarray analysis. Microarray analysis involves hybridizing fluorescently-labeled cDNA or cRNA to microarrays containing DNA probes to measure gene expression levels across thousands of genes simultaneously. Functional genomics provides a global understanding of gene function and molecular interactions through integrated omics approaches.
The document provides an overview of plant genome sequence assembly, including:
1) A brief history of sequencing technologies and their improvements over time, from Sanger sequencing to newer technologies producing longer reads.
2) Key steps in a sequencing project including read processing, filtering, and corrections before assembly into contigs and scaffolds using appropriate software.
3) Factors to consider for experimental design and assembly optimization such as sequencing depth, library types, and software choices depending on the genome and data characteristics.
Map-based cloning is a technique used to identify the genetic cause of a mutant phenotype by isolating overlapping DNA segments that progress along the chromosome toward a candidate gene. The process involves initially identifying a marker close to the gene of interest and then saturating the region with additional markers. Large populations are screened to find markers that rarely recombine with the gene. Genomic libraries are screened to find clones containing the markers, and chromosomal walking is used to obtain flanking markers on a single clone. DNA fragments between the markers are tested to rescue the wild-type phenotype and identify the candidate gene.
The document discusses various methods for studying gene expression and function, including analyzing RNA transcripts. It describes techniques like northern hybridization, DNA-mRNA hybridization, S1 nuclease mapping, primer extension, and PCR that can be used to study transcripts and locate start/stop points. The document also covers methods for studying gene regulation, such as identifying protein binding sites through gel retardation assays and footprinting, and using deletion analysis to identify control sequences.
This document discusses reporter genes, which are marker genes used to analyze gene expression. It describes the features of ideal reporter genes and several commonly used ones, including opine synthase, chloramphenicol acetyltransferase, β-glucuronidase, bacterial luciferase, firefly luciferase, and green fluorescent protein. Reporter genes allow quantification of gene expression and regulation by fusing a gene of interest to the reporter gene. The document concludes that reporter gene technology is widely used to study various cellular processes and holds promise for applications in gene therapy and drug development.
These are the first lecture slides of the BITS bioinformatics training session on the UCSC Genome Browser.
See http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203990:orange-genome-browsers-ucsc-training&catid=81:training-pages&Itemid=190
1. Gene regulatory networks have properties that enable exploratory adaptation, such as context-dependent binding and alternative splicing, that allow for a large number of expression patterns.
2. A theoretical model of a random network model with a feedback stress signal driving exploratory dynamics demonstrates how convergence to a stable phenotype is possible through a "drive reduction" principle without selection.
3. While convergence is possible, it is non-universal and depends on network properties like connection sparsity; the model shows how exploratory dynamics in gene expression can produce individual cellular adaptation within a few generations to unforeseen challenges.
The document discusses a quantitative law of reciprocity between robustness and plasticity in biology. It presents evidence that more robust circadian rhythms and oscillatory dynamics demonstrate greater plasticity in response to environmental changes. This reciprocity relationship holds across different levels of biological organization, from molecular oscillations to spatial and temporal pattern formation. The proposed mechanism involves adaptation on a limit cycle via a buffer molecule, allowing robustness of a key parameter like period while permitting plasticity in other parameters like phase.
1. Gene regulatory networks have properties that enable exploratory adaptation, such as context-dependent binding and alternative splicing, that allow for a large number of expression patterns.
2. A theoretical model of a random network model with a feedback stress signal driving exploratory dynamics demonstrates how convergence to a stable phenotype is possible through a "drive reduction" principle without selection.
3. While convergence is possible, it is non-universal and depends on network properties like connection sparsity; the model shows how exploratory dynamics in gene expression can produce individual cellular adaptation within a few generations to unforeseen challenges.
This document discusses the impact of environmental noise on neutral community dynamics and species coexistence. It first introduces neutral theory and its assumptions of equivalence among species. It then notes that most neutral models do not consider how abiotic conditions may change and affect species differently. The document examines how environmental variability is incorporated into population dynamics models through tools like the unified color noise approximation. Simulation results show that environmental noise can smear phase transitions and increase the number of coexisting species under certain conditions by creating a storage effect. The key messages are that environmental variability changes our understanding of phase transitions, can favor or disfavor species coexistence depending on how it modulates fitness, and that the interplay between timescales is important.
This document outlines the agenda for a Systems Medicine course taking place in Como, Italy in 2016. It discusses the transition from viewing diseases as caused by single molecular factors to recognizing them as network diseases influenced by multiple interacting factors. Examples are given of how systems biology has provided insights into diseases like cancer. The course aims to discuss advances that have moved medicine toward precision approaches based on individualized networks and dynamics.
This document discusses network analysis and measures of centrality and communicability in networks. It provides mathematical definitions and formulas for quantifying properties like betweenness centrality, clustering coefficient, communicability between nodes, and the number of walks and routes connecting nodes in a network. Examples of applying these metrics to real-world networks like social and biological networks are also mentioned.
This document provides an overview of a tutorial on connectome analysis given by Dr. Marcus Kaiser. The tutorial covers topics such as graph theory, spatial and topological properties of neural networks. It also discusses how brain structure is influenced by function and evolution. Computer simulations are presented that model brain dynamics and can predict the location of epileptic tissue or the effects of optogenetic stimulation. Dr. Kaiser's research group at Newcastle University studies brain connectivity across species using neuroimaging and modeling approaches.
This document summarizes Marc Barthelemy's presentation on spatial network theory and applications. The presentation covered various models of spatial networks including Voronoi tessellations, random geometric graphs, spatial generalizations of Erdos-Renyi and small-world networks, and growing network models. It also discussed optimal network design problems and models incorporating both network growth and optimization. Scaling relationships between network properties like total length and number of stations and socioeconomic factors like GDP and population were examined for subway and railway networks.
- Temporal networks are dynamic networks that change over time. They are commonly represented through temporal contact sequences or time-varying adjacency matrices.
- Key properties of temporal networks include distributions of contact durations and inter-contact times, measures of burstiness, and persistence/correlation of network structures over time.
- Analyzing temporal paths, centrality measures, motifs, and comparing empirical networks to temporal null models can provide insights into the structure and dynamics of temporal networks not evident from static representations.
1. The document discusses using call detail record (CDR) data to study how mobile phone users manage their social contacts over time and characterize or predict social turnover.
2. By detecting new and old social relationships from CDRs that show communication patterns and frequencies between users, the author aims to analyze how users' social networks evolve and change.
3. The author proposes studying properties like the distribution of inter-event times between calls to the same contact and how this distribution depends on relationship longevity to provide insights into social turnover.
This document summarizes discussions and presentations from the Como Systems Medicine course on September 30, 2016. Key points include:
- Presentations discussed approaches to precision medicine like individualized treatment based on a person's metabolism, understanding cancer at the single-cell level, and inflammation.
- Attendees participated in live polls to discuss topics like the optimal focus and registration fees for a future advanced studies school.
- Presenters covered research using metabolic maps to understand disease, challenges in modeling multifactorial diseases, and assumptions made in reconstructing cancer progression.
- The discussions emphasized reproducible science and modeling methodology, as well as exploring new frontiers in biology and the origins of life.
1. The document discusses mesoscale network structures, which are middle-scale properties between microscale (individual nodes/edges) and macroscale (overall network properties). It focuses on community structure detection but notes there are other mesoscale structures like core-periphery and roles/positions.
2. Community detection algorithms aim to find densely connected groups of nodes but may return structure even in random networks. The document advocates a cautious approach and examining multiple possible structures.
3. Other mesoscale structures discussed include bipartite structures, block models representing roles of nodes, and stochastic block models providing a statistical framework.
This document provides an overview of spatial network theory and applications. It discusses how space impacts network structure and introduces several tools for analyzing spatial networks. These include indices to characterize street and transportation networks, typologies of planar graphs based on block shape and area statistics, and methods for studying the time evolution of networks using old map digitization. Specific examples analyzed include road, power grid, airline and neural networks.
This document summarizes research on analyzing clinical and molecular cancer data to enable precision cancer medicine. It discusses analyzing tumor heterogeneity, transcriptional subtyping of colorectal cancer, identifying biomarkers of drug response, and exploring these concepts using patient-derived xenograft models. Key findings include identifying microRNAs that antagonize a poor-prognosis colorectal cancer subtype and finding kinase genes that are therapeutic targets in otherwise resistant tumor cells and xenografts.
1. Neural networks can be examined at multiple levels from individual axons between neurons to fibre tracts between brain areas.
2. Types of connectivity include structural revealed by DTI, functional from correlated activity, and effective showing causal relationships.
3. Network analysis examines topological properties like modular clusters, small-world organization with high clustering and short path lengths, as well as spatial organization of brain regions.
This document summarizes how antibiotics work, how antibiotic resistance evolves, and how physicists can help address the growing problem of antibiotic resistance. It discusses how antibiotics target bacterial processes like cell wall synthesis and protein synthesis. It also describes simple models that link the molecular mechanisms of antibiotic action to whole-cell physiology and growth. The document outlines pathways to antibiotic resistance, and how resistance can emerge more quickly in drug gradients due to strong selection at the wave front of expanding bacterial populations. It concludes by discussing opportunities for physicists to better understand biofilm infections and help design strategies to avoid antibiotic resistance.
This document discusses various topological and spatial features of brain networks, including small-world properties, motifs, clusters, degree distributions, and robustness. It provides examples of analyses conducted on structural and functional brain networks, such as detecting clusters in the cat cortex and examining the effects of simulated brain lesions. Modular organization is highlighted as important for local integration and global separation of processing. Developing brain networks are found to require less information to encode connectivity patterns compared to random networks.
Spatial neural networks tend to connect adjacent neurons to minimize wiring costs. However, some long-distance connections exist that reduce path lengths and allow for faster processing. Deficits in long-distance connectivity are linked to cognitive impairments like Alzheimer's disease and lower IQ scores.
This document discusses functional brain networks and network science approaches to studying the brain. It begins by defining complex systems and network science. It then outlines the main types of brain networks - anatomical and functional networks. Functional brain networks are constructed from time series data measuring brain activity and can be analyzed using network measures to study properties like segregation, integration and resilience.
This document discusses noise pollution, including its definition, sources, measurement, effects on the environment and humans, monitoring devices, and methods for control and prevention. It defines noise pollution as unwanted sound that penetrates the environment from an external source. Major sources listed include street traffic, railroads, airplanes, and construction. Measurement units and health impacts are also summarized, along with legislative guidelines and strategies for noise control, including reducing noise at the source, blocking transmission paths, and using protective equipment.
1. Eukaryotic genomes contain nuclear DNA as well as organelle DNA from mitochondria and chloroplasts. Genome size, or C-value, varies greatly between species from 106 bp in prokaryotes to over 1011 bp in some amphibians.
2. Renaturation kinetics can be used to measure genome complexity based on how quickly denatured DNA strands reanneal, with more common sequences reassociating faster. A COT curve plots the percentage of renatured DNA over time at different DNA concentrations.
3. Eukaryotic genomes contain genes, repetitive sequences like satellites and transposons, and non-coding DNA. While genes and complexity generally increase together in lower e
This document provides information about genetic polymorphisms and methods used to detect single nucleotide polymorphisms (SNPs), including DNA isolation, polymerase chain reaction (PCR), restriction fragment length polymorphism (RFLP) analysis, and gel electrophoresis. It defines polymorphisms and SNPs, and describes how restriction enzymes can be used to detect SNPs by cleaving DNA into different fragments depending on nucleotide variations. The document outlines the steps of PCR including melting, annealing, and extension cycles to amplify DNA, as well as how to perform restriction digests and electrophoresis to separate DNA fragments by size for analysis.
This document provides information about genetic polymorphisms and methods used to detect single nucleotide polymorphisms (SNPs), including DNA isolation, polymerase chain reaction (PCR), restriction fragment length polymorphism (RFLP), and gel electrophoresis. It defines a polymorphism as a harmless DNA variation between individuals and SNPs as single base mutations that are the most common source of genetic variation. The document outlines the steps of PCR including melting, annealing, and extension cycles to amplify DNA, as well as how restriction enzymes are used to digest PCR products into different fragments depending on sequences, which can then be separated by size via gel electrophoresis to detect SNPs.
The lecture describes the basic concepts of C-value, Cot curve and Rot curve analysis, MCQ questions regarding the same. Queries are always welcome.... Dr. Nitin Wahi (wahink@gmail.com).
"Introns: Structure and Functions" during November, 2011 (Friday Seminar activity, Department of Biotechnology, University of Agricultural Sciences, Dharwad, Karnataka) by Yogesh S Bhagat (Ph D Scholar)
Prions are infectious proteins that can replicate by converting normal prion proteins (PrP-sen) into an abnormal disease-causing form (PrP-res). PrP-res accumulates and forms amyloid fibers that are toxic to cells and ultimately cause fatal neurodegenerative diseases known as transmissible spongiform encephalopathies (TSEs). While prions do not contain genetic material, they propagate by inducing PrP-sen to adopt the abnormal PrP-res conformation. Polymerase chain reaction (PCR) is a technique used to amplify specific DNA sequences, allowing minute amounts of DNA to be analyzed. It involves repeated cycles of heating and cooling of the DNA to separate and copy the strands
Junk DNA/ Non-coding DNA and its Importance (Regulatory RNAs, RNA interferen...Pradeep Singh Narwat
The document discusses various types of non-coding DNA sequences, including repetitive sequences, transposons, non-coding RNAs, introns, and pseudogenes. It notes that while genes only make up 2-3% of human DNA, recent projects like ENCODE have found that a much larger portion of non-coding DNA is functionally important, for example through transcriptional and translational regulation of protein-coding sequences. The document outlines different classes of transposons, introns, non-coding RNAs and their various roles in gene expression, epigenetics, and genome evolution.
Techniques of SNP Genotyping can be summarized as follows:
There are several techniques for genotyping SNPs including hybridization methods, enzyme-based methods, and other methods based on physical properties of DNA. Popular hybridization methods include DASH, molecular beacons, and gene chip arrays. Common enzyme-based techniques are RFLP, Invader assay, and oligonucleotide ligation assay. Other physical property-based methods include SSCP, TGGE, and pyrosequencing. Each method has its own pros and cons related to factors like speed, cost, and accuracy. Choosing the appropriate SNP genotyping technique depends on the number of SNPs needed to be analyzed and sample size.
The document discusses several key concepts in molecular biology:
1. Cells contain DNA which encodes the genome and directs the synthesis of proteins through gene expression. The genome contains approximately 25,000-30,000 genes distributed across 23 chromosome pairs.
2. Gene expression is regulated and differs between cell types, allowing for cellular specialization. Techniques like DNA microarrays and RT-PCR are used to analyze gene expression levels.
3. Molecular biology techniques like DNA cloning, nucleic acid analysis, cell culture and genetic manipulation are used to study genes and their functions.
Real-time PCR (polymerase chain reaction) allows for amplification and quantification of DNA during the PCR process through the use of fluorescent probes such as TaqMan probes or SYBR Green. It provides advantages over conventional PCR such as faster results, higher sensitivity in detecting small changes in DNA amounts, and the ability to quantify initial template concentrations through analysis of threshold cycle values. Common applications include detection of gene expression, viral load quantification, and molecular diagnostics.
whole genome analysis
history
needs
steps involved
human genome data
NGS
pyrosequencing
illumina
SOLiD
Ion torrent
PacBio
applications
problems
benefits
Somatic cell hybridization allows for gene mapping by fusing cells from different species. This results in heterokaryons containing a mixture of chromosomes. As the hybrid cells divide, they lose chromosomes at random. Observing which chromosomes are lost along with the corresponding phenotypes allows researchers to map genes to specific chromosomes. Key aspects include using cells deficient in different enzymes to select for viable hybrids and tracking chromosome and gene retention over multiple cell lines to assign locations. This technique is an important method for gene mapping.
The genetic algorithm is a mataheuristic method that uses the metaphor of the evolutionary process of living things, especially Darwin's theory of evolution. This persentation will discuss about the fundamental of Genetic Algorithm. Download this PPT and put in "Slide Persentation (F5)" to play the animation in it.
Genome organisation in eukaryotes...........!!!!!!!!!!!manish chovatiya
This document discusses the organization of eukaryotic genomes. It explains that eukaryotic genomes are much larger than prokaryotic genomes, with most of the DNA being non-coding. Eukaryotic genomes contain multiple linear chromosomes, introns, repetitive sequences, and both coding and non-coding RNA genes. The document also describes different types of repetitive elements like tandem repeats, transposons, retrotransposons, LINEs, SINEs and their roles in increasing genome size. Overall, the document provides an overview of the complex structure of eukaryotic genomes compared to simpler prokaryotic genomes.
This document discusses methods for analyzing transgenic plants, including determining if a plant is transgenic and if transgenes are expressed. It describes established methods like PCR, Southern blots, and Northern blots. Southern blots are used to confirm transgene insertion into the genome by detecting fragments of different sizes after restriction enzyme digestion and gel electrophoresis. Northern blots detect RNA transcripts to confirm transgene expression. Proper experimental design and controls are important to avoid false positives and obtain conclusive evidence of stable transgene integration and expression.
The document discusses various components that make up genomes, including genes, repetitive sequences, and different types of DNA. It describes the human genome in particular, noting it contains around 3 billion base pairs, with 3% coding for proteins. Around 40-50% is repetitive sequences from transposition. Genomics is defined as the study of genomes, including gene mapping and sequencing. Key components of genomes discussed include transposable elements like SINEs, LINEs, LTR retrotransposons, and other interspersed repeats. Comparative analysis of genome sequences can provide insights into gene number and function.
DNA Microarray and Analysis of Metabolic Controlshilpa sharma
This document describes a study that used DNA microarrays to analyze changes in gene expression related to tryptophan metabolism in E. coli under different physiological conditions and genetic mutations. The study identified genes whose expression levels changed with tryptophan availability, tryptophan starvation, and inactivation of the tryptophan repressor. Only a small core set of operons including trp, mtr and aroH showed highly responsive changes in expression levels. mRNA levels for aromatic amino acid biosynthesis genes decreased with excess tryptophan, while only the tnaA-tnaB operon increased. The results provide quantitative validation of genes known to be involved in tryptophan metabolism.
This laboratory report summarizes an experiment exploring RNA splicing in Drosophila melanogaster. Genomic DNA and total RNA were extracted from fruit flies and used to study the rngo gene. PCR and RT-PCR were performed on the genomic DNA and cDNA samples. The genomic PCR product was cloned and sequenced. Bioinformatics analysis showed the genomic sequence was longer, containing introns absent from the cDNA, indicating splicing of the rngo pre-mRNA. Future work could investigate other splicing sites and homology to human genes.
This document summarizes a presentation on using synthetic DNA controls called "sequins" to represent the human genome for quality control in next-generation sequencing experiments. Key points:
1. Sequins are synthetic DNA or RNA molecules created to represent features of the human genome like genes and genetic variants.
2. Sequins are added to real DNA or RNA samples prior to sequencing to act as internal controls for alignment, variant detection, and gene expression quantification.
3. Analysis of sequin controls can provide metrics like accuracy, precision, sensitivity and limits of detection for sequencing experiments and bioinformatics pipelines.
PCR is a revolutionary molecular biology technique used for enzymatically replicating DNA . This technique allows a small amount of DNA molecule to be amplified many times in an exponential manner . It is commonly used in medical and biological research labs for variety of tasks such as detection of hereditary disease , identification of genetic fingerprints diagnosis of infectious disease , cloning of genes and paternity testing .
Each reaction cycle doubles the amount of DNA – a standard PCR sequence of 30 cycles creates over 1 billion copies . The thermostability of DNA polymerases is defined by how long they remain active at the extreme range of temperatures used in PCR.
There have been various thermostable polymerases identified to date, each with its optimal temperature for activity and a unique half-life profile at temperatures greater than 95°C. For example, the half-life of Taq polymerase at 95°C is 40 minutes, whereas the half-life of the hyperthermophilic Deep Vent DNA polymerase extracted from the Pyrococcus species GB-D is several hours at 98–100°C. Polymerase processivity is defined as the number of consecutive nucleotides a single enzyme can incorporate before being dislodged from the DNA template.
At 75°C, native Taq polymerases can typically amplify DNA at a rate of 10–45 nucleotides per second - that’s approximately 2 kilobases per minute!
Some DNA polymerases have been engineered to improve their binding domain, thus making them more stable than conventional Taq. For example, KAPA2G polymerase has a speed of ~150 nucleotides per second - 3-fold higher than Taq. Direct PCR cloning methods include TA and GC cloning, as well as TOPO® Cloning, and enable direct cloning of PCR fragments. For example, the TA cloning approach takes advantage of the 3’ A overhang naturally added to products by Taq polymerase following PCR. The resulting sticky ends then enable recombination with DNA fragments containing 3’ T overhangs, such as linearized vectors.
During indirect PCR cloning, the PCR products are modified prior to recombination with other DNA sequences. For example, in restriction cloning, restriction sites are frequently introduced via PCR to enable restriction digestion and ligation with linearized vectors. PCR mutagenesis is a technique used to generate site-directed sequence changes such as base substitutions, inserts and deletions.
To insert a single point mutation via mutagenesis, for example, PCR primers are designed that contain the desired base change, usually in the middle of the primer sequence. PCR is then performed with the mutagenic primers and a high-fidelity DNA polymerase, which results in the incorporation of the desired mutation into the original sequence.Allele-specific PCR is used to detect sequence variations and ultimately determine the genotype of an organism.
For allele-specific PCR, primers are designed to flank the region of interest. The most common application of PCR is gene expression analysis
Similar to Gene expression noise, regulation, and noise propagation - Erik van Nimwegen (20)
1) The document discusses multi-messenger astronomy and the detection of electromagnetic counterparts to gravitational waves, neutrinos, and cosmic rays.
2) It provides background on neutrino astronomy, gravitational wave detections from binary neutron star mergers, and kilonova emissions from such mergers.
3) The merger of GW170817 and its association with GRB170817A and kilonova AT2017gfo provided the first direct evidence that neutron star mergers are the origin of short gamma-ray bursts and produce r-process nucleosynthesis.
1) Pulsar timing arrays are searching for gravitational waves from massive black hole binaries in the nanohertz frequency range.
2) Current pulsar timing array efforts have not detected a gravitational wave signal but are placing increasingly stringent upper limits.
3) Future and more sensitive radio telescopes like FAST, MeerKAT, and the Square Kilometre Array will improve the prospects for a direct detection of gravitational waves from massive black hole binaries within the next decade.
1) Massive black hole binaries form during galaxy mergers and evolve through dynamical friction and 3-body interactions with stars until reaching separations of ~0.01 pc where gravitational wave emission takes over.
2) Gas dynamics may also drive black hole binaries to smaller separations for coalescence.
3) Black hole binary coalescence timescales are typically long, on the order of billions of years, which has implications for gravitational wave detection and triple black hole interactions.
1. The document discusses potential low frequency gravitational wave sources that could be detected by LISA, including galactic white dwarf binaries, massive black hole binaries, and extreme mass ratio inspirals.
2. LISA could detect thousands of massive black hole binaries and provide precise measurements of their parameters like mass and spin, enabling tests of general relativity and learning about black hole formation mechanisms.
3. Extreme mass ratio inspirals where a compact object spirals into a massive black hole could occur at a rate of 10-7 per year in our galaxy, allowing precision cosmology and tests of the no-hair theorem.
The document discusses Bayesian data analysis and gravitational wave detection. It provides an outline of topics to be covered including fundamentals of probability theory, stochastic processes, detection of gravitational wave signals, and inference of gravitational wave physics. It explains that Bayesian analysis is useful for gravitational wave detection because events are rare and detectors are noise dominated, requiring accurate models to detect signals in the noise.
Martin Hewitson is a physicist who has worked on projects including GEO600 calibration, LISA Pathfinder data analysis, and the LISA mission. In his lectures, he discusses why space-based gravitational wave observations are needed at lower frequencies than ground-based detectors can achieve. Lower frequencies allow observation of mergers of larger black hole and neutron star systems. The main challenges for low-frequency detection are isolating test masses from external forces and displacements, which is achieved through free-fall in space versus suspended masses on the ground. LISA will use interferometry between test masses in separate spacecraft to precisely measure gravitational waves.
The document discusses the Laser Interferometer Space Antenna (LISA) mission and its goal of achieving a strain sensitivity of around 10^-20 1/√Hz to measure distance changes of around 25 picometers per √Hz. It notes that frequency noise is currently around 0.9 millimeters per √Hz and aims to reduce this. LISA will consist of three spacecraft in an equilateral triangle formation with 2.5 million km sides to perform gravitational wave observations from space.
The document summarizes the LISA Pathfinder mission to test technologies needed for the future LISA gravitational wave observatory. It describes how LISA Pathfinder contains two test masses inside a spacecraft to test keeping the masses in near-perfect free fall, similar to individual LISA spacecraft. The document outlines the various sensors and actuators used to control the test masses and spacecraft, the goal of demonstrating positioning of test masses to less than 10 picometers. It concludes by detailing the multi-step control process used to keep the test masses and spacecraft stabilized based on sensor readings.
This document summarizes a lecture on using gravitational wave waveform models to test general relativity and probe the nature of compact objects through gravitational wave observations. It discusses how waveform models can be used to bound post-Newtonian coefficients, constrain phenomenological merger-ringdown parameters, and probe the quasi-normal modes of black hole ringdowns. Measuring multiple modes could verify the no-hair theorem and black hole uniqueness properties. Future observations from LIGO and Virgo at design sensitivity may allow high-precision black hole spectroscopy and tests of general relativity in the strong, dynamical gravity regime.
The document discusses using gravitational wave waveform models to infer astrophysical properties from observations of gravitational wave events. It describes how waveform models encode information about binary black hole parameters like mass and spin, and how Bayesian inference can be used to estimate these parameters from the detected gravitational wave signal. It also addresses assessing confidence in detections and evaluating potential modeling systematics by comparing waveform models to numerical relativity simulations.
Alessandra Buonanno gave a lecture on the analytical and numerical relativity approaches used to model gravitational waveforms from inspiraling binary systems. She discussed how post-Newtonian theory, effective one body theory, and numerical relativity are used to approximately and exactly solve Einstein's field equations. She emphasized the crucial synergy between analytical and numerical relativity approaches to develop accurate gravitational waveform models like EOBNR and Phenom that have been used to infer astrophysics from LIGO/Virgo detections.
This document summarizes a lecture on binary black holes and tests of general relativity. The lecture covers topics from current gravitational wave detections, including parameters inferred from detections like GW150914, GW151226, and others. It discusses using observations of binary black hole mergers to test predictions of general relativity like the existence of an innermost stable circular orbit and properties of the final black hole like its mass and spin.
This document discusses the challenges of developing ground-based gravitational wave detectors with improved sensitivity at low frequencies. It focuses on the issues of seismic noise, suspension thermal noise, and experimental approaches to address these issues. Specifically, it explores the Virgo monolithic suspension failures and finds excess hydroxyl groups in the fused silica fibers used, which could cause fragility. The goal is to understand failure mechanisms and improve detector sensitivity down to 10 Hz and below.
1) The document discusses using the effective one body (EOB) formalism to model gravitational wave templates for LIGO and LISA. It summarizes the number and type of templates used in LIGO's first two observing runs.
2) It also discusses using EOB, post-Newtonian theory, numerical relativity simulations, and quantum field theory to model gravitational wave emission from binary black hole and binary neutron star mergers across different mass ratio and velocity regimes.
3) The document focuses on recent work extending EOB models to higher post-Minkowskian orders and including the effects of spin and tidal interactions, with the goal of more accurate gravitational wave template modeling.
- The document discusses gravitational waves and binary systems, including perturbative computations of gravitational wave flux from binary systems up to order v7/c7.
- It covers the effective one body (EOB) method for modeling binary coalescence, including resummations of post-Newtonian results and the addition of ringdown effects. This provides the first complete waveforms for binary black hole coalescences.
- Developments are discussed such as extending EOB to include spinning bodies, comparisons to numerical relativity results, and using gravitational self-force calculations to improve EOB modeling.
1) Gravitational waves are predicted by Einstein's theory of general relativity and are generated by accelerating masses like binary star systems.
2) Modeling the motion and gravitational wave emission of compact binary systems like neutron stars and black holes requires using techniques like post-Newtonian theory, effective field theory approaches, and numerical relativity simulations.
3) Understanding strong gravitational fields like those near black holes requires tools from general relativity like multipolar expansions, matched asymptotic expansions, and analytic continuation techniques.
The document discusses gravitational waves and binary systems. It provides context on the history of gravitational wave detection, from Einstein's early work developing the theory of gravitational waves to Joseph Weber's pioneering efforts to detect them in the 1960s. It also summarizes the development of laser interferometer gravitational wave detectors by researchers in the US, Germany, Italy, and the UK beginning in the 1970s and 1980s. Key detections by LIGO and Virgo are noted, including GW150914 in 2015. Theoretical work on modeling gravitational waveforms from coalescing compact binaries is summarized, from early perturbative approaches to more recent analytical methods like the effective one-body formalism.
The document discusses econophysics and the instability of the financial system caused by credit risk. It outlines the topics to be covered, including a structural model of credit risk, numerical simulations, and a random matrix approach. The conclusions discuss how risk reduction through diversification in credit portfolios is limited by correlations between credit exposures and the presence of jumps in the economic variables modeling the credit risks. Large portfolios do not necessarily converge to a Gaussian loss distribution due to these effects.
1) The document analyzes price formation and cross-responses between correlated financial markets using empirical data from NASDAQ stocks. It finds long-memory correlations between the trades and prices of different stocks.
2) Specifically, it measures the cross-response of one stock's price changes to the trades of other stocks, and the cross-correlation between stocks' trade signs. It finds these cross-responses and cross-correlations decay slowly over time, indicating long-range correlations.
3) Averaging the cross-responses and cross-correlations across stocks reduces noise and more clearly reveals their long-memory nature. The analysis provides evidence that liquidity and trading in one stock can have prolonged impacts on price formation
This document discusses market states and correlations between financial time series. It begins by introducing the Epps effect, where measured correlations decrease with smaller time intervals, and describes compensating for this using asynchronity and tick size corrections. Non-Gaussian dependencies are then covered, showing correlations can misrepresent relationships. Market states are identified using similarity measures between correlation matrices over time. Eight distinct market states are found for the US market between 1992-2010 based on industrial sector correlations.
More from Lake Como School of Advanced Studies (20)
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Gene expression noise, regulation, and noise propagation - Erik van Nimwegen
1. Gene
expression
noise,
regula0on,
and
noise
propaga0on
Erik
van
Nimwegen
Biozentrum,
University
of
Basel,
and
Swiss
Ins8tute
of
Bioinforma8cs
Basel
Our
group
2. Cartoon
of
the
steps
in
gene
expression
Gene
X
RNA
polymerase
Gene
X
RNA
polymerase
mRNA
gene
X
Transcrip0on
rate:
rλ
mRNA
decay
rate
Protein
X
Transla0on
rate
Protein
decay
rate:
pλ
pµ
rµ
3. Gene
expression
differen3al
equa3ons
•
P
=
Amount
of
protein
X.
•
R
=
Amount
of
mRNA
X.
•
P
increases
due
to
transla0on
of
mRNA
and
decreases
due
to
protein
decay.
p p
dP
R P
dt
λ µ= −
•
R
increases
due
to
transcrip0on
and
decreases
due
to
mRNA
decay.
r r
dR
R
dt
λ µ= −
Steady-‐state:
P =
λr
λp
µr
µp
R =
λr
µr
•
In
reality
there
are
are
really
an
integer
number
p(t)
of
proteins
at
0me
t,
and
r(t)
mRNAs.
•
Numbers
may
be
small,
e.g.
there
is
only
one
copy
of
the
gene
in
the
DNA.
•
The
RNA
polymerases,
ribosomes,
and
mRNAs
are
tumbling
around
in
the
cell,
constantly
bumping
into
other
molecules
(i.e.
following
Brownian
mo0on).
Discreteness
and
Stochas3city:
4. Surprise
surprise:
Gene
expression
is
stochas3c
Low copy
Plasmid
• GFP
fluorescence
per
cell
propor0onal
to
protein
number.
• Not
surprisingly,
fluctua0ons
are
observed
between
cells.
• What
kind
of
fluctua0ons
would
one
expect
in
a
simplest
possible
model?
5. Stochas3c
transcrip3on
and
decay
Gene
X
RNA
polymerase
Gene
X
RNA
polymerase
mRNA
gene
X
Probability
per
unit
0me
to
transcribe
a
new
mRNA.
Differen0al
equa0on
for
the
distribu0on:
1 1
( )
( ) ( 1) ( ) ( ) ( )n
r n r n r r n
dP t
P t n P t n P t
dt
λ µ λ µ− += + + − +
Probability
that
there
are
n
mRNAs
at
0me
t:
rλ rµ
Pn
(t)
Probability
per
mRNA
per
unit
0me
that
it
will
decay.
6. Steady-‐state
is
Poisson
distribu3on
Probability
to
have
n
mRNAs:
Pn
=
1
n!
λr
µr
⎛
⎝
⎜
⎞
⎠
⎟
n
e
−λr /µr
Mean:
n =
λ
µ
Variance:
var(n) = n =
λ
µ
Standard-‐devia3on:
σ (n) = n
0 1 2 3 4 5
0.0
0.2
0.4
0.6
0.8
Number of mRNA n
Probability
0 2 4 6 8 10
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Number of mRNA n
Probability
0 5 10 15 20 25 30
0.00
0.02
0.04
0.06
0.08
0.10
0.12
Number of mRNA n
Probability
λr
µr
= 0.1 10r
r
λ
µ
=
λr
µr
=1
7. (Shahrezaei,
Swain
PNAS
2008)
Transla3on
amplifies
mRNA
fluctua3ons
mean
and
variance:
a =
λr
µp
b =
λp
µp
“burst
size”:
transla0ons
per
mRNA
life0me.
n = ab, var(n) = (b+1) n
λr
µr
µp
transcrip0on
mRNA
decay
transla0on
protein
decay
λp
λr
µr
λp
µp
• Proteins
are
oZen
long-‐lived:
approxima0on
protein-‐decay
slow
rela0ve
to
mRNA
decay.
• Solu0on
in
terms
of
two
ra0os:
Transcrip0on
events
per
protein
life0me.
Pn
=
Γ(a + n)
Γ(a)n!
b
b +1
⎛
⎝⎜
⎞
⎠⎟
n
1−
b
b +1
⎛
⎝⎜
⎞
⎠⎟
a
noise:
η(n) =
σ(n)
n
=
var(n)
n
2
=
b +1
n
9. Typical
genes
have
less
than
1
mRNA
per
cell
in
E
coli
Fluorescently
labeling
single
mRNAs
(Fluorescence
In
Situ
Hybridiza0on).
Coun0ng
mRNAs
per
cell
under
the
microscope.
Mean
mRNAs
per
cell
Taniguchi
et
al,
Science
(2010)
From:
Milo
and
Phillips,
Cell
Biology
by
the
numbers
10. Some
addi3onal
numbers
for
E.
coli
•
RNA
polymerases
per
cell:
1’500-‐10’000
(depending
on
growth
rate).
•
Ribosomes
per
cell:
14’000
(1
doubling
per
hour)
–
45’000
(2
doublings
per
hour).
•
mRNA
decay
rate:
1-‐15
minutes
half-‐life.
• Protein
decay
rate:
typically
a
few
hours.
• Protein
dilu0on
rate:
cell
doubling
0me,
i.e.
30
min
to
2
hours.
Bernstein
et
al,
PNAS
(2002)
Taniguchi
et
al,
Science
(2002)
Distribu3on
mRNA
half-‐lifes
Distribu3on
mean
proteins
per
cell
11. Measuring
variability
within
and
across
cells
Two
3mes
the
same
promoter
Intrinsic
and
extrinsic
noise
• Total
variance
in
fluorescence
per
cell
can
be
decomposed
into
two
parts:
• Intrinsic
=
variance
within
cell:
• Extrinsic
variance
=
the
rest,
i.e.
variability
across
cells:
vtot
= var(g) + var(r) = vi
+ ve
vi
=
1
2
(g − r)2
ve
= gr − g r
Hey!
That
covariance
could
be
nega8ve!
How
can
a
variance
be
nega8ve?
12. How
to
properly
infer
intrinsic
and
extrinsic
variance
Gives
orthodox
sta0s0cal
es0mators
that
can
give
nega0ve
es0mates.
A
Bayesian
solu3on
is
never
pathological
and
much
more
accurate
when
extrinsic
noise
is
small
Extrinsic:
Gaussian
distribu0on
of
mean
μi
across
cells
i:
Intrinsic:
Gaussian
devia0on
of
green
gi
and
red
ri
from
mean
μi:
P(gi
,ri
| µi
) =
1
2πv
exp −
(gi
− µi
)2
+ (ri
− µi
)2
2v
⎡
⎣
⎢
⎢
⎤
⎦
⎥
⎥
Posterior
for
the
intrinsic
variance
v
and
extrinsic
variance
vμ:
P(v,vµ
| D) = vµ
+ v / 2( )
−(n−1)/2
v−n/2
exp −
n
4v
(g − r)2
−
n
(2vµ
+ v)
var
r + g
2
⎛
⎝⎜
⎞
⎠⎟
⎡
⎣
⎢
⎢
⎤
⎦
⎥
⎥
Example
with
low
extrinsic
noise
Inference
based
on
only.
(g − r)2
Bayesian
result.
Result
assuming
extrinsic
noise
known.
13. Extrinsic
noise
implies
transcrip3on/transla3on/decay
rates
fluctuate
Extrinsic
noise
in
Elowitz
et
al:
Intrinsic
noise
falls
as
the
promoter
is
induced.
Extrinsic
noise
peaks
at
intermediate
induc0on.
R
Phillips
(Annu
Rev
Con
Mat
Phys,
2015)
• Transcrip0on
rate
can
vary
when
the
promoter
switches
between
different
states.
• Switching
rates
depend
on
concentra0ons
of
DNA
binding
proteins
(polymerases,
TFs).
• These
concentra0ons
will
fluctuate
from
cell
to
cell.
14. Noise
propaga3on
• Regulatory
cascade:
Gene
1
induces
gene
2.
Gene
3
cons0tu0ve.
• As
gene
1
is
induced,
its
own
noise
level
drops.
• Gene
2
goes
through
an
intermediate
peak
in
noise
level.
• Gene
3’s
noise
is
unaffected.
Interpreta3on:
At
intermediate
levels
of
gene
1,
the
promoter
of
gene
2
shows
most
switching
between
bound
and
unbound
states
and
most
sensi0vity
to
fluctua0ons
in
the
concentra0on
of
gene
1.
15. Cells
are
not
sta3c:
Inves0ga0ng
stochas0c
regulatory
dynamics
Wish
list
• Follow
growth
and
gene
expression
dynamics
in
single
cells
over
long
0me
scales.
• Accurate
quan0fica0on.
• Follow
different
cell
lineages
separately
to
allow
observa0on
of
rare
events.
• Precise
dynamical
control
over
growth
environment.
Wang
et
al.
Robust
growth
of
Escherichia
coli.
Curr
Biol.
2010
The
mother
machine
17. Switching
growth
media
between
glucose
and
lactose
• GFP/lacZ
fusion
reports
lac-‐operon
expression.
• Switch
glucose/lactose
every
4
hours.
• Immediate
growth
arrest
at
first
switch
to
lactose.
• Stochas0c
induc0on
of
lac-‐operon
and
restart
of
growth.
• Dilu0on
of
GFP/lacZ
during
glucose
phase.
• No
more
growth
arrests
upon
later
switches.
18. Automated
Image
Analysis:
The
Mother
Machine
Analyzer
Florian
Jug
Gene
Myers
MPI
Cell
Biology,
Dresden
• Tracking
and
segmenta0on
done
in
parallel
using
a
single
objec0ve
func0on.
• Interac3ve
cura3on:
• User
input
interpreted
as
addi0onal
constraints.
• Automa0c
re-‐op0miza0on.
19. Cells
expand
exponen3ally
during
their
cell
cycle
2
3
4
2
3
4
2
3
4
2
3
4
0 4 8 12 16 20
time (h)
celllength(µm)
0.970 0.975 0.980 0.985 0.990 0.995 1.000
0.0
0.2
0.4
0.6
0.8
1.0
Pearson correlation exp. growth curve
FractionCellCycles
Cumula3ve
correla3on
coeff.
of
log(size)
vs
3me
Example
growth
dynamics
of
log-‐size
vs
3me
Roughly
two-‐fold
variability
in
growth
rates
20. Fluorescence
roughly
tracks
cell
size
but
produc3on
fluctuates
significantly
Approximately
4-‐fold
varia3on
in
produc3on
rate
Examples
of
total
fluorescence
against
0me
for
single
cells
growing
in
lactose.
Distribu0on
of
GFP
molecules
produced
per
second.
21. Distribu3on
of
total
fluorescence
and
fluorescence
concentra3ons
5000 10000 15000 20000 25000 30000 35000
0.00000
0.00005
0.00010
0.00015
Fluorescence HAUL
Probabilitydensity
Total Fluorescence Distribution
m=10'616, s=2911, sêm=0.274
8.5 9.0 9.5 10.0 10.5
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Log Fluorescence HAUL
Probabilitydensity
Total Log Fluorescence Distribution
m=9.23,s2
=0.07
4000 6000 8000 10000 12000
0.0000
0.0002
0.0004
0.0006
0.0008
Fluorescence concentrationHAUêmicronL
Probabilitydensity
Fluorescence Concentration Distribution
m=4278, s=661, sêm=0.154
8.0 8.5 9.0 9.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Log Fluorescence concentrationHAUêmicronL
Probabilitydensity
Log Fluorescence Concentration Distribution
m=8.35,s2
=0.022
Very
roughly
log-‐normal
distribu0ons.
Concentra0on
has
significantly
less
varia0on.
22. Measuring
transcrip3on
from
all
E.
coli
promoters
in
single
cells
• GFP
fluorescence
per
cell
propor0onal
to
protein
number.
• GFP
levels
of
single
cells
can
be
measured
in
high-‐throughput
using
FACS.
• Quan0ta0vely
characterize
the
distribu0on
of
expression
levels
across
single
cells,
for
all
E.
coli
promoters.
ORF1
ORF2
ORF4
E. coli genomeORF3
Plasmid
Zaslaver et al.
2006
Silander
et
al.
PLoS
genet
2012
Wolf
et
al.
eLife
2015
23. FACS:
Measuring
and
selec3ng
single
cells
• Cells
move
one-‐by-‐one
in
a
flow
channel.
• Each
cell
passes
in
front
of
a
laser
and
its
fluorescence
is
measured.
• By
selec0vely
charging
par0cles
based
on
their
measured
fluorescence,
one
can
select
cells
whose
fluorescence
lies
in
a
certain
range.
25. Means
and
variances
of
na3ve
E.
coli
promoters
• Variance
in
log-‐expression
in
shows
a
trend
of
decreasing
with
mean
expression.
• Different
promoters
with
same
mean
can
show
significantly
different
variance.
• There
seems
to
be
a
clear
lower
bound
on
variance
as
a
func0on
of
mean.
5 6 7 8 9 10 11
0.0
0.2
0.4
0.6
0.8
Mean Log@GFP IntensityD
VarianceLog@GFPpercellD
background
2
*
background
26. 7 8 9 10 11 12 13
0.0
0.2
0.4
0.6
0.8
Mean Log@proteins per cellD
VarianceLog@proteinspercellD
7 8
0.0
0.2
0.4
0.6
0.8
Excessnoise
Means
and
variances
of
na3ve
E.
coli
promoters
Red
curve:
σab
2
= 0.025, b = 450
n = ab, var(n) = (b+1) nAt
constant
transcrip0on/transla0on/decay
rates:
Assume
a
and
b
both
fluctuate:
var(n) = (b +1) n +σab
2
n
2
nmeas
= nbg
+ n + ε var(n) var log(nmeas
)⎡⎣ ⎤⎦ = σab
2
1−
nbg
nmeas
⎛
⎝
⎜
⎞
⎠
⎟
2
+
(b +1)
nmeas
1−
nbg
nmeas
⎛
⎝
⎜
⎞
⎠
⎟
27. Noise
levels
vary
across
na3ve
E.
coli
promoters
7 8 9 10 11 12 13
0.0
0.2
0.4
0.6
0.8
Mean Log@ proteins per cellsD
Excessnoise
Excess
noise
(variance
–
lower
bound
as
func.
mean)
Selec3on
on
noise
levels
High
noise
DriZ?
Selected
for
noise?
Low
noise.
Selec0on
to
minimize
noise?
What
noise
would
one
get
without
selec3on?
Evolve
synthe8c
promoters
in
a
precisely
controlled
selec0ve
environment.
29. Evolu0on
of
popula0on
expression
levels
Selec0ng
for
Medium
expression
29
Selec0ng
for
High
expression
30. Expression
distribu0ons
of
individual
synthe0c
promoters
• We
isolated
~400
clones
from
evolu0onary
runs
for
both
medium
and
high
expression.
• Measured
each
clone’s
expression
distribu0on.
How
do
noise
levels
of
synthe3c
promoters
compare
with
those
of
na3ve
promoters?
31. Na0ve
promoters
Synthe0c
promoters
• Synthe0c
promoters
were
not
selected
on
their
noise
proper0es.
• Low
noise
is
the
default
behavior
of
E.
coli
promoters.
• Selec0on
must
have
acted
so
as
to
increase
the
noise
levels
of
some
na0ve
promoters.
Iden0cal
distribu0ons
at
the
low
noise
end.
High
noise
enriched
in
na0ve
promoters.
Selec0on
caused
increased
noise
in
a
substan0al
frac0on
na0ve
promoters
What
is
`special’
about
na3ve
promoters
that
show
high
noise?
32. Noisy
genes
have
more
regulatory
inputs
• 185
E.
coli
transcrip0on
factors
(TFs).
•
4123
known
regulatory
interac0ons
TF
→
promoter.
Genes
with
higher
noise
have
(on
average)
higher
numbers
of
known
regulatory
inputs.
2
or
more
inputs
1
known
input
no
known
inputs
synthe0c
proms.
Why
is
there
a
general
associa3on
between
noise
and
regula3on?
Why
did
selec3on
cause
noise
to
increase?
33. Noise-‐propaga3on:
nuisance
or
opportunity?
Noise
as
an
unavoidable
side-‐effect
of
regula3on
• Explains
the
general
associa0on
of
noise
and
regula0on.
• `Fluctua0on-‐dissipa0on
rela0on’:
Genes
that
need
complex
regula0on
unavoidably
couple
to
the
noise
in
their
regulators.
• Generally
assumed
to
be
detrimental:
reduces
the
accuracy
of
regula0on.
Stochas3city
as
a
bet-‐hedging
strategy
• Phenotypic
diversity
can
generally
be
selected
for
in
fluctua0ng
environments.
• Maybe
noise-‐propaga0on
can
be
beneficial
in
some
circumstances?
Let’s
do
some
theory
on
how
gene
expression
noise
affects
fitness
34. Fitness
func0on
in
a
single
environment
f (x | µ*,τ ) = exp −
(x −µ* )2
2τ 2
"
#
$
%
&
'
p(x | µ,σ ) =
1
2πσ
exp −
(x −µ)2
2σ 2
"
#
$
%
&
'
f (µ,σ | µ*,τ ) = dxp(x | µ,σ ) f (x | µ*,τ ) =∫
τ 2
τ 2
+σ 2
exp −
(µ −µ* )2
2(τ 2
+σ 2
)
#
$
%
&
'
(
The
fitness
of
a
promoter
`genotype’
(frac0on
of
its
cells
selected)
is
a
convolu0on
of
these
two
func0ons
(approx.
area
on
the
intersec0on):
Fitness
(probability
to
be
selected):
Promoter
expression
distribu0on:
σ = 0.1
µ µ*
τ
37. As
mean
moves
away
from
the
op0mum
there
is
a
bifurca0on
to
nonzero
op0mal
noise
f (µ,σ | µ*,τ ) =
τ 2
τ 2
+σ 2
exp −
(µ −µ* )2
2(τ 2
+σ 2
)
"
#
$
%
&
'
f (µ = 8.0,σ = 0.05) = 0.0077
7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4
0.0
0.2
0.4
0.6
0.8
1.0
Log expression
ExpressionêSelectionprobability
f (µ = 8.0,σ = 0.1) = 0.066
7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4
0.0
0.2
0.4
0.6
0.8
1.0
Log expression
ExpressionêSelectionprobability
`Bifurca3on’
in
op3mal
σ
When
,
the
op0mal
noise
level
is
non-‐zero:
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Expression deviation »mu-mu*»
Optimalsigma
Op3mal
σ
σ* = (µ −µ* )2
−τ 2
τ = 0.05
τ = 0.2µ −µ*
≥ τ
38. Variable
environment:
Fitness
of
an
unregulated
gene
log f (µ,σ )[ ]= −
(µ −µe )2
2(τ 2
+σ 2
)
+
1
2
log
τ 2
τ 2
+σ 2
"
#
$
%
&
'Log-‐fitness
in
a
variable
environment:
Assuming
no
regula0on,
op0mal
mean
equals
Log-‐fitness
becomes:
Op3mal
noise
matches
the
varia3on
in
desired
expression
levels:
log f (µ,σ )[ ]= −
var(µe )
2(τ 2
+σ 2
)
+
1
2
log
τ 2
τ 2
+σ 2
"
#
$
%
&
'
This
is
the
bet
hedging
scenario.
But:
Wouldn’t
it
be
beer
to
evolve
gene
regula0on?
σopt
2
= var(µe )−τ 2
µ = µe
39. Effects
of
coupling
a
gene
to
a
regulator
Regulator’s
ac0vity
Gene
coupled
to
the
regulator.
Gene
without
regula0on
TF
TF
Two
main
effects
on
the
gene’s
expression:
1. Condi3on-‐response:
Mean
depends
on
regulator’s
(condi0on-‐dependent)
ac0vity.
2. Noise-‐propaga3on:
Noise
increases
due
to
propaga0on
of
the
regulator’s
noise.
We
developed
a
general
theory
to
calculate
how
these
effects
conspire
to
affect
fitness.
40. Fitness
depends
on
only
4
effec3ve
parameters
Varia0on
in
desired
levels:
V
στ
1.
Expression
mismatch:
Y 2
=
V
σ 2
+τ 2
Varia0on
in
regulator
levels:
Vr
σr
2.
Signal-‐to-‐noise
of
the
regulator:
S2
=
Vr
σr
2
3.
Correla3on
regulator/desired
levels:
R
Fitness
effect
of
the
regulatory
interac3on:
4.
Coupling
strength:
X
log[ f ]= −
1
2
Y 2
(1− R2
)+ SX − RY( )
2
(1+ X 2
)
−
1
2
log 1+ X 2"
#
$
%
Scenario:
Start
with
unregulated
promoter.
What
fitness
can
be
obtained
by
coupling
to
regulator
with
signal-‐to-‐noise
S
and
correla0on
R?
41. Fitness
with
op0mal
coupling
to
a
regulator
of
given
correla0on
R
and
signal-‐to-‐noise
S
Fitness
of
the
unregulated
promoter.
Y=4
Perfect
correla0on
No
correla0on
Noisy
regulator
Precise
regulator
42. Coupling
to
a
near
op3mal
regulator:
condi3on-‐response
effect
Y=4
TF
TF
σtot = 0.16
R = 0.95
S = 3.3
Fitness
of
the
unregulated
promoter.
43. Coupling
to
a
noisy
uncorrelated
regulator:
noise-‐propaga3on
implements
bet
hedging
strategy
Y=4
TF
TF
σtot = 0.55
R = 0
S = 0.19
Fitness
of
the
unregulated
promoter.
44. Intermediate
case:
a
moderately
correlated
regulator
Y=4
TF
TF
σtot = 0.23
R = 0.64
S = 2.45
Fitness
of
the
unregulated
promoter.
45. Op0mal
S
at
a
given
R.
Y=4
Condi3on-‐response
and
noise-‐propaga3on
typically
act
in
concert
Regulator
too
noisy.
Regulator
not
noisy
enough.
• Noise-‐propaga0on
is
oZen
func8onal,
ac0ng
as
a
rudimentary
form
of
regula0on.
• De
novo
evolu0on
of
regula0on:
Star0ng
from
pure
noise-‐propaga0on
(R=0,S=0)
there
is
a
con0nuum
of
solu0ons
of
increasing
accuracy
along
which
condi0on-‐
response
and
noise-‐propaga0on
op0mally
complement
each
other.
• Regulated
genes
are
noisy
because,
whenever
the
condi0on-‐response
is
imperfect,
maximal
fitness
requires
noisy
regulators.
Summary
Theory:
46. 0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
Y: Expression mismatch
R:Correlationofregulator'sexpressionwithdesired-levels
σtot
2
=σ 2
Low
noise
regime:
Promoters
with
low
expression
mismatch
Y<1
`do
not
bother’
to
be
regulated.
For
extremely
correlated
regulators,
zero
noise-‐propaga0on
is
the
op0mum.
Phase
diagram
of
final
noise
aZer
coupling
to
regulators
with
op0mal
noise
levels.
47. 0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
Y: Expression mismatch
R:Correlationofregulator'sexpressionwithdesired-levels
σtot
2
=σ 2
Noise-‐propaga3on
regime:
The
final
noise
level
matches
the
frac0on
of
variance
in
desired
levels
not
tracked
by
the
condi0on-‐response.
σtot
2
= (1− R2
)var(µe )−τ 2
Phase
diagram
of
final
noise
aZer
coupling
to
regulators
with
op0mal
noise
levels.
Amount
of
regula3on
required.
Variance
in
desired
levels
Selec3on
tolerance
Limited
accuracy
of
the
condi3on-‐response.
Frac3on
variance
not
tracked
by
regula3on.
48. Conclusions
signal
regulator
• We
evolved
synthe0c
promoters
de
novo
in
E.
coli
under
carefully-‐
controlled
selec0ve
condi0ons.
• No
evidence
E.
coli
promoters
have
been
selected
to
lower
noise.
• Regulated
genes
have
been
selected
to
increase
noise.
Experimental
observa3ons
Theory
• Coupling
a
regulator
to
a
target
promoter
has
two
effects:
1. Condi0on-‐response.
2. Noise-‐propaga0on.
• Noise-‐propaga0on
alone
can
act
as
a
rudimentary
form
of
regula0on.
• Accurate
regula0on
can
evolve
smoothly
along
a
con0nuum
in
which
noise-‐propaga0on
and
condi0on-‐response
act
in
concert.
• Whenever
the
condi0on-‐response
has
limited
accuracy,
noisy
regula0on
is
preferred.
• Explains
the
general
associa0on
between
noise
and
regula0on.
49. Thank
you!
Luise
Wolf
Olin
Silander
Theory/computa3on
PhD
and
post-‐doc
posi3ons
available!
This
work:
Our
group