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

Paradigms in Synthetic Biology Part II: Semiotics and Economics

In the first post in this series (See PartI: Analog to Digital), I point out how synthetic biology will require researchers and engineers to remain flexible with regards to the conceptual framework they use. Indeed, entirely new concepts may be necessary for this field, which is the intersection of engineering and molecular biology (which is not as fully understood as other sciences which have been grappled by engineers). Treating a genetic network or transcription circuit as components that follow digital logic has it's advantages, but also it's limitations (mostly due to the real physical nature of transcription and molecular biology occurring in the cell). Other paradigms, such as analog computing, can also be applied and sometimes inform more powerful designs in synthetic biology.

Beyond different types of computing, it may be helpful to borrow concepts and framework from a wide variety of fields. Below, I discuss several other fields and concepts which may find use in synthetic biology. One prism through which molecular biology can be viewed is that of semiotics; the study of information and signs. Even the field of economics may have concepts which biologists can find useful. After all, economists need to model complex studies, which many different actors / agents, with a great deal of uncertainty.

How do you see Synthetic Biology? Is there a certain paradigm or field that you think Synthetic Biologists can borrow useful concepts from? Does all of this dense, abstract blather simply amount to hot air? Feel free to share by leaving a comment below.

I recently attended the iGEM 2013 world championship jamboree. Stay tuned for a future post reflecting on my experience!