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How to turn data into knowledge: 10 conceptual frameworks that are universal to the pedagogy and communication of biology.
How to turn data into knowledge: 10 conceptual frameworks that are universal to the pedagogy and communication of biology.

How to turn data into knowledge: 10 conceptual frameworks that are universal to the pedagogy and communication of biology.

10 conceptual frameworks that make learning easier.

Students majoring in Biology or Biochemistry frequently voice their feedback to me on how dissatisfied they are with their area of study. The most common complaint comes with the viewpoint that biology is just memorization with a little bit of reasoning. Many students upon graduating do not feel like a “real” scientist, capable of critical thinking and having a successful career taking on challenging work. It is my long held belief that this stems from biochemistry and biology having an inherent lack of a strong quantitative theoretical framework, the gold standard of modern science. This lack of a theoretical framework is the primary culprit for the structure of higher education in biology and biochemistry being primarily focused on mindlessly memorizing facts and figures instead of asking questions or even utilizing central themes that connect one idea to another.


Memorization is the primary factor students need to consider in order to integrate themselves into the system of academia and adhere to its definition of success. Admittedly, it does take a lot of discipline to study like this and utilize one’s time memorizing reaction pathways and enzyme schematics in order to pass an exam, only to later forget the details and the underlying concepts (if any). What’s worse is that for many would-be scientists and physicians, this mindless memorization is a determinant for admittance into their fields of interest. Most medical or graduate schools won’t even look at an application if the associated GPA is less than a minimum value. A GPA that is largely dependent on students parroting back information from their professor’s lectures (regardless of the factual nature of this information) instead of building their own frameworks, ideas, or identifying patterns.

What is learning?

learning is best defined, and most productively implemented as the development and discovery of new theoretical and conceptual frameworks. Learning is not memorization, nor is it helpful for memorization to be the end goal.

Understanding of central themes and patterns of a theoretical framework will never leave your mind; the pedantic underlying details will almost inevitably fade from your memory, therefore my advice for any would be professional scientist is to develop as many theoretical and conceptual frameworks as possible.

I have contemplated some of the possible problems that are holding biology back (both the science itself, as well as how it is taught). I have also listed my own observations that may help students establish an effective framework and move forward towards a goal of learning.

Problem 1: Life is complex

 First, let us just admit that life and its maintenance are the most complex processes known to man: it takes matter and converts it back and forth into energy in tiny little compartments within cells. Second, life can be viewed through many different conceptual lenses. None of these are concise, however. We do not have a working definition of what life is vs. what life is not. These ideas can range from arguments of life being anything that fits a compiled list of observations, to being a physical state of matter, to something that can be described in the framework of thermodynamics. The difficulty in application of these ideas arises in understanding the emergent properties of biological systems, such as consciousness or even homeostasis.

 Now that we have established that biology is a complex subject, a pressing question presents itself to be answered. What needs to be done in order to understand and explain this complexity, rather than just describe it with routine memorization that is quickly becoming more and more obsolete within the Information Age? We will come back to this later.

Note: an understanding of the difference between a description and an explanation is critical in this context, hence why I used italics for emphasis.

 Problem 2: Complexity cannot be understood with memorization and mindless data acquisition 

One of my favorite TV series of all time is the 1990s remake of The Outer Limits. As I was thinking about how much memorization biology students are responsible for in their academic careers, one episode titled, “Stream of Consciousness” comes to mind. I would highly recommend watching this episode (the entire series is accessible via hulu) . The basic summary of the episode is that at some point in the future internet access would be granted to people via neural implants and humans would, unaware of it, become slaves to a central AI that was obsessed with data mining. No matter how obscure and how pointless said data is. One victim in the episode ends up going so far as to count the number of hairs on their body until they die from sleep deprivation and exhaustion. This is exactly what an archetypal biology student experiences in studying for their undergrad degree: obsessive and pointless data mining (memorizing biochemical reaction pathways and gross anatomy) to the point of damaging their health.


The image above is from the aforementioned episode. At least the characters get food, housing, medical care, and don’t go into their mid-20’s with $70,000 of debt from which they can never declare bankruptcy.

 Syndey Brenner within his 2002 Nobel Prize acceptance lecture said, “We are drowning in a sea of data and starving for knowledge. The biological sciences have exploded, largely through our unprecedented power to accumulate descriptive facts, [but] we need to turn data into knowledge, and we need a framework to do it.”

Geoffrey West, my favorite physicist turned Ecologist, has pointed out in his book, Scale, the success of the Large Hadron Collider (LHC) at CERN. The LHC produced massive amounts of data, currently over 200 petabytes.

Regardless of this massive “sea of data,” CERN was able to find evidence of the Higgs field because they had a strong quantitative theoretical framework to guide them through finding this needle in a haystack of data.

As an example, let’s compare the LHC with another “Big Data” project: ENCODE (a project to understand the “junk?” regions of DNA). Unlike the LHC, ENCODE was done mostly through the use of blindly analyzing statistical relationships and with zero theoretical foundation underlying it. This lead to a vast array of criticism directed at the project authors’ interpretation. To this day, there is no consensus among geneticists of what the data obtained from ENCODE means.

(AN: this is probably why you have never heard about ENCODE, it was a bust for big data and big science).

Stuart Kauffman pointed out, long before ENCODE began, that if you just look at the statistical relationships among components in biology, it will lead you down a blind alley. You will conclude that the heart exists solely to add weight to the chest: filling up the space between the lungs.

What this means is that we cannot rely on statistical and phenomenological approaches towards understanding biology , we must strive to adopt a mechanistic quantitative theoretical framework to explain processes involved in living things, with functional roles being accounted for.

Unlike physics, biology does not have a quantitative theoretical framework guiding it. Natural Selection: the Central Unifying theory of Biology, discovered by Darwin and Wallace, is itself intrinsically qualitative. The power of this idea has advanced biology forward in ways that mirrors how Galileo advanced physics. The momentum of Natural Selection to explain complex ideas in biology has maintained itself for centuries, but the time has come for biology to embrace the more quantitative frameworks, rather than qualitative.

While Ecology and Systems Biology (a couple of specializations that do have a more abstract and quantitative foundation) are incredibly powerful tools to utilize for processing biological data, they are not themselves utilized for teaching the theoretical foundation needed for many other areas of biology such as molecular biology, immunology, or microbiology. Furthermore, many, MANY students in the United States can obtain bachelor’s degrees in biology, where Algebra and Statistics are the only math requirements for them. I view this as a testament of how poor higher education is for biology as well as to how much growing up this field still has to do in order to be considered a ‘real’ science. Biology needs to remove itself from phenomenological and statistical approaches towards understanding and embrace a fully quantitative mechanistic theoretical framework that “turns data into knowledge”.

What are some ideas that establish a strong theoretical foundation that makes conceptualizing biology much easier than mindless memorization? Here are my 10 recommendations, while not all of them are quantitative I am certainly advocating that biology adopt more quantitative and mechanistic theoretical frameworks in its approach :

1.    Information and “it from bit” philosophy. This idea first came about from the works of the physicist John Wheeler, but its application extends far beyond his discipline. (you can find the original post on “It from bit” here)

This idea is incredibly useful in that is allows us to simplify things into a predictive framework that is not overwhelmed by an insurmountable information overload. When complicated things such as ribosomes, RNA, nucleoui, or chromatin become reduced down to “physical substrates of abstract information” you end up being able to view biology as merely complex adaptive systems that are exchanging and replicating information in non-equillibirating thermodynamics.

This is especially apparent in developmental biology: The linear order of hox genes is conserved because it can convey information**. The symmetry-breaking in spiral (determinant) cleavage conveys information about cellular differentiation, whereas radial (indeterminate) cleavage does not. Viewing things, such as environmental cues, hormones, or proteins as merely “information” provides a strong framework for understanding adaptive, self-organizing systems such as cells, tissues, and embryos.

image credit PhiLiP
image credit: Pearson

2.    Viewing cells, organisms, and ecosystems in the more abstract framework of “complex adaptive systems.

I won’t go into full detail, but I will summarize to say that understanding complexity is not about understanding the individual parts and their properties, but understanding the manner in which the individual parts are arranged. How they are interconnected, and how this leads to a systems macroscopic behavior.

Viewing things in this framework is especially helpful for studying biology. As a first year student I felt lost in this haze of memorizing data that only pertains to a hyper-specialized area. However, viewing life in the framework of complex adaptive systems allowed me to see universal abstract patterns that made digesting the material much easier. Next to evolution by Natural Selection there is not one single idea I have come across that can successfully connect so many areas of study such as microbiology, ecology, biochemistry, and physiology.


 3.    Endosymbiotic theory: Just how Anatomy makes absolutely no sense outside of the context of evolution (take a look at the cranial nerves and you will see that humans are definitely lacking in intelligent design). Eukaryotic molecular biology cannot be understood without understanding how these structures emerged and what the underlying pressure was that initiated Eukaryotic evolution. This also ties into the more abstract concepts, such as altruism and division of labor for optimization and task completion.

4.    R vs. K selection theory: This is an idea that applies far beyond the size of an organism and their reproductive rate. It applies at every level of life from DNA structure, and transcription to other cellular processes. You cannot understand complexity within biology without understanding R vs. K selection theory and what it implies.

5.    2nd law of Thermodynamics and the numerous forms it takes:

“If your theory is found to be against the second Law of Thermodynamics, I give you no hope.” – Albert Einstein

There is not one single concept that is more universal than entropy. Between Shannon, Boltzman, Gibbs and numerous others: you can explain why we age, why we never get a 100% yield in chemistry, why black holes hint to a holographic universe, and why time only flows in one direction (yes, I am aware that this statement is not 100% in text) . The Laws of Thermodynamics comes back again and again in biology, medicine, chemistry and physics, and is hands down the most universally observed and applicable phenomenon. It lays a foundation for literally everything in science.


Honorable Mention: a common misconception I found myself frequently making as a student was the difference between equilibrium and steady state.

Equilibrium vs Steady State: The word Homeostasis is often conflated with “equilibrium” among biologists. If a biological reaction in your body reaches “equilibrium” you have died. I want to make this clear that we exist in a steady state, and that homeostasis is just that: a steady sate at a large scale perspective. It is NOT equilibrium. Living things are not in equilibrium, the water molecules, phosphate, and amino acids in your body want to disperse out and diffuse away like the molecules of gas inside a can of deodorant spray, living things are predicated on the ability to hold themselves together and to do so requires a constant input of energy.

6.    The role of physics. In biology, we rarely talk about why there are no “flying bears” or Godzilla-like creatures that we see in sci-fi and fantasy books, despite the fact that such a hypothetical creature would certainly have strong Darwinian fitness. The reason for this is often due to the fact that we don’t discuss the restraints that the laws of physics place on natural selection. From determining the maximum size of mammals, to why our arms and microtubules have a cylindrical shape, to criticizing the BMI, physics plays a significant role in setting the limits on natural selection.

7.  Fractals and fractal dimensions. Fractals are ubiquitous in nature. But rarely do textbooks ever explain WHY this is the case and what it implies. Fractals offer us the ability to understand networks and connectivity within a complex adaptive system on a more quantitative and intuitive level.


Amazon.com: Laminated 32x24 inches Poster: Tree Branches Silhouette Black  White Fractal Pattern Bare Winter Defoliated Contrast Abstract Division  Roots: Posters & Prints
Is this a river, blood vessels, or tree branches?

8.    Selfish Genes: The story of the idea of “gene selection” serves an an example of how science works. Namely, that by pointing out and investigating a paradox, you can discover hidden details and a wealth of knowledge. The idea that natural selection does NOT act on the individual but rather, on the genes offers a solution to the paradox of altruistic behavior. This applies not just to animal behavior, but down to the very cells inside your body.


9.    The Scientific Method: While it seems obvious, you would be very surprised how many “scientists” do not understand the scientific method. Allow me to clarify: medicine is not science, nursing is not science , psychology is not science, sociology is not science. I am not saying these disciplines are invalid or not needed, but there is a very severe problem when we start to masquerade these disciplines as something they are not. They are based off science.

Stephen Jay Gould would consider this an encroachment of Non Overlapping Magisteria (NOMA). If you are working with human beings in your control group, and you are not violating every ethical standard known to man, you are not adhering to the scientific method. If your hypothesis is predicated on proving a negative, you are not adhering to the scientific method. If you are not making objective, quantitative measurements, and if you are not making objective predictions that emerge from a strong theoretical framework, you are not adhering to the scientific method.

10. Evolution by Natural Selection. It should be obvious, but you cannot hope to understand biology without understanding the central unifying theory of it. It’s alarming how many institutions of higher education offer degrees in biology or biochemistry without establishing any theoretical foundation. Or worse attempt to replace it with creationism. Regardless of what you are studying specifically: developmental biology, ecology, biochemistry, genetics, and etc. It will always help you if you can connect the ideas you are learning about to Evolution by Natural Selection.


Why is the linear order of Hox Genes Conserved?

** Since most readers of this are arguably science geeks I figured I would elaborate on this topic since it is almost never stated explicitly and, to the annoyance of others, often stated implicitly, the full details have not been worked out but the following explanation is to the fullest I have been able to understand it:

As a wave of gastrulation sweeps across the embryo, these cells start to activate the most readily available “open” or Eurochromatic genes. This unraveling happens over time in a linear unidirectional fashion. This unraveling sets a “clock” on the hox genes. By the time the gastrulation wave has reached its most posterior position of the embryo, the more “distal” Hox genes are now available as the DNA unwinds and open to RNA polymerases. Thus, hox genes can use the unraveling of DNA as a means to make a clock for gene activation susceptibility in a linear fashion. This is why the linear structure of hox genes is conserved and why hox genes coordinate with boy plane strucures in a linear fashion, it contains information. This is also why hox genes contain fewer introns, making them highly atypical when compared with most eukaryotic gene groups.