Makers Movement and Chemistry?


A revolution is going on, and as a scientist, I want to be part of it. It’s a revolution that started in 2008. Honestly, it started way before the 2008 (Neanderthals were crafting tools by themselves). Year after year the makers movement is getting stronger and stronger and I can assume that this is due to the cheaply available and open source 3D printers and easily programmable microcontrollers.

3D Printers are not so new, but now they are cheap, open source and easily obtainable. You can imagine whatever you want, draw it and print it in a matter of hours. So are the new generations of open source hardware / open source software microcontrollers like Arduino (Massimo Banzi TED talk is quite inspiring) or even more sophisticated computers like Raspberry Pi. The technologies that few years ago were expensive and difficult, are now broadly available and “easy”.

Unfortunately scientists are well known for hiding themselves in their ivory towers, or ivory basements, depending on your funding agency. This is not the time for closing ourself out of the real world. This it’s the perfect time for acting, learning something new, using our brain outside the box and do something innovative.

There are two main reasons why we should embrace this revolution:

1 – Money
Everything is working on money and I always had the feeling that scientists have been exploited for a long time. Just open whatever catalog is on your desk and check the smallest plastic things that they are selling. Is it twice or more expensive of what you thought, isn’t it? Just as an example 6 gel combs for the gel electrophoresis can cost more than 300$. You can print them for 1$ ( Similarly, there are countless plastic parts in your lab that you can print/replace/repair without wasting your grant. I bet you would like to spend your money on doing something scientifically relevant and not using the for lab supply producers donations.

Money is quite important but my main driving force is:

2 – Innovation
You and only you know what you need for your project. You can buy whatever is on the market, but it will rarely fit 100% your needs. Now you can couple a 3D printer with a microcontroller, assemble a circuit and write some lines of code, and you can virtually do whatever you want. This will give us the freedom of moving from an idea to a working object that fits our needs in the lab. I’m not talking only about static 3D objects, but about programmable microcontrollers and sensors. You can detect something from an instrument, process it on the microcontroller and this will control another instrument autonomously. And this is not something trivial. Naturally you need to learn at least the basics of 3D drawing, electronics and programming. But, well, I’m a nerd, learning new stuff it’s not a big issue 🙂


Just few examples (there are hundreds online) of what you can do yourself for your lab/experiments:

Punk Science on Make Magazine 31
3D reactionware
from Lee Cronin (I found his TED talk little bit speculative, but it’s a TED talk… so it’s ok… I guess)
Fraction collector for Chromatography (I should modify this one and do it with an arduino controller)
Magnetic stirrer
Scanning Electron Microscope 🙂
pHduino (Arduino pH meter)
Arduino Spectrometer
DNAquiri (not really interesting, but quite funny)

Where am I now? I know a little bit of 3D and planning to buy a personal 3D printer soon (I’m still in scanning mode to find the perfect one for me). Last week I bought an arduino starting kit (it’s less than 100€) and I’m on my second day of playing around with it. So far I did just few projects to learn the basics (how to transform an analog input into a digital output and a little bit of programming).

IMG 0814This one for example reads the temperature from my finger and then turns on the LEDs according to my temperature.

My suggestion is that we, as scientists, should embrace and take part in this revolution. It’s interesting, useful and well, if you are a nerd, even funny.
What do you think about it? Do you have suggestions or comments or what-so-ever? Contact me by mail, smoke signal or twitter @V_Saggiomo

 and thanks to Piotr Nowak for the English support.


Paper Explained: Using thermodynamically controlled networks to assess molecular similarity

I always wanted to do this kind of blog post. The idea is to explain a chemistry paper without jargon and just a little bit of chemistry. Let’s see if I can mange to do it:

Today I will (try to) explain our recent paper: Systems chemistry: using thermodynamically controlled networks to assess molecular similarity. It’s an open access paper, you are more than welcome to download and share it.


Why this research?

The similarity concept is quite hard to explain, and it can easily go into a philosophical discussion. Remaining on the scientific approach, take a look at these two molecules:




They look similar, isn’t it? But when you smell them the upper one will smell like coconut, while the bottom one like peach. That is quite a difference. This is because we have thousand of receptors in our body that can differentiate between these two molecules. This is a key point, it’s not only one receptor doing all the job, but it’s rather a network of receptors.

Medicinal chemistry and drug development are the main targets of this kind of research. We already have a number of working drugs, now we want to find some effective new molecules. Think for example at the antibacterial drugs, most of the standard one are ineffective today and we need new ones. So far, most of the pharmaceutical industries screening is based on computational approach.

We wanted an experimental setup with test molecules and receptors.The goal of our research was to exploit a dynamic network of receptor for discover “intrinsic similarity” of a set of molecules. The term “intrinsic similarity” means that the dynamic network itself will set his own parameters for assessing similarity. 


How it works? And what is a dynamic network?

In chemistry some reactions are irreversible, some are reversible. A dynamic network is formed by linking different chemical building blocks through reversible reactions. This means that the building blocks can exchange with one another for forming different products. 


In this paper three different building blocks are able to form six different macrocycles. The amount of each macrocycle is variable and it’s dependent by the addition of an “effector”. For example if we add an effector that can be bound to one specific macrocycle, the amount of this latter will increase. This also means that we can use the concentration of each single macrocycle as variable on the addition of different effectors. We can simply add a number of different effectors and then compare the six variables of the dynamic network after each addition. If two or more effectors change those six variables in a similar way, this means that those effectors are similar. In a few words, we are using this dynamic network for generating a number of receptor for different effectors.


Comparing six variables is straightforward and it can be done by hand. However a computer is way faster then us in comparing different numbers. We used a basic algorithm for comparing all the six variables (clustering analysis). 

As preliminary test we used 25 different effectors. The selection was done based on chemical groups that are already used as drugs or have something in common with well known drugs. This specific dynamic network was able to distinguish two big sets of molecules: one with ethylamine group and one without it. Apparently, the discriminant of this specific dynamic network was the presence of ethylamine moiety. 


We tested also another approach. We forced another algorithm to see the similarity that we want to screen. We teach the computer which molecules are similar for us (for a specific parameter that we want to screen) and train it with the variables. After that the algorithm was trained to screen for “our” similarity, we fed it with some unknown molecules. At this point it compared this variables with the ones acquired during the training and it told us if this unknown was more similar to a set or molecules or to another.


In principle this can be used for screening for biological active molecules. We can train the algorithm with molecules that are active on a specific protein, or are liposoluble, or are DNA intercalator and so on. Then we can simply test a new synthesized molecule with the dynamic network, and the algorithm will put this new molecule in one of the classes.



This specific dynamic network was used only to proof the concept of assessing molecular similarity using a dynamic network. Now it is time to use more complex networks. More and more different macrocycles mean more receptors, more variables and possibly a better discrimination.

Another great point of this approach is that it can be completely automated. You can use robots for mixing the samples, injecting in the HPLC for the analysis, another computer could read the output and do the analysis. All of this without your presence:


Then again, if you want to read the paper, it’s “free” and open access on the Journal of Systems Chemistry