6 replies to this topic

[maestro949]

This is beautiful...

Link to Full PDF Article (Warning: Long) : In Silico Systems Analysis of Biopathways

Overall it's an excellent document regarding modelling and simulation of biopathways. Among many other trinkets, it discusses the issues surrounding the massive growth in sequence, pathway and protein data and the need to organize it as well. 168 pages of juiciness though I'm not going to finish reading it today as my wife is forcing me to go out and interact with other dreadful homosapiens. I guess it's still better than having to watch American Idol. [huh]

First Paragraph of Abstract

In the past decade with the advent of high-throughput technologies, biology has migrated from a descriptive science to a predictive one. A vast amount of information on the metabolism have been produced; a number of specific genetic/metabolic databases and computational systems have been developed, which makes it possible for biologists to perform in silico analysis of metabolism. With experimental data from laboratory, biologists wish to systematically conduct their analysis with an easy-to-use computational system. One major task is to implement molecular information systems that will allow to integrate different molecular database systems, and to design analysis tools (e.g. simulators of complex metabolic reactions). Three key problems are involved:

1) Modeling and simulation of biological processes;
2) Reconstruction of metabolic pathways, leading to predictions about the integrated function of the network; and
3) Comparison of metabolism, providing an important way to reveal the functional relationship between a set of metabolic pathways.

Conclusion

The rapid development of molecular biology and achievements of modern technology have raised many questions of great bioinformatics interest. Analysis of biopathways is one of the key topics in the post-genomic era. In order to understand the cellular mechanisms, to automatically retrieve metabolic information and predict metabolic pathways, and also to perform comparison of biopathways, we have to develop and implement useful methodologies, algorithms and tools for the analysis of complex biopathways. In this thesis we have investigated several problems of biopathway analysis based on the above considerations.

1) Modeling and simulation of biopathways The hybrid Petri net has been exploited for modeling and simulation of gene regulated metabolic networks. A global Petri net modeling and simulation strategy and technique is described to systematically investigate metabolic networks. The methodology of this model can be used to all other metabolic networks or the virtual cell metabolism. Moreover we discussed the perspective of Petri nets on modeling and simulation metabolic networks.

A Biology Petri Net Markup Language (BioPNML) for biological data interchange among diverse biological simulators and Petri net tools has been proposed. The BioPNML is designed to provide a starting point for the development of a standard interchange format for Bioinformatics and Petri nets. The language makes it possible to present biology Petri net diagrams between all supported hardware platforms and versions. It is also designed to associate Petri net models and other known metabolic simulators.

2) Prediction of metabolic pathways A web-based system for prediction of metabolic pathways has been developed. The system, PathAligner, allows to reconstruct metabolic pathways from rudimentary elements such as genes, sequences, enzymes, metabolites, etc., and to extract metabolic information from biological databases via the Internet. PathAligner also provides a navigation platform to investigate more related metabolic information, and transforms the output data into XML-files for further modeling and simulation. Using the PathAligner system, it is possible to construct a complete Petri net model of biopathway from a rudimentary dataset.

3) Alignment of biopathways
A global definition of bioprocess pathways has been presented. A new method to align metabolic pathway has been described and implemented into the PathAligner system. The algorithm is based on strip scoring the similarity of 4-heirachical EC numbers involved in the pathways. We have set up the STCDB database. STCDB is an information system on cellular signal transductions. It recommends a classification of cellular signal transduction, and
attempts to standardize the representation of signaling pathways. Every characterized signal transduction is assigned a unique 4-heirachical ST number. Our alignment algorithm can be applied to both metabolic pathways and signaling pathways. The general representation of alignment of biopathways is possible by using the recommended signal transduction classification system and the introduced alignment algorithm. In addition, a concrete biological example has been studied. A detailed model of the urea cycle has been modeled and systematically analyzed. The discoveries of transcription factors and their associated diseases are useful for the treatment of the urea cycle disorders. The process of “from sequence to structure to function to application” will dominate bioinformatics in the next decades. Biopathways presents many questions and problems worthy to focus on. Some are well studied while others are entirely open problems. We hope that our work has brought us a small step forward in applying computational methods to handle the complexity of metabolic data and that it may some day bring us closer to understand life itself.
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Edited by maestro949, 10 June 2006 - 12:43 PM.

[Live Forever]

Anything is better than American Idol.


Nice find!

[olaf.larsson]

Good .pdf What education do you have meastro are you doing bioinfo yourself?

[maestro949]

Good .pdf What education do you have meastro are you doing bioinfo yourself?

I have an undergrad degree in management information systems but my real education came from working at startup telecom companies architecting software for VOIP networks. I've been filling the both the director and lead designer role for a team of 13 engineers for the past 5 years.

Presently I am doing the bioinfo research myself though I have been contemplating a career switch once we sell off this current start up ( probably another year or two). The debate I'm struggling with is a.) do I just stay on the CIO path I'm on now, b.) take a couple of years off and go back to school, c.) hop on another venture opportunity or d.) just remain an armchair scientist and work with the "open" source/standards/etc. community.

[maestro949]

This paragraph from the dissertation, especially the last two lines, is why we need to fully model the complexity, put it into a simulation and then tinker with it there. Otherwise we're just playing whack-a-mole in our attempts to modify our DNA and pathways towards longevity.


--Begin Snip ---

Life is the process of metabolism that transforms compounds such as carbohydrates, amino acids and lipids, and the energy required and all the other components that take up living systems to synthesize them and to use them in creating proteins and cellular structures as well as sustaining life. A cell contains a great numbers of organelles, specific proteins, and much more (Figure 2.1.1a). There are thousands of biochemical reactions taking place per second in a living cell. In Escherichia coli, for instance, there are 225,000 proteins, 15,000 ribosomes, 170,000 tRNA-molecules, 15,000,000 small organic molecules and 25,000,000 ions inside the a few µm cell [Goo93]. There are estimated 1014-1016 biochemical reactions in a cell [End01]. These reactions are interconnected by the metabolic molecules. Many molecules involved in one reaction can also be found in other reactions where the molecules act as substrate or activator or repressor, the activities of enzymes are enhanced or inhibited by some molecules. Proteins and enzymes are synthesized from encoding genes which can also be switched on or off by some other molecules. Thus a densely connected, intricate and precisely regulated reaction network is built (Figure 2.1.1b). These connected biochemical reaction is normally called a metabolic network. Obviously, the more interconnections exist, the harder it gets to predict how the system will react. When systems reach a certain size, they will be become unmanageable and difficult to understand without the help of computational support. It also gets harder to change any part of the system without influencing other parts.

---End Snip---

[olaf.larsson]

225,000 proteins, 15,000 ribosomes, 170,000 tRNA-molecules, 15,000,000 small organic molecules and 25,000,000 ions inside the a few µm cell

The interessting is: How many different proteins are there? How many different kinds organic molecules are there..? etc.
If all these proteins consist of the same AA-sequence the komplexity level is not very high, if they all are unique the complexity level is high.

[maestro949]

The interessting is: How many different proteins are there? How many different kinds organic molecules are there..? etc.
If all these proteins consist of the same AA-sequence the komplexity level is not very high, if they all are unique the complexity level is high.

Many of the molecules are in a constant state of change either binding, moving or entirely disolving into smaller molecules depending on what chain reaction they are contributing to at a precise moment. One protein may serve many pathways which makes the complexity either higher so the study and simulation of proteins nor pathways alone isn't sufficient but rather the study of both of them in conjuction with each other and every other pathway they affect. The fact that you can't build an agent based model where each protein is an agent that simply has function(s) really bums me out. A model where agents-as-proteins that can break into smaller agents with completely different functions based on their new chemical structure is wildly more complex. It just puts you right back at having to model every friggin' chemical reaction based on the atomic makeup. grrr.

Edited by maestro949, 15 March 2006 - 02:00 PM.