Tree Building, part 1

November 21, 2011

For those of you who have actually been reading and are curious, my BLAST finally finished. It only took 9 days, which was nice considering I’ve heard BLAST’s of that size can take months. Finally finished running the myriad programs required to extract data from the proteomes on Friday… and now I’m trying to figure out where and how to utilize this data so I can actually get meaningful info from it.
Beside that, I’m currently working on building a phylogenetic tree using the genomes of the same Archaea I just did BLAST’s of. This tree building is the first part of my actual project for the lab, and I’m pretty excited about it. We’ve decided to use codons to build the tree, which is very time intensive but incredibly accurate and reliable. But, as happens a lot in this field, the software we’re using to build the tree doesn’t support codon models; more specifically, they convert nucleotide models to codon models, but the way in which they do this is less accurate than just using a codon model. So I’ve been tasked with figuring out how to modify the script the software uses to allow us to use the Goldman-Yang (1994) Codon Substitution model. So at the moment I find myself staring at XML scripts and Goldman and Yang (1994) to see what needs to be modified, how, and why. Yay…‽
Hopefully more to come soon.


Yeah yeah yeah, I know, they aren’t called “enraged” anymore, they’re called Activated Macrophages.  But I concur with my Microbiology professor that “Enraged Macrophage” sounds better while being more descriptive.  But anyways, that’s not the point.

I was trying to find a catchy title related to Cell Bio, but there isn’t much in that field that lends itself to catchy AND pun-ny titles.  Although “See You In The ER” might have been ok… Alternatively, “I Ran, I Ran-GEF’ed So Far Away” is just bad, and I’ve been trying to figure out ways to use COPII, MAPK, NLS, and a handful of others with no success.  This is way off topic, though, so let me get to the purpose of this.

I need to blog more, which is something I’ve been saying almost ever since I started this damned thing.  I like blogging, and I like blogging about more interesting or relevant things than just the doldrums of my day-to-day.  It’s hard for me to find something to consistently write about, however, so my plans to be more consistent usually fall apart.  I’m hoping to change that by chronicling my Misadventures in Bioinformatics.  Hey… that’s not a bad title.  But titles aside, I think this is actually something I can accomplish.  I have to write a weekly “blog” for my Modeling in Biology class anyways, and that class is also having me put together a model of my own, complete with a Lit Review, a proposal, and, of course, a model that I have to present and defend.  How does a quarter-long class get me to write more consistently?  Well, in addition to the class I’ve (finally) begun working in the lab I’ve been trying to get into since my Freshman year, and I’m working with Metagenomic data of a very large number of Archaea; Halophilic Archaea, to be precise  (“Halophile Ever Know” might have been a decent title too.  But now I really need to stop).  And I’ve managed to find connections between the modeling project for my class and my lab work in the lab.  Both of which give me something to talk about at regular intervals, as I slog through everything.  It may or may not be interesting, but it’s at least an excuse to blog more often, and about things I actually can say something about.

So that brings me to where I am currently: trying to narrow down my modeling project while trying to relearn Unix/bash and “relearn” Perl, along with learning bioinformatics the old-fashioned way — Coffee, Google, bang head against wall, repeat until you have a vague idea of what you’re doing.  I’m not new to bioinformatics, but I’m extremely new to genomic data from multiple genomes, database management, protein family annotation, and constructing phylogenies from phylogenomic data.  And like all good bioinformatics projects, I’ve been sent out into the wild with nothing more than a pat on the back, some vague info, and a dark spot on the horizon that I’m supposed to walk towards.  Oh, and Google.  But Google pre-Google maps, because it can’t tell me what that dark spot on the horizon is.

So what’s there to look forward to? Well, data and info, for one.  And the excitement of finally (and hopefully soon) figuring out how to do everything I need to do efficiently.  I say that because the people in my lab basically created a lot of the methods that we and many other labs are using to do research of this sort, and they pull their hair out constantly.  They still have issues with everything they use, but they’ve at least found ways to make the head-bashing minimal and more efficient.  But what I’m really, really, really, REALLY excited about is the potential for my modeling project and my lab work to overlap and (hopefully) turn into my own actual research project, complete with experiments, data, and maybe even publication.

So yeah, what is my modeling project?  I’m trying to look into and model the interactions between microbes and humans that might cause tumor formation and cancer.  While I might be working on the genomics of haloarchaea, the major research focus of my lab is in microbe-host interactions.  But hey, what’s that?  An article from 2010 describes finding Halophilic Archaea in human intestines?  Some of the species found in the study are some of the species who’s genomes we’re studying?  Even if there really isn’t anything there, it’s a start.  It’s something I can keep an eye on.  And it’s something I can blog about.


This is from a blog post I had to write for one of my classes this quarter, BIS 132: Dynamic Modeling in Biology. I hope you enjoy it and find the subject as fascinating as I do!

Instead of writing on one of the provided prompts for this week’s blog, I decided to write about something that is specifically interesting to me and, I hope, will be interesting to you as well. I did this because I felt that the 300 word summary for the homework assignment wasn’t sufficient to fully capture the research and implications. That and the fact that I’m extremely excited about this research now that I’ve read through it and have had a chance to dig into it a little bit. So let’s begin, shall we?

The article I am writing about is called “A Dynamical Systems Model for Combinatorial Cancer Therapy Enhances Oncolytic Adenovirus Efficacy by MEK-Inhibition.” It was published this past February in PLoS Computational Biology, and was authored by 4 researches from MIT and UCSF. The article discusses the use of oncolytic adenoviruses for the treatment of metastatic cancers, as metastatic cancers are very dangerous and non-surgical treatments are quite often ineffective. Then again, surgical treatments for these types of cancers are often not very effective as well. So why use viruses? Well, viruses, specifically adenoviruses, have been used as vectors to deliver recombinant DNA to targeted cells for a small number of diseases. Adenoviruses are found in vertebrates, are the cause of conjunctivitis (“Pink Eye”) among other viral diseases, and have double stranded DNA, much like Humans and most other animals. Adenoviruses lacking the E1B-55K gene, which can inhibit the tumor-suppressing protein p53, can selectively target cancerous cells, as mutations to p53 are the most common types of cancerous cells and when p53 is active it can inhibit the action of the adenovirus. Thus, they typically can only infect cancerous cells where a p53 mutation is the cause of the cancer and cannot affect healthy cells, leaving them alone.

Here’s where it gets interesting, at least to me: these oncolytic adenoviruses, including ONYX-015 which is the particular virus studied in this model, require the protein CAR (Coxackievirus-Adenovirus Receptor) to be on the surface of the cell for the virus to be able to attach to and infect the cell. The CAR protein, however, is often not expressed in cancerous cells. To induce expression of this protein requires disruption of the Mitrogen-Activated Protein Kinase Kinase (MEK/MAPK2) pathway; disrupting this pathway arrests, or freezes, the cell in the G1 phase of the cell cycle, which is the phase preceding the S, or DNA Synthesis, phase. When the cell is frozen in the G1 phase, the virus is unable to replicate, and therefore can’t spread and continue to lyse cancerous cells. So how does one kill a tumor with a virus if the virus can’t attach to the tumor cell or replicate if it can attach? This is exactly what the authors of the paper wanted to find out and describe using a dynamic model.

To model the most efficient way to combine treatments to increase CAR expression while not freezing the cell in the G1 phase, the authors constructed a dynamic model using a four-state nonlinear Ordinary Differential Equation, which operates much like a bathtub model. To do this the authors had to use experimental data to quantify CAR expression, tumor cell proliferation, adenovirus infection, cell viability, and viral replication both in the presence and absence of MEK-pathway inhibition. This led them to the four-state ODE, where the four states, which are the state variables, are cell states during the treatment process: 1) uninfected cell density, 2) G1-arrested cell density, 3) untreated and infected cell density, and 4) MEK-inhibited and infected cell density. In addition to this, the total cell population was included as a state variable. The parameters used were the rate of cell proliferation and the rate of infection, where the cells can be either treated or untreated. To simplify the model, delays caused by cell cycle phase transitions were ignored, as were dose and treatment times. Additionally, it was assumed that prolonged treatment would not increase infection and that the cancer cells were uniform.

What was predicted by this model is that two-day pretreatment of cancerous cells with an MEK-inhibitor will almost double the expression of the CAR protein. If this inhibition is stopped when the adenovirus is introduced the cell is allowed to go to the S phase of the cell cycle and the virus is replicated, resulting in cancer cell lysis. It was also predicted that increased cell density at the time of infection will reduce the efficacy of infection. Both of these predictions were shown to be correct during later in vitro experiments. What was even more significant is that the model and these experiments showed that infection during G1 phase arrest is coincident with the greatest amount of virus production and the greatest amount of cancer cell lysis. This is significant for two reasons: one, the more obvious, is that this is when the virus infection should be started to maximize treatment efficacy, and two, which isn’t as obvious initially, is that currently we don’t know much about adenovirus replication in humans, but this points to the G1-S phase transition as being critical to virus replication, which has implications throughout virology, medicine, and cellular biology. Also found is that CAR expression at the time of infection is not the only determining factor for this therapy. The other factors remain to be seen, but nonetheless this model has led to discoveries about adenoviruses and this treatment that were not expected beforehand.

The authors admit, and I agree, that more could be added to the model to make it more accurate. This added accuracy can only come from further in vitro and in vivo experiments in order to elucidate other factors that may influence this type of treatment. That being said, the model data, when compared to data gathered during experiments, is very similar, with the model data for pre-treatment, simultaneous, and post-treatment simulations being within 19% of experimental data or less (the best was within 8% for simultaneous treatment simulations). This shows that the model has an incredible amount of accurate predictive power as is, and this predictive power can only increase as more data and knowledge are accumulated. This predictive power is seen when the authors acknowledge that the simultaneous and post-treatment protocols involved experimental procedures that were not taken into account during model development; in other words, the model came very close to predicting experimental data before other factors were known. I am extremely excited about the potential and promise of further research in this area, and I hope that I was able to accurately convey that through this blog post. Hopefully when more is published I will be able to comment on that in this blog as well.

Bagheri, N., Shiina, M., Lauffenburger, D. A., & Korn, W. M. (2011). A Dynamical Systems Model for Combinatorial Cancer Therapy Enhances Oncolytic Adenovirus Efficacy by MEK-Inhibition. (C. V. Rao, Ed.)PLoS Computational Biology, 7(2), e1001085. Retrieved from


April 28, 2010

First: New Deftones = amazing.  Here:

Second: An excerpt from the first draft of my short story due in a little under a week.

Memories flooded his thoughts, memories of he and Peters in Iraq.  Peters was the guy who always had a crazy question, and Tim didn’t mind trying to explain it to him.  One time Tim was sitting up in his bed, books taking up the extra space around him, studying cardiac disease for his Cardiology rotation that week.  It was five in the morning, and he hadn’t been to bed yet, when Peters came back off of a convoy.  He tossed his rifle on his bed and set down his Kevlar helmet as he stared at Tim.

“Hey, uh, Doc.  I got a question for you.”

“What’s up?” Tim said, not looking up from his books.

“So, I was wondering: what could you do for a guy who got his balls shot off? Like, really shot off.”

Tim looked at Peters over the top of his glasses as he thought about his answer.  After a few seconds he said quickly, “Hold pressure, try to pack what’s left with gauze.  Hope he doesn’t bleed out.  I mean, he probably lost his dick, too, if it isn’t just badly injured to start.”

Peters stared at Tim, his mouth open.  “That’s it?  No trying to reattach it? This guy isn’t going to be a guy anymore and you’re just gonna let that happen?”

“No, not at all.  But it’s not my place to put everything back together, assuming there’s anything to suture back in the first place.  Dude, look, I can’t give sutures while bouncing in the back of a Humvee in the dark.  Impossible.  And I’m more worried about him dying than about him losing his dick.  Life over limb, you know?  Besides, if it ever happens maybe you can, uh, ‘comfort‘ his girlfriend.”

“That’s fucked up, man.  What if it was me?”

“You’d be dickless, and I wouldn’t feel guilty at all.”  Tim tried to keep a straight face, but it wasn’t working.  He started laughing the longer he thought about Peters talking with a higher voice.  “Hah, you’d have to use the female latrines!”

It’s very rough, I know, but I happen to like the dialogue.  Then again, “Kill your darlings”.  We’ll see what becomes of it.