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!
The Language of the Cell: Biosemiotics
Semiotics is the study of signs and information, traditionally associated with the field of linguistics. Biosemiotics arises from the fact that, on the level of molecular biology, there is a flow of information within the cell. The sequence of nucleotides in a DNA polymer represents (genetic) information, which can be transformed into different types of information such the sequence and structure of a protein and the transmission of a signal through a cascade of proteins (i.e. a signaling pathway).
When I first became interested in molecular biology, I was struck by the parallels between the flow and consequences of genetic information in a cell and how information can be used to program computers. Although this interpretation isn't quite what is described by scholars in the biosemiotics field, it nonetheless represents a useful way for the synthetic biologist to think about information in the cell. The programs in molecular biology, however, can take different forms, a distinct one for every process in the cell that deals with information transfer. Some of these languages depend upon each other. The program of a genetic network depends, to some extent, upon it's cellular context: the size and growth conditions of the cell, and the speed at which transcription factors are synthesized and diffuse. Recognizing how a diverse array of biological information can influence the behavior of a single system is important when designing one that is artificial.
Biosemiotics is an interesting field in it's own light, despite the fact that it is at times difficult for a classically biologist to penetrate. A heavy focus in the field is on evolution and how information and signs in biological systems change over time. Since I do not have the space or expertise for a more comprehensive discussion of the field (a discussion that would ultimately stray from the main topic, synthetic biology), I encourage readers to follow the links below for further reading.
Links for Further Reading:
Biosemiotics: Towards a New Synthesis in Biology
Biosemiotics (Wikipedia)
International society for Biosemiotics
Biosemiosis (Blog)
Learning from Economics
The study of economics is important for molecular and synthetic biology in more way than one. Economics can inform what applications of synthetic biology will be profitable (or at least adequate solutions to real economic challenges - not necessarily ones done for profit). The field of economics ultimately influence funding levels appropriated for science by government agencies and others, perhaps as much influence as the merit of the science itself. However, in this post I will focus on how concepts, modeling, and paradigms from economics may find utility when applied to synthetic biology.
Economics is sometimes referred to as 'the dismal science'. Although this phrase was coined over a hundred and fifty years ago as a rebuke to a rival scholar (who the rival and opposing belief was is a matter of debate and myth), I have seen it used in a fashion lamenting the difficulties of the field of economics. After all, economists try to model incredibly complex systems, with many agents, actors and forces. Not only do they attempt to study and model these systems, but like the synthetic biologists [do to their systems] they attempt to design or at least influence these systems to produce a desired behavior. The interactions between different people or consumers can influence the average opinion of a particular product, just as the interactions between different individual proteins (each with it's own innate characteristics, but sometimes uncertain behavior) can direct the behavior of the entire cell.
Drawing an analogy between people and proteins (or any other biological entity, for that matter) has a number of strengths. First, in both economics and molecular biology, the average value for a particular system may not be reflected in its individual members. The reverse is also often true, where studying the individuals in isolation does not reliably allow one to capture the average or emergent properties of a populations (although statistics are used to determine which studies are more reliable than others). Finally, I think employing the analogy between people and proteins makes understanding molecular biology easier for our own mind. In the classroom I have found success 'personifying' molecules and proteins. Giving purpose and personality to a protein often makes it easier to grasp how it will interact with other factors. The evolutionary biologist in me will claim that this reflects an ability of our brains (selected for in pre-historic man) to more easily understand and compute concepts related to other people. Of course, any such analogy must only be used within it's limits, and should be crafted to reflect the biochemical reality as closely as possible.
Another comparable field that studies a complex system with emergent would be that of the climatologist. In all three cases (all of which are, or will be, vitally important to the economy), the field grapples not only with the tremendous complexity of the system but also with uncertainty. Neither the economist or the weatherman can predict the future, and synthetic biologists many times cannot account for the behavior of factors and proteins with no known function and activity. Indeed, certain branches of mathematics and economics have been developed to aid models built with great deals of uncertainty. The apply named uncertainty analysis deals directly with this problem, and has even been employed by synthetic biologists on occasion (for example, the 2013 iGEM team from Manchester, UK).
Links for further reading
Will the Real Engineers Please Stand Up*
http://socfinance.wordpress.com/2013/08/27/will-the-real-engineers-please-stand-up/
Will the Real Engineers Please Stand Up*
http://socfinance.wordpress.com/2013/08/27/will-the-real-engineers-please-stand-up/
Anatomy of a Hypothesis
For many who have read Thomas Kuhn's The Structure of Scientific Revolutions, changes in paradigms in science appear to be rare and monumental occasions, made when the prevailing theory fails to explain a preponderance of newly observed phenomenon. Kuhn paints a picture (mostly from the study of physics, if I am not mistaken) of a cycle where a scientific theory is protected by entrenched scientists, until it eventually collapses under extreme stress a leads to a chaotic period until a new theory is agreed upon.
Not all scientific disciplines follow the Kuhn model for Scientific Revolutions / Progress, but some of the elements are similar. In fact, I prefer to view science in a from a more incremental perspective. The large changes and periods of upheaval that Kuhn describes are likely to be preceding by many cases in which the prevailing theories generated a hypothesis which was rejected by the results of an experiment. These mini-revolutions can be very important to the progress of science. Of course, this also depends upon the perspectives and behavior of other scientists in the field (they may resist as some or even as many changes as possible).
What does such an abstract discussion of the philosophy of science have to do with the engineering discipline of Synthetic Biology? Any proper hypothesis consists of a disprovable prediction, based upon some previous knowledge, theory and / or paradigm (or several different types of each). I argue that even the vocabulary used to formulate a hypothesis will reflect a particular paradigm. This is partly demonstrated by the discussion of biosemiotics above; this paradigm is reflected every time a researcher talks about a 'high-fidelity' polymerase or antigen 'presentation' , he/she is borrowing vocabulary most often associated with communication, information flow and sign processes. Even if they are not explicitly stated, the shared conceptual framework is present. This framework may also extend across a study beyond the particular instance in which it is used; other associated concepts are likely to be used by a researcher to either explain or hypothesize about other characteristics about a system.
For example, since tmRNA and the system of trans-translation acts upon ribosomes that are unproductively stalled, it is referred to as a ribosome rescue system. This has led to work that characterized its integration with other housekeeping, rescue, and emergency response systems in the bacterial cell. However, the trans-translation system does not only rescue distressed ribosomes, but is also an important part of certain gene regulatory networks in different bacteria. Indeed, the dynamics of induction at the natural Lac promoter are influenced by the (relatively) obscure trans-translation system. In this case, thinking of tmRNA only as a rescue device does not capture it's full role in the cell. Here you can notice that in fact both approaches and paradigms are correct, since tmRNA serves multiple roles. Recognizing both is important for understanding the relationship between tmRNA activity and the rest of the cell's behavior.
Despite my analysis above, I am not going to argue that borrowing concepts from another field has only a negative impact on science and synthetic biology. This is true even if the borrowing is heavy and extensive. In fact, if the parallels between the two fields are strong, this can greatly facilitate research and the communication of research findings. However, the scientists must remain flexible in their approach, and especially critical when a design fails or a hypothesis is disproven. In fact, conceptual progress in science may be made by creative researchers who encountered with a rejected hypothesis. This hypothesis is based upon a particular paradigm or vocabulary, and upon refutation of such a hypothesis the researchers do not need to borrow ideas from yet another field, but rather can decide to invent their own concepts and terminology. Perhaps in the future, instead of cells being described as miniature computers, our technology will be describe in biological terms, some of which may not be available (although if synthetic biology fulfills it's promise most of the technology will indeed be biological, obviating the need for an analogy!).
There is an intimate connection between engineering technologies and the science that they are based upon, as the previous paragraphs attempt to demonstrate (at least in an abstract sense). This is perhaps no where more true than in synthetic biology; an engineering discipline based upon a science (Molecular Biology) which is not fully understood. In turn, synthetic biologists must see themselves as scientists as much as engineers, for their efforts will help deepen the understanding the the science that underlies their efforts. In the oscillator example above, questions about the paradigm, together with an understanding of the molecular biology occurring in their system (as opposed to taking a strictly 'black-box' view of the system) and some imagination may lead to insights and new paradigms in synthetic biology.
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