Biotechnology is a topic of great interest for me. A blend of engineering and computer science, with the study of complex living organisms, biotechnology captures the essence of both biology and technology. But business in biology? At first glance, introducing this concept seems incongruous or unlikely. But recently, the emerging discipline of systems biology — operating at the confluence of these two fields — does just that! Interning at NASA this past fall, I was able to analyze the unique perspective that systems thinking brings to biosciences and the numerous benefits of such an approach to other disciplines (like astrobiology or planetary protection). This post summarizes what I did this summer — my technical learnings and my research insights — as well as some pointers for other high-schoolers looking to intern at NASA.
Technical Learning: The Ins and Outs of Systems Biology
In order to understand the applications of systems biology to assist research at NASA, the first step is to get a solid understanding of systems thinking, and more specifically, systems biology. Over the course of the first few weeks of my internship, I spent time understanding and interpreting the basics of systems thinking.
A Crash Course in Systems Thinking: The Beginning of my Exploration in Systems Biology
Systems thinking can be defined as a philosophy that tries to identify interactions between specific parts of systems and use these interactions to identify how the system works. This philosophy can be compared to building a jigsaw puzzle; you first try to make small sections of pieces that you are confident fit together in the puzzle, then use these sections to identify the bigger picture. This type of thinking is common in many different fields. Business management uses systems thinking to help build out strategic plans that target shared resources and dependencies between different company workgroups. Manufacturing uses this type of thinking to assemble large-scale machines by modeling the entire machine and identifying the interactions between different parts to create a high-quality and effective machine.
Systems thinking is a very valuable approach due to five unique advantages it has over the standard philosophy (the reductionist philosophy) associated with biology: First, it is a very holistic approach and the primary focus is not to zoom down and look at things at a minute scale, but rather look at the bigger picture to identify a network of interactions. Second, this holistic style, coupled with the bottoms-up strategy mentioned in the previous paragraph makes it easier to trace important pathways in organizations, and, in my case, biological models. Third, systems thinking also allows the formation of a larger network of networks (a large collection of systems placed together, an example of which is the Human Body and its organ systems). This type of network array makes it easier to make inferences about the way one system may influence a behavioral change in another system and vice versa. For example, it may help neuroscientists to see how the muscular system, skeletal system, and nervous system all work in synchrony to cause the movement of body parts.
Fourth, mathematical and computerized models are often used to help produce these models and provide added insights to key datasets. For example, a research group in the Weizmann Institute in Israel created a computational tool that helps recognize short loops (common recurring patterns) in large and complex gene regulation and expression networks. Similarly, machine learning, artificial intelligence, and other computational models can help enable biologists to go deeper in their quest to identify the functions of specific biological molecules and processes. Finally, other mathematical models have helped in generalizing behaviors of specific actions in organisms: one of the starting points for systems biology came from a mathematical model that illustrated how action potentials passed through an axon of a neuron in the nervous system. Such models can be beneficial in the many other subsets of biology as well.
These five characteristics help bring a new perspective to viewing biology and provide a framework from which discoveries and insights can be made.
Systems Archetypes: Applying Systems Thinking to Real-World Situations
After getting a basic understanding of systems thinking, the next step was to learn more about the common systems archetypes that I was likely to encounter. Systems archetypes are a generalized set of loops that can be applied to any system to identify interactions in the system.
In order to fully understand systems archetypes, there are a few important terms to know. All of these systems’ archetypes contain multiple loops which come in two different classes. The result that is expected to occur is known as the intended consequence (IC) feedback loop, while the unexpected loop is (very appropriately) dubbed the unintended consequence (UC) feedback loop. These two loops also come in two flavors: a balancing loop that attempts to reduce the impact of the system, and a reinforcing loop, that attempts to increase the impact of the system.
There are four major systems archetypes: (1) underachievement, (2) out-of-control, (3) relative achievement, and (4) relative control. These are summarized in the table below. For each of the problem archetypes mentioned below, a solution archetype helps fix the problem. Problem archetypes are archetypes whose intended consequence feedback loop causes negative unintended consequence feedback loops that create problems for organizations and systems. Solution archetypes help to create a connection between the input of the intended consequence feedback loop and an input of the unintended consequence feedback loop that helps to solve the problem.
Systems Archetypes: Applying Systems Thinking to High School Life
All of these archetypes have numerous applications in the real world. For example, as a high schooler, I find myself susceptible to distractions. One of the major video game fads right now is “Among Us”: a space-themed game of deduction, strategy, and a hint of chaos. If I were to put a break in my schedule to play Among Us, I am hoping to relax and feel less stressed (which in this case, is the balancing loop). However, these breaks actually work against me: not only have I depleted my time resources, but I am also now more stressed having to play catchup and have to later pull a potential all-nighter to finish my homework (an example of a reinforcing loop that has amplified the problem rather than address it). A solution archetype to this problem may be to take shorter stretch breaks when I have homework or to mitigate my distractions. Another alternative may be to take my break after I finish my homework. This brief example is a perfect case study of an out-of-control loop system in the real world.
Another example is an underachievement loop that I noticed occurring in my high school robotics team. As a junior, I take interest in a lot of school clubs. One of these is the school robotics team (which is sponsored by NASA!), where I am an officer. During our design process, we often go through a conceptual visualization using 2D sketches, refine these ideas, solid-model them, then make machinist drawings that could be used for manufacturing these parts. Often, we decide to speed up this design process iteratively, which creates the need for a lot of parts that have to be made. However, the balancing loop is that we may not have enough machinists skilled in a specific machine, and this creates a bottleneck in the system. Once we understood this, to better counter this, we designed specific fall workshops to help train new members on our team to be able to use the machines to mitigate a resource issue. Now we have more machinists than parts to make!
With these examples, we found out that systems archetypes work even with an average teenager’s lifestyle, but how might it be applied to more complex fields of study, like astrobiology or synthetic biosciences? In order to answer this question, I spent the next two weeks looking into the applications of these systems archetypes to the general field of biology, and the specific field of astrobiology.
Multidisciplinary Applications: Systems Thinking meets Astrobiology
You may be wondering: what is astrobiology? According to Wikipedia, astrobiology is defined as “an interdisciplinary scientific field concerned with the origins, early evolution, distribution, and future of life in the universe.” Astrobiology considers the question of whether extraterrestrial life exists, and if it does, how humans can detect it.” Fascinating, isn’t it? So how can we apply systems thinking to astrobiology?
As systems archetypes are frameworks that are generalized to work for any type of system, they can be used in any type of field to analyze common recurring patterns in datasets and networks. The characteristics of systems biology mentioned earlier also can be applied to any field. In my internship, I first chose to analyze microsystems in protein pathways before moving on to apply the systems thinking approach to the larger field of Astrobiology.
With my research with proteins, I focused primarily on the enzyme kinase, which is known to act as a signal enzyme in signal transduction pathways, amplifying initial receptor signals through a complex method of phosphorylating kinase molecules exponentially. I observed that this system follows an out-of-control archetype, as the system continues to amplify the signal without a stopping mechanism. The body’s in-built solution mechanism is to add transcription factors and other cytosolic targets in close proximity to the phosphorylated protein kinases so that the phosphates from the kinase molecules can be transferred to these molecules, instigating a cellular response. While not as complex as other systems, the microsystem in the cellular pathway provides a good starting point to analyze the application of systems thinking in a biological setting.
When looking at Systems Thinking models in Astrobiology, a paper by researchers in Poland, titled “Synthetic connectivity, emergence, and self-regeneration in the network of prebiotic chemistry,” was highly useful. The paper illustrated the use of computer networks and holistic paradigms to find the chemical pathways taken by prebiotic molecules to create biotic molecules (such as amino acids, mRNA, etc.). This paper created their own network tool called Allchemy, which provided the researchers with insights into the emergence of such biological molecules given the initial conditions of six naturally occurring molecules (Water, Ammonia, Nitrogen Gas, Methane, Hydrogen Cyanide, and Hydrogen Sulfide). Although this paper was targeted towards researching prebiotic conditions on Earth, I was able to identify the potential use of Allchemy to discern prebiotic pathways given changes in temperature, pressure, and gravity. For example, the high temperature often denatures specific proteins made from some of these initial condition pathways, and as such may cause different chemical pathways to occur due to changes in the state of matter, shape, or function. Microgravity, perhaps the most important factor in astrobiology, may cause different intermolecular/intramolecular forces to occur, again causing potential changes to the shape and function of molecules crucial to the formation of life (at least on Earth). Through Allchemy, the bioscience research community may be able to uncover more about how prebiotic chemical pathways can be altered or modified, a major leap towards identifying potential life on other planets.
Although this article highlights the use cases of systems thinking at NASA’s bioscience departments, these processes can also be applied to other biotech areas. Bioengineering companies everywhere can benefit from using systems thinking to analyze sterilization techniques, management processes, production methods, temporal delays in organism’s chemical pathways, etc. The possibilities of using systems thinking in biosciences are only beginning to be uncovered, and with more researchers adopting such an approach, I think we are in for an exciting future.
Interested in applying to internships, but don’t know where to start? Attached below is my paired article on tips and tricks while applying to such programs
Hari is a rising senior interested in the applications of robotics and business to biosciences. He has always been interested in the intersection of biology and engineering, and he is an active contributor to his high school robotics team. In his free time, he loves to read, binge-watch tv shows, and spend time browsing the internet. Hari has, in the past, shadowed a Stanford emeritus dermatologist (where he learned about global health), created biology courses that harness robotics to teach biological concepts (e.g., evolution/natural selection), and worked with NASA to apply systems thinking frameworks to various space biosciences divisions. Hari hopes to pursue a degree in biomedical engineering, with the option to enter a medical pathway in the future.
Wernicke, Sebastian, and Florian Rasche. “FANMOD: a Tool for Fast Network Motif Detection.” OUP Academic, Oxford University Press, 2 Feb. 2006, academic.oup.com/bioinformatics/article/22/9/1152/199945.
Wolstenholme, E. F. “Towards the Definition and Use of a Core Set of Archetypal Structures in System Dynamics.” Wiley Online Library, John Wiley & Sons, Ltd, 27 Jan. 2003, onlinelibrary.wiley.com/doi/abs/10.1002/sdr.259.
Wołos, Agnieszka, et al. “Synthetic Connectivity, Emergence, and Self-Regeneration in the Network of Prebiotic Chemistry.” Science, American Association for the Advancement of Science, 25 Sept. 2020, science.sciencemag.org/content/369/6511/eaaw1955.
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