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What Is Metabolomics?

What Is MetabolomicsYou might have heard that some scientists sequenced the human genome and this would bring the cure to many diseases.

Sorry to disappoint you but in biology and health there is much more than meets the eye.

Your genetic heritage might indicate that your have more chances than usual of developing a disease, but there are many other factors that will contribute to this finally occuring.

Metabolomics studies the footprint of these factors in order to give a more accurate picture of your condition.

In this post you will find what is metabolomics explained in simple words and with examples of application areas.

Metabolomics in simple words

Metabolomics surveys the metabolites that are present in organisms.

What are metabolites?

Metabolites are small molecules, smaller than genes or proteins. They are involved in metabolism as intermediates or products of metabolic processes.

Human examples of metabolites are hormones, amino acids, lipids, vitamins, and antioxidants, among others. Not only humans have metabolites, in fact all live beings need metabolites for their functioning.

Metabolites are responsible for the taste of tomatoes, the smell of plants, the color of fishes, and the poison of spiders.

Why is metabolomics useful?

Metabolomics describes the phenotype of an organism, which quoting Wikipedia is:

the composite of an organism’s observable characteristics or traits: such as its morphology, development, biochemical or physiological properties, phenology, behavior, and products of behavior (such as a bird’s nest).

Phenotypes result from the expression of an organism’s genes as well as the influence of environmental factors and the interactions between the two.

The presence or absence of metabolites (and their concentrations) describes the state of an organism.

A practical application of this definition: Metabolomics can also describe the metabolic signatures of disease versus normality.

Imagine two identical twin brothers (who share almost identical genes). One has a stroke at 45 while the other lives healthy till he dies at 90. How can this happen if they are almost identical? Easy, lifestyle.

Can you guess who was having 2 meals a day at McRonald’s?

If you would have used metabolomics to check their lipid profile, you would have seen that the one with the stroke had a LDL cholesterol level abnormally high.

Grape tomatoes.

Examples Of Metabolomics Research

Metabolomics has proved useful to answer biological questions, for instance to determine the effect of a clinical intervention (aka, if I do this to these people, what do I see?) or to unravel the mechanisms of disease, to name a few.

If you are wondering what is metabolomics doing in the real world and what kind of biological questions metabolomics can answer, check these two examples.

How can I make my tomatoes tastier?

Imagine a company that sells tomato seeds want to improve their tomatoes in terms of taste, texture, aroma, and other properties you could not imagine a tomato possess.

Here the company can have two options, either modify genetically the tomatoes or to cross different breeds.

Next, a panel of tomato tasters (you guessed it, taster like with wine) give scores for these properties of each tomato sample.

Now, remember that metabolites are responsible for the taste and other properties of tomatoes.

By comparing the metabolic profiles of tomato types that scored high and low in each property, one can discover which metabolites contribute positively and negatively to the quality of tomatoes.

Is this diet good for reducing cholesterol?

Since you are interested in nutrition and health, you are curious about which diet improves your cholesterol, this is, bad cholesterol goes down, good cholesterol goes up.

The study is designed as follows: 4 different diets plus an extra control diet are chosen. You select 5 groups of people and each one gets a diet.

After some weeks, blood samples from the participants are collected and measured to determine the concentration of different cholesterol metabolites. And there you have your ideal diet to improve you cholesterol levels.

Sounds simple, huh? In fact, this kind of research, if you want solid results, requires a joint effort of many research centers and hundreds of participants.

Check this research about the best diet for  weight-loss maintenance.

Their goal was to decide which combination of high/low protein and high/low glycemic index was the, so you avoid the “rebound effect” (spoiler alert: the best diet is high protein and low GI).


Microfluidic devices

Future Directions Of Metabolomics

Single Cell Metabolomics

Two individuals have different metabolic profiles if we measure their urine or blood. In the same fashion, different cell types in our body have different profiles.

In a not-so-far future, we will be able to extract a tiny sample from a single liver cell and compare its metabolic profile to the profile of a white blood cell, for instance.

By doing so, we will be able to focus on the specific organ or tissue involved in our research.

Metabolomics For Personalized Health

Have you heard of what is doing with gene analysis? you spit in a tube, send it by post and you get a detailed report on your genetic predisposition for a long list of diseases.

Now imagine the same, maybe instead of saliva samples with urine, but you will get a metabolic report, which describes your current health and if you are in any developmental stage of a disease.


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10 Most Viewed Posts From 2011

10 Most Viewed Posts In 2011

This is me in Koh Chang (Thailand) during Xmas 2011 holidays

Dear readers, thanks for coming to my site. These are the 10 most viewed posts in my blog during 2011. I started to blog seriously from September 2011 and I am quite happy with the amount of readers I got in 3 months.


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Understanding And Classifying Metabolite Space And Metabolite-Likeness

I have recently published a paper in PLoS ONE titled Understanding and Classifying Metabolite Space and Metabolite-Likeness.

Understanding and Classifying Metabolite Space and Metabolite-Likeness. 2011, PLoS ONE
Julio E. Peironcely, Theo Reijmers, Leon Coulier, Andreas Bender and Thomas Hankemeier.



Short Descritption Of The Paper

We have studied the chemical space of human metabolites.

We have built a computational model that assigns a metabolite-likeness score to input molecules.

We have studied which physicochemical properties and moieties are characteristic of human metabolites.

Long Description Of The Paper

This work describes the application of computational tools to predict the metabolite-likeness score of a molecule, i.e. how much it resembles a metabolite, and to acquire a global understanding of how the space of human metabolites is organized. In order to achieve this we made use of the state of the art in machine learning and the most comprehensive collection of human metabolites. To our understanding, we are the first to provide a metabolite-likeness score and to elaborate on its use for metabolomics, specifically in metabolite identification.

The use of computational models to classify metabolites and to compare them to other families of compounds has previously been published, but no prospectively validated papers have appeared.  The major contribution of the current work is threefold:

  1. We employ and validate a variety of computational methods to not only predict if a molecule is a metabolite or not, but also to assign a metabolite-likeness score to molecules.
  2. Three prospective validation sets are used to demonstrate the generalizability of our models and their applicability for drug research and metabolomics.
  3. We investigate which physicochemical properties and chemical moieties are characteristic of the metabolite space.

We believe that our work would be of interest to medicinal chemists performing drug research, but specially to scientists in the field of metabolomics. Our aim is to show the metabolomics community how they can benefit from chemoinformatics.

Therefore, we devise further development and application of this work to accelerate discoveries concerning metabolite identification.

Calculate Metabolite-Likeness Of Your Molecules

If you would like to calculate the Metabolite-Likeness of some molecules, please contact me ( peyron #at#

Ditto if you want to know more about this work or to collaborate with me.

Structure Generation, Metabolite Space, and Metabolite Likeness

These are the slides of my talk “Structure Generation, Metabolite Space, and Metabolite Likeness” at the Unilever Center for Molecular Informatics in November 2011, Cambridge, UK. I presented these slides during my UK road tour in November 2011.


Structure Generation, Metabolite Space, and Metabolite Likeness


The chemoinformatics tools presented here are developed for metabolomics, specifically for metabolite identification. From our mass spectrometry experiments, and after data analysis, we want to identify certain metabolites of interest.

Of these molecules we might know the elemental composition (which and of how many atoms they are composed) and maybe one or more fragments (maybe a ring, a chain, or a functional group).

With this information we have to propose candidate structures for our unknowns. If we don’t find them in a database, we should generate the chemical structures with a computer tool, the structure generator.

Chemical Structure Generation

In this part of the talk I describe structure generator I have developed. It relies on the canonical augmentation approach proposed by Brendan McKay and it makes use of the Chemistry Development Kit (CDK).

The main use of this tool is: for a given elemental composition and prescribed non-overlaping fragment(s), it exhaustively produces all non-duplicate chemical structures.

Since the output list can be very large, we want to keep only those molecules that are likely to be metabolites. Therefore, we have developed a model that predicts the percentage of Metabolite Likeness of a molecule, this is, how likely a molecule is to be a metabolite.

Metabolite Space and Metabolite Likeness

We have combined 3 classifiers and 5 molecular representations to build metabolite likeness models. We wanted to see which combination could discriminate better metabolites from non metabolites.

The best models have been validated with a prospective validation set to asses that it can classify well new and unseen molecules.

We expect to use this model to rank candidate structures for unknown metabolites. Also, we hope these tools help scientist working in metabolite identification.


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