Working with Arbitrary Units

Summary

This Synthetic Biology One video delves into the concept of 'arbitrary units' often found in scientific papers, particularly in synthetic biology. The creator highlights the confusion that arises from the term, as it indicates measurements that don't hold a standard physical unit, making them seem meaningless. However, the term signifies that the data can only be compared within a single experiment. Through a detailed example of measuring the fluorescence of a genetically engineered bacterial culture, the intricacies of arbitrary measurements are explained. The key focus is on the importance of context, comparison, and working within the linear range of equipment to derive meaningful conclusions.

Highlights

• Arbitrary units are not standard physical measurements, leading to confusion but serving a practical purpose in experiments 🔄.
• Measurements in arbitrary units are context-specific, valid only within a single experiment's framework 🎯.
• Using controls enhances the significance of results obtained in arbitrary units by providing comparison points ⚖️.
• Arbitrary units necessitate understanding equipment limits, particularly the linear range, for reliable results 🚥.
• Working with arbitrary units allows researchers flexibility without universal measurement standards 🧪.

Key Takeaways

• Arbitrary units are common in scientific data but indicate contextual measurements rather than standard ones 🚦.
• Measurements in arbitrary units can only be compared within the same experiment 🔬.
• Understanding equipment's linear range is essential when working with arbitrary units for accuracy 📏.
• Arbitrary units allow for flexibility in lab work, avoiding the need for universal standards across all labs 🌍.
• Always include controls in experiments to make meaningful comparisons with arbitrary units ✅.

Overview

Navigating the world of arbitrary units can initially seem perplexing. These units pop up in scientific papers, causing quite the stir for rookies who are told measurements need physical units to be valid. However, arbitrary units play a crucial role, especially when indicating contextual data that isn’t directly comparable across different labs or experiments.

An illustrative example of measuring fluorescence in engineered bacteria helps unpack this concept. Arbitrary units are pivotal in discerning if the transformation works, but standalone numbers in these units don't inherently mean much. The trick? Drawing comparisons within the specific experiment, bolstered by the consistent inclusion of control samples, is key to extracting valid data from these seemingly whimsical numbers.

The video emphasizes the tightrope walk involved in maintaining precision with arbitrary units. Researchers must account for the equipment's linear range to ensure that the meaning drawn from their measurements remains accurate and reliable. This flexibility means not every lab needs to use identical equipment—a boon for scientists who can tailor their tools to their experiment without losing scientific fidelity.

Working with Arbitrary Units Transcription

• 00:00 - 00:30 hi everybody welcome back to synthetic biology one today I want to talk about arbitrary units when you look through scientific papers especially in this
• 00:30 - 01:00 field you see the term come up all the time somebody took a measurement they plotted it on a graph and they labeled it arbitrary units now I remember when I was first starting out and I found this super confusing on the one hand I had my teachers and they would say every measurement needs to be reported in physical units if you don't indicate the units of measurement then your data is worthless but then on the other hand I would look at these figures reported in
• 01:00 - 01:30 arbitrary units and it's like the author's we're saying yeah whatever here's some units but they're just arbitrary so don't even worry about it right and it's like which is it make up your mind bro well it turns out that measurement units do matter and they especially matter when they are arbitrary because it warns us about an important limitation on how we can interpret arbitrary units specifically arbitrary units can't be directly
• 01:30 - 02:00 compared in different experiments or in different labs or even really in the same lab from day to day they only allow us to make comparisons within the context of a single experiment let's take a specific example to see how this works today I'm gonna measure the fluorescence of a bacterial culture I've engineered this culture to express GFP in a green fluorescent protein and I want to know if my transformation worked are my engineered cells fluorescent well
• 02:00 - 02:30 lucky for me the fluorescence properties of GFP make it easy to measure so I grow a culture of bacterial cells and I transfer a specific volume into a fluorometer the fluorometer shines light on the cells at the excitation wavelength of GFP then uses an optical filter to collect the light at the emission wavelength for GFP the emitted light is transformed into an electrical signal that is reported by the computer as a number let's say 100 it's 100 a lot
• 02:30 - 03:00 is it a little my transformation work well without more information we have no way of knowing let's look at all the different choices that make this measurement specific to my lab and to my hands I chose how many cells to load into the thermometer if I put more cells I'd get more fluorescence the fluorometer shines a light on the cells this could be a
• 03:00 - 03:30 strong light or a weak light depending on the kind of machine that I bought then the machine collects the light and the internal circuits turn that light intensity into a number but all that depends very specifically on how the machine was built now I certainly don't know everything about how a fluorometer is engineered but all of these little choices affect my measurement 100 1000 1 million or if I give my cells to you and you repeat the measurement in your lab
• 03:30 - 04:00 with different equipment you're bound to get a different number the number is arbitrary what this means for us effectively is that no single measurement contains information by itself it's meaningless but we can make our measurement meaningful again by establishing a point of comparison because I'm a super good scientist I never forget to include a control so now let's take another population of cells we'll call them wild-type cells that
• 04:00 - 04:30 don't express GFP we take the same volume put them in the same fluorometer and take another measurement we get another number also in arbitrary units this time it's for all of the specific details of our experimental setup still apply but now they apply equally to each measurement so it's valid for us to say that my transformed cells show 25 times more fluorescence than the non engineered cells if you do the experiment in your lab
• 04:30 - 05:00 you'll get different numbers but you'll get the same ratio we might say 25 to 1 or we might say 25 million to 1 million you should always keep this in mind when you see data reported in arbitrary units only the relative values are meaningful never be tricked into comparing arbitrary units between experiments or between labs finally to be really confident in working with arbitrary units we need to confirm one more assumption that makes all of this work
• 05:00 - 05:30 specifically we need to be sure that 25 times more signal really means 25 times more fluorescence imagine for example that I loaded only a very small number of cells onto the machine fewer than it could detect I might get a value of zero for the experiment and zero for the control or imagine that I just crammed that machine full of cells enough to completely saturate the detector I might get a value of 1,000 for the experiment
• 05:30 - 06:00 and 999 for the control we need to be working in a sweet spot where the Machine is sensitive but not saturated a place where 25 times the fluorescence really means 25 times this signal we call this sweet spot the linear range and we'll want to know it for almost any equipment that we use to make measurements for a fluorometer egg for example we can find this range by loading a standard fluorescent molecule at known concentrations eventually
• 06:00 - 06:30 doubling the concentration of the molecule will stop doubling the fluorescence readings that's how we know we've left the linear range and that comparisons using arbitrary units are no longer valid arbitrary units are a useful way to report data like fluorescence data that can easily vary between labs and between instruments it's still perfectly valid to compare the measurements within a single controlled experiment and it doesn't require that every lab on earth by
• 06:30 - 07:00 exactly the same equipment and work in exactly the same way just remember don't be impressed if you see someone measured 9,000 arbitrary units because it could just as easily be ten and don't expect the arbitrary units that you measure to match with numbers that come from someone else when it comes to arbitrary units you just do you and you remember until next time you are special