BIOINFORMATICS IN CELL BIOLOGY Bioinformatics Bioinformatics term was coined by Paulien Hogeweg in 1970 to refer to the study of information processes in biotic systems. Bioinformatics has become an important part of many areas of biology as it is the application of computer technology to the management of biological information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to different fields. The need for bioinformatics came up after the availability of genomic information resulting from the Human Genome Project publicly. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. CellBiology It is the area of science which deals with cells- i.e. different perspectives of cells- signalling, metabolism, protein trafficking in cell, growth and differentiation of cell. Therefore, research in cell biology is closely related to genetics, biochemistry, molecular biology, immunology, and developmental biology. Thus to cater to the needs of the interdisciplinary tasks of cell biology concepts, it was needed to look into the problem from all aspects and so came up the field of Bioinformatics in cell biology. Use of bioinformatics in cell biology field deals with mainly use of certain softwares and databases that help us to predict in silico certain important aspects. One such technique used is Computational Genomics which refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data. It may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes (rather than individual genes) to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. Advantages The areas of Cell Biology has revolutionized science the last few decades, and play a critical role in virtually every aspect of the life sciences, including such vital areas as medicine, pharmacology, ecology, chemistry, environmental science, and agriculture. Bioinformatics combines the advances in cell biology with the power of computer-based information technology. The far-reaching impact of these new disciplines has changed the way we fight disease, produce food, monitor the environment, and deal with crime. They have literally transformed our society, our language, and our economy. The explosion of the commercial applications of these disciplines which has resulted in the biotechnology industry, has changed the economy of the world. explosion has produced a vital, ever-growing demand for individuals with solid interdisciplinary training in these areas.
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BIOINFORMATICS IN CELL BIOLOGY
Bioinformatics Bioinformatics term was coined by Paulien Hogeweg in 1970 to refer to the study of information processes in biotic systems. Bioinformatics has become an important part of many areas of biology as it is the application of computer technology to the management of biological information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to different fields. The need for bioinformatics came up after the availability of genomic information resulting from the Human Genome Project publicly. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge.
CellBiology It is the area of science which deals with cells- i.e. different perspectives of cells- signalling, metabolism, protein trafficking in cell, growth and differentiation of cell. Therefore, research in cell biology is closely related to genetics, biochemistry, molecular biology, immunology, and developmental biology.
Thus to cater to the needs of the interdisciplinary tasks of cell biology concepts, it was needed to look into the problem from all aspects and so came up the field of Bioinformatics in cell biology. Use of bioinformatics in cell biology field deals with mainly use of certain softwares and databases that help us to predict in silico certain important aspects.
One such technique used is Computational Genomics which refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data. It may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes (rather than individual genes) to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond.
Advantages The areas of Cell Biology has revolutionized science the last few decades, and play a critical role in virtually every aspect of the life sciences, including such vital areas as medicine, pharmacology, ecology, chemistry, environmental science, and agriculture. Bioinformatics combines the advances in cell biology with the power of computer-based information technology.
The far-reaching impact of these new disciplines has changed the way we fight disease, produce food, monitor the environment, and deal with crime. They have literally transformed our society, our language, and our economy. The explosion of the commercial applications of these disciplines which has resulted in the biotechnology industry, has changed the economy of the world. explosion has produced a vital, ever-growing demand for individuals with solid interdisciplinary training in these areas.
For many years, the working of the cell was a mystery. Also the quest was to understand the interaction of the cell with its neighbouring cells and its environment. All these questions needed to be dealt with. So an initiative of using bioinformatics tools to atleast predict various features was done. Things that could be predicted were proteins topology, homology of proteins, compartmentalization of proteins etc. Also one of the most important aspect was the evolutionary conserved nature of a gene or a protein in a particular pathway that could be analyzed using bioinformatics and hence provide an idea about using a model organism. The most important cellular components which seem to be the genome, the transcripts and the proteins. The characterization and analysis of these three types of cellular components leads to genomics, transcriptomics and proteomics. These all when together lead to the field of bioinformatics. Biologists can now routinely monitor the gene expression at the genomic scale over time or compare gene expression between cells of a particular cell type. Thus in order to analyze the cellular components and their interactions, one needs to take into consideration proteomics, genomics and transcriptomics data.
There are various tools and softwares that enable us to perform predictions in field of cell biology:
Protein Function Analysis : BLAST ( Basic Local Alignment Search Tool) Structural Analysis : PROSPECT (PROtein Structure Prediction and Evaluation Computer ToolKit) Sequence Analysis : COPIA (COnsensus Pattern Identification and Analysis), tool for discovering motifs. References:
Bioinformatics in cell biology Bioinformatics is an interdisciplinary scientific field that develops methods for storing, retrieving, organizing and analyzing biological data. Bioinformatics is conceptualizing biology in terms of molecules (in sense of physical chemistry) and then applying informatics techniques (derived from disciplines such as applied math, computer sciences and statistics) to understand and organize the information associated with these molecules on a large scale. The field of bioinformatics includes database development, data management, software (algorithm) development, modeling (simulation), and quantitative analysis. Algorithmic development is an important part of bioinformatics, and techniques and algorithms were specifically developed for the analysis of biological data (e.g., the dynamic programming algorithm for sequence alignment).
In the modern era of computers and information technology bioinformatics has become an important part of many areas of biology like molecular biology ,genetics and genomics ,structural biology ,systems biology and cell biology etc .
Cell biology is a scientific discipline that studies cells – their physiological properties, their structure, the organelles they contain, interactions with their environment, their life cycle, division and death at both macromolecular as well as on microscopic level. The domain of bioinformatics has significant effect on field of cell biology and has provided the discipline with revolutionary technologies over the last few decades. Few areas of cell biology where bioinformatics has significant effect are :
Protein-protein and protein-DNA interactions : An important task in deciphering protein function is the identification of other entities with which it interacts. Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography and protein nuclear magnetic resonance spectroscopy (protein NMR) but it is not possible to determine all probable interactions by performing protein–protein interaction experiments. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational algorithms, termed docking algorithms, is hence very useful for studying molecular interactions.
Analysis of large scale 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), RNA-Seq, also known as "Whole Transcriptome Shotgun Sequencing" (WTSS), 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, under different developmental condition and different tissues.
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.
Network analysis seeks to understand the relationships within biological networks such as metabolic or protein-protein interaction network. Network biology often attempts to integrate many different data types, such as proteins, small molecules, gene expression data, and others, which are all connected physically and/or functionally .
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
High-throughput single cell data analysis: Computational techniques are used to analyze high-throughput, low-measurement single cell data, such as that obtained from flow cytometry. These methods typically involve finding populations of cells that are relevant to a particular disease state or experimental condition
Pharmacological Relevance: The tools of bioinformatics are also helpful in drug discovery, diagnosis and disease management. Using computational tools to identify and validate new drug targets, more specific medicines that act on the cause not merely the symptoms of the disease can be developed. .Prediction and analysis of protein 3D structure is used to develop drugs and understand drug resistance.
Drug design exploits the knowledge of the 3D structure of the binding site (or the structure of the complex with a ligand) to construct potential drugs, for example inhibitors of viral proteins or RNA. In addition to the 3D structure, a force field is necessary to evaluate the interaction between the protein and a ligand (to predict binding energies). In virtual screening, a library of molecules is tested on the computer for their capacities to bind to the macromolecule
Drug targets in infectious organisms can be revealed by whole genome comparisons of infectious and non–infectious organisms. The analysis of single nucleotide polymorphisms reveals genes potentially responsible for genetic diseases.
Patient databases with genetic profiles, e.g. for cardiovascular diseases, diabetes, cancer, etc. may play an important role in the future for individual health care, by integrating personal genetic profile into diagnosis, despite obvious ethical problems.
High-throughput image analysis : used for high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology, Bioimage informatics), morphometrics ,clinical image analysis and visualization
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. Knowledge of this structure is vital in understanding the function of the protein
Analysis of regulation: Regulation is the complex orchestration of events starting with an extracellular signal such as a hormone and leading to an increase or decrease in the activity of one or more proteins.
In brief it can be said that bioinformatics has incredible contribution to various disciplines of cell biology and in present era knowledge of modern bioinformatics tools is necessary along with basic and experimental biology.
(MAMTA) Bioinformatics in cell biology Bioinformatics is an interdisciplinary research area that is the interface between the biological and computational sciences. The ultimate goal of bioinformatics is to uncover the wealth of biological information hidden in the mass of data and obtain a clearer insight into the fundamental biology of organisms. This new knowledge could have profound impacts on fields as varied as human health, agriculture, the environment, energy and biotechnology. . Nowadays before doing any experiment in lab its bioinformatic analysis is done first, starting from sequencing, and then finding evolutionarily related species which will very beautifully crave out all the secrets of a new protein.and all this information is gained without the use of any reagents, protocols or standardization what all is needed is just an internet connection .the wet lab experimental confirmation to these hypothesized data is compulsory but still it solves a lot of problems associated with a new biomolecule .if experiments done for a newly identified enzyme or protein it will take almost 5-6 years or may be more , for its characterization but with the help of bioinformatics this job can be done within significant lesser time relatively. In experimental molecular biology, bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics and genomics, it aids in sequencing and annotating genomes and their observed mutations. It plays a role in the textual mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It plays a role in the analysis of gene and protein expression and regulation. Bioinformatics tools aid in the comparison of genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modeling of DNA, RNA, and protein structures as well as molecular interactions. Bioinformatics as a science can provide input to all previously mentioned scientific fields, as the recording and processing of detailed biological data is the first step towards doing something with them. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of
data. This includes nucleotide and amino acid sequences, protein domains, and protein structures. The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology includes the development and implementation of tools that enable efficient access to, use and management of, various types of information ,the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes A major application of bioinformatics in cell biology is use of software tools to create new families of drugs based on structure-function knowledge rather than synthesizing and screening random variants of molecules known to be bioactive. This involves docking variants of drug structures into binding sites of protein models in silico with the objective of selecting candidate structures for synthesis. Not alone is a model of the target protein required but the effect of interaction of the ligand on the protein structure must also be modelled. Bioinformatic databases have begun to serve an valuable repositories for this data, attempting to maintain cellular context for the information. Although still in their infancy, biochemical network models have proven to be useful for integrating this information to generate holistic understanding of cellular behavior. Bioinformatics research will continue to bridge the gap between molecular biology and network understanding, facilitating the reconstruction of biochemical pathways and leading to the analysis of function of cellular signaling. REFERENCES
1. Mount, David W. (May 2002). Bioinformatics: Sequence and Genome Analysis. Spring Harbor Press. ISBN 0- 879-69608-7
2. Bioinformatics - Wikipedia, the free encyclopedia 3. .Bioinformatics and cellular signaling(Jason Papin and Shankar
Bioinformatics is an interdisciplinary scientific field that develops methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. The primary goal of bioinformatics is to increase the understanding of biological processes and on developing and applying computationally intensive techniques to achieve this goal.
The publication of the first draft of the human genomic DNA sequence in 2001 heralded a new era in biology. The life sciences continue to be transformed by the rapid accumulation of a rich array of data of diverse types. In order to access and exploit this information, biologists have become increasingly dependent on computational approaches to access, annotate, and analyze these data sets.
BIOINFORMATICS AND BIOLOGICAL APPROACHES :
Bioinformatics has become an important part of many areas of biology. In experimental molecular and cell biology, bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. It plays a role in the analysis of gene and protein expression and regulation. Bioinformatics tools aid in the comparison of genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular and cell biology
For many years, a challenging question in the mind of many leading biologists is how living cells work. A living cell is a system with cellular components interacting with each other and with extracellular environment, and these interactions determine the fate of the cell. To understand how living cells work, these cellular components and their interactions would need to be identified and characterized. The most important cellular components happen to be the genome, the transcripts and the proteins. The characterization and analysis of these three types of cellular components leads to genomics, transcriptomics and proteomics that jointly drive the development of bioinformatics. Genomics leads to various developments. One of them is to allow a much faster identification of proteins by combining mass spectrometry data with genomic databases. Biologists can now routinely monitor the gene expression at the genomic scale over time or compare gene expression between cells of a particular cell type. However, there are two major problems with the transcriptomic data. The first is that the relative abundance of transcripts is not always a good predictor of the relative abundance of proteins. Many proteins that are produced as a result of alternative splicing and posttranslational modification will not be revealed in the analysis of transcriptomic data. Thus in order to
characterize the cellular components and their interactions, one needs the corroboration of proteomic, genomic and transcriptomic data. Therefore we can conclude that bioinformatics has three facets labelled proteomics, genomics and transcriptomics and that it deals mainly with characterizing cellular components and their interactions. But bioinformatics goes beyond this. Genes and genomes have evolved from time immemorial, as do interactions among genes and gene products. The genomic change is particularly well exemplified by various diseases all evolving quickly as a result of mutation, recombination and selection. Studying the dynamic nature of genes and genomes, tracing their phylogenetic relationships and reconstructing their ancestral states allow biologists to gain the advantages offered by the various tools of bioinformatics. Thus, bioinformatics has revolutioniesd the way cellular information can now be retrieved from any source or database provided. Software and tools :
Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs web-services available from various bioinformatics companies or public institutions. Bioinformatics uses many areas of computer science, statistics, mathematics and engineering to process biological data. Some of the key Web sites used for bioinformatics are:
Human Genome http://www.ncbi.nlm.nih.gov/genome/guide/human/
References : www.wikipedia.org http://www.springer.com/978-0-387-71336-6 Shainan Hora
BIOINFORMATICS AND CELL BIOLOGY
1. INTRODUCTION The publication of the first draft of the human genomic DNA sequence in 2001 (Lander et al., 2001 ; Venter et al., 2001 ) heralded a new era in biology. The life sciences continue to be transformed by the rapid accumulation of a rich array of data of diverse types. In order to access and exploit this information, biologists have become increasingly dependent on computational approaches to access, annotate, and analyze these data sets—that is, the goal of bioinformatics. (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC256976/) Bioinformatics is the application of computer technology to the management and analysis of biological data. The result is that computers are being used to gather, store, analyze and merge biological data. Bioinformatics is an interdisciplinary research area that is the interface between the biological and computational sciences. The ultimate goal of bioinformatics is to uncover the wealth of biological information hidden in the mass of data and obtain a clearer insight into the fundamental biology of organisms. This new knowledge could have profound impacts on fields as varied as human health, agriculture, the environment, energy and biotechnology.
2. WHY IS BIOINFORMATICS IMPORTANT?
The genome sequencing projects has produced large amounts of nucleotide and protein sequence data. Traditionally, molecular biology research was carried out entirely at the experimental laboratory bench but the huge increase in the scale of data being produced in this genomic era has seen a need to incorporate computers into this research process.
Sequence generation, and its subsequent storage, interpretation and analysis are entirely computer dependent tasks. However, the molecular biology of an organism is a very complex issue with research being carried out at different levels including the genome, proteome, transcriptome and metabalome levels. Following on from the explosion in volume of genomic data, similar increases in data have been observed in the fields of proteomics, transcriptomics and metabolomics.
The first challenge facing the bioinformatics community today is the intelligent and efficient storage of this mass of data. It is important to provide easy and reliable access to this data. The data itself is meaningless before analysis and the sheer volume present makes it impossible for even a trained biologist to begin to interpret it manually. Therefore, incisive computer tools must be developed to allow the extraction of meaningful biological information.
There are three central biological processes around which bioinformatics tools must be developed:
DNA sequence determines protein sequence
Protein sequence determines protein structure
Protein structure determines protein function
The integration of information learned about these key biological processes should allow us to achieve the long-term goal of the complete understanding of the biology of organisms.
Figure 1.1: Overview of various subfields of bioinformatics. Bio computing tool development is at the foundation of all bioinformatics analysis. The applications of the tools fall into three areas: sequence analysis, structure analysis, and function analysis. There are intrinsic connections between different
areas of analyses represented by bars between the boxes.
Bioinformatics has not only become essential for basic genomic and molecular biology research, but is having a major impact on many areas of biotechnology and biomedical sciences. It has applications, for example, in knowledge-based drug design, forensic DNA analysis, and agricultural biotechnology. This informatics-based approach significantly reduces the time and cost necessary to develop drugs with higher potency, fewer side effects, and less toxicity than using the traditional trial-and-error approach. It plays important role in cell biology also for example in protein cell sorting , which is a very important function of a cell to regulate protein trafficking.
PROTEIN SORTING Sub-cellular localization is an integral part of protein functionality. The study of protein sorting has become a central theme in modern cell biology. Identifying protein sub-cellular localization is an important aspect of functional annotation, as it helps to narrow down its putative functions. Studying protein sorting experimentally in a cell is a tedious work. Various computational methods have been developed to predict the sub-cellular localization signals. Here are some of the most frequently used programs for the prediction of sub-cellular localization and protein sorting signals with reasonable accuracy (65% to 75%).
Bioinformatics is an interdisciplinary scientific field that develops methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge.
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), RNA-Seq, also known as "Whole Transcriptome Shotgun Sequencing" (WTSS), or various applications of multiplexed in-situ hybridization.
Analysis of regulation Bioinformatics techniques have been applied to explore various processes. For example, promoter analysis involves the identification and study of sequence motifs in the DNA 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. Examples of clustering algorithms applied in gene clustering are k-means clustering, self-organizing maps (SOMs), hierarchical clustering, and consensus clustering methods such as the Bi-CoPaM.
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.
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.
Molecular interaction Main article: Protein–protein interaction prediction
Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography and protein nuclear magnetic resonance spectroscopy (protein NMR) and a central question in structural bioinformatics is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without performing 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 work to be done in this field.
Other interactions encountered in the field include Protein–ligand (including drug) and protein–peptide. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational algorithms, termed docking algorithms, for studying molecular interactions.
Software and tools
Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services available from various bioinformatics companies or public institutions.
Open-source bioinformatics software The range of open-source software packages includes Bioconductor, BioPerl, Biopython, BioJava, BioRuby, Bioclipse, EMBOSS, .NET Bio, Taverna workbench, and UGENE. In order to maintain this tradition and create further opportunities, the non-profit Open Bioinformatics Foundation[16] have supported the annual Bioinformatics Open Source Conference (BOSC) since 2000.
Web services in bioinformatics SOAP- and REST-based 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 main advantages derive from the fact that end users do not have to deal with software and database maintenance overheads.
Basic bioinformatics services are classified by the EBI into three categories: SSS (Sequence Search Services), MSA (Multiple Sequence Alignment), and BSA (Biological Sequence Analysis). The availability of these service-oriented bioinformatics resources demonstrate the applicability of web-based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single, standalone or web-based interface, to integrative, distributed and extensible bioinformatics workflow management systems.
gpDB: A database of GPCRs, G-proteins, Effectors and their interactions. Submitted by-
Psort web server: http://psort.nibb.ac.jp/
prediction of protein localization sites in cells from their primary amino acid sequence
Lobzang Tsering
BIOINFORMATICS IN CELL BIOLOGY
Bioinformatics is an interdisciplinary scientific field that develops methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, statistics, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. In order to study how normal cellular activities are altered in different disease states, the biological data is combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. The goal of bioinformatics is to increase the understanding of biological processes. It is a computational approach nowadays scientists are following to reduce their work load and time and getting a suitable data.
If one needs to know about the integral membrane proteins, the sequence of the protein can be predicted and it can be analyzed that how many amino acid and what type of amino acids are present in the membrane region.
No. of transmembrane helices can be predicted. Topology of protein can be studied using bioinformatics as to know which
terminus is outside and inside of the cell. Using BLAST it can be seen how much similar is particular protein to another
which can help in classifying the proteins/enzymes in the cell to particular family. Receptor dimerization can also be studied as if it’s a homodimer both the protein
should correspond to similar sequence and if it’s a hetrodimer the sequence of both the proteins will be different.
To check for the exact no. of phosphorylation site, sequecing can be done on a receptor using Mass Spectrometry through PMF. This can hepl to make phospho-specific antibody which can hepl to learn about different proteins interacting with the receptor.
What is the effect of mutation on a protein can be studied which cuold tell about the alteration in its structure and further its effect on function can be predicted.
Different sites of post translation modification such as phosphorylation,biotinylation, glycosylation etc can also be studied by sequencing the protein in a cell inorder to learn about structural stability in cell and its membrane.
It can also help to study the first step of cell signalling i.e, ligand and receptor binding through sequence and aso through the 3D structure of the complex and how the ligand is bringing about the change in conformation in receptor.
Location of protein can also be predicted.
Experiment can be designed based on length of protein to find out how much activity is performed by the particular domain of the protein .
Bioinformatics can be useful in making primers for PCR e.g. in overlapping PCR where one needs to delete the middle section of a domain.
Many exercises can be performed to learn about human genome well. e.g. DNA sequence of a 6.6-kb BamHI fragment from the genome that contains the human H-Ras gene. This piece of DNA transforms NIH 3T3 cells to cancer cells when only a single nucleotide in codon 12 of the Ras gene is changed from G to T, resulting in the substitution of valine for glycine in the encoded protein As children work together in groups to carry out the conceptual translation of the gene from the nucleotide sequence, students occasionally make mistakes, making it necessary for the group to backtrackor even start over. It impresses them to see how precise the cellular machinery must be to faithfully duplicate and express the information in DNA. As they work through this exercise, they also encounter what is often a surprise—an intron that interrupts a codon. Once again, they see how precise the cell must be, in terms of splicing the transcribed RNA. Finally, a remarkable aspect of human gene organization is seen: only about one tenth of this genomic fragment encodes the H-Ras protein.
Source of unknown protein or DNA sequence can be known can be known by sequencing followed by doing the protein or nucleotide blast.
Location of affected amino acid can also be predicted. These tools can be applied to give insight into the function of a protein that has
obvious relevance to human medicine.
In experimental molecular biology, bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics and genomics, it aids in sequencing and annotating genomes and their observed mutations. It plays a role in the textual mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It plays a role in the analysis of gene and protein expression and regulation. Bioinformatics tools aid in the comparison of genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modeling of DNA, RNA, and protein structures as well as molecular interactions.
By: Gagandeep Kaur
BIOINFORMATICS IN CELL BIOLOGY
Bioinformatics is the application of computer technology to the management and analysis of biological data. The result is that computers are being used to gather, store, analyse and merge biological data. Bioinformatics is an interdisciplinary research area that is the interface between the biological and computational sciences. The ultimate goal of bioinformatics is to uncover the wealth of biological information hidden in the mass of data and obtain a clearer insight into the fundamental biology of organisms. This new knowledge could have profound impacts on fields as varied as human health, agriculture, the environment, energy and biotechnology. Bioinformatics uses many areas of computer science, statistics, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory,system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML,Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning different DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures. There are two fundamental ways of modelling a Biological system (e.g., living cell) both coming under Bioinformatic approaches.
Static Sequences – Proteins, Nucleic acids and Peptides Interaction data among the above entities including microarray data and
Networks of proteins, metabolites Dynamic
Structures – Proteins, Nucleic acids, Ligands (including metabolites and drugs) and Peptides (structures studied with bioinformatics tools are not considered static anymore and their dynamics is often the core of the structural studies)
Systems Biology comes under this category including reaction fluxes and variable concentrations of metabolites
Multi-Agent Based modelling approaches capturing cellular events such as signalling, transcription and reaction dynamics
It helps analyze and catalogue the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modeling of DNA, RNA, and protein structures as well as molecular interactions. Bioinformatics can be used as an alternative to microarray for looking at differential gene expression in some circumstances. Digital differential display(DDD) is a method for comparing EST-based expression profiles in different tissues or conditions from various libraries or between pools of EST libraries. An EST library contains short sequences cloned from the total cellular mRNA (converted to cDNA) of a particular tissue or particular condition. The theory is that genes expressed at a high level will be
represented by more ESTs than those expressed at a lower level. Genes whose expression levels differ significantly from one set of EST libraries to the next are identical using a statistical test. The NCBI’s UniGene database forms the core of the DDD method. In general, it is difficult to compare and integrate data from different laboratories and experiments. Therefore a group of scientists have set up a consortium (the MGED society) to standardize the output and annotation of microarray data, which will facilitate sharing the data and creating a concolidated database. The set of standardization rules is called MIAME, which stands for Minimum Information About a Microarray Experiment. This includes information that is essential for someone else to interpret the results of the experiment and even to reproduce the experiment. Reconstruction of biochemical pathways is a complex task. In metabolism, databases like KEGG and EcoCyc serve as valuable resources for metabolic networks. Such extensive and well-curated databases are not yet available for cellular signaling. The role of each protein in a signaling network is to communicate the signal from one node to the next, and to accomplish this the protein has to be in a defined signaling ‘state’. The state of a signaling molecule is characterized by covalent modifications of the native polypeptide, the substrates and/or ligands bound to the protein, its state of association with other protein partners, and its location in the cell. Several efforts are underway currently to build databases of biochemical signaling pathways and networks of pathways. These databases are also combined into larger infrastructures containing graphical user interfaces and some rudimentary analysis tools. The semantic network is an effective method to model cell signaling pathways. The semantic model allows proper representation and integration of information on biological structures and their interactions at different levels. The reconstruction of the cell signaling network in the macrophage allowed detailed investigation of connections among various essential molecules and reflected the cause-effect relationships among signaling events. The simulation demonstrated the dynamics of the semantic network, where a change of states on a molecule can alter its function and potentially cause a chain-reaction effect in the system. The transition from genomic and proteomic ‘parts lists’ to fully reconstructed biochemical network models is of critical importance in understanding how cells respond to the milieu of environmental stimuli and developmental cues. Concerted research efforts including large-scale expression profiling of cells under varied conditions and the subsequent development of microarray data standards have generated a wealth of data crucial to the fulfillment of the goal to understand cellular network behavior. Bioinformatic databases have begun to serve as invaluable repositories for this data, attempting to maintain cellular context for the information. Although still in their infancy, biochemical network models have proven to be useful for integrating this information to generate holistic understanding of cellular behavior. Bioinformatics research will continue to bridge the gap between molecular biology and network understanding, facilitating the reconstruction of biochemical pathways and leading to the analysis of function of cellular signaling. References :
1. http://en.wikipedia.org/wiki/Bioinformatics 2. Molecular Biology and Bioinformatics techniques, 2003 3. Current Opinion in Biotechnology 2004, 15:78–81 4. BMC Bioinformatics 2004, 5:156
- PRIYANKA
Bioinformatics & it’s Application in Cell Biology
Biology as defined earlier as a natural science concerned with the study of life and living organisms, including their structure, function, growth, evolution, etc. It has further developed with the development of internet and computer. With the help of technology scientists were recently able to sequence Human Genome & of many other organisms but the major challenge was where to store the data. For example, DNA identification. Every species or human beings have particular DNA strands that contain the genetic instructions used in the development and functioning of all known living organisms. By identifying DNA information one can trace generations’ links and can find the root of different disease. It is hard to manage this information. So, in order to collect and link DNA information from all over the world and to solve many medical complications, development of bioinformatics played a major role.
What is BIOINFORMATICS ??
Is it just an integration of biology and computer?? The answer is No, it is properly defined as conceptualizing biology in terms of molecules (in the sense of physical chemistry) and applying “informatics techniques" (derived from disciplines such as applied maths, computer science and statistics) to understand and organize the information associated with these molecules, on a large scale. In short, bioinformatics is a management information system for molecular biology and has many practical applications. In 1970 Paulien Hogeweg, coined the term "Bioinformatics". Margaret Oakley Dayhoff, & David Lipman, (director of the National Center for Biotechnology Information,) are described as "mother and father of bioinformatics."
LINK BETWEEN CELL BIOLOGY AND BIOINFORMATICS??
Now, Cellular biology has not been confined only to the structure of cell but it has spread out it branches in diverse ways. The communication between cells that goes through cell signalling includes various biochemical interactions and processes. These protein protein interactions, analysis of proper protein expression, gene expressions etc can be easily studied with the help of BIOINFORMATICS. Bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data.
Bioinformatics tools aid in the comparison of genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modeling of DNA, RNA, and protein structures as well as molecular interactions. It entails creation and advancements of DATABASES. There’re many kind of biological databases:-
Primary Databases:- The International Nucleotide Sequence Database (INSD) consists of the following databases.
DNA Data Bank of Japan (National Institute of Genetics)
European Nucleotide Archive (European Bioinformatics Institute)
GenBank (National Center for Biotechnology Information)
These are repositories for nucleotide sequence data from all organisms. These three databases are primary databases, as they house original sequence data.
Secondary Databases:- Those data that are derived from the analysis or treatement of primary data such as secondary structures, hydrophobicity plots, and domain are stored in secondary databases. Eg : CATH, PROSITE of Swiss Institute of Bioinformatics.
Some of the databases that’re useful in extracting information about cellular signaling are:-
i. Protein Protein Interaction BIND Biomolecular Interaction Network Database
BioGRID [12] A General Repository for Interaction Datasets (Samuel Lunenfeld Research Institute)
CCSB Interactome DIP Database of Interacting Proteins
ii. Signal transduction pathway databases
Cancer Cell Map Netpath - A curated resource of signal transduction pathways in humans NCI-Nature Pathway Interaction Database The Cell Collective
The major work on biology includes sequence analysis of many organisms and NCBI is major portal which help us to gain access to all databases whether it’s protein, DNA or RNA. One can simply compare the genome sequence between any organisms in the world provide their information is stored in databases and can bring out evolutionary link. Similarly, protein structure prediction or it’s interaction with other molecules can be easily find out. Protein - protein interactions and their homologues, conserved domains can also be searched through the help of bioinformatics. Some of the tools are: - EXPASY, BLAST, VMD etc APPLICATIONS IN CELL BIOLOGY The information about protein - protein interactions, their structures, molecular
interactions, and the databases of signal transduction pathways help us to gather and understand Cellular signalling, cellular development, and processing in a much better way.
Also, it helps us to design DRUGS some of which works by inhibiting/stimulating cellular pathways. Hence, to understand their mechanisms and molecular interactions we need many softwares like JMOL(analyse 3D structures) , etc
It helps in compiling expression data for cells affected by different diseases, eg cancer and ateriosclerosis and comparing the measurements against normal expression level.
Finally, we can conclude that bioinformatics is playing a vital role in development of society by providing quick information and making research fast. It is using today’s computer technology and biological research together very efficiently. This field is going to generate more opportunities in future for all people working in different areas.
Bioinformatics is the application of computer technology for the management of molecular and biological information. Computers are used to gather, store, analyse, process and integrate biological information so that it can be retrieved whenever required by both common people and scientist using either public or private database. Databases and information systems are used to store and organize data, algorithms used to analyse data allowing data mining, image processing and simulation and algorithms are in turn based on theoretical foundations like IT theory, mathematics and statistics. Commonly used tools and software are Java, C++, SQL, XML etc Thus, today it’s an emerging field having intense potential as career opportunity for those who are interested in merging biological understanding with IT, mathematics and statistical analysis. It major task is to develop software tools to generate useful biological knowledge like tools for DNA, RNA and protein sequence analysis, protein structure prediction soft wares, tools to analyse and store microarray experiment information etc. But it is different from biological computation which combines computer science and engineering subfields using bioengineering and biology to generate biological computer. The word “BIOINFORMATIC” was coined by Paulien Hogweg in 1970, to refer to study the information processes in biotic systems. In early days of bioinformatics, it was used to to study processes like formation of complex social interaction structures based on behavioural rules and information accumulation and model development for prebiotic evolution. Elvin A. Kabat, also an important contributor in this field, pioneered biological sequence analysis in 1970. Also a renowned personality in this field was Margaret Oakley Dayhoff, titled as “Mother and Father of Bioinformatics”. . Huge amount of data that was generated post Human Genome Project gave rise to development of this science to store and analyse nucleotide and protein sequence. Also it was a step ahead than older version in way that data was now made accessible to reader. Bioinformatics goals have now evolved such that most pressing task is analysis and interpretation of data so that various cellular component and activities can be under stood in a better way in order to correlate it with diseased condition and to develop solution to that pathological condition. So, important sub-disciplines within bioinformatics include:
a) Developing tools for efficient access, use and management of various type of data and information stored in databases,
b) Developing algorithms and statistics to co-relate data from different type of data bases to understand evolution and different biological processes. e.g. tools to predict genes from DNA sequence, cluster protein sequences into families of related sequences etc.
Approach to achieve the goals is purely computational which includes pattern recognition, data mining, prediction, machine learning algorithms and visualization. Various research efforts include sequence alignment, gene finding, drug design, protein structure alignment and prediction, protein-protein interactions, PTM prediction, subcellular localization, genome- wide studies and modelling of evolution based on protein sequence and structure homology. There are ideally two ways in which modelling is done in bio informatics – Static modelling: involves sequence studies of proteins’ nucleic acids and peptides based on interaction data between these entities obtained from microarray etc.; Dynamic modelling: includes systems biology and multi-agent based modelling approaches capturing cellular events like signalling and transcription.
Bioinformatics has become an important part of many areas of biology. In experimental molecular biology, bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics and genomics, it aids in sequencing and annotating genomes and their observed mutations. It plays a role in the textual mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It plays a role in the analysis of gene and protein expression and regulation. Bioinformatics tools aid in the comparison of genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology. At a more in integrative level, it helps analyse and catalogue the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modelling of DNA, RNA and protein structures as well as molecular interactions. DNA sequence of thousands of organisms stored in databases is analysed to determine genes that encode polypeptides, RNA genes, regulatory sequences, structural motifs and repetitive sequences. A comparison of genes within specie or between different species can show similarities between protein functions, or relations between species using phylogenetic trees. Today computer programs such as BLAST are used daily to search sequences from more than 2,60,000 organisms, containing over 190 billion nucleotides. These programs can compensate for mutations in the DNA sequence, to identify sequences that are related, but not identical. Another aspect of bioinformatics in sequence analysis is annotation. This involves computational gene finding to search for protein coding genes, RNA genes and other functional sequences within a genome. Bioinformatics helps to bridge the gap between genome and proteome projects- for example, in the use of DNA sequences for protein identification. In the field of evolutionary biology, bioinformatics enable researchers to trace the evolution of a large no. 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, horizontal gene transfer and prediction of factors important in bacterial speciation, building of complex computational models of populations to predict the outcome of the systems overtime track and share information on an increasingly large no. of species and organisms. Most important use of bioinformatics is use of sequence data to map the genes of complex diseases such as infertility, breast cancer and Alzheimer’s Disease. These analyses can pin point the mutations the genes. In cell biology field, regulation of various cellular processes can be studied using bioinformatics. Regulation is the complex orchestration of events staring with an extracellular signal such as a hormone and leading to an increase or decrease in the activity of one or more proteins. For example, promoter analysis involves the identification and study of sequence motifs in the DNA surrounding the coding region of gene. This motifs influence the extent to which that region is transcribed into mRNA. In single cell organisms, one might compare stages of cell cycle, along with various conditions (heat shock, starvation, etc.). One can then apply clustering algorithms to that expression data to determine which genes are co-expressed e.g. k-means clustering. Protein structure prediction is another important application of bioinformatics. The primary sequence can be easily determined from the sequence on the gene that codes for it. In majority cases, this sequence uniquely determines a structure in its native environment and this structure is vital in understanding the function of the proteins. But validity of these predicted structures is still a unsolved problem. Post translation modifications and subcellular localization of proteins is also an important aspect of protein function and many prediction tools has been developed using bioinformatics to do the job.
Thus, there is an unlimited use of bioinformatics in various fields of biology which makes its understanding very efficient.
By: Madhu Baghel
Bioinformatics in cell biology.
INTRODUCTION :
The term “bioinformatics” was coined in 1970 by Hogeweg and Hesper which means “the study of informatic processes in biotic systems”
Bioinformatics includes a suite of methods, which are cheap, approachable, and many of which are easily accessible without any sort of specialized bioinformatics training. It is an interdisciplinary scientific stream that develops method for storing, retrieving, organizing and analyzing biological data. Its primary goal is to develop basic understanding of biological processes. It involves developing specific software and tools that helps to generate biological knowledge. It involves various sub-fields like computer science, computer engineering, statistics, mathematics, along with biology logics and bioengineering. It entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Bioinformatics approaches for modeling a biological system (living cell ) are static and dynamic approach. The static approach includes sequencing of protein, DNA, RNA and their interactions with one other. While the dynamic approach includes structural analysis, system biology and multi-agent based modeling.
BIOINFORMATICS TOOLS USED:
To obtain nucleotide sequence – National Center for Biotechnology Information (NCBI), through its database GenBank; nucleotide sequence database of European Molecular Biology Laboratory (EMBL) and DNA Databank of Japan (DDBJ).
Post translational modifications – NetPhos 2.0 server for predication of phosphorylation sites; SUMOplot analysis tool to predict the probability of sumoylation sites; The Sulfinator for tyrosine sulfation sites; ProP 1.0 Server for arginine and lysine propeptide cleavage sites; UBPred for protein ubiquitination sites.
To search homology – pairwise or multiple sequence alignment using NCBI Tool, ClustalW. If no homologs exist, then identification of conserved domains and motifs using NCBI’s Conserved Domain search (CD-search).
For structural analysis – Protein Data Bank (PDB) governed by Research Collaboratory for Structural Bioinformatics (RCSB),
For modeling of macromolecular structures – Jmol, BioBlender, UCSFChimera, PsiPred, VMDL
BIOINFORMATICS OF SINGLE CELL GENOMICS :
Along with novel sequencing technologies, there are several new exciting techniques like Multiple Displacement Amplification (MDA) that amplify entire genomes from a few cells or even a single cell, and Multiple Annealing, Looping Based Amplification Cycle (MALBAC) method, which combines features of linear amplification with PCR.
Single cell genomic analysis provides a more precise measurement and decisive move toward a fundamental understanding of biology of cell.
Single cell sequencing has broad amplification in biology and medicine; like in characterization of earliest events in embryogenesis; in study of microorganisms that cannot be cultured; in the study of tumor heterogeneity and microenvironment. But the single cell sequencing requires the extra step that amplifies the genome of single cell, as the single cell has lower genome coverage and it is high amplification bias.
BIOINFORMATICS IN IMMUNOLOGY :
Along with the use of bioinformatics in the study of genetics and evolution, it has been also used in immunology research. The combination of immunology and computational biology is referred as immunomics or computational immunology.
Bioinformatics techniques can be used to model the major histocompatibility complex heterozygosity (MHC), their effects on other cells, way of interaction, their frequency and many more.
Tools that can be used by immunologist for transcriptomics using microarray, RNASequencing (RNAseq)- NCBI’s Gene Expression Omnibus, EBI’s gene Expression Atlas, Gene skyline; Modules and Regulators
BIOINFORMATICS IN CELL SIGNALLING:
To study the cellular signaling, there is requirement of measurement of various cellular components and their properties. It requires the combination of novel experimental and computational tools.
Molecule Pages Database from the Alliance for Cellular signalling, is a database for signaling proteins, their intracellular network maps, different functional states and their transitions between different states.
KEGG and EcoCyc, are databases for metabolic networks and protein interaction data.
Databases like DIP, BIND, Biocarta describes molecules involves in pathways and their reaction maps.
EMP and BRENDA for enzyme kinetics
- Pooja badhwar
Systems Biology in cellular signaling Since the days of Norbert Weiner, system-level understanding has been a recurrent theme in biological science. System-level understanding, the approach advocated in systems biology requires a shift in our notion of “what to look for” in biology. While an understanding of genes and proteins continues to be important, the focus is on understanding a system’s structure and dynamics. Because a system is not just an assembly of genes and proteins, its properties cannot be fully understood merely by drawing diagrams of their interconnections. Although such a diagram represents an important first step, it is analogous to a static roadmap, whereas what we really seek to know are the traffic patterns, why such traffic patterns emerge, and how we can control them. If we consider a cell as a complete electrical circuit with lots of “electrical lines” diverging and converging then we should ask questions like: 1. What is the voltage on each signal line? 2. How are the signals encoded? How can we stabilize the voltage against noise and external fluctuations? 3. And how do the circuits react when a malfunction occurs in the system? 4. What are the design principles and possible circuit patterns, and how can we modify them to improve system performance?
Hypothesis-driven research in systems biology. A cycle of research begins with the selection of contradictory issues of biological significance and the creation of a model representing the phenomenon. The model represents a computable set of assumptions and hypotheses that need to be tested or supported experimentally.Computational experiments, such as simulation, on models reveal computational adequacy of the assumptions and hypotheses embedded in each model. Models that pass this test become subjects of a thorough system analysis where a number of predictions may be made Successful experiments are those that eliminate inadequate models. Models that survive this cycle are deemed to be consistent with existing experimental evidence. In Cell Signalling Cell signaling pathways interact with one another to form networks in mammalian systems. Such networks are complex in their organization and exhibit emergent properties. Analysis of signaling
networks requires a combination of experimental and theoretical approaches including the development and analysis of models. Recent analyses of signaling pathways in mammalian systems have revealed that cellular signals do not necessarily propagate in a linear fashion. Instead, cellular signaling networks are made up of highly connected modules that can be used to regulate multiple functions in a context dependent manner. In the field of pharmacology and drug discovery, modeling has often used to study of receptor-ligand interactions and pharmacokinetics. In this assignment I have explained the systems level approach to understand EGF-EGFR pathway.
Signalling through the ERBB/HER receptors is intricately involved in human cancerand already serves as a target for several cancer drugs. Because of its inherent complexity, it is useful to envision ERBB signalling as a bow-tie-configured, evolvable network, which shares modularity, redundancy and control circuits with robust biological and engineered systems. Because network fragility is an inevitable trade-off of robustness, systems-level understanding is expected to generate therapeutic opportunities to intercept aberrant
A systems perspective of the ERBB network. A reductionist view of the bow-tie-architectured signaling network is represented. The heart of the system is a core process, a collection of biochemical interactions, which are tightly coupled to each other and interface with two sets of components: three input modules, each comprising an ERBB receptor tyrosine kinase; and a large group of partly redundant ligand growth factors. The output of the core process is translated to gene expression through multiple transcription factors. Depending on the exact combination of transcription factors and the cellular context, the output of the network regulates cell behaviour. The system maintains two steady states (bistability), for which inter-conversions depend on ligand binding. The fail-safe (robustness) action of the system is conferred by structural modularity and functional redundancy, along with rich and stringent system controls. An important positive regulator is ERBB2, a co-receptor. Heterodimerization between ERBB2 and any of the three ERBB input modules enhances and prolongs the respective output. The described emergence of a systems-level view of EGF–ERBB signalling, along with the first large computational models of the system, are natural sequels of the post-genomic phase of
ERBB research: most components of the signalling network are well characterized,and their connectivities are rapidly becoming apparent.The systems-level perspective provides a useful framework that accommodates the increasingly voluminous amounts of incoming experimental data. Because ERBB signalling is so pivotal to some of the most virulent human malignancies, reliable quantitative modelling will probably identify new targets for cancer therapy, as well as predict the consequences of combining specific drugs and clinical procedures. Although the future medical promise is truly great, fulfilling this promise will require a transformation in the way biologists conduct experiments, analyse data and share datasets. References 1. Kitano, H. Systems biology: a brief overview. Science (New York, N.Y.) 295, 1662-1664 (2002). 2. Hood, L., Heath, J.R., Phelps, M.E. & Lin, B. Systems biology and new technologies enable predictive and preventative medicine. Science (New York, N.Y.) 306, 640-643 (2004). 3. Eungdamrong, N.J. & Iyengar, R. Modeling cell signaling networks. Biology of the cell / under the auspices of the European Cell Biology Organization 96, 355-362 (2004). 4. Butcher, E.C., Berg, E.L. & Kunkel, E.J. Systems biology in drug discovery. Nature biotechnology 22, 1253-1259 (2004). 5. Nusser-Stein, S. et al. Cell-cycle regulation of NOTCH signaling during C. elegans vulval development. Molecular systems biology 8, 618 (2012). 6. Citri, A. & Yarden, Y. EGF-ERBB signalling: towards the systems level. Nature reviews. Molecular cell biology 7, 505-516 (2006).
