Envisioning Change in Science Communication

“You never step in the same river twice.”

Or so the ancient Greek philosopher Heraclitus once pointed out, as he posited the concept of “change” as a fundamental property of the universe.

Side note, this was around the time Buddha supposedly attained enlightenment and the world population was estimated to be a third of the current US population. And centuries after the I Ching attempted to say something similar in far more ambiguous terms.

Biochemical Pathways: A design challenge

If science aims to describe how the world works, and science involves change, then science communication must be adequately equipped to handle that responsibility.

I recently ran into the problem of envisioning change while designing a better visual framework for biochemistry. My prior review of chemical language systems proved to me how poorly change is modeled in current chemistry. So the challenge is clear: How do you best communicate change throughout biochemical pathways?

To further understand the problem, let’s dive deep into a couple different types of change: Iterative change, phase shifts, and stasis.

Type 1: Iterative Change

This is the type of change I originally set out to understand, and is where I’ll spend the most time.

LEGO Instructions

These are interesting because LEGO’s goal is to help young children build new things out of smaller things, step-by-step. In many ways, that’s chemistry!

BrickInstructions.com provides a fascinating collection of Lego manuals all the way back to 1965. Here are a couple that stood out:

What seems to be missing from these illustrations is a visual distinction between each step’s old and new components. For this, there’s actually a great example from a book about plant chemicals.

Don’t look at the words — just see if you can identify each molecular LEGO using the provided keys:

There are some good ideas being attempted here. These pages are strong in their use of defined symbols for key elements, providing a full cast of characters working across each iteration. They even sprinkle in a bit of context — for example, a separate symbol for atmospheric oxygen (▲) in addition to non-atmospheric oxygen (O).

Of course it could be improved. Using consistent styling for sub-components across models would go a long way, along with more emphasis on the changes rather than the system as a whole. And it needs color! Never underestimate the power of color as a differentiator in modeling systems.

Names… galore

“It is exactly through the science of names that we have Western science.”
Alan Watts

With the growing complexity of information today, naming of physical units is becoming increasingly difficult. Hundreds of new genes and proteins being discovered each month, new pathways being identified, new bacteria species discovered. The number of names increases with each new discovery.

So given that iterative change consists of chained iterations, or a pathway, maybe the chain itself thus becomes the primary unit. With this, then each iteration becomes defined by the type of change applied to it, or the step number.

Photoshop Transformations

Kind of like Photoshop’s history. Note the lack of detailed information provided — a rotation is described as “Free Transform”.

Actions are approached differently, providing more details and indicating an amount of control over the outcome:

Of further note here is that the Actions require an initial input, as separate from the transformations applied to that input. Notably, each transformation is described using plain old English.

And it gets even better! The actions are applied through functions of Stop (⬛️), Record (⚫)and Play (▶︎), adding a playful, interactive dimension to the transformation.

Iteration schmiteration

There’s probably a better word than “iteration” for this idea, for “iteration” implies the same function applied to x over time, not allowing the possibility of different functions. This is important for these kinds of systems, as each step is often functionally different.

What defines iterative change?

A quick brain dump, definitely not rigorous:

• Naming systems can be pathway dependent.
• Clear causes and effects.
• Multiple steps within a larger unit of transformation histories.
• Units are defined by slight differences.
• What changes matters as much or more than what’s not changed.

Type 2: Phase Shifts

Sometimes a lot of change happens at once, and the equilibrium of the system shifts as a whole. If the entire system is shifting at once, then that poses a different set of challenges in describing change.

I’m using this term a bit differently than Chemists or Physicists would use it, but maybe we can call this the Designer’s definition: Multiple equilibriums provided by different variable configurations. In biological systems, this is an innate part of many life processes.

I’m not going to spend much time on this one, since it lies at a higher level of abstraction than is relevant for many molecular transformations.

Biochemical Phase Shifts

From a chemical perspective, a few things stand out as possibly being characterized by phase shifts (or our definition, anyway).

• Phenotypes & Gene Expression – We’re learning that many characteristics are not controlled by single genes but rather hundreds of genes working in concert. In some sense, then, this system takes on a life of its own and each individual gene becomes less important, compared to the broader phenotype.
• Hormones – I’ll call them the spice of life: Small doses of hormones can trigger enormous effects on life states. Puberty, plant growth stages, and metamorphosis are some examples controlled by hormones.
• pH Triggers – This is more of a hunch than anything. Maybe changes in pH can cause phase shifts? Maybe.

What defines this type?

Phase shifts deserve a much deeper dive than I’ve provided here.. But here are a few quick thoughts:

• Changes span multiple orders of magnitude.
• Characterized by compound, non-linear processes.
• More impacted variables means more information prioritization, thus likely to be more abstracted.
• Requires more information to meaningfully explain.

Type 0: Stasis

Sometimes the best way to understand something is to understand its opposite or its absence. If the world is in constant flux, yet modern models assume a certain permanence or “source of truth” as some like to say, what does that mean for modeling?

For modeling scientific systems, stasis can be thought of as removing the time variable from a model. But Heraclitus told us everything is constantly changing!

So if time is removed from a model, what are we actually modeling? Here are some possibilities. Down the rabbit hole we go…

• An idealized model, formed through fundamentals.
• An idealized model, formed through reduction.
• A statistical mean, given a specific point in time or across a range of time.
• An assortment of forms.
• Dude, don’t. Just stop overthinking this. Look! A white rabbit!

Conclusion

If we’re to consider change as categorized between iterative and phase-shifted, then those two kinds of system changes turn out to be quite different.

And if we’re to allow for the possibility of no change occurring in a system, thennn you might want to take that one up with Heraclitus and the I Ching.