Bio::Blogs #8
The eighth edition of Bio::Blogs is coming up soon. It is going to be published here on Public Rambling tomorrow night. I almost forgot that this month is shorter than usual :). There are currently 3 submissions sent in. Pick something from your blogs that is bioinformatic related and sent it in (bioblogs at gmail) or leave the link in the comments. Please also let me know if you mind that I create a PDF file including your submission for offline reading. If you don;t have a blog start one and let me know or just send in a link to something that you particularly liked.
Tuesday, February 27, 2007
The future impact of genome synthesis
The synthesis blog pointed to a detailed report discussing the economical importance of impending advances in biological engineering. The study, supported by DOE, DuPont Corporation and The Berkley Nanosciences Nanoengineering Institute tries to cover the main driving forces for biotechnology innovation, it's possible future applications and economical impact. The last chapter is dedicated to envisioning future scenarios for synthetic biology based on different assumptions about important factors that could determine the progress of this technology.
While the scenarios described in the end of the report might be useful to track the speed and mode of evolution of this emerging technology, the most relevant section for life scientists is arguably the one discussing possible applications of genome synthesis.
There are three main applications listed:
Chemicals: Engineering new production pathways and creating new products
Energy: Opening new biological routes for energy transformation
Synthetic Vaccines: Opportunities for rapid-response biosecurity
The best examples of synthetic biology research have consisted up to now mostly of simple toy examples. Usually simple circuits are created and studied to detail but few have obvious immediate practical applications. Are we currently at the inflection point, were synthetic biology research will produce more practical applications or is the complexity of living systems still too large a barrier ?
One example of the use of synthetic biology in chemical production is the work of Dae-Kyun Ro and colleagues in the Keasling lab (free PDF). They re-engineered S. cerevisiae to produce artemisinic acid, a precursor of the malaria drug Artemisinin.
Keasling is also one of the researchers involved in the Helios project. An effort directed at developing technology for solar fuel generation (in the form of biofuel). The project is also headed by Nobel prize laureate Steve Chu that explains the project in this video presentation.
The synthesis blog pointed to a detailed report discussing the economical importance of impending advances in biological engineering. The study, supported by DOE, DuPont Corporation and The Berkley Nanosciences Nanoengineering Institute tries to cover the main driving forces for biotechnology innovation, it's possible future applications and economical impact. The last chapter is dedicated to envisioning future scenarios for synthetic biology based on different assumptions about important factors that could determine the progress of this technology.
While the scenarios described in the end of the report might be useful to track the speed and mode of evolution of this emerging technology, the most relevant section for life scientists is arguably the one discussing possible applications of genome synthesis.
There are three main applications listed:
Chemicals: Engineering new production pathways and creating new products
Energy: Opening new biological routes for energy transformation
Synthetic Vaccines: Opportunities for rapid-response biosecurity
The best examples of synthetic biology research have consisted up to now mostly of simple toy examples. Usually simple circuits are created and studied to detail but few have obvious immediate practical applications. Are we currently at the inflection point, were synthetic biology research will produce more practical applications or is the complexity of living systems still too large a barrier ?One example of the use of synthetic biology in chemical production is the work of Dae-Kyun Ro and colleagues in the Keasling lab (free PDF). They re-engineered S. cerevisiae to produce artemisinic acid, a precursor of the malaria drug Artemisinin.
Keasling is also one of the researchers involved in the Helios project. An effort directed at developing technology for solar fuel generation (in the form of biofuel). The project is also headed by Nobel prize laureate Steve Chu that explains the project in this video presentation.
Friday, February 23, 2007
Traveling around (Boston, San Francisco)
I have been traveling during the past week. I have been in Boston and I am now in San Francisco (labs: Marc Vidal, Wendel Lim, Adam Arkin).It would be interesting to be able to talk about some of the nice projects I have heard about but I guess it is really not up to me to make this public. Some of it is on their webpages. That leaves very little to say about the trip in respect to science. So instead, here is a picture I took in San Francisco :)
I am looking for a place to start a postdoc after the summer time. Even if I don't move to the states it is very unlikely that I will stay in Germany. This will be my 3rd country and 7th city. It is funny that so many of the grants that are currently available in Europe for postdocs are incentives to increase mobility. Isn't it time to also create some incentives to settling down ? I am 28 and this will be my first postdoc but eventually I will get tired of moving around. I hope by then it will be easier to stay.
I have been traveling during the past week. I have been in Boston and I am now in San Francisco (labs: Marc Vidal, Wendel Lim, Adam Arkin).It would be interesting to be able to talk about some of the nice projects I have heard about but I guess it is really not up to me to make this public. Some of it is on their webpages. That leaves very little to say about the trip in respect to science. So instead, here is a picture I took in San Francisco :)
I am looking for a place to start a postdoc after the summer time. Even if I don't move to the states it is very unlikely that I will stay in Germany. This will be my 3rd country and 7th city. It is funny that so many of the grants that are currently available in Europe for postdocs are incentives to increase mobility. Isn't it time to also create some incentives to settling down ? I am 28 and this will be my first postdoc but eventually I will get tired of moving around. I hope by then it will be easier to stay.
Wednesday, February 07, 2007
in sillico network reconstruction (using expression data)
In my last post I commented on a paper that tried to find the best mathematical model for a cellular pathway. In that paper they used information on known and predicted protein interactions. This time I want to mention a paper, published in Nature Mol. Systems Biology, that attempts to reconstruct gene regulatory networks from gene expression data and Chip-chip data.
The authors were interested in determining how/when transcription factors regulate their target genes over time. One novelty introduced in this work was the focus on bifurcation events in gene expression. They tried to look for cases where a groups of genes clearly bifurcated into two groups at a particular time point. Combining these patterns of bifurcation with experimental binding data for transcription factors they tried to predict what transcription factors regulate these group of genes. There is a simple example shown in figure 1, reproduced below.
In this toy example there is a bifurcation event at 1 h and another at the 2h time point. All of the genes are assigned to a gene expression path. In this case, the red genes are those that are very likely to show a down regulation in between the 1st and 2nd hour and stay at the same level of expression from then on. Once the genes have been assigned it is possible to search for transcription factors that are significantly associate to each gene expression path. For example in this case, TF A is strongly associated to the pink trajectory. This means that many of the genes in the pink group have a known binding site for TF A in their promoter region.
To test their approach, the authors studied the amino-acid starvation in S. cerevisiae. In figure 2 they summarize the reconstructed dynamic map. The result is the association of TFs to groups of genes and the changes in expression of these genes over time during amino acid starvation.
One interesting finding from this map was that Ino4 activates a group of genes related to lipid metabolism starting at the 2h time point. Since Ino4 binding sites had only been profiled by Chip-chip in YPD media and not in a.a. starvation, this is a novel result obtained using their method.
To further test the significance of their observation they performed Chip-chip assays of Ino4 in amino acid starvation. They confirmed that Ino4 binds many more promoters during amino acid starvation as compared to synthetic complete glucose media. Out of 207 genes bound by Ino4 (specifically during AA starvation) 34 were also among the genes assigned to the Ino4 gene path obtained from their approach.
This results confirmed the usefulness of this computational approach to reconstruct gene regulatory networks from gene expression data and TF binding site information.
The authors then go on to study the regulation of other conditions.
For anyone curious enough about the method, this was done using Hidden Markov Models (see here for available primer on HMMs).
In my last post I commented on a paper that tried to find the best mathematical model for a cellular pathway. In that paper they used information on known and predicted protein interactions. This time I want to mention a paper, published in Nature Mol. Systems Biology, that attempts to reconstruct gene regulatory networks from gene expression data and Chip-chip data.
The authors were interested in determining how/when transcription factors regulate their target genes over time. One novelty introduced in this work was the focus on bifurcation events in gene expression. They tried to look for cases where a groups of genes clearly bifurcated into two groups at a particular time point. Combining these patterns of bifurcation with experimental binding data for transcription factors they tried to predict what transcription factors regulate these group of genes. There is a simple example shown in figure 1, reproduced below.
In this toy example there is a bifurcation event at 1 h and another at the 2h time point. All of the genes are assigned to a gene expression path. In this case, the red genes are those that are very likely to show a down regulation in between the 1st and 2nd hour and stay at the same level of expression from then on. Once the genes have been assigned it is possible to search for transcription factors that are significantly associate to each gene expression path. For example in this case, TF A is strongly associated to the pink trajectory. This means that many of the genes in the pink group have a known binding site for TF A in their promoter region.
To test their approach, the authors studied the amino-acid starvation in S. cerevisiae. In figure 2 they summarize the reconstructed dynamic map. The result is the association of TFs to groups of genes and the changes in expression of these genes over time during amino acid starvation.
One interesting finding from this map was that Ino4 activates a group of genes related to lipid metabolism starting at the 2h time point. Since Ino4 binding sites had only been profiled by Chip-chip in YPD media and not in a.a. starvation, this is a novel result obtained using their method.
To further test the significance of their observation they performed Chip-chip assays of Ino4 in amino acid starvation. They confirmed that Ino4 binds many more promoters during amino acid starvation as compared to synthetic complete glucose media. Out of 207 genes bound by Ino4 (specifically during AA starvation) 34 were also among the genes assigned to the Ino4 gene path obtained from their approach.
This results confirmed the usefulness of this computational approach to reconstruct gene regulatory networks from gene expression data and TF binding site information.
The authors then go on to study the regulation of other conditions.
For anyone curious enough about the method, this was done using Hidden Markov Models (see here for available primer on HMMs).
Tuesday, February 06, 2007
In silico network reconstruction
It is day one of Just Science week and I want to tell you about a recent paper that was published in BMC Systems Biology by Rui Alves and Albert Sorribas. It is about a general approach to integrate information to come up with models for cellular pathways. What does this mean and why is this important ?
Increasingly the scientific knowledge is being stored in databases (literature, protein structures, gene expression, protein-protein interactions, protein-DNA interactions, etc). The general idea behind the work described is that we should be able to use the accumulated information about cellular pathways to extract models of how the cell's components interact to preform their functions. By models I mean a formal representation that can tell us how the components' concentrations and activities change with time.
There are several works already dealing with this problem of trying to reconstruct cellular networks from large data sources but I found this article particularly interesting because it uses so many of these methods.
To give you an idea I reproduce below figure 4 of the paper with a diagram of the method (click to zoom in):
The authors have pulled in experimentally known interactions and combined them with putative interactions obtained from docking and phylogenetic based predictions. These predicted networks are then converted to several possible mathematical models that are examined under different parameter conditions and compared with known experimental values.
This method should be particularly suited for a case when some of the genes in the pathway are known and there are experimental measured outputs for the pathway that can be compared with the predictions from the putative pathway models.
Ideally this whole procedure would be fully converted into an automatic pipeline that could be used by people that are not so familiar with the tools.
I will try to stick with the same theme during the week, hopefully covering different methods to achieve the same thing.
It is day one of Just Science week and I want to tell you about a recent paper that was published in BMC Systems Biology by Rui Alves and Albert Sorribas. It is about a general approach to integrate information to come up with models for cellular pathways. What does this mean and why is this important ?
Increasingly the scientific knowledge is being stored in databases (literature, protein structures, gene expression, protein-protein interactions, protein-DNA interactions, etc). The general idea behind the work described is that we should be able to use the accumulated information about cellular pathways to extract models of how the cell's components interact to preform their functions. By models I mean a formal representation that can tell us how the components' concentrations and activities change with time.
There are several works already dealing with this problem of trying to reconstruct cellular networks from large data sources but I found this article particularly interesting because it uses so many of these methods.
To give you an idea I reproduce below figure 4 of the paper with a diagram of the method (click to zoom in):
The authors have pulled in experimentally known interactions and combined them with putative interactions obtained from docking and phylogenetic based predictions. These predicted networks are then converted to several possible mathematical models that are examined under different parameter conditions and compared with known experimental values.
This method should be particularly suited for a case when some of the genes in the pathway are known and there are experimental measured outputs for the pathway that can be compared with the predictions from the putative pathway models.
Ideally this whole procedure would be fully converted into an automatic pipeline that could be used by people that are not so familiar with the tools.
I will try to stick with the same theme during the week, hopefully covering different methods to achieve the same thing.
Sunday, February 04, 2007
Publishing greasemonkey scripts (update)
A while ago I asked if greasemonkey scripts should be published in peer reviewed journals or if blogs could be a more suitable way of distributing these tools. The blog post was triggered by the publication of iHOPerator, mentioned also by Deepak.
I would like to thank one of the authors, Benjamin Good, and a BMC editor, Matt Hodgkinson, for taking the time to post their opinion in the comments. In summary they both argue that this publication helps raise awareness to greasemonkey and related technologies.
For me this exchange in the comments exemplifies the usefulness of the web for discussing science. The comments on this paper are aggregated in this Postgenomic entry and anyone could, in principle, participate no matter where they are.
This also reminded me of a discussion I had with someone here at EMBL recently. If web based discussions like this take off then authors might have a higher work load in trying to keep up with what is being said about their works. If a misinterpretation occurs it has a higher potential for spreading online. On the other hand, these sorts of web discussion help to level the playing field for manuscripts. In the near future it might not matter so much were the paper is published but if attracted the attention of the people in the field.
A while ago I asked if greasemonkey scripts should be published in peer reviewed journals or if blogs could be a more suitable way of distributing these tools. The blog post was triggered by the publication of iHOPerator, mentioned also by Deepak.
I would like to thank one of the authors, Benjamin Good, and a BMC editor, Matt Hodgkinson, for taking the time to post their opinion in the comments. In summary they both argue that this publication helps raise awareness to greasemonkey and related technologies.
For me this exchange in the comments exemplifies the usefulness of the web for discussing science. The comments on this paper are aggregated in this Postgenomic entry and anyone could, in principle, participate no matter where they are.
This also reminded me of a discussion I had with someone here at EMBL recently. If web based discussions like this take off then authors might have a higher work load in trying to keep up with what is being said about their works. If a misinterpretation occurs it has a higher potential for spreading online. On the other hand, these sorts of web discussion help to level the playing field for manuscripts. In the near future it might not matter so much were the paper is published but if attracted the attention of the people in the field.
Friday, February 02, 2007
Just Science
(Via RPM, Razib, Chris, Arunn) Next week is Just Science week. I will try to review recent papers on cellular networks, systems/synthetic biology and evolution that I found interesting.
(Via RPM, Razib, Chris, Arunn) Next week is Just Science week. I will try to review recent papers on cellular networks, systems/synthetic biology and evolution that I found interesting.
Bio::Blogs #7
The February edition of Bio::Blogs was just published in BioHacking. Thanks to Paras for editing it. He highlighted some blogs related to synthetic biology and some of the recent bioinformatics posts from Neil, Sandra and Pierre.
The 8th edition will be coming back here. The participation has been generally going down so Bio::Blogs might, in the near future, morph to something else.
Just to give it a little twist I thought that it could be interesting to add a PDF version of Bio::Blogs (with the permission of the authors of course). So, for the next month entries I will be asking if the authors concede that the blog posts be compiled into a printable document.
Entries can be submitted until the end of February to bioblogs at gmail.
The February edition of Bio::Blogs was just published in BioHacking. Thanks to Paras for editing it. He highlighted some blogs related to synthetic biology and some of the recent bioinformatics posts from Neil, Sandra and Pierre.
The 8th edition will be coming back here. The participation has been generally going down so Bio::Blogs might, in the near future, morph to something else.
Just to give it a little twist I thought that it could be interesting to add a PDF version of Bio::Blogs (with the permission of the authors of course). So, for the next month entries I will be asking if the authors concede that the blog posts be compiled into a printable document.
Entries can be submitted until the end of February to bioblogs at gmail.
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