Till 2015-Some unresolved questions in genomics research?

Here are opinions of some experts:

1. Michael Wendell from Washington University in St. Louis says "A very broad goal of "genomics" and its companion "bioinformatics" is to transition us beyond the purely empirical state of knowing gene (and pathway) lists, their basic functions and interactions, etc. to a state where we understand the (almost certainly non-linear) set of control laws that govern the cellular and larger-scale biological machinery of an organism. As Eric Lander said many times regarding the first human genome sequence: it's a list of parts. At the moment, one might say that genomics is now figuring how they fit together and what types of problems "broken parts" can cause. Ultimately, we need to know how these parts *work as a system* (at all scales), so that predictive models can be constructed. As a corollary, I think genomics will see an increasing influx of workers who specialize in these types of problems, especially from the engineering and mathematical realms."

2. Alex Ignatov of Research Center of Phytoengineering says most important stuff to consider is:
1) What is the mechanism of genome dynamics
2) What is the mechanism of information transfer from gene to phenotypic trait?
3) Is there any general rules ant purposes or evolution? "

3. Mark M. Abitol of Necker University Hospital and INSERM CENTER UMR says "The most immediate questions to solve seem to me the following ones :
1) Identifying and clarifying the different families of Non Coding RNAs and the role of each non Coding RNA
2) There are many  genes encoding small peptides apparently and it is likely that most of them have not yet be identified. For Those identified, the role of these peptides are still unknown in many instances
3) the links between energy expenditure and the occurence of biological events or processes are in most instances unknown
4) The energetic cost of alternative splicing is still unknown and the same holds true for translation
5) Energy homeostasis is still an enormous conundrum without a crystal clear overwiew and modelization
6) The dynamics of genomes remains largely a mystery
7) DNA DAMAGE RESPONSE and DNA repair remain a huge fields of  mysteries even though a lot of work has provided many answers
8) The RNA world remains largely unexplored
9) The mathematic modelization of biological pathways and of life is in its infancy."

4. Miguel Andarde of Johannes Gutenberg-Universität Mainz says "The 3D structure of genomes, their dynamic changes and their control."

5. Y. Tom Yang from First Dimension Biosciences (Suzhou) LTD says "Seeking "The Main Program"

I like to watch kids playing an orchestra. "An orchestra is a large instrumental ensemble that contains sections of string, brass, woodwind, and percussion instruments." There is a single person, you may or may not notice his existence. Yet, his hand moves, with a baton or not, drive the entire orchestra to make the fantastic music we enjoy. Is a "life" like an orchestra? Are there many instruments that work seamlessly together? If yes, who is "The Conductor"?

Here is another comparison:

To program with modern languages (C++, Java, Python, etc), we would have basic modules (i.e. classes), and objects for these classes; and then these modules would be linked together by a "Main" program. The "Main Program" usually takes some input arguments, and then call upon these objects to achieve a function. The Main operates essentially in the time domain and its working logic is determined by the status reported back from those objects. "The Main Program" is the system integrator and decision maker. It is part of the system, but in the mean time it is kind of beyond the system.

In genetic programs (i.e. all life forms), the architect should be similar to concerts and to computer programming. For simplicity, I will just use the case of programming. The genes are "classes". Each transcription of a gene is an "instance" of the "class", hence, an "object". The collection of genes are the working parts. They are the classes for programming. That said, how can we make the genes to work together? By comparing it to programming, we know we need a drive, i.e. "the Main Program".

Where "The Main Program" is located in the genome? How "the Main" was written? Nobody knows yet. Presumably it is a COMMON part that is within every genome for all life forms, being it be bacteria, eukaryotes, fungi, plants, but excluding virus. It should be mostly composed of genes in the class of "Transcription Factors", as it needs to call upon many genes, and to conduct a symphony.

So if we study some model systems, from the time of "fertilization" in diploid genomes, or from the time of budding in a yeast, the "Main" is kicked off. Through an evolutionary study of the pathways, we should be able to pinpoint to this Main. It should contain some massive genes (large in size) which acts as integrators of the system states, ie. the switch board protein, which then makes a decision and turns on a group of other genes through cascades.

Let's we join hand to find this "The Main Program". It is the "Conductor of Life". From there, the genetic logic will become clear. "

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Approval of Zarxio by FDA: Avenue for less expensive treatment options in expensive healthcare market

Zarxio is the first biosimilar by Sandoz biopharmaceuticals to be approved in USA [1] and naming conventions for biosimilars are likely to follow shortly afterwards. This marks the country’s catchup with Europe, where biosimilars have already been in the market for years. Zarxio is analogous to Amgen’s Neupogen and acts by decreasing rates of infection in chemotherapy receiving patients with nonmyeloid malignancies. Some patients and medical experts have voiced their concerns for Biologics as they are genetically engineered substances mimicking a certain type of drug and may be dangerous or have low quality. One impediment might be that Zarxio is not purely generic i-e biosimilar but not interchangeable and cannot be substituted for Nupogen without physician’s approval. The FDA briefing document however, states that the drug carries no meaningful differences to its reference counterpart and meets the required conditions of safety, purity and potency (http://www.fda.gov/downloads/ForConsumers/ConsumerUpdates/UCM436715.pdf). Regulatory concepts such as “biosimilar,” “highly similar” and “no clinically meaningful differences” are new and vulnerable to different elucidations and interpretations by the targeted general public, sales forces and physicians. The authorization, however, guarantees versatility in the drugs but also public felicitation due to a huge reduction in prices.

The approval of Zarxio is basically an upshot of the Biologics Price Competition and Innovation Act 2010. Congressional budget prophets also purported that biosimilar drugs could benefit federal government and the consumers by incentivizing market competition and saving a great deal of money. This poses a serious threat to Amgen which spawned a revenue of $1.2 billion by the reference drug Nupogen [2] in 2014 alone (http://www.fiercepharma.com/story/judge-says-novartis-can-launch-biosimilar-amgens-neupogen/2015-03-20). Amgen filed lawsuit against Sandoz for patent infringement and unfair competition. District court has ruled out Amgen’s motion for prevention of Zarxio’s marketing (http://www.natlawreview.com/article/district-court-rules-against-amgen-s-motion-preliminary-injunction-to-prevent-market) and the commercialization of Zarxio is anticipated to remove Neupogen’s hit standing in a couple of years. Initially, it was speculated that biosimilars will be sold 40-50% cheaper than costs of original reference products in USA. However, presumed the complications of bringing biosimilar drugs to market, several analysts have stated apprehensions that the actual savings to US customers could be considerably lower than expected. According to Andre Mulchay, it costs almost $300 for a single injection by Neupogen and Zarxio could benefit the public by provision of a 35% discount ($105 per injection) (http://www.modernhealthcare.com/article/20150107/NEWS/301079947). According to Express Scripts, its availability in the U.S. market could save $5.7 billion in drug costs over the next 10 years (http://www.allgov.com/news/where-is-the-money-going/fda-opens-door-to-less-expensive-drugs-150320?news=856011). Sandoz, however, has not specified the market price for Zarxio yet and currently staying off the market, probably due to patent battle with Amgen. European Union’s substantial market of biosimilars could be used for conjecturing monetary benefits for USA but degree of biosimilar market astuteness varies substantially across the European Union, owing to differences in payer systems and policies, laws related to drug substitution, and the overall size of the generics market within each country. Average price discount is around 25% (http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_100.pdf). Environment for biosimilars is more complicated and legally challenging in USA than in other countries and US federal and state regulatory, pricing, and reimbursement policies will play a key role in determining future cost savings.
The approval, however, marks the dawn of a new era in treatment and setting up of glorious pathway for biosimilar centered small companies like Epirus, Coherus Biosciences and Pfenex. They are now, ready to step in after Sandoz and their share prices have already boosted after the word got out for approval of Zarxio.


References

1. Burki, Talha Khan. (2015). First biosimilar drug approved in the USA. The Lancet Oncol.
2. Senior, M. (2015). First US biosimilar edges towards market. Nat. Biotechnol., 33(3): 222-223.

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Kinetic modelling of metabolic pathways

Reposted from biostars

http://bioinformatics.knowledgeblog.org/2011/06/21/kinetic-modelling-of-metabolic-pathways/

Authors

Natalie J Stanford^1,2 , Kieran Smallbone^2,3
1. Integrative Systems Biology Doctoral Training Centre; 2. Manchester Centre for Integrative Systems Biology; and 3. School of Mathematics, University of Manchester, M13 9PL, UK.

Abstract

We show how to build a kinetic model of a metabolic pathway. We provide the example of building a model of glycerol synthesis using Copasi. However, the techniques required remain the same for any pathway, using any software.

Introduction

A mathematical description of a kinetic metabolic model may be given in differential equation form as $latex x^\prime = N v (x, y, p)$ $latex x(0) = x_0$ and this may be used as a guide as to the data required to create and parameterise a kinetic model. First, N is the stoichiometric matrix, which may be derived from the topology of the model. x denotes metabolite concentrations. y denotes boundary metabolites, whose concentrations are not allowed to vary, but do affect the reaction rates. Initial concentrations for both x and y must be defined, though note that only concentrations x will change over time. Finally, v denotes reaction rates; these are dependent on kinetic mechanisms, concentrations x and y and parameters p.

Stoichiometry

The first stage in kinetic modelling is defining the pathway of interest and its boundaries, which are captured mathematically in the stoichiometric matrix. As an example, we shall use a model of glycerol synthesis containing only two reactions [1], though the same procedure can be used for more complex systems. The model’s two reactions are glycerol 3-phosphate dehydrogenase (GPD) – which has reactants DHAP and NADH and products glycerol 3-phosphate (G3P) and NAD – and glycerol 3-phosphatase (GPP), which has reactant G3P and product glycerol (Gly) and phosphate (Phi). We shall show here how to implement the model using Copasi [2], but the techniques we describe will be applicable to any software. To enter the stoichiometric knowledge in Copasi, navigate to
Model > Biochemical > Reactions
Double click on the equation box, which should take you to the reaction interface (see figure 2). In the chemical equation box enter
DHAP + NADH = G3P + NAD
Click commit and navigate back to Model > Biochemical > Reactions

Figure 1: Navigating to the reaction interface

Figure 2: The reaction interface.

Note: You may notice that Copasi automatically defines a rate law. This is fine, and will be addressed later in the tutorial. Double click the new reaction box, and enter the following in the chemical equation box G3P -> Gly + Phi Note: You may have noticed that in reaction 1 we use = to signify the conversion between substrates and products, whilst reaction 2 uses ->. The former is used to represent a reversible chemical reaction. The latter signifies an irreversible reaction. It is important to note that Copasi will not let you assign pre-defined rate laws for reversible reactions that are irreversible and vice versa. However, if you choose to define your own rate law, these constraints can be violated. This will be discussed in more detail when we address entering rate laws. Navigating now to
Model > Biochemical > Species

Figure 3: The species interface

we see that the six metabolic species have already been added to the model. However, we do not want to simulate changes in concentration of the boundary metabolites in our system. To address this, change Simulation Type in DHAP from reactions to fixed, and repeat this for the other boundary metabolites, Gly, NAD, NADH and Phi.

Figure 4: Changing the metabolite boundaries

The stoichiometry matrix N is now found at
Model > Mathematical > Matrices
As expected, it contains only G3P, which is produced by the first reaction and consumed by the second.

Figure 5: The stoichiometric matrix.

Metabolites

In order to build a kinetic model, concentration values must be provided for all metabolites. Typically these will come from metabolomics measurements, or databases such as HMDB [3]. In Table 1, the concentrations for all metabolites in our model are set out. These may be added by accessing the Species menu in Copasi, and entering the concentration as the initial concentration (see figure 6).

Figure 6: Setting the initial concentration.

ADP, ATP and F16BP are included in the model as modifiers of GPD (Reaction 1). This is represented in Copasi by changing the reaction’s definition to
DHAP + NADH = G3P + NAD; ADP ATP F16BP

metabolite	concentration (mM)
ADP	2.17
ATP	2.37
DHAP	0.59
F16BP	6.01
G3P	0
Gly	15.1
NAD	1.45
NADH	1.87
Phi	1

Table 1: Metabolite concentrations used in the model. It is important to ensure that consistent units are used throughout the model. Navigating to Model, we change Time, Quantity Unit and Volume Unit to min, mmol and l, respectively. In this way, all concentrations are measured in units of mmol/l = mM, and all fluxes in units mM/min.

Figure 7: Model unit interface.

Rate equations

Kinetic rate laws v may be derived from knowledge of the mechanism underlying the enzymatic process. In many cases these are not known, and we must instead resort to approximations such as linlog [4, 5], power law [6, 7] or convenience kinetics [8]. For our model, the rate of GPD is known to be
$latex v_1 = \frac{\frac{Vf1}{K1nadh \cdot K1dhap} \left(NADH \cdot DHAP – \frac{NAD \cdot G3P}{Keq1} \right)} { \left(1 + \frac{F16BP}{K1f16bp} + \frac{ATP}{K1atp} + \frac{ADP}{K1adp} \right) \left( 1 + \frac{NADH}{K1nadh} + \frac{NAD}{K1nad} \right) \left(1+ \frac{DHAP}{K1dhap} + \frac{G3P}{K1g3p} \right)}$
whilst the rate of GPP is given by
$latex v_2 = \frac{\frac{V2 \cdot G3P}{K2g3p}} {\left( 1+ \frac{G3P}{K2g3p} \right) \left( 1+ \frac{Phi}{K2phi} \right)}$
Copasi has a wide variety of inbuilt kinetic laws that may be accessed via the Functions menu. However, the forms above must be entered manually; this is achieved by returning to the Reactions menu and selecting GPD (Reaction 1). Click New Rate Law and enter the following
Vf1/(K1nadh*K1dhap)*(NADH*DHAP-NAD*G3P/Keq1)/((1+F16BP/K1f16bp+ATP/K1atp+ADP/K1adp)*(1+NADH/K1nadh+NAD/K1nad)*(1+DHAP/K1dhap+G3P/K1g3p))
Once the equation has been entered you then need to associate each identifier as a Substrate (NADH, DHAP), Product (NAD, G3P), Modifier (F16BP, ATP, ADP) or Parameter(all other identifiers). This can be seen in figure 8

Figure 8: Entering a new rate law.

Repeat for GPP (Reaction 2)
V2*G3P/K2g3p/((1+G3P/K2g3p)*(1+Phi/K2phi))
once again identifying Substrate (G3P), Product (Gly, Phi) or Parameter (all other identifiers). Returning to the Reactionsmenu, these formulae need to be selected again under Rate Law for each reaction. Where there are multiple substrates, products or modifiers within a reaction it is important to check that the correct identifier is associated with the correct metabolite. These are circled in figure 9

Figure 9: Selecting the rate law.

Kinetic parameters

The kinetic formulae above lead to a range of kinetic parameters. Ideally, these would be measured using enzymatic assay, When this is not possible, databases such as Brenda [9] or Sabio-RK [10] could be used. In Table 2, the parameter values in our model are set out. These can be modified in the same interface highlighted in figure 9.

parameter	value	units
V1	147	mM/min
Keq	10000	1
K1adp	2	mM
K1atp	0.73	mM
K1dhap	0.54	mM
K1f16bp	4.8	mM
K1g3p	1.2	mM
K1nad	0.93	mM
K1nadh	0.023	mM
V2	53	mM/min
K2g3p	3.5	mM
K2phi	1	mM

Table 2: Parameter values used in the model.

Analysis

Having built a model of the system, we would like to use mathematical tools to further our understanding of its behaviour. Copasi provides a number of tools to carry out these tasks under the Tasks menu. In the original paper [1], the authors investigate the response of flux through the pathway to changes in boundary metabolite concentrations. This analysis may be performed running the Sensitivities task, with Function: Concentration Fluxes of Reactions and Variable: Initial Concentrations. Its results are presented in Table 3.

metabolite	response
ADP	-0.141
ATP	-0.420
DHAP	0.475
F16BP	-0.162
G3P	0
Gly	0
NAD	-0.0159
NADH	0.0260
Phi	-0.0741

Table 3: Scaled response coeffcients of flux to metabolite concentrations.

Systems biology standards

Describing mathematical models as above is unwieldy and error-prone, and naturally leads to difficulties in reproduction of results. Thus researchers have developed SBML (the Systems Biology Markup Language [11]). A computer-readable format for representing models of biological processes. Exchanging our models in SBML format (rather than e.g. Copasi’s own xml format) not only allows us to describe our model in unambiguous terms. It also gives us access to over 200 software tools specialising in many elements from data integration to visualisation [12]. SBML can be combined with MIRIAM [13] to annotate the entities of those models; for example, by marking-up the molecule G3P as CHEBI:15978 allows its unambiguous identification and automatically links to many additional sources of information. This may be achieved in Copasi under the Species submenu Annotation. BioModels.net [14] is a modelling repository containing hundreds of models marked up with the above standards. If you are having trouble reproducing the glycerol model described above, it is available from the repository at BIOMD0000000076.

References

1. G. Cronwright and J. Rohwer, “Metabolic control analysis of glycerol synthesis in Saccharomyces cerevisiae,” Applied and Environmental Microbiology, vol. 68, pp. 4448–4456, 2002.
2. S. Hoops, S. Sahle, R. Gauges, C. Lee, J. Pahle, N. Simus, M. Singhal, L. Xu, P. Mendes, and U. Kummer, “COPASI – a COmplex PAthway SImulator,” Bioinformatics, vol. 22, pp. 3067–74, Dec. 2006.
3. D. S. Wishart, D. Tzur, C. Knox, R. Eisner, A. C. Guo, N. Young, D. Cheng, K. Jewell, D. Arndt, S. Sawhney, C. Fung, L. Nikolai, M. Lewis, M.A. Coutouly, I. Forsythe, P. Tang, S. Shrivastava, K. Jeroncic, P. Stothard, G. Amegbey, D. Block, D. D. Hau, J. Wagner, J. Miniaci, M. Clements, M. Gebremedhin, N. Guo, Y. Zhang, G. E. Duggan, G. D. Macinnis, A. M. Weljie, R. Dowlatabadi, F. Bamforth, D. Clive, R. Greiner, L. Li, T. Marrie, B. D. Sykes, H. J. Vogel, and L. Querengesser, “HMDB: the Human Metabolome Database,” Nucleic Acids Res, vol. 35, pp. D521–6, Jan. 2007.
4. D. Visser and J. J. Heijnen, “Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics,” Metabolic engineering, vol. 5, pp. 164–76, July 2003.
5. K. Smallbone, E. Simeonidis, D. S. Broomhead, and D. B. Kell, “Something from nothing: bridging the gap between constraint-based and kinetic modelling.,” The FEBS journal, vol. 274, pp. 5576–85, Nov. 2007.
6. M. A. Savageau, Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology. Addison-Wesley Pub (Sd), 1976.
7. J. J. Heijnen, “Approximative kinetic formats used in metabolic network modeling,” Biotechnology and bioengineering, vol. 91, pp. 534–45, Sept. 2005.
8. W. Liebermeister and E. Klipp, “Bringing metabolic networks to life: convenience rate law and thermodynamic constraints,” Theor Biol Med Model, vol. 3, p. 41, 2006.
9. M. Scheer, A. Grote, A. Chang, I. Schomburg, C. Munaretto, M. Rother, C. Sohngen, M. Stelzer, J. Thiele, and D. Schomburg, “BRENDA, the enzyme information system in 2011,” Nucleic Acids Research, vol. 39, pp. D670–D676, Nov. 2010.
10. U. Leser, F. Naumann, B. Eckman, U. Wittig, M. Golebiewski, R. Kania, O. Krebs, S. Mir, A. Weidemann, S. Anstein, J. Saric, and I. Rojas, Data Integration in the Life Sciences, vol. 4075 of Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006.
11. M. Hucka, A. Finney, H. M. Sauro, H. Bolouri, J. C. Doyle, H. Kitano, A. P. Arkin, B. J. Bornstein, D. Bray, A. Cornish-Bowden, A. A. Cuellar, S. Dronov, E. D. Gilles, M. Ginkel, V. Gor, I. I. Goryanin, W. J. Hedley, T. C. Hodgman, J.-H. Hofmeyr, P. J. Hunter, N. S. Juty, J. L. Kasberger, A. Kremling, U. Kummer, N. Le Nov`re, L. M.e Loew, D. Lucio, P. Mendes, E. Minch, E. D. Mjolsness, Y. Nakayama, M. R. Nelson, P. F. Nielsen, T. Sakurada, J. C. Schaff, B. E. Shapiro, T. S. Shimizu, H. D. Spence, J. Stelling, K. Takahashi, M. Tomita, J. Wagner, and J. Wang, “The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models,” Bioinformatics, vol. 19, pp. 524–531, Mar. 2003.
12. SBML software guide
13. N. Le Novère, A. Finney, M. Hucka, U. S. Bhalla, F. Campagne, J. Collado-Vides, E. J. Crampin, M. Halstead, E. Klipp, P. Mendes, P. Nielsen, H. Sauro, B. Shapiro, J. L. Snoep, H. D. Spence, and B. L. Wanner, “Minimum information requested in the annotation of biochemical models (MIRIAM),” Nature Biotechnology, vol. 23, pp. 1509–15, Dec. 2005.
14. “BioModels database – A database of annotated published models”

Tags: copasi, metabolic, modelling, simulation

• Citation

  Please cite this article as: Natalie Stanford,Kieran Smallbone (2011) Kinetic modelling of metabolic pathways. The Bioinformatics Knowledgeblog. http://bioinformatics.knowledgeblog.org/2011/06/21/kinetic-modelling-of-metabolic-pathways/

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Sources of biological pathways

Interpretation database
Name: Hora suite
Location:http://www.paternostrolab.org/
Description: HORA suite (Human blOod Range vAlidator) is a Java application developed to validate the metabolomic analysis of human blood. It bases the search work on a database, built in our laboratory, that stores the normal plasma and serum range concentrations of metabolites.

Biological pathways analysis tools
Name: Pathvisio
Location: http://www.pathvisio.org/
Description: PathVisio is a pathway visualization and editing program using pathways in the GPML format. It is meant to be flexible enough to display many different types of data, such as microarray data and proteomics data, on familiar biological pathways. The available GPML pathways are converted directly from GenMAPP pathways, and should appear exactly the same.

Freeware Advantages: easy to create your own pathways and visualize your experimental data. Possibilities to add literature references to the links drawn. Good tutorials are available on the website. Visualization of different datatypes in one pathway is possible (i.e. metabolite data and gene data). Metabolite data can be uploaded by HMDB code and CAS number. Pathways can eventually be uploaded in wikipathways to make them publicly available (www.wikipathways.org).

Drawbacks: No possibilities to upload data on the level of enzymes (i.e. EC numbers). No possibilities to combine information available in the pathways into biological networks. No biological descriptions available for metabolites.

Name: Ingenuity
Location: http://www.ingenuity.com/products/pathways_analysis.html
Description: Ingenuity Pathways Analysis (IPA) is an all-in-one software application that enables researchers to model, analyze, and understand the complex biological and chemical systems at the core of life science research. IPA has been broadly adopted by the life sciences research community and cited in hundreds of peer-reviewed journal articles.

Commercial Advantages: straightforward and user friendly pathway visualization and analysis tool. Extensive help files are available. Metabolomics data can be uploaded as KEGG and PubChem ID. Every metabolite has its own ‘chem view’ page, summarizing chemical information, but also biological information such as ‘what does the metabolite regulate’, ‘by what is the metabolite regulated’, ‘what is its role in the cell’, ‘what does it bind’, and ‘what diseases are associated with the metabolite’. This biological information is created by textmining tools. References and citations of the information behind these biological associations are easily accessible. The tool creates on the basis of the textmining information and pathways biological networks of your uploaded metabolites (on the basis of the default algorithm ‘shortest path’).

Drawbacks: the information gathered by textmining tools are not manually curated by IPA, so you need to this by yourself and therefore be very careful in the interpretation of your results! There are no possibilities to visualize and analyze different datatypes (i.e. metabolite data combined with transcriptomics data). No possibilities to visualize & analyze your data on the level of biofluids such as blood and/or urine. Not species specific. No possibilities to edit own pathways or networks.

Name: Metacore
Location: http://www.genego.com/metacore.php
Description: MetaCore™ is an integrated software suite for functional analysis of experimental data. The scope of data types includes microarray and SAGE gene expression, SNPs and CGH arrays, proteomics, metabolomics, pathway analysis, Y2H and other custom interactions. MetaCore™ is based on a proprietary manually curated database of human protein-protein, protein-DNA and protein compound interactions, metabolic and signaling pathways and the effects of bioactive molecules in gene expression. The analytical package includes easy to use, intuitive tools for data visualization, mapping and exchange, multiple networking algorithms and filters.

Commercial, access available through NuGO ()Advantages: A nice pathway analysis and visualization tool. Good tutorials are available and there is a monthly online webex meeting in which Genego leads you through the Metacore software package. Metacore recognizes the following metabolite ID’s: chemical name, Brutto Formula, Molecular Weight, SMILES, InChI, CAS Number, KeGG ID, PubChem Compound ID, GeneGo Compound ID. For metabolomics, especially the biological network algorithms are interesting, which makes it easy to mine your data in a biological context. References behind biological links are easily accessible. It is possible to visualize mouse, rat, worm, fly, yeast and dog data on maps and networks. Moreover you can mine your data in real time: multiple data points, conditions, time-series, treatments can be visualized through animated videos. Finally you can apply disease, tissue, functional processes and sub-cellular localization filters to focus networks on information that is relevant to your study. It is possible to visualize different datatypes (i.e. metabolite and genedata).


Drawbacks: No possibilities to visualize & analyze your data on the level of biofluids such as blood and/or urine. There are possibilities to edit and create your own pathways, however you need an extension of your license with MapEditor software. Statistical algorithms available to calculate which pathways are enriched are not suitable for metabolomics data (it assumes that all metabolites available in the database are measured and quantified). No biological descriptions available for metabolites.
Name: Pathway HunterLocation: http://pht.tu-bs.de/PHT/
Description: Pathway Hunter Tool (PHT) is a robust and user friendly Systems Biology-based BioInformatics tool to process biologically relevant information. The tool finds all the valid bio-chemical shortest paths that connect two molecules in selected organisms. Further it generates a list of potential drug targets for the selected genomes based on the production/consumption load on metabolites or enzymes. This requires further in vitro/ in vivo verification. The tool presents overall connectivity information for the selected organisms.
Freeware

Name: VANTED
Location: http://vanted.ipk-gatersleben.de/
Description: This system makes it possible to load and edit graphs, which may represent biological pathways or functional hierarchies. It is possible to map experimental datasets onto the graph elements and visualize time series data or data of different genotypes or environmental conditions in the context of a the underlying biological processes. Built-in statistic functions allow a fast evaluation of the data (e.g. t-Test or correlation analysis).
Shareware

Modeling tools
Name: JWS online
Location: http://jjj.biochem.sun.ac.za/
Description: JWS Online is a Systems Biology tool for simulation of kinetic models from a curated model database. Click on the Model Database tab to access the models, or on the other tabs for more information.
Freeware

Name: Biomodels
Location: http://www.ebi.ac.uk/biomodels/
Description: BioModels Database is a data resource that allows biologists to store, search and retrieve published mathematical models of biological interests. Models present in BioModels Database are annotated and linked to relevant data resources, such as publications, databases of compounds and pathways, controlled vocabularies, etc.
Freeware

Name: COPASI: Complex Pathway Simulator
Location: http://www.copasi.org/tiki-index.php
Description: COPASI is a software application for simulation and analysis of biochemical networks.
Shareware

Name: SimBiology
Location: http://www.mathworks.com/products/simbiology/
Description: SimBiology® extends MATLAB® with tools for modeling, simulating, and analyzing biochemical pathways. You can create your own block diagram model using predefined blocks. You can manually enter in compartments, species, parameters, reactions, events, rules, kinetic laws, and units, or read in Systems Biology Mark-Up Language (SBML) models. SimBiology software lets you simulate a model using stochastic or deterministic solvers and analyze your pathway with tools such as parameter estimation and sensitivity analysis. A graphical user interface (GUI) provides access to command-line functionality and lets you create and manage compartments, reactions, events, species, parameters, rules, and units.
Commercial

Name: ByoDyn
Location: http://cbbl.imim.es:8080/ByoDyn
Description: ByoDyn has been designed to provide an easily extendable computational framework to estimate and analyze parameters in highly uncharacterized models.ByoDyn includes a set of tools to 1) integrate ordinary differential equations (ODEs), including systems with events, rules (differential algebraic equations, DAE) and delays built from a given biological model; 2) globally optimize the parameters that fit the provided experimental information and evaluate the sensitivity of the model with respect to the different parameters; and 3) include the sensitivity of the parameters in an optimal experimental design pipeline. The program makes use of external software, providing a Python binding schema that allows the user to easily implement new software in the desired calculation protocol. The program benefits from its interface with the SBML library, which ensures communication with other existing tools in the field.
Freeware

Name: CellDesigner
Location: http://www.systems-biology.org/cd/
Description: CellDesigner is a structured diagram editor for drawing gene-regulatory and biochemical networks. Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano, and are stored using the Systems Biology Markup Language (SBML), a standard for representing models of biochemical and gene-regulatory networks. Networks are able to link with simulation and other analysis packages through Systems Biology Workbench (SBW).
Freeware

Name: Cytoscape
 Location: http://www.cytoscape.org/
Description: Cytoscape is an open source bioinformatics software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data. Although Cytoscape was originally designed for biological research, now it is a general platform for complex network analysis and visualization. Cytoscape core distribution provides a basic set of features for data integration and visualization. Additional features are available as plugins. Plugins are available for network and molecular profiling analyses, new layouts, additional file format support, scripting, and connection with databases. Plugins may be developed by anyone using the Cytoscape open API based on Java technology and plugin community development is encouraged. Most of the plugins are freely available.
Freeware

Sources of biological pathways
Name: ExPASy
Location: http://www.expasy.ch/cgi-bin/search-biochem-index
Description: This page gives access to the digitized version of the Roche Applied Science "Biochemical Pathways" wall chart.

Name: Wikipathways
 Location: http://www.wikipathways.org
Description: Wikipathways is a online pathway database.

Name: KEGG: Kyoto Encyclopedia of Genes and Genomes
Location: http://www.genome.jp/kegg/Description: The goal of this website is to build a bioinformatics resource as complete computer representation of the cell, the organism, and the biosphere, which will enable computational prediction of higher-level complexity of cellular processes and organism behaviors from genomic and molecular information. For metabolites especially http://www.genome.ad.jp/kegg/ligand.html, which includes possibilities to search for chemical formula, name, exact mass, and pathway.
Name: Sigma Aldrich clickable metabolic pathway mapLocation: http://www.sigmaaldrich.com/img/assets/4202/MetabolicPathways_6_17_04_.pdf

Name: Nicholson minimaps
Location: http://www.sigmaaldrich.com/Area_of_Interest/Life_Science/Metabolomics/Key_Resources/Metabolic_Pathways/IUBMB_Nicholson_Minimaps.html
Nicholson minimaps give an overview of major individual metabolic pathways.

Name: Metacyc
Location: http://metacyc.org/
Metacyc is a database of nonredundant, experimentally elucidated metabolic pathways (<300 organisms).

Name: Reactome
Location: http://www.reactome.org/
The Reactome project is a collaboration among Cold Spring Harbor Laboratory, The European Bioinformatics Institute, and The Gene Ontology Consortium to develop a curated resource of core pathways and reactions in human biology. The information in this database is authored by biological researchers with expertise in their fields, maintained by the Reactome editorial staff, and cross-referenced with the sequence databases at NCBI, Ensembl and UniProt, the UCSC Genome Browser , HapMap, KEGG(Gene and Compound ), ChEBI, PubMed and GO. In addition to curated human events, inferred orthologous events in 22 non-human species including mouse, rat, chicken, puffer fish, worm, fly, yeast, two plants and E.coli are also available. A description of Reactome has been published in Genome Biology genomebiology.com/2007/8/3/r39.


Reference: Ma, H. W. & Goryanin, I. (2008) Human metabolic network reconstruction and its impact on drug discovery and development. Drug Discov Today, 13: 402-408

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Literature review:Summing up!

Literature reviews are very demanding if done properly and in huge demand as they are a sum of all the stuff going on in particular facet of knowledge. They serve as an amalgamated source of information as one does not have to open each and every paper and study it to know about what's going on around in last few years in a particular field.They also serve as a wonderful source of lectures for students and save many professors the heck of preparing lectures (sorry! not for all professors).
But the aim of today's post is not to discuss these points but provide tips on how to write literature review that is sound and professional.Some of the key points to remember are:
1.After choosing a topic, search and research literature.
When searching the literature for pertinent papers and reviews, the usual rules apply:

1. be thorough,
2. use different keywords and database sources (e.g., DBLP, Google Scholar, ISI Proceedings, JSTOR Search, Medline, Scopus, Web of Science), and
3. look at who has cited past relevant papers and book chapters.

2.keep track of the search items you use (so that your search can be replicated)
3.keep a list of papers whose pdfs you cannot access immediately (so as to retrieve them later with alternative strategies).
4.use a paper management system (e.g., Mendeley, Papers, Qiqqa, Sente)
5.Define early in the process some criteria for exclusion of irrelevant papers (these criteria can then be described in the review to help define its scope)
6.Do not just look for research papers in the area you wish to review, but also seek previous reviews.
7.Discuss in your review the approaches, limitations, and conclusions of past reviews.
8.Try to find a new angle that has not been covered adequately in the previous reviews
9.Incorporate new material that has inevitably accumulated since their appearance.
10.Keep focused, don't deviate from topic but do keep it of broad interest as well.
11.Aside from consistency, critical analysis is of utmost importance.
12.Get feedback from peers before final submission.

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Figures for publication: Data demands to be presented beautifully

Science cannot do without diagrams and figures. Getting better visualization of scientific data provides better graphical interface between reader and the data.One should tailor the images to the audience, i-e not depict only few direct steps and skip details if not intending it for experts only. Fine tune images with a suitable colour choice. Scaling should be checked. Avoid chartjunk or visual pollution. One can break image into chunks to make convenient for audience to capture details.Right selection of tools is of utmost importance. captions should be a must and resolution adjust to the best dpi possible. Most journals demand from 600 dpi-1000 dpi.


Some journals even provide tools for graphical check before submission as RapidInspector (http://rapidinspector.cadmus.com/zwi/index.jsp) by the Journal 'Protein: structure, function and bioinformatics.

Some resources of my choice are mentioned below:

D3.js: D3 for Data-Driven Documents is a JavaScript library that helps create and control interactive data-based graphical forms that can be run in web browsers, examples are shown in the gallery at http://github.com/mbostock/d3/wiki/Galle​ry.

GIMP:GNU Image Manipulation Program is an excellent application for tasks as photo retouching, image composition, and image authoring.

Matplotlib:a python plotting library, primarily for 2-D plotting but with some 3-D support, produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. It comes with a huge gallery of examples that cover virtually all scientific domains (http://matplotlib.org/gallery.html).

R : a powerful language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible.

Inkscape:professional vector graphics editor. It allows you to design complex figures and can be used, for example, to improve a script-generated figure or to read a PDF file in order to extract figures and transform them any way you like.

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Anecdote (Your advisor matters 100%,choose wisely)

One sunny day a rabbit came out of her hole in the ground to enjoy the fine weather. The day was so nice that she became careless and a fox snuck up behind her and caught her.

"I am going to eat you for lunch!" said the fox.

"Wait!" replied the rabbit, "You should at least wait a few days."

"Oh yeah? Why should I wait?"

"Well, I am just finishing my thesis on 'The Superiority of Rabbits over Foxes and Wolves'."

"Are you crazy? I should eat you right now! Everybody knows that a fox will always win over a rabbit."

"Not really, not according to my research. If you like, you can come into my hole and read it for yourself. If you are not convinced, you can go ahead and have me for lunch."

"You really are crazy!" But since the fox was curious and had nothing to lose, he went with the rabbit. The fox never came out.

A few days later the rabbit was again taking a break from writing and sure enough, a wolf came out of the bushes and was ready to set upon her.

"Wait!" yelled the rabbit, "you can't eat me right now."

"And why might that be, my furry appetizer?"

"I am almost finished writing my thesis on 'The Superiority of Rabbits over Foxes and Wolves'."

The wolf laughed so hard that it almost lost its grip on the rabbit.

"Maybe I shouldn't eat you. You really are sick...in the head. You might have something contagious."

"Come and read it for yourself. You can eat me afterward if you disagree with my conclusions."

So the wolf went down into the rabbit's hole...and never came out.

The rabbit finished her thesis and was out celebrating in the local lettuce patch. Another rabbit came along and asked, "What's up? You seem very happy."

"Yup, I just finished my thesis."

"Congratulations. What's it about?"

"'The Superiority of Rabbits over Foxes and Wolves'."

"Are you sure? That doesn't sound right."

"Oh yes. Come and read it for yourself."

So together they went down into the rabbit's hole. As they entered, the friend saw the typical graduate student abode, albeit a rather messy one after writing a thesis.

The computer with the controversial work was in one corner. To the right there was a pile of fox bones, to the left a pile of wolf bones. And in the middle was a large, well-fed lion.

The moral of the story:

The title of your thesis doesn't matter. The subject doesn't matter. The research doesn't matter. All that matters is who your advisor is!

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Tools to check plagiarism

1. Anti-Plagiarism Anti-Plagiarism is a software designed to effectively detect and thereby prevent plagiarism. It is a versatile tool to deal with World Wide Web copy-pasting information from the assignment of authorship. The goal of this program is to help reduce the impact of plagiarism on education and educational institutions. At present, it distributes free software to detect plagiarism. Checking documents in a format *.rtf, *.doc, *.docx, *.pdf
2. DupliChecker DupliChecker is a tool 100% free to use. Just copy-paste, or upload your essay, thesis, website content or articles, and click ‘search’, and you’ll get the analysis reports within seconds.
3. PaperRater Paper Rater offers three tools: Grammar Checking, Plagiarism Detection and Writing Suggestions. It is a free resource that is developed and maintained by linguistics professionals and graduate students. It is absolutely free to use and it allows you to check for plagiarized parts in your students’ essays.
4. Plagiarisma.net Plagiarisma has a search box as well as a software download available for Windows. Users can also search for entire URLs and files in HTML, DOC, DOCX, RTF, TXT, ODT and PDF formats.
5. PlagiarismChecker  PlagiarismChecker.com makes it simple for educators to check whether a student's paper has been copied from the Internet. Users can also use the "Author" option to check if others have plagiarized their work online. It is very easy to use as it does not require any download or installation.
6. Plagium Plagium is a free plagiarism detection tool. It’s very easy to use. All you have to do is paste in the original portion of text (max 250 characters) and hit "search. It is available in six languages and an Alert feature is also available.
7. PlagTracker Plagtracker is another online plagiarism detection service that checks whether similar text content appears elsewhere on the web. It starts scanning all internet pages and more than 20 million  academic works for any plagiarized copy. After scanning, you will receive a report with details about your work.
8. Viper  Viper is a fast plagiarism detection tools with the ability to scan your document through more than 10 billion resources, such as academic essays and other online sources, offering side-by-side comparisons for plagiarism. It’s free and you can download it very easily. Just keep in mind that it requires a download. Just note that Viper is available to Microsoft Windows users only.
9. SeeSources SeeSources is an online, automatic and free plagiarism checker. Choose MS Word in the formats (.doc/ .docx) or HTML in the formats (.htm) or text (.txt) or text document (max. 300kB, 1000 words). With "Start Analysis" the source search begins.  You will be updated about the progress continuously, search takes about 1 minute per document.
10. Plagiarism Detector Plagiarism Detector is a software especially designed keeping the growing content requirement over the internet in mind. Equally useful for teachers, students and website owners. It scans the documents and detects plagiarism and provides an instant report. Your content should not be in a specific format. You simply need to copy/paste your content in the provided window and press search button. This is it!

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2D plot of transmembrane protein

I had to make a representation of transmembrane protein of RND superfamily protein and two tools which I found suitable were:
TMRPres2D available at http://bioinformatics.biol.uoa.gr/TMRPres2D/download.jsp
and Protter available at http://wlab.ethz.ch/protter/start/
which is an online version of Textopo.

TMRPres2D did not give a good figure 

while Protter like this:

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Websites to check impact factor of journals for 2014

1. http://www.citefactor.org/journal-impact-factor-list-2014..., 
 2. http://globalimpactfactor.com/journals-list/ 
3. http://www.scijournal.org/

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Resource for microbial genomics and metagenomics

1.CAMERA
2.EcoCyc a database that describes the genome and the biochemical machinery of the model organism E. coli K-12
3.Ensembl Genomes
4.National Microbial Pathogen Data Resource
5.RegulonDB RegulonDB is a model of the complex regulation of transcription initiation or regulatory network of the cell E. coli K-12.
6.SEED platform for microbial genome analysis includes all complete microbial genomes, and most partial genomes. The platform is used to annotate microbial genomes using subsystems.
7.INTEGRALL: Database dedicated to integrons, bacterial genetic elements involved in the antibiotic resistance
8. EzGenome, comprehensive information about manually curated genome projects of prokaryotes (archaea and bacteria)

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