Professional Issues in IT course project presentation to discuss how DNA can be used to store and manipulate information. Also, I discussed why or how can we use DNA in computing.
Bio computers use systems of biologically derived molecules—such as DNA and proteins—to perform computational calculations involving storing, retrieving, and processing data. The development of biocomputers has been made possible by the expanding new science of nanobiotechnology.
Bio computing uses DNA and biochemical processes to store and manipulate data similarly to human biology. DNA can store vast amounts of data densely due to its structure of paired chemical bases. A DNA computer operates massively in parallel and with extraordinary energy efficiency compared to conventional computers. While DNA computing shows potential for medical and data applications, it still requires further development to overcome challenges such as reduced accuracy compared to conventional computing.
DNA computing has several advantages including performing millions of operations in parallel, using large amounts of data storage in a small space, being lightweight, low power, and environmentally friendly. However, it also faces challenges such as molecular operations not being perfect and having a relatively high error rate. DNA computing shows promise for medical applications such as cancer diagnosis and targeted drug delivery but introducing genetic material into humans safely requires overcoming challenges like immune system reactions. While concerns around ethics and computers/DNA taking control exist, DNA computing remains an emerging field with opportunities in healthcare.
Biological computers use biological components like DNA to store and process data analogous to human body processes. They are implantable devices with a CPU and use DNA as software to monitor body activities and process data faster than traditional computers. DNA contains all genetic information in its molecular structure and biological computers use DNA computing, storing information in DNA molecules that can perform calculations much faster than regular computers using DNA's four basic components - adenine, cytosine, guanine and thymine. While biological computers are more efficient, accurate and environmentally friendly than traditional silicon-based computers, they also face challenges like potential hacking, need for human assistance, DNA degradation over time and rare pairing errors.
Bioinformatics in biotechnology by kk sahu KAUSHAL SAHU
Introduction
Bioinformatics – definition
History
Required skills
Core areas of bioinformatics
Components of bioinformatics
Nomenclature system in bioinformatics
Biological databases
Types of database
Bioinformatics tools
Applications of bioinformatics
Conclusion
References
This document discusses DNA computing, which uses DNA molecules to solve computational problems. It begins by introducing DNA computing and its inventor Leonard Adleman. It then explains what DNA is composed of and how its dense information storage and parallelism make it well-suited for computation. The document outlines Adleman's solution to the Hamiltonian Path Problem using DNA and provides an example of applying DNA computing to the Traveling Salesman Problem. It acknowledges current limitations of DNA computing compared to electronic computers but envisions future applications and improvements that may one day replace silicon-based computers.
Uses of Artificial Intelligence in BioinformaticsPragya Pai
This presentation is about the usage of Artificial Intelligence in Bioinformatics. These slides give the basic knowledge about usage of Artificial Intelligence in Bioinformatics.
Bio computers use systems of biologically derived molecules—such as DNA and proteins—to perform computational calculations involving storing, retrieving, and processing data. The development of biocomputers has been made possible by the expanding new science of nanobiotechnology.
Bio computing uses DNA and biochemical processes to store and manipulate data similarly to human biology. DNA can store vast amounts of data densely due to its structure of paired chemical bases. A DNA computer operates massively in parallel and with extraordinary energy efficiency compared to conventional computers. While DNA computing shows potential for medical and data applications, it still requires further development to overcome challenges such as reduced accuracy compared to conventional computing.
DNA computing has several advantages including performing millions of operations in parallel, using large amounts of data storage in a small space, being lightweight, low power, and environmentally friendly. However, it also faces challenges such as molecular operations not being perfect and having a relatively high error rate. DNA computing shows promise for medical applications such as cancer diagnosis and targeted drug delivery but introducing genetic material into humans safely requires overcoming challenges like immune system reactions. While concerns around ethics and computers/DNA taking control exist, DNA computing remains an emerging field with opportunities in healthcare.
Biological computers use biological components like DNA to store and process data analogous to human body processes. They are implantable devices with a CPU and use DNA as software to monitor body activities and process data faster than traditional computers. DNA contains all genetic information in its molecular structure and biological computers use DNA computing, storing information in DNA molecules that can perform calculations much faster than regular computers using DNA's four basic components - adenine, cytosine, guanine and thymine. While biological computers are more efficient, accurate and environmentally friendly than traditional silicon-based computers, they also face challenges like potential hacking, need for human assistance, DNA degradation over time and rare pairing errors.
Bioinformatics in biotechnology by kk sahu KAUSHAL SAHU
Introduction
Bioinformatics – definition
History
Required skills
Core areas of bioinformatics
Components of bioinformatics
Nomenclature system in bioinformatics
Biological databases
Types of database
Bioinformatics tools
Applications of bioinformatics
Conclusion
References
This document discusses DNA computing, which uses DNA molecules to solve computational problems. It begins by introducing DNA computing and its inventor Leonard Adleman. It then explains what DNA is composed of and how its dense information storage and parallelism make it well-suited for computation. The document outlines Adleman's solution to the Hamiltonian Path Problem using DNA and provides an example of applying DNA computing to the Traveling Salesman Problem. It acknowledges current limitations of DNA computing compared to electronic computers but envisions future applications and improvements that may one day replace silicon-based computers.
Uses of Artificial Intelligence in BioinformaticsPragya Pai
This presentation is about the usage of Artificial Intelligence in Bioinformatics. These slides give the basic knowledge about usage of Artificial Intelligence in Bioinformatics.
This document discusses DNA computing and provides an overview of key concepts. It summarizes Adleman's 1994 experiment solving the Hamiltonian path problem using DNA strands to represent graph connections. While DNA computing shows promise for massively parallel processing, current limitations include slow laboratory procedures and inability to represent data universally. Future advances may address these issues and enable DNA computers to solve certain complex problems not feasible with electronic computers.
This document discusses analyzing and visualizing gene expression data. It defines key terms like genes and gene expression data. It also describes clustering gene expression data using k-means clustering to group genes based on similarity in a dataset of yeast cell cycle genes. Finally, it discusses visualizing gene expression data using techniques like vector fusion, nMDS, and PCA to project high-dimensional gene expression datasets into 2D or 3D spaces.
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
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.
The document discusses DNA computing and its advantages over traditional computers. It begins with an introduction by Debadarshi Mishra on the topic. DNA computers use enzymes that react with DNA strands in a chain reaction to perform computations in parallel, unlike traditional computers that use binary. DNA computers are smaller, faster, and can solve problems with many possible solutions simultaneously. Their potential applications include monitoring health and generating customized drugs. While still in development, DNA computing represents a new approach to computation at the molecular level.
DNA computing is a novel approach that uses DNA, RNA, and biochemical reactions to solve computational problems. The document outlines Adleman's experiment using DNA to solve the Hamiltonian path problem. It then discusses applications of DNA computing such as solving NP-complete problems, data storage, DNA sequencing, and mutation detection. Finally, it compares DNA computers to conventional computers, noting DNA's ability to perform massive parallelism but its sensitivity to chemical deterioration.
The document summarizes a seminar presentation on DNA computing. It introduces DNA computing and its potential applications, advantages, and current limitations. DNA computing utilizes the massive parallelism of DNA strands to solve complex computational problems. While DNA computing offers advantages like vast storage capacity and parallel processing, its practical applications are currently limited by errors, time-consuming procedures, and the lack of a universal data representation method. The future of DNA computing depends on overcoming these challenges and finding suitable applications that leverage its unique capabilities.
The document presents information on DNA computing. It discusses how DNA computing uses the properties of DNA to perform massively parallel computations. It provides background on DNA computing, including its history starting with Leonard Adleman's 1994 proof-of-concept. The document also outlines applications of DNA computing, advantages such as performing millions of operations simultaneously, and current limitations like requiring large amounts of DNA and time-consuming laboratory procedures.
Scoring schemes in bioinformatics (blosum)SumatiHajela
This document discusses scoring schemes in bioinformatics, specifically BLOSUM (BLOcks SUbstitution Matrix). It introduces BLOSUM, describing that it is based on conserved amino acid patterns from multiple sequence alignments. It then explains the BLOSUM-62 matrix and the BLOSUM scoring algorithm. The document contrasts BLOSUM with PAM matrices, noting key differences like BLOSUM being based on direct observations while PAM uses evolutionary modeling. Finally, it outlines the significance of scoring matrices for detecting distant evolutionary relationships between protein sequences.
The document presents an overview of DNA computers. DNA computers use DNA molecules as the data storage medium and enzymes as the processing units. Some key advantages of DNA computers include massive data storage capacity using a small physical space, highly parallel processing, and low cost. However, DNA computers also currently have limitations such as high error rates and the need for human assistance in laboratory procedures. Potential applications of DNA computing include DNA chips, genetic programming, and pharmaceutical analysis. While DNA computers show promise, further work is still needed to develop them into a practical product.
The document discusses various types of biological databases including nucleotide databases, genomic databases, protein databases, and metabolic databases. It provides examples of several specific databases, such as Nucleotide databases like GenBank, genomic databases like Entrez Genome, protein databases like UniProt, and metabolic databases like KEGG. It also discusses the different levels of data in biological databases from primary data directly from experiments to secondary data that is analyzed and derived from primary data.
This document discusses using DNA for digital data storage. It provides background on DNA and how digital data can be encoded in DNA sequences. Key advantages of DNA storage include extremely high storage density, longevity, and security. Challenges include slow retrieval speeds and high costs. Recent developments show promise, such as Microsoft demonstrating storing 200 MB of data in a single drop of liquid DNA. DNA may provide vastly more efficient long-term storage than current technologies.
Interactomics, Integromics to Systems Biology: Next Animal Biotechnology Fron...Varij Nayan
“Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological
systems”
(David Baltimore, Nobel Laureate)
Applying Hidden Markov Models to Bioinformaticsbutest
This document discusses the application of hidden Markov models (HMMs) to bioinformatics. HMMs were first developed in the 1960s and applied to speech recognition in the 1970s. In the 1980s, HMMs began to be used for analyzing biological sequences like DNA. HMMs are a powerful tool for bioinformatics because they allow probabilistic modeling of linear sequences and labeling problems. The document provides an example of how an HMM can be used to identify a splice site in a DNA sequence by modeling exon, intron, and splice site states and emissions.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
The document discusses transcriptomics and the relationship between transcriptome size and organism complexity. It questions how gene expression contributes to transcriptome size and what new studies reveal about size and complexity. Specifically, it notes that alternative splicing and RNA editing increase transcriptome size and complexity. It also discusses that the human genome is pervasively transcribed, with one stretch of DNA encoding many RNAs, including microRNAs, which control mRNA expression and are involved in development, gene regulation, and diseases like cancer.
This document provides an introduction to biological databases and bioinformatics tools. It defines biological sequences and databases, and describes the types of bioinformatics databases including primary, secondary, and composite databases. Examples of specific biological databases like GenBank, EMBL, and SwissProt are outlined. Common bioinformatics tools for sequence analysis, structural analysis, protein function analysis, and homology/similarity searches are listed, including BLAST, FASTA, EMBOSS, ClustalW, and RasMol. Finally, important bioinformatics resources on the web are highlighted.
Scientists have successfully stored 700 terabytes of data in a single gram of DNA, vastly exceeding previous DNA data density records. DNA is an ideal storage medium as it is incredibly dense, with each DNA base representing a binary digit. Additionally, DNA is very stable and can preserve data for hundreds of thousands of years without needing to be kept in controlled environments like other storage methods. Researchers are also exploring using DNA to build biological computers and memory devices, taking advantage of DNA's ability to store and process genetic information.
DNA has potential as a digital data storage medium due to its ability to densely store information for thousands of years. Information can be encoded in DNA by assigning a DNA nucleotide sequence to represent digital data through a coding table. Researchers have demonstrated storing several files like an operating system and movie totaling 2.14 megabytes in DNA. DNA storage has advantages of being stable and dense, with potential to store 2.2 petabytes per gram. However, the speed of reading and writing data is currently slow and costly. Future improvements may enable uses like DNA computing, nanotechnology, and personal storage applications.
This document discusses DNA computing and provides an overview of key concepts. It summarizes Adleman's 1994 experiment solving the Hamiltonian path problem using DNA strands to represent graph connections. While DNA computing shows promise for massively parallel processing, current limitations include slow laboratory procedures and inability to represent data universally. Future advances may address these issues and enable DNA computers to solve certain complex problems not feasible with electronic computers.
This document discusses analyzing and visualizing gene expression data. It defines key terms like genes and gene expression data. It also describes clustering gene expression data using k-means clustering to group genes based on similarity in a dataset of yeast cell cycle genes. Finally, it discusses visualizing gene expression data using techniques like vector fusion, nMDS, and PCA to project high-dimensional gene expression datasets into 2D or 3D spaces.
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
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.
The document discusses DNA computing and its advantages over traditional computers. It begins with an introduction by Debadarshi Mishra on the topic. DNA computers use enzymes that react with DNA strands in a chain reaction to perform computations in parallel, unlike traditional computers that use binary. DNA computers are smaller, faster, and can solve problems with many possible solutions simultaneously. Their potential applications include monitoring health and generating customized drugs. While still in development, DNA computing represents a new approach to computation at the molecular level.
DNA computing is a novel approach that uses DNA, RNA, and biochemical reactions to solve computational problems. The document outlines Adleman's experiment using DNA to solve the Hamiltonian path problem. It then discusses applications of DNA computing such as solving NP-complete problems, data storage, DNA sequencing, and mutation detection. Finally, it compares DNA computers to conventional computers, noting DNA's ability to perform massive parallelism but its sensitivity to chemical deterioration.
The document summarizes a seminar presentation on DNA computing. It introduces DNA computing and its potential applications, advantages, and current limitations. DNA computing utilizes the massive parallelism of DNA strands to solve complex computational problems. While DNA computing offers advantages like vast storage capacity and parallel processing, its practical applications are currently limited by errors, time-consuming procedures, and the lack of a universal data representation method. The future of DNA computing depends on overcoming these challenges and finding suitable applications that leverage its unique capabilities.
The document presents information on DNA computing. It discusses how DNA computing uses the properties of DNA to perform massively parallel computations. It provides background on DNA computing, including its history starting with Leonard Adleman's 1994 proof-of-concept. The document also outlines applications of DNA computing, advantages such as performing millions of operations simultaneously, and current limitations like requiring large amounts of DNA and time-consuming laboratory procedures.
Scoring schemes in bioinformatics (blosum)SumatiHajela
This document discusses scoring schemes in bioinformatics, specifically BLOSUM (BLOcks SUbstitution Matrix). It introduces BLOSUM, describing that it is based on conserved amino acid patterns from multiple sequence alignments. It then explains the BLOSUM-62 matrix and the BLOSUM scoring algorithm. The document contrasts BLOSUM with PAM matrices, noting key differences like BLOSUM being based on direct observations while PAM uses evolutionary modeling. Finally, it outlines the significance of scoring matrices for detecting distant evolutionary relationships between protein sequences.
The document presents an overview of DNA computers. DNA computers use DNA molecules as the data storage medium and enzymes as the processing units. Some key advantages of DNA computers include massive data storage capacity using a small physical space, highly parallel processing, and low cost. However, DNA computers also currently have limitations such as high error rates and the need for human assistance in laboratory procedures. Potential applications of DNA computing include DNA chips, genetic programming, and pharmaceutical analysis. While DNA computers show promise, further work is still needed to develop them into a practical product.
The document discusses various types of biological databases including nucleotide databases, genomic databases, protein databases, and metabolic databases. It provides examples of several specific databases, such as Nucleotide databases like GenBank, genomic databases like Entrez Genome, protein databases like UniProt, and metabolic databases like KEGG. It also discusses the different levels of data in biological databases from primary data directly from experiments to secondary data that is analyzed and derived from primary data.
This document discusses using DNA for digital data storage. It provides background on DNA and how digital data can be encoded in DNA sequences. Key advantages of DNA storage include extremely high storage density, longevity, and security. Challenges include slow retrieval speeds and high costs. Recent developments show promise, such as Microsoft demonstrating storing 200 MB of data in a single drop of liquid DNA. DNA may provide vastly more efficient long-term storage than current technologies.
Interactomics, Integromics to Systems Biology: Next Animal Biotechnology Fron...Varij Nayan
“Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological
systems”
(David Baltimore, Nobel Laureate)
Applying Hidden Markov Models to Bioinformaticsbutest
This document discusses the application of hidden Markov models (HMMs) to bioinformatics. HMMs were first developed in the 1960s and applied to speech recognition in the 1970s. In the 1980s, HMMs began to be used for analyzing biological sequences like DNA. HMMs are a powerful tool for bioinformatics because they allow probabilistic modeling of linear sequences and labeling problems. The document provides an example of how an HMM can be used to identify a splice site in a DNA sequence by modeling exon, intron, and splice site states and emissions.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
The document discusses transcriptomics and the relationship between transcriptome size and organism complexity. It questions how gene expression contributes to transcriptome size and what new studies reveal about size and complexity. Specifically, it notes that alternative splicing and RNA editing increase transcriptome size and complexity. It also discusses that the human genome is pervasively transcribed, with one stretch of DNA encoding many RNAs, including microRNAs, which control mRNA expression and are involved in development, gene regulation, and diseases like cancer.
This document provides an introduction to biological databases and bioinformatics tools. It defines biological sequences and databases, and describes the types of bioinformatics databases including primary, secondary, and composite databases. Examples of specific biological databases like GenBank, EMBL, and SwissProt are outlined. Common bioinformatics tools for sequence analysis, structural analysis, protein function analysis, and homology/similarity searches are listed, including BLAST, FASTA, EMBOSS, ClustalW, and RasMol. Finally, important bioinformatics resources on the web are highlighted.
Scientists have successfully stored 700 terabytes of data in a single gram of DNA, vastly exceeding previous DNA data density records. DNA is an ideal storage medium as it is incredibly dense, with each DNA base representing a binary digit. Additionally, DNA is very stable and can preserve data for hundreds of thousands of years without needing to be kept in controlled environments like other storage methods. Researchers are also exploring using DNA to build biological computers and memory devices, taking advantage of DNA's ability to store and process genetic information.
DNA has potential as a digital data storage medium due to its ability to densely store information for thousands of years. Information can be encoded in DNA by assigning a DNA nucleotide sequence to represent digital data through a coding table. Researchers have demonstrated storing several files like an operating system and movie totaling 2.14 megabytes in DNA. DNA storage has advantages of being stable and dense, with potential to store 2.2 petabytes per gram. However, the speed of reading and writing data is currently slow and costly. Future improvements may enable uses like DNA computing, nanotechnology, and personal storage applications.
This document discusses DNA digital storage. It begins by summarizing early pioneering approaches to storing digital data in DNA from 2013, including storing 11 JPEG images and Martin Luther King Jr.'s "I Have a Dream" speech. It then provides an overview of DNA structure and how single nucleotides can represent 2 bits of information. The document outlines the process of encoding data in DNA through various encoding methods, synthesizing DNA, using polymerase chain reaction to amplify DNA, sequencing DNA to decode the stored data. It discusses challenges including the current low speed of writing and rewriting data and high chance of errors during synthesis, as well as advantages like DNA's ability to store huge amounts of data densely and durably.
A camera holds a year's worth of photos and videos now. Storage is measured in petabytes. PCs may no longer need booting. The memristor is a new circuit element that was theorized in 1971 and first created in 2008. It is a resistor that remembers the amount of charge that flows through it. Memristors could replace other memory technologies due to benefits like non-volatility, faster speeds, and higher density. They may also enable new applications in AI, flexible displays, sensors, and more lifelike computing.
DNA shows promise as a long-term information storage solution. It is stable, durable, and can store vast amounts of data in a small physical space. The document outlines how DNA can be used to store digital files by encoding the information into DNA sequences using a quaternary coding system. As an example, researchers were able to store several works by Shakespeare, scientific papers, images and audio clips in DNA. While the speed and cost of reading and writing to DNA are currently limitations, the technology is improving rapidly and DNA may become a practical large-scale storage solution within the next 5-10 years.
The document discusses various genomic and proteomic tools and techniques that have revolutionized the field of microbial physiology. The advent of personal computers, the Internet, and rapid DNA sequencing techniques has fueled this renaissance by enabling widespread sharing of information among scientists. Genomic tools like gene cloning and sequencing provide insights into complete genetic instructions, while proteomic techniques examine dynamic protein expression and interactions. A variety of methods are described, including two-dimensional gel electrophoresis, mass spectrometry, and gene arrays.
The document discusses protein-based memory storage as a promising new technology to compete with existing memory storage methods. It describes how bacteriorhodopsin, a light-sensitive protein found in halobacteria, undergoes reversible changes in absorption of light and can be used to store data in a 3D optical memory. Bacteriorhodopsin has desirable properties such as stability at high temperatures, fast switching time, and potential for high density data storage. The document outlines how bacteriorhodopsin undergoes a photocycle in response to light, changing its optical and electrical characteristics and allowing it to function as an optical memory storage medium.
DNA sequencing determines the order of nucleotides in a gene through four main steps: (1) PCR amplifies and fragments chromosomes, (2) a sequencing reaction uses dideoxyribonucleotides to randomly cut strands ending with each individual nucleotide, (3) gel electrophoresis separates the fragments by size and fluorescent dyes identify the end nucleotide, and (4) computers scan and interpret the gel to print the nucleotide sequence. Current limitations are that only 500-900 bases can be read per run and less expensive methods are less accurate. Some also have ethical concerns about DNA sequencing becoming too common and personal genetic information being abused.
DNA sequencing determines the order of nucleotides in a gene through four main steps: (1) PCR amplifies and fragments chromosomes, (2) a sequencing reaction uses dideoxyribonucleotides to randomly cut strands ending with each individual nucleotide, (3) gel electrophoresis separates the fragments by size and fluorescent dyes identify the end nucleotide, and (4) computers scan and interpret the gel to print the nucleotide sequence. Current limitations are that only 500-900 bases can be read per run and less expensive methods are less accurate. Some also have ethical concerns about privacy if sequencing becomes too common.
Blue brain technology aims to create a synthetic brain using nanobots and supercomputing. It would function similarly to the human brain by taking inputs, processing information, and producing outputs. A key goal is to upload the contents of natural brains into virtual brains to preserve human intelligence, knowledge, and skills indefinitely. Realizing such a brain would require powerful hardware and software like large memory, fast processors, and programs to translate between neural and digital signals. Potential advantages include enhanced memory and decision making. Disadvantages could include overreliance on computers and misuse of personal information.
DNA digital data storage is the process of encoding and decoding binary data to and from synthesized strands of DNA. While DNA as a storage medium has enormous potential because of its high storage density, its practical use is currently severely limited because of its high cost and very slow read and write times.
DNA digital data storage is the process of encoding and decoding binary data to and from synthesized strands of DNA. While DNA as a storage medium has enormous potential because of its high storage density, its practical use is currently severely limited because of its high cost and very slow read and write times.
This document discusses using DNA as a digital storage medium. It provides an introduction to DNA digital storage and outlines the structure and coding methods used to encode digital data in DNA. The document explains how source data is converted to a tertiary code and then mapped to DNA nucleobases to encode the information. It describes some potential applications of DNA storage such as archiving and discusses how companies are developing DNA storage technologies that could store massive amounts of data in very small physical volumes.
This seminar presentation discusses DNA data storage. It provides background on DNA and its structure, then summarizes the history of data storage from punch cards to modern technologies. Challenges of big data are reviewed. DNA is proposed as a storage medium due to its high density and longevity. The presentation explains how data is stored in DNA using algorithms and techniques like polymerase chain reaction. Current research by Microsoft is discussed as a case study. Both advantages like density and disadvantages like cost and read speeds are presented. Applications and future potential are considered.
London Exponential Technologies Meetup, July 2017Peter Morgan
This document summarizes an inaugural meetup on exponential technologies held in London on May 10, 2016. It discusses several major exponential technologies including artificial intelligence, longevity, bioengineering, and quantum computing. For each technology, it provides examples of major areas of work and companies involved. It also lists recommended reading materials and upcoming meetup topics on related subjects such as robotics, human longevity, biotech, and blockchain. The meetup was sponsored by Innovation Warehouse, O'Reilly, and Nvidia.
Molecular computing is an emerging field to which chemistry,
biophysics, molecular biology, electronic engineering, solid state physics and computer science contribute to a large extent. It involves the encoding, manipulation and retrieval of information at a macro molecular level in contrast to the current techniques, which accomplish the above functions
via IC miniaturization of bulk devices. Bio-molecular computers have the real potential for solving problems of high computational complexities and therefore, many problems are still associated with this field.
DNA computing is a novel approach that uses DNA, genetic material, and biochemical processes to solve computational problems. DNA computers have significant potential advantages over traditional silicon-based computers, including massive parallelism, large storage capacity, and low energy usage. However, DNA computing also faces challenges such as slow operation speeds and reliability issues that need to be addressed through ongoing research.
DNA sequencing is the process of determining the precise order of nucleotides in a DNA molecule. There are two main historical methods - the Maxam-Gilbert chemical method from 1977 and the Sanger dideoxy chain termination method from 1977, which is still commonly used. Next generation sequencing uses massively parallel sequencing to produce millions of short DNA fragments at once, reducing costs significantly. However, next generation sequencing produces huge amounts of data that presents challenges for data storage, analysis, and interpretation.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
3. Data / Information storage
• Paper data storage
• Computer data storage
• DNA digital data storage
4. Why DNA-Storage?
1. DNA has already stood the test of time
scientists have managed to extract mitochondrial DNA from
human remains that are 65,000 years old
2. DNA is universal
If DNA is used to store information, the archeologists and
historians of the future will be able to quickly decipher the
code
3. DNA can be easily replicated
Polymerase chain reaction (PCR)
6. Computing – Information processing
• Binary Language
• ASCII Codes
• Logic Gates
• Quantum Computing
• Quantum states of subatomic particles to store information
• Biocomputing
• Amino acids
10. Future potential of bio computers
• Some people believe that bio computers have great
potential, but this has yet to be demonstrated.
• Solve np and np-hard problems
• Cryptography
• Bioinformatics problems and simulations