Holiday and Honeymoon Hiatus

There is a lot of interesting science to write about this season, but unfortunately I have been kept mute by a number of obligations. It has been a busy semester teaching (and grading), and the holiday seasons means time with family and/or searching for gifts. More importantly, my pending nuptials have demanded time for preparation; finding the venue, getting the tux, and so forth. Although my loving fiancee handled a majority of the details, much of it was happily done together. Even happier will be the time we spend on our honeymoon in the coming few weeks, but of course blogging will not be part of my agenda on that trip.

As a teaser for the content when I return in 2015, here are some of the articles for which I had planned summary and commentary.

Ovchinnikov, S., Kamisetty, H., Baker, D. (2014) Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information eLife, 02030
[PMC4034769]

This paper uses a bioinformatic approach to determine co-evolution between bacterial proteins on a large scale. This type of analysis had been done previously, but usually only with a handful of proteins at a time. Here, Baker and colleagues analyze protein-protein residue pairs that display co-evolution across all proteins in the ribosome, among other bacterial complexes, and then map these predicted interacting pairs back onto the known structures of the proteins.

A companion site to this work, gremlin.bakerlab.org, allows you to download the sequences and alignments used in the study, view the results for any two proteins analyzes, and also perform your own comparisons by inputting sequences to their server.

*

Gardin, J., et al (2014) Measurement of average decoding rates of the 61 sense codons in vivo. eLife 03735
[PMID:25347064]

During my time at Stony Brook I learnt of the work by Professors Skiena, Wimmer, and Futcher into analyzing codon bias and codon pair bias (indeed, I even took a computer science class offered by Skiena). This research was interesting, and I have since kept an eye out for further developments from this group. In this paper, they use ribosome profiling data to determine the average decoding rate at each codon, to provide some evidence for a mechanism of codon bias. Previously, slower decoding at rare codons had only been inferred based on low tRNA abundance and observations of ribosome stalling.

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Venkataraman, K., Zafar, H., Karzai, W. (2014) Distinct tmRNA sequence elements facilitate RNase R engagement on rescued ribosomes for selective nonstop mRNA decay. Nulceic Acids Research, 42 (17): 11192-11202
[PMC4176180]

Venkataraman, K., Guja, KE., Garcia-Diaz, M., Karzai, W. (2014) Non-stop mRNA decay: a special attribute of trans-translation mediated ribosome rescue. Front. Microbiol. 11, 5:93
[PMC3949413]

These two articles are from my former colleagues at the Karzai lab, and help add insight into a complex between the ribosome and RNase-R, important during trans-translation.

The Central Dogma Cafe

Molecular Biology is vitally important for Modern Biology, but can sometimes seem foreign or complicated to new students and the lay person. At the heart of this study of the inner workings of the cell is the transfer of information from DNA to RNA and finally into Protein, which is responsible for a lion's share of activity in the cell.

I have previously tried to explain the importance of, and relationship between, these three molecules by using an analogy of sheet music being read by a musician and instrument to produce sound. While it is a good analogy, I think I have developed an even better one, that of a recipe being used by a chef to prepare a meal. In lieu of writing out a detailed explanation of this analogy (which I may eventually do), I have posted a short video as the first part of my new video series, Life Science in 6 Minutes. 

As it turns out, I was not the first to use the recipe and cooking analogy in molecular biology. At least one other video, courtsey of Bozeman Science, used this analogy. One of my former colleagues also employed this analogy, quiet well in fact, at her blog It's Like Biology. In the remainder of this article, I share ways in which I have expanded upon this analogy, as well as how others have explained the Central Dogma.

Feel free to share your comments both here or on my youtube page. What is your favorite way to explain concepts in molecular biology, microbiology, and synthetic biology?

Impressions from iGEM 2014 Giant Jamboree

The International Genetically Engineered Machine Competition, or iGEM, is an annual event in which teams, mostly comprised of undergraduates, will compete against one another to design and create novel applications in the field of synthetic biology. The atmosphere surrounding the competition and the projects is a refreshing one: they offer enticing visions of how the field of synthetic biology can grow, and how the science can be communicated in an exciting, approachable and often colorful way. Furthermore, the iGEM teams are often highly interdisciplinary, featuring biologists, programmers, mathematics, and even artists (for communication and design).

Below are my impressions from the annual end of the year jamboree in which teams present their work. Last year, iGEM was organized into regional jamborees, with the finalists from each regional group being allowed to participate in the championship jamboree in the Stata Center at MIT. In an earlier post at this blog, you can find my impressions of the 2013 championship jamboree. This year, all teams participated in one giant jamboree, held in the Hynes Convention Center in Boston, MA. This jamboree was very well organized and was a very enjoyable event to attend, a testament to the efforts of the iGEM staff and volunteers. 

Although every team and project is unique, many share similar themes. The jamboree this year was organized into several tracks, such as Energy or Health and Medicine. Within each track (or even across tracks), several teams had similar projects. Some of the more common themes this year included:

• Modification of Bacillus subtilis to detect and/or eliminate pathogens (plant or human pathogens, usually fungal in nature)
• Engineering strains of bacteria (typically E. coli) for bioremediation or biofuel production
• Creation of packing material, patterned paper, and even clothing out of bacterially derived cellulose (or other microbe-derived products)

In the remainder of this post, I discuss some of the iGEM team projects in detail, and some other impressions of the event. Please select 'Read More' to see the rest of this article. I also attended the 'Funders Panel' session. This panel featured leaders of community labs, members of large budget teams, and even a science minister / official for Slovenia and Canada (the former featuring a perspective on funding science and iGEM teams with a limited budget). Even representatives from business and capital investment firms were present.

What was your favorite iGEM team or project this year? Please feel free to share your thoughts and comments below!

A Return to Blogging: The Year in Review

After a nearly year absence, Just Me and Eubacteria is back! The past year has been a busy one, not only for science but also for this humble author: I have moved from adjunct positions at several institutions to a full-time professor position at Kingsborough Community College, where I teach several courses (including Microbiology in Health and Disease). I am also engaging in starting my own research projects at the College, with a focus towards translation, synthetic biology, and iGEM. On a personal note, I am now engaged and will be married before the end of the year.

All of these changes unfortunately necessitated a break from blogging, from which I am only now settled enough to begin again. To re-open this blog, I have designed its appearance, and for the first post in almost a year, I will review some of the more interesting papers that have been published in synthetic biology and microbiology in 2014.

In the remainder of this post, you can find the citation, link, and summary from a number of selected publications from PubMed Central (PMC) that I found to be particularly interesting. In the coming months, I will provide a more detailed analysis of some of these papers (among others). In addition, you can continue to look forward to articles about synthetic biology, microbiology education. For example, I will soon be posting my impressions of the giant iGEM jamboree this year, which I again attended as a volunteer. 

Here are some of the articles of interest from 2014:

A Rapid and Simple Method for DNA Engineering Using Cycled Ligation Assembly
Roth, T., Milenkovic, L., and Scott, M. (2014) PLoS One 9(9):e107329
PMC4167330

Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information
Ovchinnikov, S., Kamisetty, H., Baker, D. (2014) eLife 3: e02030
PMC4034769

Programmed Allee effect in bacteria causes a tradeoff between population spread and survival
Smith, R., et al (2014) PNAS 111(5); 1969-1974
PMC3918787

Cell-surface receptor control that depends on the size of a synthetic equilateral RNA-protein complex
Fujita, et al (2014) Science Reports 4:6422
PMC4168268

See Stay tuned for more great blog posts! I should be posting about once a week, except for a break in mid-December. Select read more to find links and summaries for more interesting articles from 2014! Think of a paper I missed? Feel free to comment below.

Biologists Swimming in a Mathematical Ocean: Modeling Gene Expression

Math is everywhere in today's society, and advanced mathematics have fueled the engineering efforts which underly almost all technology. In the past, biology may have been a refuge for scientists that were math-adverse. This is not longer true; instead, biologists should openly embrace higher mathematics and employ these techniques in a variety of ways. Perhaps the most relevant application to the synthetic biologists is for developing mathematical models of gene expression and cellular behavior.

Biologists make models all the time: any hypothesis, for example, is a reflection of some model held by the scientist which describes how a certain system works. Many times, this model may be implicit in a description for a biological process. More powerful models are explicitly formulated using the language of mathematics. Leveraging mathematics allows quantitative predictions of a cell's behavior or gene expression pattern to be made with great precision. Engineering the metabolism of a cell by modulating expression of many different genes requires sophisticated and (as much as possible) accurate models.

How then, is the a biologist with limited mathematical training to understand models of gene expression, let alone craft ones themselves? Diving into advanced literature on the subject can lead one to practically drown in a sea of differential equations, boolean logic, and matrices. I must even admit to having trouble staying afloat at first: my formal mathematics education stopped at 'Calculus II for Bio Majors" at Cook College, Rutgers (a course which covered matrices but not differential equations). Fortunately, my brother is quiet adept at this sort of math and has provided me with some excellent tutorials.

Even if you don't have a skilled mathematician in the family, there are resources to help you learn gene expression modeling (or any modeling of a biological system). Below I feature links to several of these resources. The resource which I have found to have the best blend of exhaustive yet accessible explanation is a thesis from 2010 written by Hosam Abdel Aleem. I'm sure there are other articles and publications out there that are as good, if not even better, than Dr. Aleem's work, but his is a delightful read and quiet approachable (it's also freely available).

An Algebraic Approach to Modelling the Regulation of Gene Expression

https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-scw:108029&datastreamId=FULL-TEXT.PDF

This thesis throughly explains the philosophy behind modeling, as well as how to construct mathematical models of gene expression in detail. The author covers not only modeling by differential equations (continuous), but also boolean models (binary) and his own methods (discrete but with multiple values). I highly recommend this read to anybody that is interested in modeling gene expression but doesn't know where to start.

In future posts, I will provide more detail of my own experience learning some of this material, including some step by step examples of how to create a model, solve or analyze the associated equations, and calculate the results. Until that time, I'd like to wish all of my readers a very Happy Thanksgiving!

For more links to resources for mathematical modeling of gene expression, select 'Read More'. Did I miss an important resource, or do you have a favorite method for modeling transcription and translation? Please share your thoughts below as a comment!

Bacteria learn how to take a pulse: programming microbes to convert digital light signals to analog gene expression.

What do telecommunications, power delivery, and your audio system* all have in common? For starters, their underlying electrical systems use digital pulses, alternating ON and OFF states over time. These pulse patterns and the way they change, known as pulse width modulation or PWM, can encode and transmit information. Now, research from a team of British and American scientists have made a surprising addition to the list of systems that can decode information in pulse widths: Escherichica coli (E. coli), a bacteria normally found in our gut. 

In an article recently published in the Journal of Molecular Biology, the research team describes genetic modifications to E. coli that enable it to read the pulse width modulation of alternating green and red lights. The gene expression of a reporter protein represented an analog output in response to this digital pattern of light color. In essence, scientists have been able to replicate in bacteria a process important in electrical engineering.

The creation of a system in E. coli capable of decoding PWM information is a significant step forward in the field of synthetic biology. This field, which sits at the intersection between biology and engineering, attempts to design artificial sensing and gene regulatory networks in bacteria. Perhaps most exciting, however, is the potential to use microbes like this one described in this study as an interface between digital signals from machines and the biological activity of cells.

For more detail and commentary about this study, please select 'Read More'. Which ways do you think PWM sensing in E. coli should be used? How would you continue this study? Comments are welcome below!

*not all audio systems utilize PWM, if I am not mistaken

Impressions from iGEM WCJ 2013

The International Genetically Engineered Machine competition, or iGEM, is an annual event in which teams of undergraduates compete to develop the best synthetic biology project. Their results are presented, and prizes awarded, at conference events dubbed iGEM jamborees. 

The iGEM 2013 event featured hundreds of teams. After qualifying at regional jamborees in North America, Asia, Latin America, and Europe, many teams converged at the Stata Center in MIT between November 1st and November 4th for the World Championship Jamboree.

I attended (as a volunteer) the Championship Jamboree this year. It was a great experience, and is something I recommend to anybody that is interested in the field of synthetic biology but cannot themselves join an iGEM team. In the rest of this post, I will share my impressions of the Jamboree and highlight some of my favorite teams and projects from this year.

If you are interested in learning more about all of this year's projects, and see their presentations from the World Championship Jamboree, you can visit the iGEM 2013 livestream channel for archived videos. HD videos and other files (including photos) should be or will be available on the main iGEM website. (For example, the finalists and medalists presentations are available, both video and poster files).

What do you think about iGEM, and which team or project was your favorite? Please share your thoughts in a comment below!

Note: I do not own, or claim any rights to, the official iGEM logo shown above; it was taken from igem.org