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Y Saturday, August 11, 2007Y
3:09 PM

Bioinformatics and computational biology involve the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry, and biochemistry to solve biological problems usually on the molecular level. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution.


introduction

The terms bioinformatics and computational biology are often used interchangeably. However bioinformatics more properly refers to the creation and advancement of algorithms, computational and statistical techniques, and theory to solve formal and practical problems inspired from the management and analysis of biological data. Computational biology, on the other hand, refers to hypothesis-driven investigation of a specific biological problem using computers, carried out with experimental or simulated data, with the primary goal of discovery and the advancement of biological knowledge. Put more simply, bioinformatics is concerned with the information while computational biology is concerned with the hypotheses. A similar distinction is made by National Institutes of Health in their working definitions of Bioinformatics and Computational Biology, where it is further emphasized that there is a tight coupling of developments and knowledge between the more hypothesis-driven research in computational biology and technique-driven research in bioinformatics. Bioinformatics is also often specified as an applied subfield of the more general discipline of Biomedical informatics.

A common thread in projects in bioinformatics and computational biology is the use of mathematical tools to extract useful information from data produced by high-throughput biological techniques such as genome sequencing. A representative problem in bioinformatics is the assembly of high-quality genome sequences from fragmentary "shotgun" DNA sequencing. Other common problems include the study of gene regulation using data from microarrays or mass spectrometry.

[edit] Major research areas

[edit] Sequence analysis

Since the Phage Φ-X174 was sequenced in 1977, the DNA sequences of hundreds of organisms have been decoded and stored in databases. The information is analyzed to determine genes that encode polypeptides, as well as regulatory sequences. A comparison of genes within a species or between different species can show similarities between protein functions, or relations between species (the use of molecular systematics to construct phylogenetic trees). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Today, computer programs are used to search the genome of thousands of organisms, containing billions of nucleotides. These programs would compensate for mutations (exchanged, deleted or inserted bases) in the DNA sequence, in order to identify sequences that are related, but not identical. A variant of this sequence alignment is used in the sequencing process itself. The so-called shotgun sequencing technique (which was used, for example, by The Institute for Genomic Research to sequence the first bacterial genome, Haemophilus influenzae) does not give a sequential list of nucleotides, but instead the sequences of thousands of small DNA fragments (each about 600-800 nucleotides long). The ends of these fragments overlap and, when aligned in the right way, make up the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. In the case of the Human Genome Project, it took several months of CPU time (on a circa-2000 vintage DEC Alpha computer) to assemble the fragments. Shotgun sequencing is the method of choice for virtually all genomes sequenced today, and genome assembly algorithms are a critical area of bioinformatics research.

Another aspect of bioinformatics in sequence analysis is the automatic search for genes and regulatory sequences within a genome. Not all of the nucleotides within a genome are genes. Within the genome of higher organisms, large parts of the DNA do not serve any obvious purpose. This so-called junk DNA may, however, contain unrecognized functional elements. Bioinformatics helps to bridge the gap between genome and proteome projects--for example, in the use of DNA sequences for protein identification.

See also: sequence analysis, sequence profiling tool, sequence motif.

[edit] Genome annotation

Main article: Gene finding

In the context of genomics, annotation is the process of marking the genes and other biological features in a DNA sequence. The first genome annotation software system was designed in 1995 by Dr. Owen White, who was part of the team that sequenced and analyzed the first genome of a free-living organism to be decoded, the bacterium Haemophilus influenzae. Dr. White built a software system to find the genes (places in the DNA sequence that encode a protein), the transfer RNA, and other features, and to make initial assignments of function to those genes. Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA are constantly changing and improving.

[edit] Computational evolutionary biology

Evolutionary biology is the study of the origin and descent of species, as well as their change over time. Informatics has assisted evolutionary biologists in several key ways; it has enabled researchers to:

  • trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone,
  • more recently, compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, lateral gene transfer, and the prediction of bacterial speciation factors,
  • build complex computational models of populations to predict the outcome of the system over time
  • track and share information on an increasingly large number of species and organisms

Future work endeavours to reconstruct the now more complex tree of life.

The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology, but the two areas are unrelated.

[edit] Measuring biodiversity

Biodiversity of an ecosystem might be defined as the total genomic complement of a particular environment, from all of the species present, whether it is a biofilm in an abandoned mine, a drop of sea water, a scoop of soil, or the entire biosphere of the planet Earth. Databases are used to collect the species names, descriptions, distributions, genetic information, status and size of populations, habitat needs, and how each organism interacts with other species. Specialized software programs are used to find, visualize, and analyze the information, and most importantly, communicate it to other people. Computer simulations model such things as population dynamics, or calculate the cumulative genetic health of a breeding pool (in agriculture) or endangered population (in conservation). One very exciting potential of this field is that entire DNA sequences, or genomes of endangered species can be preserved, allowing the results of Nature's genetic experiment to be remembered in silico, and possibly reused in the future, even if that species is eventually lost.

Important projects: Species 2000 project; uBio Project.

[edit] Analysis of gene expression

The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate signal from noise in high-throughput gene expression studies. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.

[edit] Analysis of regulation

Regulation is the complex orchestration of events starting with an extra-cellular signal and ultimately leading to an increase or decrease in the activity of one or more protein molecules. Bioinformatics techniques have been applied to explore various steps in this process. For example, promoter analysis involves the elucidation and study of sequence motifs in the genomic region surrounding the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the cell cycle, along with various stress conditions (heat shock, starvation, etc.). One can then apply clustering algorithms to that expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented regulatory elements.

[edit] Analysis of protein expression

Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein are detected.

[edit] Analysis of mutations in cancer

In cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways. Massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer. Bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare the sequencing results to the growing collection of human genome sequences and germline polymorphisms. New physical detection technology are employed, such as oligonucleotide microarrays to identify chromosomal gains and losses (called comparative genomic hybridization), and single nucleotide polymorphism arrays to detect known point mutations. These detection methods simultaneously measure several hundred thousand sites throughout the genome, and when used in high-throughput to measure thousands of samples, generate terabytes of data per experiment. Again the massive amounts and new types of data generate new opportunities for bioinformaticians. The data is often found to contain considerable variability, or noise, and thus Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes.

Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent across many tumors .

[edit] Prediction of protein structure

Protein structure prediction is another important application of bioinformatics. The amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it. In the vast majority of cases, this primary structure uniquely determines a structure in its native environment. (Of course, there are exceptions, such as the bovine spongiform encephalopathy - aka Mad Cow Disease - prion.) Knowledge of this structure is vital in understanding the function of the protein. For lack of better terms, structural information is usually classified as one of secondary, tertiary and quaternary structure. A viable general solution to such predictions remains an open problem. As of now, most efforts have been directed towards heuristics that work most of the time.

One of the key ideas in bioinformatics is the notion of homology. In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. In a technique called homology modeling, this information is used to predict the structure of a protein once the structure of a homologous protein is known. This currently remains the only way to predict protein structures reliably.

One example of this is the similar protein homology between hemoglobin in humans and the hemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting oxygen in the organism. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes.

Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling.

See also structural motif and structural domain.

[edit] Comparative genomics

The core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. It is these intergenomic maps that make it possible to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. Ultimately, whole genomes are involved in processes of hybridization, polyploidization and endosymbiosis, often leading to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectra of algorithmic, statistical and mathematical techniques, ranging from exact, heuristics, fixed parameter and approximation algorithms for problems based on parsimony models to Markov Chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models.

Many of these studies are based on the homology detection and protein families computation.

See also comparative genomics, bayesian network and protein family.

[edit] Modeling biological systems

Main article: Systems biology

Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

[edit] High-throughput image analysis

Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. Modern image analysis systems augment an observer's ability to make measurements from a large or complex set of images, by improving accuracy, objectivity, or speed. A fully developed analysis system may completely replace the observer. Although these systems are not unique to biomedical imagery, biomedical imaging is becoming more important for both diagnostics and research. Some examples are:

  • high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology)
  • morphometrics
  • clinical image analysis and visualization
  • determining the real-time air-flow patterns in breathing lungs of living animals
  • quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury
  • making behavioral observations from extended video recordings of laboratory animals
  • infrared measurements for metabolic activity determination

[edit] Protein-protein docking

In the last two decades, tens of thousands of protein three-dimensional structures are determined by X-ray crystallography and Protein nuclear magnetic resonance spectroscopy (protein NMR). One central question for the biological scientist is whether it is practical to predict possible protein-protein interactions only based on these 3D shapes, without doing protein-protein interaction experiments. A variety of methods have been developed to tackle the Protein-protein docking problem, though it seems that there is still much place to work on in this field.

[edit] Software tools

Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalong web-services. The computational biology tool best-known among biologists is probably BLAST, an algorithm for determining the similarity of arbitrary sequences against other sequences, possibly from curated databases of protein or DNA sequences. The NCBI provides a popular web-based implementation that searches their databases.

SOAP-based (Service Oriented Architecture Protocol) interfaces have been developed for a wide variety of bioinformatics applications allowing an application running on one computer in one part of the world to use algorithms, data and computing resources on servers in other parts of the world. The availability of these SOAP-based bioinformatics web services through systems such as the BioMoby service register demonstrate the applicability of web based bioinformatics solutions. These tools range from a collection of standalone tools with a common data format under a single, standalone or web-based interface, to integrative and extensible bioinformatics workflow management systems.



en.wikipedia.org/wiki/Bioinformatics

Y Monday, July 30, 2007Y
10:31 PM
Applied for:

MOE Scholarship, shortlisted, but I rejected them on the spot during interview;
Public Service Commission Scholarship,
And one other scholarship that I cant even remember now

USP program, shortlisted, interview, didnt get in.
Food Science n tech;

And now finally, computational biology.

comp bio.

I am glad of cos, but I am still having doubts whether I should get out of my comfort zone or not.

Computing, mathematics and biology. Not a combination that I will choose.
I can stick to what I feel the most comfortable. Life science.
But. I want to explore more to life. To learn more, gain more knowledge, and have more meaning to life.

I know I am not a computer person, but perhaps, if I am willing to put in more effort, to put in more time, computing will not be a problem for me.

Maths, a little more effort shouldn’t be a problem.

I am scare, really scare to get out of my comfort zone.
Moreover, I did not even consider school of computing before.
But I know, this is the track that can allow me to pursue my diverse interest, and to develop a critical and analytical mindset.
I have to force myself out of my comfort zone. There is a more thing in life than biology.

On the way home, I was just thinking on the train.
Fear engulfed me, n I really feel like breaking down.
Computational biology.
Bioinformatics.
A total new world to me.
I am scare.
This course is tough, I have been warned.

I want to try something different from others I guess.
I do not want to stay with the flow.
The mainstream.
Although I am scare, but I want to try it out, I want to overcome my fear to programming, computers. I want to know more.

So please, what I need is encouragements and support from my friends, that I can do it. No matter how difficult it will going to be, remind me to preserve, and to keep a positive mindset, cos this is what I have chosen. Please. Every discouragement does make an impact to me.

If this is what God had planned for me, I will undergo it with endurance and a positive mindset. No complains and complete trust in him. Cos I know He will always be there for me and I know that what ever He planned is for my own good. . It is a challenge from him, to develop me into who I will going to be.

Give me all support and encouragements, cos I think I will need it badly for time to come. But I will try my best.

Loves,
Adeline
Confused. Scared. Lost.

Y Sunday, July 29, 2007Y
3:12 PM
Greed:Low
Gluttony:Medium
Wrath:Low
Sloth:Medium
Envy:Medium
Lust:Very Low
Pride:High

The Seven Deadly Sins Quiz on 4degreez.com

1:20 AM
Went PRP with BK. Well, as I was staring out into the sea, thoughts came into my mind.

1) What LX said yesterday (fri) night, really make some sense after all. Perhaps He is trying to make me realized that I am, indeed, ready to take the next step – to understand more deeply.

2) I was suddenly reminded of what happened last year during the camp ignite. While the camp coordinators were taking a break, I actually managed to escape my responsibility of further planning and make my way alone to PRP. Sitting on the sand and staring out into the sea under the stars scattered sky for more than an hour, I felt so lost and burdened, with all the planning and coordinating of the camp. I wondered what I was doing all these for. i thought about what do I want in my life actually. And most importantly that was when I realized that that relationship was impossible to continue. Somehow, it was that time over there, alone, that I managed to sort out what was happening in my life.

Upon going back, the concern from friends and the smiles and laughter of the kids, make me realized that all those work and tears, was worth it after all. That was what I was doing for. Thinking back now, the memories still remain quite vividly.

Somehow or another, after that, I managed to sort out my life.

3) Sea. Calm yet violent. It is contradicting - the destructions it can bring and, the sort of peacefulness and tranquility it can have on a person. Memories of the tsunami came flooding back - The Day of Judgment. Homes, family and lives were ruined. Pray that the people over at purket had managed to get bits and pieces of their lives together now. They are trying hard.

I miss the innocent nature of the children.

I miss their smiles.

I miss their laughter.

I miss playing and talking photos with them.

I miss making the fishing nets, even thought I got blisters from that.

I miss sorting out and painting the wood.

I miss the scary looking hotel.

I miss hanging out late into the night with the guys
- Playing cards, chatting
- exploring the village

I miss having breakfast, watching soccer/thai palace with tian, huilin and Alvin in the middle of the night.

I miss having Spy with tian n vera.

I miss having breakfast with chu and the exco.

There are so many tiny details that I miss. Too bad it is impossible to reverse time. 5days, but it is a lifetime memory, a lifetime impact.


Perhaps it was all these memories that I was quiet than. I guess, I just have to get used to a new chapter of my life.

u can do it!
~positivc&actions~

Y Saturday, July 28, 2007Y
1:54 AM
I always thought I am a person who knows what I want to do in life, but in fact, I am not. I want to try out too many things, I want to excel tot many things, and I am making my life tough by thinking too much. I should learn to take it easy and just follow the flow, like the rest? Hm...

I still think the most important thing is to be happy, and contended with what you have. Although so, I am still learning how to achieve that. It is not easy. but still, keep a positive mind.

Oh ya. I didn't get into food sci n tech. somehow I am glad. At least one more choice is out. Those that remain are life sci, and computational biology. No matter which first major I am doing, pray that I can get management as my second major. That is what I want, and I am sure about it.

Y Monday, July 23, 2007Y
11:06 PM
So your meeting is more important than your daughter?

Y Friday, July 20, 2007Y
5:16 PM
I am sad and disappointed. If I had put in more effort, if I had study harder, and got a overseas scholarship, I will not be coped in Singapore, and that local U, doing a degree which I am not at least interested in. there is numerous ‘if’, but now, it is a fact that I have to tolerate 3 years in a nus, find a good job, and earn enough for me to pursue my interest and study the course that I want – nutrition. Did some research under the recommendation by Ron, and it does make me feel worse, knowing that doing a degree in Berkeley or UCLA, is almost not possible. The tuition fee itself is unbearable. A year there can total up to what I will spend in local uni for 3 years. If nus or any other local uni offers a major in nutrition, that will be the best. But for now, I just have to make do with it.

Y Tuesday, July 17, 2007Y
11:18 AM
I was reading a friend's blog and I realized, how lucky am I to find the meaning of my life, the meaning of living, when others are still searching aimlessly for theirs. As what PSi said 'For him, I will live', and that is the idea that is gradually became my main philosophy to life. It has been a difficult path, why not lives the life that is besotted to the fullest? Stay positive!

This is random. I was partially asleep last night when a sudden thought/feeling came to me. 'His love is just so great and overwhelming'.

I do agree.

aDe~