The many faces of modelling in systems biology

By Nicolas Gambardella

COVID-19 pandemics put mathematical models of biological relevance all over daily newspapers and TV news, raising their profile for the non-scientists. In the life sciences, while mathematical models have always been at the core of some disciplines, such as genetics, they really became mainstream with the rebirth of systems biology a couple of decades ago. However, there are many different modelling approaches, and even specialists often ignore methods they do not use regularly or have not been taught.

After a historical overview, this blog post will then attempt to classify the main types of models used in systems biology according to their principal modalities.

What is the goal of using mathematical models in the life sciences in the first place? Three main aims came out roughly sequentially during the XX century, following the path physics followed over the past two millennia. First, mathematics helps describe the structure and dynamics of living forms and their productions. These models may rely on supposed underlying laws, be purely descriptives, such as the allometric laws. A great example is the famous book “On Growth and Form” by D’Arcy Thompson, attempting to understand living forms based on physical laws.

The second aim is to explain the shape and function of living forms. How do the properties of life’s building blocks explain what we can observe? In their masterwork, Alan Hodgkin and Andrew Huxley predicted the existence of ionic channels within the cell membrane and, using a mathematical model, explained how neurons generate action potentials (a work for which they got the Nobel prize).

Finally, can we predict how a system will behave, and can we invent new systems that will behave the way we want them to? This is the purpose of synthetic biology, exemplified in the figure below by the pioneering work of Michael Elowitz and Stanislas Leibler, who built the “repressilator”, a synthetic construct exhibiting sustained oscillations maintained through generations of bacteria.

Obviously, there are no strict boundaries between the three aims, and most models seek to describe, explain, and predict the structures and behaviours of living systems.

A major shift in the use of mathematical models was the introduction of numerical simulations, made feasible by the invention of computers. The benefits have been laid out by one of the inventors of such computers in an article that indeed contained complex mathematical models but no simulations. In his famous 1952 paper introducing morphogens, Alan Turing suggested that using a digital computer to simulate specific cases of a biological system would allow avoiding the oversimplifications required by analytic solutions.

This wish was granted the very same year first by Britton Chance, who built a computer (an analogue one at the time) specifically to solve a differential equation model of a small biochemical pathway.

1952 was also when Hodgkin and Huxley published the action potential model mentioned above, a real Annus Mirabilis for computational modelling in the life sciences.

Before going further, we should ask ourselves, “what is a mathematical model”? According to Wikipedia (as of 4th July 2022), A mathematical model is a description of a system using mathematical concepts and language. I consider that three essential categories of components form mathematical models in systems biology.

Variables represent what we want to know or what we want to compare with the observations. They can characterise a physical entity, e.g., the concentration of a substance, the length of an object or the duration of an event, or be derived from the model itself, for instance, the maximum velocity of an enzymatic reaction. 

Relationships mathematically link variables together and represent what we know or what we want to test. They can be static, an affinity constant linked to concentrations or dynamic, such as a rate of change depending on concentrations. Relationships are not necessarily equations. For instance, a sampling might link a variable to a statistical distribution, and logic models use logical statements to attribute values to variables. 

Finally, a much-underestimated category is formed by constraints put on the model. Those represent the context of the modelling project or what we consciously decide to ignore. Some constraints are properties of the world, e.g., a concentration must always be positive, the total energy is conserved, and some are properties of the model, such as boundary conditions and objective functions for optimisation procedures. Initial conditions – the values of variables before starting a numerical simulation – are also crucial since a given model might behave differently with different initial conditions, even if neither variables nor relationships are changed.

However, the mathematical model itself is only one brick of a systems biology’s modelling and simulation project as in any natural science domain. Since these models aim to be mechanistic, i.e., anchored in underlying molecular, cellular, tissular, and physiological processes, the first step is to conceptualise a “biological model”. For instance, a biochemical pathway will be a collection of chemical reactions. In the case of Hodgkin and Huxley, who did not know the underlying molecules, the mechanism was based on an electrical analogy, ionic channels being represented by electrical conductances. The “mathematical model” is made up of mathematical relationships linking the variables and constraints. A “computational model”, using the “mathematical model” in conjunction with observed or estimated values, is then simulated. The result is compared with observations, and the loop is iterated.

Now let’s explore the different facets of models used in systems biology, and marvel at their diversity

The variables of a model can represent biological reality at different granularities. In some logical models (often wrongly called Boolean networks), a variable can represent a state, such as presence or absence, 1, 2, 3. Detailed models at the “mesoscopic scale” might represent individual molecules, where a separate variable represents every single particle. Variables can also represent discrete populations of molecules, for instance, the number of molecules of a given chemical class, whose evolution is simulated by stochastic algorithms. In chemical kinetics, the variables whose change is determined using ordinary differential equations often represent continuous concentrations. Finally, some models gloss over the physical parts altogether and use fields to represent what could happen to them.

Numerical simulations most often represent the evolution of variable values over “time”. However, the granularity of this “time” may vary. At an extreme, we have models with no representation of time, such as regression models, or implicit representation of time, such as steady-states models. In logical models, as in Petri Net, simulations usually progress along a pseudo-time, where one cannot compare numbers of steps. Time can be discrete, numerical simulations computing a system’s state at fixed intervals, for instance, one second. Finally, models can consider time as continuous, simulations being iterated at various timepoints decided by numerical solvers (note that software might still return results at fixed intervals).

Spacetime being a thing, we also have as many different representations of space. Starting with no space at all, for instance, in noncompartmental analyses of pharmacokinetic models. Space can also be represented by a single homogenous (well-stirred) and isotropic compartment or several of them connected by variables and relationships (multi-compartment models, a.k.a. bathtub models). Cellular automata constitute a particular case, where each compartment is also a model variable whose status depends on its neighbours’. An extension of the multi-compartment modelling represents realistic biological structures using finite elements, each considered homogeneous and isotropic. Finally, space might be represented by continuous variables, where the trajectory of each molecule can be simulated.

Variability and noise are unavoidable parts of any observation of the natural world, including living systems. Variability can be extrinsic (e.g., due to technical variability), or intrinsic (e.g., actual differences between cells or samples). True noise depends on the size of the system. Taking all those into account in the models can thus be important, and different approaches present different levels and types of stochasticity. As with the other modalities above, stochasticity might be entirely absent, models and simulations being deterministic. One can add different and arbitrary types of noise to simulations with stochastic differential equations. The stochastic aspect might instead emerge directly from the structure of the model, as with the Stochastic Simulation Algorithms (a.k.a. algorithms of the “Gillespie” type). Variability can also be taken into account prior to the simulations, for instance, by sampling initial conditions from distributions, as with ensemble modelling. Finally, in probabilistic models such as Markov models, the entire iteration of the system is based on the probabilities of switching states.

Finally, there are two large families of models based on the type of algorithms used to update the variables. One can compute a variable’s new value by calculating its value either using numerical combinations of previous variables’ values or logic rules taking into account other variable states. Contrary to widespread belief, not all logic models use pseudo-time and Boolean variables. Stochastic Boolean networks can use continuous time, and fuzzy logic models can base their decision on continuous variable values.

Those modalities can be combined in many ways to produce an extremely rich toolkit of modelling approaches. One of the most frequent sources of pain when modelling biological systems is to start with a methodological a priori, often because we are comfortable with an approach, we have the necessary software, or we don’t know better. Doing so can result in under-determined models, endless iterations and failure to get any result.  The choice of a modelling approach should be first and foremost based on 1) the question asked and 2) the data available to build and validate the model.

References

Chance, B., Greenstein, D. S., Higgins, J. & Yang, C. C. (1952) The mechanism of catalase action. II. Electric analog computer studies. Arch. Biochem. Biophys. 37: 322–339. doi:10.1016/0003-9861(52)90195-1

Hodgkin, A.L., Huxley, A.F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology117 (4): 500–44. doi:10.1113/jphysiol.1952.sp004764

Stanislas Leibler; Elowitz, Michael B. (2000-01-20). “A synthetic oscillatory network of transcriptional regulators”. Nature 403 (6767): 335–338. doi:10.1038/35002125.

Thompson, D. W., 1917. On Growth and Form. Cambridge University Press.

Turing, A. M. (1952). “The chemical basis of morphogenesis”. Philosophical Transactions of the Royal Society of London B237 (641): 37–72. doi:10.1098/rstb.1952.0012.

SBGN-ML, SBML visual packages, CellDesigner format, what are they? When should I use them?

By Nicolas Gambardella

[disclaimer: I wrote the first version of this text in 2018. Some of its descriptions might thus be slightly out of date]

Visual representation of biochemical pathways has been a critical tool for understanding the cellular and molecular systems for a long time. Any knowledge integration project involves a jigsaw puzzle step, where different pieces must be put together. When Feynman cheekily wrote on his blackboard just before his death, “What I cannot create I do not understand”, he meant that he only fully understood a system once he derived a (mathematical) model for it; and interestingly, Feynman is also famous for one of the earliest standard graphical representations of reaction networks, namely the Feynman diagrams to represent models of subatomic particle interactions. The earliest metabolic “map” I possess comes from the 3rd edition of “Outlines of Biochemistry” by Gortner published in 1949. I would be happy to hear if you have older ones.

(I let you find out all the inconsistencies, confusing bits, and error-generating features in this map. This might be food for another text, but I believe this to be a great example to support the creation of standards, best practices, and software tools!)

Until recently, those diagrams were drawn mainly by hand, initially on paper, then using drawing software. There was little thought spent on consistency, visual semantics, or interoperability. This state of affairs changed in the 1990s as part of Systems Biology’s revival. The other thing that changed in the 1990s was the widespread use of computers and software tools to build and analyse models. The child of both trends was the development of standard computer-readable formats to represent biological networks.

When drawing a knowledge representation map, one can divide the decision-making process, and therefore the things we need to encode in order to share the map, into three parts:

What – How can people identify what I represent? A biochemical map is a network built up from nodes linked by arcs. The network may contain only one type of node, for instance, a protein-protein interaction network or an influence network, or be a bipartite graph, like a reaction network – one type of node representing the pools involved in the reactions, the other representing the reactions themselves. One decision is the shape to use for each node so that it carries visual information about the nature of what it represents. Another concerns the arcs linking the nodes, which can also contain visual clues, such as directionality, sign, type of influence, and more. All this must be encoded in some way, either semantically (a code identifying the type of glyphs from an agreed-up list of codes) or graphical (embedding an image or describing the node).

Where – After choosing the glyphs, one needs to place them. The relative position of the information should not always carry much information, but there are some cases where it must, e.g. members of complexes, inclusion in compartments, etc. Furthermore, there is no denying that the relative position of glyphs is also used to convey more subjective information. For instance, a linear chain of reactions induces the idea of a flow, much better than a set of reactions going randomly up and down, right and left. Another unwritten convention is to represent membrane signal transduction on the top of the maps, with the “end-result” – often effect on gene expression – at the bottom, with the idea of a cascading flux of information. The coordinates of the glyphs must then be shared as well.

How – Finally, the impact of a visual representation also depends on aesthetic factors. The relative size of glyphs and labels, the thickness of arcs, the colours, shades and textures, all influence the facility with which viewers absorb the information contained in a map. Relying on such aspects to interpret the meaning of a map should be avoided, particularly if the map is to be shared between different media, where rendering could affect the final aspect. Nevertheless, wanting to keep this aspect as close as possible makes sense.

A bit of history

Different formats have been developed over the years to cover these different aspects with different accuracy and constraints. In order to understand why we have such a variety of description formats on offer, a bit of history might be helpful. Being able to encode the graphical representation of models in SBML was mentioned as early as 2000 (Andrew Finney. Possible Extensions to the Systems Biology Markup Language. 27 November 2000).

In 2002, the group of Hiroaki Kitano presented a graphical editor for the Systems Biology Markup Language (SBML, Hucka et al 2003, Keating et al 2020), called SBedit, and proposed extensions to SBML necessary for encoding maps (Tanimura et al. Proposal for SBEdit’s extension of SBML-Level-1. 8 July 2002). This software later became CellDesigner (Funahashi et al 2003), a full-featured modelling developing environment using SBML as its native format. All graphical information is encoded in CellDesigner-specific annotations using the SBML extension system. In addition to the layout (the where), CellDesigner proposed a set of standardised glyphs to use for representing different types of molecular entities and different relationships (the what) (Kitano et al 2003). At the same time, Herbert Sauro developed an extension to SBML to encode the maps designed in the software JDesigner (Herbert Sauro. JDesigner SBMLAnnotation. 8 January 2003). Both CellDesigner and JDesigner annotations could also encode the appearance of glyphs (how).

In 2003, Gauges et al (Gauges et al. Including Layout information in SBML files. 13 May 2003) proposed to split the description of the layout (the where) and the rendering (the what and the how) and focus on the layout part in SBML (Gauges et al 2006). Eventually, this effort led to the development of two SBML Level 3 Packages, Layout (Gauges et al 2015, Keating et al 2020) and Render (Bergmann et al 2017, Keating et al 2020).

Once the SBML Layout annotations were finalised, the SBML and BioPAX communities came together to standardise visual representations for biochemical pathways. This led to the Systems Biology Graphical Notation, a set of three standard graphical languages with agreed-upon symbols and rules to assemble them (the whatLe Novère et al 2009). While the shape of SBGN glyphs determines their meaning, neither their placement in the map nor their graphical attributes (colour, texture, edge thickness, the how) affect the map semantics. SBGN maps are ultimately images and can be exchanged as such, either in bitmaps or vector graphics. They are also graphs and can be exchanged using graph formats like GraphML. However, sharing and editing SBGN maps would be much easier if more semantics were encoded than graphical details. This feeling led to the development of SBGN-ML (van Iersel et al 2012), which encodes not only the SBGN part of SBGN maps but also the layout and size of graph elements.

So we have at least three solutions to encode biochemical maps using XML standards from the COMBINE community (Hucka et al 2015): 1) SBGN-ML, 2) SBML with Layout extension (controlled Layout annotations in Level 2 and Layout package in Level 3) and 3) SBML with proprietary extensions. Regarding the latter, we will only consider the CellDesigner variant for two reasons. Firstly, CellDesigner is the most used graphical model designer in systems biology (at the time of writing, the articles describing the software have been cited over 1000 times). Secondly, CellDesigner’s SBML extensions are used in other software tools. These three solutions are not equivalent; they present different advantages and disadvantages, and round-tripping is generally impossible.

SBGN-ML

Curiously, despite its name, SBGN-ML does not explicitly describe the SBGN part of the maps (the what). Since the shape of nodes is a standard, it is only necessary to mention their type, and any supporting software will know which symbol to use. For instance, SBGN-ML will not specify that a protein X must be represented with a round-corner rectangle. It will only say that there is a macromolecule X at a particular position with given width and height. Any SBGN-supporting software must know that a round-corner rectangle represents a macromolecule. The consequence is that SBGN-ML cannot be used to encode maps using non-SBGN symbols. However, software tools can decide to use different symbols attributed to a given class of SBGN objects while rendering the maps. For example, instead of using a round-corner rectangle each time a glyph’s class is macromolecule, it could use a star. The resulting image would not be an SBGN map. However, if modified and saved back in SBGN-ML, it could be recognised by another supporting software. Such behaviour is not to be encouraged if we want people to get used to SBGN symbols, but it provides a certain level of interoperability.

What SBGN-ML explicitly describes instead are the parts that SBGN itself does not regulate but are specific to the map. That includes the size of the glyphs (bounding box), the textual labels, as well as the positions of glyphs (the where). SBGN-ML currently does not encode rendering properties such as text size, colours and textures (the how). However, the language provides an element extension, analogous to the SBML annotation, that allows augmenting the language. One can use this element to extend each glyph or encode style, and the community started to do so in an agreed-upon manner.

Note that SBGN-ML only encodes the graph. While it contains a certain amount of biological semantics – linked to the identity of the glyphs – it is not a general-purpose format that would encode advanced semantics of regulatory features such as BioPAX (Demir et al. 2010), or mathematical relationships such as SBML. However, users can distribute SBML files along with SBGN-ML files, for instance, in a COMBINE Archive (Bergmann et al 2014). Unfortunately, there is currently no blessed way to map an SBML element, such as a particular species, to a given SBGN-ML glyph.

SBML Level 3 + Layout and Render packages

As we mentioned before, SBML Level 3 provides two packages helping with the visual representations of networks: Layout (the where) and Render (the how). Contrarily to SBGN-ML, which is meant to describe maps in a standard graphical notation, the SBML Level 3 packages do not restrict the way one represents biochemical networks. This provides more flexibility to the user but decreases the “stand-alone” semantics content of the representations. I.e. if non-standard symbols are used, their meaning must be defined in an external legend. It is, of course, possible to use only SBGN glyphs to encode maps. The visual rendering of such a file will be SBGN, but the automatic analysis of the underlying format will be more challenging.

The SBML Layout package permits encoding the position of objects, points, curves and bounding boxes. Curves can have complex shapes encoded as Béziers curves. The package allows distinguishing between different general types of nodes such as compartments, molecular species, reactions and text. However, there is few

If we are trying to visualise a model, one advantage of using SBML packages is that all the information is included in a single file, providing a straightforward mapping between the model constructs and their representation. This goes a long way to solve the issue of the biological semantics mentioned above since it can be retrieved from the SBML Core elements linked to the Layout elements. Let us note that while SBML Layout+Render do not encode the nature of the objects represented by the glyphs (the what) using specific structures, this can be retrieved via the attributes sboTerm of the corresponding SBML Core elements, using the appropriate values from the Systems Biology Ontology (Courtot et al 2011).

CellDesigner notation

CellDesigner uses SBML (currently Level 2) as its native language. However, it extended it with its own proprietary annotation, keeping the SBML perfectly valid (which is also the way software tools such as JDesigner operate). Visually, the CellDesigner notation is close to SBGN Process Descriptions, having been the strongest inspiration for the community effort. CellDesigner offers an SBGN-View mode, that produces graphs closer to pure SBGN PD.

CellDesigner’s SBML extensions increase the semantics of SBML elements such as molecular species or regulatory arcs in a way not dissimilar to SBGN-ML. In addition, it provides a description of each glyph linked to the SBML elements, covering the ground of SBML Layout and Render. The SBML extensions being specific to CellDesigner, they do not offer the flexibility of SBML Render. However, the limited spectrum of possibilities might make the support easier.

 CellDesigner notationSBML Layout+RenderSBGN-ML
Encodes the what
Encodes the where
Encodes the how
Contains the mathematical model part
Writing supported by more than 1 tool
Reading supported by more than 1 tool
Is a community standard

Examples of usages and conversions

Now let us see the three formats in action. We start with SBGN-ML. First, we can load a model – for instance from BioModels (Chelliah et al 2015 ) – in CellDesigner (version 4.4 at the time of writing). Here we will use the model BIOMD0000000010, an SBML version of the MAP kinase model described in Kholodenko et al (2000).

From an SBML file that does not contain any visual representation, CellDesigner created one using its auto-layout functions. One can then export an SBGN-ML file. This SBGN-ML file can be imported, for instance, in Cytoscape (Shannon et al.  2003) 2.8 using the CySBGN plugin (Gonçalves et al 2013).

The position and size of nodes are conserved, but edges have different sizes (and the catalysis glyph is wrong). The same SBGN-ML file can be open in the online SBGN editor Newt.

An alternative to CellDesigner to produce the SBGN-ML map could be Vanted (Junker et al 2006, version 2.6.4 at the time of writing). Using the same model from BioModels, we can auto-layout the map (we used the organic layout here) and then convert the graph to SBGN using the SBGN-ED plugin (Czauderna et al 2010).

The map can then be saved as SBGN-ML and as before, opened in Newt.

The positions of the nodes are conserved. However, the connection of edges is a bit different. In that case, Newt is slightly more SBGN compliant.

Then, let us start with a vanilla SBML file. We can import our BIOMD0000000010 model in COPASI  (Hoops et al 2006, version 4.22 at the time of writing). COPASI now offers auto-layout capabilities, with the possibility of manually editing the resulting maps.

When we export the model in SBML, it will now contain the map encoded with the Layout and Render packages. When the model is uploaded in any software tool supporting the packages, we will retrieve the map. For instance, we can use the SBML Layout Viewer. Note that if the layout is conserved, it is not the case with the rendering.

Alternatively, we can load the model to CellDesigner, and manually generate a nice map (NB: a CellDesigner plugin that can read SBML Layout was implemented during Google Summer of Code 2014 . It is part of the JSBML project).

We can create an SBML Layout using the CellDesigner layout converter. Then, when we import the model in COPASI, we can visualise the map encoded in Layout. NB: here, the difference in appearance is due to a problem in the CellDesigner converter, not COPASI.

The same model can be loaded in the SBML Layout Viewer.

How do I choose between the formats?

There is, unfortunately, no unique solution at the moment. The main question one has to ask is what do we want to do with the visual maps?

Are they meant to be a visual representation of an underlying model, the model being the critical part that needs to be exchanged? If that is the case, SBML packages or CellDesigner notation should be used.

Does the project mostly/only involves graphical representations, and those must be exchanged? CellDesigner or SBGN-ML would therefore be better.

Does the rendering of graphical elements matter? In that case, SBML packages or CellDesigner notations are currently better (but that is going to change soon).

Is standardisation important for the project, in addition to immediate interoperability? If yes, SBML packages or SBGN-ML would be the way to go.

All those questions and more have to be clearly spelt out at the beginning of a project. The answer will quickly emerge from the answers.

Acknowledgements

Thanks to Frank Bergmann, Andreas Dräger, Akira Funahashi, Sarah Keating, Herbert Sauro for help and corrections.

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De la ponctuation des listes, Une Règle pour les Gouverner Toutes

Par Nicolas Gambardella

Quelle ponctuation utiliser dans les listes à puces ou numérotées ? Si l’on essaie de comprendre les règles en accumulant les exemples, le découragement vient rapidement, car il semble que le hasard règne en maître. Si l’on trouve une liste de ces règles, le soulagement est de courte durée tant celles-ci semblent arbitraires et les exceptions nombreuses. Je vais proposer à la fin de ce billet un algorithme graphique en deux parties, pour vous aider à choisir la casse des premières lettres des éléments de liste et la ponctuation à la fin de chaque élément. Mais avant, ça je vais fusionner toutes les règles en une seule, et présenter des exemples vous aidant à la comprendre.

Avertissement : ce billet ne concerne que les listes insérées dans des textes. S’il s’agit de listes insérées dans des diapositives de présentations, la rapidité de lecture et la facilité de mise en page doit être prise en compte, et on préférera souvent commencer chaque élément par une majuscule et omettre la ponctuation à la fin de celui-ci.

Revenons à cette fameuse unique Règle pour les Gouverner Toutes ; quelle est-elle ? C’est tout simple :

Si on supprime le formatage de la liste, à savoir, passages à la ligne et puces,
la ponctuation du texte résultant doit rester correcte.

Prenons une liste simple.

Le cerveau comporte trois grandes parties :
le prosencéphale ;
le mésencéphale ;
le rhombencéphale.

Si l’on supprime le formatage de la liste ci-dessus, on obtient :

Le cerveau comporte trois grandes parties : le prosencéphale ; le mésencéphale ; le rhombencéphale.

La ponctuation de cette phrase est tout à fait correcte puisqu’elle commence par une majuscule, ne contient pas de majuscules au milieu, suivant les deux-points et les point-virgules, et se termine par un point.

Aparté : le tiret commençant l’entrée d’une liste est un tiret semi-cadratin « – ». Ce n’est ni un trait d’union « - », ni un tiret cadratin « — », ni bien sûr le symbole mathématique moins « − ». Ce tiret est suivi d’une espace insécable, comme il l’est quand il précède une incise. Fin de l’aparté.

On ferme chaque élément par une ponctuation non fermante, sauf le dernier. On aurait pu remplacer les point-virgules par des virgules. Dans ce cas, on aurait également pu ajouter la conjonction de coordination « et » après le pénultième élément (après la virgule !).

Le cerveau comporte trois grandes parties :
– le prosencéphale,
– le mésencéphale, et
– le rhombencéphale.

L’absence de ponctuation à la fin de chaque élément, comme l’utilisation d’un point rend la ponctuation incorrecte :

Le cerveau comporte trois grandes parties : le prosencéphale le mésencéphale le rhombencéphale

Le cerveau comporte trois grandes parties : le prosencéphale. le mésencéphale. le rhombencéphale.

Ce serait également le cas si nous avions commencé chaque élément par une majuscule.

Le cerveau comporte trois grandes parties : Le prosencéphale ; Le mésencéphale ; Le rhombencéphale.

Dans la liste précédente, les éléments ne sont pas indépendants de la phrase d’entrée. Ce n’est parfois pas le cas, par exemple si cette dernière est du type « Remarques : » ou « À noter : » (mais pas « À noter que : »). Dans ces cas-là, les éléments de la liste sont des phrases complètes, indépendantes non seulement de la phrase d’entrée, mais également l’une de l’autre. Elles sont ponctuées en conséquence.

Remarques :
– Le prosencéphale  contient la vaste majorité des neurones du cerveau des êtres humains.
– Le mésencéphale est atteint dans la maladie de Parkinson.
– Le rhombencéphale contrôle les fonctions vitales et autonomes.

Remarques : Le prosencéphale  contient la vaste majorité des neurones du cerveau des êtres humains. Le mésencéphale est atteint dans la maladie de Parkinson. Le rhombencéphale contrôle les fonctions vitales et autonomes.

Les listes ci-dessus étaient précédées d’une phrase ouverte, se terminant par deux-points. Cela aurait également été le cas si elle s’était terminée par un point-virgule ou une virgule (voir plus bas le cas des listes emboîtées). Si la phrase d’entrée est fermée, c’est-à-dire qu’elle se termine par un point, un point d’exclamation ou un point d’interrogation, les règles sont différentes, car les éléments sont eux-mêmes des phrases.

Laquelle des informations suivantes est erronée ?
– Le prosencéphale  est la partie la plus antérieure du cerveau.
– Le mésencéphale est atteint dans le diabète sucré.
– Le rhombencéphale contrôle les fonctions vitales et autonomes.

Laquelle des informations suivantes est erronée ? Le prosencéphale  est la partie la plus antérieure du cerveau. Le mésencéphale est atteint dans le diabète sucré. Le rhombencéphale contrôle les fonctions vitales et autonomes.

On voit bien que débuter les éléments par des minuscules et les achever par un point virgule ou une virgule produirait ici une structure incorrecte.

Laquelle des informations suivantes est erronée ? le prosencéphale  est la partie la plus antérieure du cerveau, le mésencéphale est atteint dans le diabète sucré, le rhombencéphale contrôle les fonctions vitales et autonomes.

Ce qui nous amène aux questions à choix multiples (QCM). Cette situation est quelque peu différente, car les éléments de la liste sont des alternatives.

Quelle est la partie du cerveau la plus développée chez l’être humain ?
– Le prosencéphale.
– Le mésencéphale.
– Le rhombencéphale.

La liste peut dès lors être pliée en phrases différentes, correspondant à chaque élément.

Quelle est la partie du cerveau la plus développée chez l’être humain ? Le prosencéphale.

Quelle est la partie du cerveau la plus développée chez l’être humain ? Le mésencéphale.

Quelle est la partie du cerveau la plus développée chez l’être humain ? Le rhombencéphale.

Pour finir, abordons le sujet des listes emboîtées. Les règles restent les mêmes. Le dernier élément d’une liste de niveau supérieur joue toutefois le rôle de phrase entrante pour la liste de niveau inférieur. On utilisera des ponctuations différentes pour les éléments de niveaux différente. Typiquement, une virgule terminera des éléments au sein d’un élément terminé par un point-virgule.

Le cerveau comporte trois grandes parties :
– le prosencéphale qui est la partie la plus antérieure formée de deux parties,
  • le télencéphale qui est responsable des fonctions supérieures,
 • le diencéphale qui relaie les entrées sensorielles ;
– le mésencéphale ;
– le rhombencéphale qui est la partie postérieure formée de deux parties,
  • le métencéphale qui comprend le cervelet,
  • le myélencéphale qui est aussi appelé bulbe rachidien.

Si l’on supprime les listes de second niveau, on obtient la ponctuation correcte suivante :

Le cerveau comporte trois grandes parties :
– le prosencéphale qui est la partie la plus antérieure formée de deux parties, le télencéphale qui est responsable des fonctions supérieures, le diencéphale qui relaie les entrées sensorielles ;
– le mésencéphale ;
– le rhombencéphale qui est la partie postérieure formée de deux parties, le métencéphale qui comprend le cervelet, le myélencéphale qui est aussi appelé bulbe rachidien.

La liste entièrement pliée devient :

Le cerveau comporte trois grandes parties : le prosencéphale qui est la partie la plus antérieure formée de deux parties, le télencéphale qui est responsable des fonctions supérieures, le diencéphale qui relaie les entrées sensorielles ; le mésencéphale ; le rhombencéphale qui est la partie postérieure formée de deux parties, le métencéphale qui comprend le cervelet, le myélencéphale qui est aussi appelé bulbe rachidien.

Essayons de construire un algorithme qui nous permet de trouver à coup sûr quelle casse et quelle ponctuation utiliser. Commençons par la casse de début d’élément.

Nous pouvons ensuite nous tourner vers la ponctuation terminant les entrées. Notons que je ne considère ici qu’un seul niveau d’imbrication. L’algorithme pourrait être généralisé en remplaçant points-virgules et virgules par ponctuation ouverte de niveau n et n+1.

Et voilà ! Vous pouvez maintenant être confiant·e dans le formatage de votre liste !

Quelques faux-amis peu connus

Par Nicolas Gambardella

Tout le monde connaît les faux-amis anglais-français comme actually et actuellement, le second signifiant « maintenant » tandis que le premier signifie « en réalité ». Mais certains faux-amis sont plus rares ou plus subtils. En voici quelques-uns auxquels un traducteur doit faire attention. Je mettrai cette liste à jour au fur et à mesure que j’en rencontrerai de nouveaux.

Adresser et to address

Tout comme le verbe anglais to address, le verbe français adresser possède un grand nombre d’acceptions dont certaines sont partagées. « To address a letter » signifie « adresser une lettre ». « To address someone » signifie « s’adresser à quelqu’un » Cependant, l’un comme l’autre présente des significations qui lui sont propres. Attention donc aux faux-amis. « to address a problem or a question » se traduit par « s’occuper d’un problème » ou « répondre à une question ». Pour répondre à des questions métaphysiques, on peut « s’adresser à la philosophie », qui en anglais se traduira pas « to turn to phylosophy ».

Déception et deception

En anglais, une deception est un mensonge, une tromperie, une action visant à induire quelqu’un en erreur. Cette signification a disparu en français, où une déception est le sentiment la tristesse ressentit lorsqu’un espoir n’est pas rempli. La traduction anglaise de déception est disappointment.

Accord et accord

Dans le cadre d’un traitement, un le français accord correspond à l’anglais assent (donner son accord). En anglais, un accord est une adhésion thérapeutique, une convergence de vue avec la personne prescrivant le traitement (les opinions sont en accord).

Cave et cave

En français, la cave est une pièce en sous-sol, par exemple pour conserver le vin, et se traduit par cellar en anglais. En anglais, a cave est un trou dans un relief rocheux, traduit par caverne ou grotte en français.

Mental et mental

En anatomie, l’adjectif anglais mental se réfère au menton (du latin mentum), comme dans « mental foramen ». En français, l’adjectif correct est mentonnier, mental faisant référence au latin mens, l’esprit.

Crâne et crane

En anglais, crane signifie grue, que ce soit l’oiseau ou la machine. Le français, crâne se traduit par l’anglais skull.

Lunatique et lunatic

En anglais, une personne lunatic est un·e fo·u·olle (loony), tandis qu’en français un lunatique est quelqu’un qui change d’opinion sur un coup de tête.

Dramatique et dramatic

En anglais, dramatic peut signifier « soudain et frappant » et avoir une connotation positive (par exemple, « a dramatic increase of cancer remissions »). L’utilisation du français dramatique ici impliquerait une tragédie avec des conséquences très négatives. La traduction française correcte est spectaculaire, « une augmentation spectaculaire des rémissions de cancer ».

Diaphorétique et diaphoretic

Assez technique et subtil, mais sémantiquement et médicalement important. L’adjectif français diaphorétique signifie uniquement « qui fait transpirer », tandis que l’adjectif anglais diaphoretic signifie également « transpirer excessivement », tant pour une personne que pour une peau.

Adhésion (thérapeutique) et (medical) adherence

En anglais, l’adherence d’un patient est le respect scrupuleux d’un traitement, y compris la posologie des médicaments, le calendrier d’administration et toute autre mesure prescrite. Elle est traduite par le français observance. Alors qu’en français, l’adhésion thérapeutique correspond à l’anglais concordance lorsque le patient est d’accord avec le choix fait et les décisions prises par le personnel de santé, et qu’il devient un participant actif de son traitement. Notez qu’en français, adhésion et adhérence sont utilisées dans des contextes différents.

Affecter et to affect

L’anglais to affect, qui signifie avoir un effet sur quelque chose, est (devrait être) traduit par influer sur. Le français affecter signifie adopter, prétendre si l’on parle de l’attitude d’une personne, et présenter si l’on parle des caractéristiques d’une chose.

Fastidieux et fastidious

En français, fastidieux présente une connotation négative, décrivant quelque chose de répétitif et d’ennuyeux. La traduction anglaise est tedious. Au contraire, en anglais, fastidious peut avoir une connotation positive, décrivant quelqu’un qui se soucie de la précision et des détails, correspondant au français pointilleux.

Légume et legume

En anglais, un legume est une plante (ou son fruit) appartenant à la famille des Leguminosae, comme les haricots, les pois, les cacahuètes ou les lentilles. La traduction française est légumineuse. En français, un légume est toute plante potagère cultivée pour l’alimentation, correspondant à l’anglais vegetable. En français, un végétal est toute plante, champignon ou algue.

Vocable et vocable

En anglais, un vocable est un énoncé non verbal, tel que « la la la », « Huh », etc. À l’opposé, en français, un vocable est un mot ou une expression dont la sémantique est très précise, parfois contextuelle.

Employé et employee

En anglais, un employee est une personne payée par une autre pour fournir un travail. En français, les employés forment une catégorie de travailleurs dont le travail n’est pas manuel, mais qui n’occupent pas une position de cadre. La traduction française correcte d’employee est salarié.

Idiome et idiom

En français, idiome signifie dialecte. En anglais, un idiom est une expression dont le sens est figuratif. La traduction française d’idiom est idiotisme. En anglais, l’idiotism est la condition rendant idiot.

Criquet et cricket

La traduction française de l’anglais cricket est grillon. Le criquet français est traduit en anglais par grasshopper. En fait, les Français ont volé le nom anglais pour nommer par erreur tous les insectes du sous-ordre Caelifera. Ceux-ci sont divisés en locustes s’ils peuvent former des populations migrantes et en sauteriaux s’ils ne le peuvent pas. Là, où cela devient amusant, c’est que locuste est le latin pour sauterelle. Par conséquent, les criquets français sont divisés en sauteriaux et en sauterelles !

Pétulant et petulant

Petulant en anglais et pétulant en français ont des significations légèrement différentes. L’ancienne dirigeante des libéraux démocrates, Jo Swinson, a été traitée de petulant par un député travailliste. Il voulait dire par là qu’elle avait mauvais caractère, était boudeuse. En français, pétulante signifie dynamique, plein d’énergie, ce qu’était aussi Jo Swinson… peut-être un peu trop.

Some less-known false friends

By Nicolas Gambardella

Everyone knows English-French “false friends” such as actually and actuellement, the second meaning “currently” while the first means “in reality”. But some false friends are rarer or subtler. Below are a few of them that a translator should worry about. I will update the list as I come across new ones.

To address versus adresser

Like the English verb to address, the French verb adresser has many meanings, some of which are shared. “To address a letter” means “adresser une lettre”. “To address someone” means “s’adresser à quelque’un”. However, both also present specific meanings. So beware of false friends. “To address a problem or a question” is translated into “s’occuper d’un problème” or “répondre à une question”. To answer metaphysical questions, one can, in French, “s’adresser à la philosophie”, which in English is translated as “to turn to phylosophy”.

Deception versus déception

In English, a “deception” is a lie, a sham, an action aimed at misleading someone. This meaning has disappeared in French, where a “déception” is the feeling of sadness felt when a hope is not fulfilled. The English translation of “déception” is “disappointment”.

Accord versus accord

In the context of treatment, an accord in French is an assent in English (to give one’s assent). An accord in English is an adherence, a congruence of views with the person prescribing the treatment (opinions are accorded).

Cave versus cave

In French, la cave is a room in the basement, for example for storing wine, and is translated as cellar in English. In English, a cave is a hole in a rocky outcrop, translated as caverne or grotte in French.

Mental versus mental

In anatomy, the English adjective mental refers to the chin (from Latin mentum), as in “mental foramen”. In French, the correct adjective is mentonnier, mental referring to the Latin mens, the mind.

Crane versus crâne

The English crane corresponds to the French grue, whether it is the bird or the machine. The French crâne is translated as skull.

Lunatic versus lunatique

In English, a lunatic person is crazy (loony), while in French a lunatique is someone who changes their opinion on a whim.

Dramatic versus dramatique

In English, dramatic can mean “sudden and striking” and be very positive (e.g., a dramatic increase of cancer remissions). Using the French dramatique here would imply a tragedy with very negative consequences. The proper French translation is spectaculaire, “une augmentation spectaculaire des rémissions de cancer”.

Diaphoretic versus diaphorétique

Quite technical and subtle, but semantically and medically significant. The French adjective diaphorétique only means “that causes perspiration”, while the English adjective diaphoretic also means “perspiring excessively”, both for a person or a skin.

(medical) Adherence versus adhésion (thérapeutique)

In English, a patient’s adherence to treatment is careful compliance with the treatment regimen, including drug dosing, schedule, and other prescribed measures. It is translated by the French observance. While the French adhésion thérapeutique corresponds to the English concordance between the patient and the health care professional when the patient is on-board with the choice made and the decisions taken, and became an active participant in their treatment. Note that in French, adhésion and adhérence are used in different contexts.

To affect versus affecter

The English to affect, meaning to have an effect on something, is (should be) translated by influer sur. The French affecter means to adopt, to pretend if we are talking about a person’s attitude, and to present if we are talking about the features of a thing.

Fastidious versus fastidieux

In French, fastidieux has a negative overtone, describing something repetitive and boring. The English translation is tedious. On the contrary, in English, fastidious may have a positive overtone, describing someone who cares about accuracy and the details, corresponding to the French pointilleux.

legume versus légume

In English, a legume is a plant (or its fruit) belonging to the Leguminosae family, such as beans, peas, peanuts, or lentils. The French translation is légumineuse. In French, a légume is any garden plant cultivated for nutrition, corresponding to English vegetable. In French, a végétal is any plant, mushroom, or alga.

Vocable versus vocable

In English, a vocable is a non-word utterance, such as “la la la”, “Huh”, etc. Now let’s U-turn, in French, a vocable is a word or an expression with very precise, sometimes contextual, semantics.

Employee versus employé

In English, an employee is someone paid by someone else to work for them. In French, employés is a category of workers, whose work is not manual and who are not in a managing position. The correct French translation of employee is salarié.

Idiom versus idiome

In French, idiome means dialect. In English, idiom is an expression with a non-literal meaning. The French version of idiom is idiotisme. In English, idiotism is the condition to be an idiot.

Cricket versus criquet

The French translation of English cricket is grillon. The French criquet is translated into English by grasshopper. In fact, the French stole the English name to mistakenly name all insects of the suborder Caelifera. These are divided into locustes if they can form migrant populations and in sauteriaux if they cannot. Now, the interesting bit is that locuste is the latin name for sauterelle. Therefore, the French criquets are divided in sauteriaux and sauterelles!

Petulant versus pétulant

Petulant in English and pétulant in French have slightly different meanings. The once liberal democrat leader Jo Swinson was called petulant by a labour frontbencher. He meant it as bad-tempered, sulky. In French, pétulant means dynamic, full of energy, which is also what Jo Swinson was… perhaps a bit too much.

Événement indésirable, effet indésirable, effet secondaire

Par Nicolas Gambardella

[English version]

Comme nous l’a montré le déluge de communication autour des vaccins contre la covid-19, la terminologie de pharmacovigilance (le suivi de la sécurité des médicaments, à savoir leur innocuité et leur tolérabilité) peut entraîner de la confusion, voire nourrir les acteurs de la désinformation. l’Organisation mondiale de la santé (OMS) fournit des définitions claires de termes précis qui sont malheureusement souvent détournés de leur sens premier.

Les événements indésirables (adverse event en anglais) recouvrent tout ce dont souffrent les personnes dans les périodes suivant l’administration d’un traitement (qu’il soit prophylactique ou thérapeutique). Les périodes concernées peuvent varier très largement. Un des principaux outils de pharmacovigilance est le recueil des signalements de tels événements indésirable. C’est par exemple le rôle du VAERS (Vaccine Adverse Event Reporting System) des Centers for Disease Control and Prevention (CDC) et de la Food and Drug Administration (FDA) aux États-unis, de l’ANSM (Agence nationale de sécurité du médicament et des produits de santé) en France, ou encore de la MHRA (Medicines & Healthcare products Regulatory Agency) au Royaume-Uni. La survenue ou l’incidence de ces événements ne sont pas nécessairement liées au traitement. Par exemple, dans les cas des vaccins contre la covid-19, la MHRA répertoriait les chutes, les électrocutions, les morsures d’insectes et les accidents de voitures. Bien que l’incidence de ces événements puisse être affectée par certains médicaments, il est peu probable que ce soit le cas pour des vaccins.

Si l’événement peut engager le pronostic vital, on parle d’événement indésirable grave (serious adverse event en anglais). Rappelons la différence entre sévère et grave (severe et serious en anglais). La sévérité est liée à l’intensité d’un phénomène. La gravité est liée aux conséquences de ce phénomène. Un symptôme ou un signe clinique peut être sévère sans être avoir de conséquences majeurs sur la santé et vice-versa. À noter que la gravité dépend du contexte personnel et environnemental. Selon les antécédents du patient et ses circonstances, un événement peut être bénin ou grave.

Quand l’événement indésirable est prouvé être directement en rapport avec le traitement, qu’il soit entraîné par le traitement lui-même ou par les circonstances de son administration, on parle d’événement indésirable associé aux soins (treatment-emergent adverse event en anglais)

Un effet indésirable (adverse effect ou adverse reaction en anglais) est un événement indésirable directement causé par le traitement. Il est à noter que tous les événements indésirables d’un certain type ne sont pas dus au traitement et donc des effets indésirable. Par exemple, les événements thromboemboliques et les myocardites sont des événements relativement fréquents et qui sont parmi les complications principales de la covid-19. Bien que les vaccins à adénovirus et à ARNm, respectivement, aient montré un accroissement de leur incidence dans certaines populations, des analyses statistiques poussées ont été nécessaires

Un effet secondaire (side effect en anglais) est un effet directement dû au traitement, mais qui n’est pas nécessairement indésirable. Par exemple, l’inhibition de l’agrégation plaquettaire par l’aspirine est utilisée pour prévenir la formation de caillots sanguins.

Adverse event, adverse effect, side effect

by Nicolas Gambardella

[Version en français]

As the deluge of communication around the covid-19 vaccines has shown us, the terminology of pharmacovigilance (the monitoring of drug safety, i.e. safety and tolerability) can lead to confusion and even feed the actors of misinformation. The World Health Organization (WHO) provides clear definitions of specific terms, unfortunately often misused.

Adverse events (événements indésirables in French) are anything that people suffer in the periods following the administration of a treatment (whether prophylactic or therapeutic). The periods involved can vary widely. One of the main tools of pharmacovigilance is the collection of reports of such adverse events. This is, for example, the role of the VAERS (Vaccine Adverse Event Reporting System) of the Centers for Disease Control and Prevention (CDC) and the Food and Drug Administration (FDA) in the United States, of the ANSM (Agence nationale de sécurité du médicament et des produits de santé) in France and of the
MHRA (Medicines & Healthcare products Regulatory Agency) in the UK. The occurrence or incidence of these events is not necessarily related to the treatment. For example, in the cases of covid-19 vaccines, the MHRA listed falls, electrocutions, insect bites and car accidents. Although the incidence of these events may be affected by some drugs, this is unlikely to be the case for vaccines.

If the event is life-threatening, it is called a serious adverse event (événement indésirable grave in French). Remember the difference between severe and serious (sévère et grave in French). Severity is linked to the intensity of a phenomenon. Seriousness is related to the consequences of this phenomenon. A symptom or clinical sign can be severe without having significant implications on health and vice versa. We should note that severity depends on the personal and environmental context. Depending on the patient’s history and circumstances, an event may be mild or severe.

When the adverse event is proven to be directly related to the treatment, whether it is caused by the treatment itself or by the circumstances of its administration, it is called a treatment-emergent adverse event (événement indésirable associé aux soins in French)

An adverse effect or adverse reaction (effet indésirable in French) is an undesirable event directly caused by the treatment. Let’s note that not all adverse events of a particular type are caused by the treatment and are therefore adverse reactions. For example, thromboembolic events and myocarditis are relatively common events and are among the main complications of covid-19. Although adenovirus and mRNA vaccines, respectively, have shown an increased incidence in specific populations, further statistical analysis was required

A side effect (effet secondaire en français) is an effect that is directly caused by the treatment but is not necessarily adverse. For example, platelet aggregation inhibition by aspirin is used to prevent blood clots.

Venous thromboembolic diseases

By Nicolas Gambardella

[Version en français]

We are all familiar with the words ‘blood clots’, ‘stroke’ and ‘heart attack’. However, before the media deluge devoted to the extremely rare side effects of certain COVID-19 vaccines, few outside the medical community had heard of thromboembolic events.

The central player in the drama is the thrombus, also known as blood clot. The blood clot is the product of coagulation. The formation of a clot stops a haemorrhage when the blood vessel wall is damaged. The first step is forming a platelet plug formed by the aggregation of platelets, or thrombocytes. The thrombus is then consolidated by strands of fibrin.

A thrombus can block vessels, especially if they are already narrowed, for example in atherosclerosis. This thrombosis impedes blood flow. Thrombosis occurs mainly when the blood flow is slow and steady (otherwise, the clots are torn off). This is why they are primarily found in the veins, forming deep vein thrombosis, also called deep phlebitis, or superficial thrombophlebitis.

A thrombus can break off, forming an embolus that travels through the vessels following the blood flow. If the vessels become smaller, the embolus is more likely to block them. Such an embolism decreases the blood supply downstream, depriving the tissues of oxygen, something called ischaemia, leading to tissue necrosis or infarction.

In the veins, oxygen-deprived blood flows from the small vessels to the large vessels. Therefore, if a clot breaks loose, it does not block the downstream vessels and travels to the heart. It is then sent by the heart into the pulmonary artery. This artery, in turn, splits into smaller and smaller branches, and the clot can then block the circulation. This is a pulmonary embolism. Deep vein thrombosis and pulmonary embolism are two manifestations of venous thromboembolism or phlebitis.

In the arteries, blood flow is rapid and pulsating. As a result, arterial thrombosis is quite rare. However, as the circulation moves from large to small vessels, embolisms are common. The most common examples are coronary artery embolisms, causing destruction of the heart muscle, a myocardial infarction, and cerebral artery embolisms causing cerebral infarction, one of two types of stroke – the other being cerebral haemorrhage.

This brings us to a very rare complication of COVID-19 vaccination with adenovirus vector vaccines such as Vaxzevria from Oxford University and AstraZeneca, and Ad26.COV2.S from Janssen. This complication is called “vaccine-induced prothrombotic immune thrombocytopenia (VITP)”. Indeed, in extremely rare cases, these vaccines induce antibodies to recognise the protein “platelet factor 4“, which activates platelets and causes their aggregation, leading to thrombosis.

Let’s reiterate that these cases are extremely rare, and their incidence is much lower than that observed after infection with SARS-CoV-2, thromboembolic events being one of the main complications of COVID-19.

Les maladies thromboemboliques veineuses

Par Nicolas Gambardella

[English version]

Nous sommes tous familiers des mots « caillots sanguins », « AVC » et « infarctus ». Cependant, avant le déluge médiatique consacré aux effets secondaires extrêmement rares de certains vaccins contre la covid-19, bien peu en dehors de la communauté médicale avaient entendu parler des événements thromboemboliques.

L’acteur central du drame est le thrombus, aussi appelé caillot sanguin. Le caillot sanguin est le produit de la coagulation sanguine. La formation d’un caillot permet d’arrêter une hémorragie lorsque la paroi d’un vaisseau sanguin en endommagée. La première étape est la formation d’un clou plaquettaire formé par l’agrégation de plaquettes, ou thrombocytes. Le thrombus est ensuite consolidé par des brins de fibrine.

Un thrombus peut bloquer les vaisseaux, en particulier s’ils sont déjà rétrécis par exemple dans les cas d’athérosclérose. Cette thrombose entrave la circulation sanguine. Les thromboses surviennent principalement lorsque le débit sanguin est lent et régulier (sinon les caillots sont arrachés). C’est pourquoi on les trouve surtout associées aux veines, formant des thromboses veineuses profondes aussi appelés phlébites profondes, ou superficielles, aussi appelées périphlébites.

Un thrombus peut se détacher, formant un embole qui voyage dans les vaisseaux en suivant la circulation sanguine. Si les vaisseaux deviennent plus petits, ces emboles ont plus de chances de les bloquer. Cette embolie diminue l’irrigation en aval, privant les tissues d’oxygène, ce qu’on appelle une ischémie, ce qui entraîne une nécrose des tissus, ou infarctus.

Dans les veines, le sang privé d’oxygène circule des petits vaisseaux vers les grands vaisseaux. De ce fait, si un caillot se détache, ils ne bloquent pas les vaisseaux en aval, et voyage jusqu’au cœur. Il est alors envoyé par le cœur dans l’artère pulmonaire. Cette artère, quant à elle, se divise en branches de plus en plus petites, et le caillot peut alors bloquer la circulation. C’est une embolie pulmonaire. Les thromboses veineuses profondes et les embolies pulmonaires sont les deux manifestations de la la maladie thromboembolique veineuse ou phlébite.

Dans les artères, la circulation sanguine est rapide et pulsée. De ce fait, les thromboses artérielles sont assez rares. En revanche, la circulation allant des gros vaisseaux vers les petits, les embolies sont fréquentes. Les exemples les plus fréquents sont les embolies des artères coronaires, causant une destruction du muscle du cœur, un infarctus du myocarde, et les embolies des artères cérébrales causant un infarctus cérébral, un des deux types d’accidents vasculaires cérébraux (AVC) – l’autre étant constitué par les hémorragies cérébrales.

Ce qui nous amène à une complication très rare de la vaccination contre la covid-19 par des vaccins utilisant des vecteurs adénovirus comme Vaxzevria de l’université d’Oxford et AstraZeneca, et Ad26.COV2.S de Janssen. Cette complication est la « thrombopénie immunitaire prothrombotique induite par le vaccin (TIPIV) ». En effet, dans des cas extrêmement rares, ces vaccins induisent la production d’anticorps reconnaissant la protéine « facteur plaquettaire 4 » qui active les plaquettes et cause leur agrégation, entraînant des thromboses.

Rappelons une fois encore que ces cas sont extrêmement rares, et leur incidence est bien inférieure à celle observée après infection par le virus SARS-CoV-2, les événements thromboemboliques étant une des complications principales de la covid-19.

Merging differential expression and Gene Ontology enrichment in a single plot

By Nicolas Gambardella

I recently came across the package GOplot by Wencke Walter http://wencke.github.io/. In particular, I liked the function GOBubble. However, I found it difficult to customise the plot. In particular, I wanted to colour the bubbles differently, and to control the plotting area. So I took the idea and extended it. Many aspects of the plot can be configured. It is a work in progress. Not all features of GOBubble are implemented at the moment. For instance, we cannot separate the different branches of Gene Ontology, or add a table listing labelled terms. I also have a few ideas to make the plot more versatile. If you have suggestions, please tell me. The code and the example below can be found at:
Main script: plotGODESeq.R
Demo script: usePlotGODESeq.R
DESeq data used by the script: DESeq-example.csv
GO data used by the script: GO-example.csv
Help: README.html

What we want to obtain at the end is the following plot:

The function plotGODESeq() takes two mandatory inputs: 1) a file containing Gene Ontology enrichment data, 2) a file containing differential gene expression data. Note that the function works better if the dataset is limited, in particular the number of GO terms. It is useful to analyse the effect of a perturbation, chemical or genetic, or to compare two cell types that are not too dissimilar. Comparing samples that exhibit several thousands of differentially expressed genes, resulting in thousands of enriched GO terms, will not only slow the function to a halt, it is also useless (GO enrichment should not be used in these conditions anyway. The results always show things like “neuronal transmission” enriched in neurons versus “immune process” enriched in leucocytes). A large variety of other arguments can be used to customise the plot, but none are mandatory.

To use the function, you need to source the script from where it is; In this example, it is located in the session directory. (I know I should make a package of the function. On my ToDo list)

source('plotGODESeq.R')

Input

The Gene Ontology enrichment data must be a data frame containing at least the columns: ID – the identifier of the GO term, description– the description of the term, Enrich – the ratio of observed over expected enriched genes annotated with the GO term, FDR – the False Discovery Rate (a.k.a. adjusted p-value), computed e.g. with the Benjamini-Hochberg correction, and genes – the list of observed genes annotated with the GO term. Any other column can be present. It will not be taken into account. The order of columns does not matter. Here we will load results coming from and analysis run on the server WebGestalt. Feel free to use whatever Gene Ontology enrichment tool you want, as far as the format of the input fits.

# load results from WebGestalt
goenrich_data <- read.table("GO-example.csv", 
                            sep="\t",fill=T,quote="\"",header=T)

# rename the columns to make them less weird 
# and compatible with the GOPlot package
colnames(goenrich_data)[
colnames(goenrich_data) %in% c("geneset","R","OverlapGene_UserID")
] <- c("ID","Enrich","genes")

# remove commas from GO term descriptions, because they suck
goenrich_data$description <- gsub(',',"",goenrich_data$description)

The differential expression data must be a data frame in which rownames are the gene symbols, from the same namespace as the genes column of the GO enrichment data above. In addition, one column must be namedlog2FoldChange, containing the quantitative difference of expression between two conditions. Any other column can be present. It will not be taken into account. The order of columns does not matter.

# Load results from DESeq2
deseq_data <- read.table("DESeq-example.csv", 
                         sep=",",fill=T,header=T,row.names=1)

Now we can create the plot.

plotGODESeq(goenrich_data,deseq_data)

The y-axis is the negative log of the FDR (adjusted p-value). The x-axis is the zscore, that is for a given GO term:

(nb(genes up) – nb(genes down))/sqrt(nb(genes up) + nb(genes down))

The genes associated with each GO term are taken from the GO enrichment input, while the up or down nature of each gene is taken from the differential expression input file. The area of each bubble is proportional to the enrichment (number of observed genes divided by number of expected genes). This is the proper way of doing it, rather than using the radius, although of course, the visual impact is less important.

Choosing what to plot

The console output tells us that we plotted 1431 bubbles. That is not very pretty or informative … The first thing we can note is that we have a big mess at the bottom of the plot, which corresponds to the highest values of FDR. Let’s restrict ourselves to the most significant results, by setting the argument maxFDR to 10-8.

This is better. We now plot only 181 GO terms. Note the large number of terms aligned at the top of the plot. Those are terms with an FDR of 0. The Y axis being logarithmic, we plot them by setting their FDR to a tenth of the smallest non-0 value. GO over-representation results are often very redundant. We can use GOplot’s function reduce_overlap by setting the argument collapse to the proportion of genes that needs to be identical so that GO terms are merged in one bubble. Let’s use collapse=0.9 (GO terms are merged if 90% of the annotated genes are identical).

Now we only plot 62 bubbles, i.e. two-third of the terms are now “hidden”. Use this procedure with caution. Note how the plot now looks distorted towards one condition. More “green” terms have been hidden than “red” terms.

The colour used by default for the bubbles is the zscore. It is kind of redundant with the x-axis. Also, the zscore only considers the number of genes up or down-regulated. It does not take into account the amplitude of the change. By setting the argument color to l2fc, we can use the average fold change of all the genes annotated with the GO term instead.

Now we can see that while the proportion of genes annotated by GO:0006333 that are down-regulated is lower than for GO:0008380, the amplitude of their average down-regulation is larger.

WARNING: The current code does not work if the color scheme chosen for the bubbles is based on a variable, l2fc or zscore, that do not contain negative and positive values. Sometimes, the “collapsing” can cause this situation, if there is an initial unbalance between zscores and/or l2fc. It is a bug, I know. On the ToDo list …

Using GO identifiers is handy and terse, but since I do not know GO by heart, it makes the plot hard to interpret. We can use the full description of each term instead, by setting the argument label to description.

Customising the bubbles

The width of the labels can be modified by setting the argument wrap to the maximum number of characters (the default used here is 15). Depending on the breadth of values for FDR and zscore, the buble size can be an issue, either by overlapping too much or on the contrary by being tiny. We can change that by the argument scale which scales the radius of the bubbles. Let’s fix it to 0.7, to decrease the size of each bubble by a third (the radius, not the area!).

There is often a big crowd of terms at the bottom and centre of the plot. This is not so clear here, with the harsh FDR threshold, but look at the first plot of the post. These terms are generally the least interesting, since they have a lower significance (higher FDR) and mild zscore. We can decide to label the bubbles only under a certain FDR with the argument maxFDRLab and/or above a certain absolute zscore with the argument minZscoreLab. Let’s fix them to 1e-12 and 2 respectively.

Finally, you are perhaps not too fond of the default color scheme. This can be changed with the arguments lowCol, midCol, highCol. Let’s set them to  “deepskyblue4”, “#DDDDDD” and “firebrick”,

Customising the plotting area

The first modifications my collaborators asked me to introduce were to centre the plot on a zscore of 0 and to add space around so they could annotate the plot. One can centre the plot by declaring centered = TRUE (the default is FALSE). Since our example is extremely skewed towards negative zscores, this would not be a good idea. However, adding some space on both sides will come in handy in the last step of beautification. We can do that by declaring extrawidth=3 (default is 1).

The legend position can be optimised with the arguments leghoffset and legvoffset. Setting them to {-0.5,1.5}

plotGODESeq(goenrich_data,
            deseq_data,
            maxFDR = 1e-8,
            collapse = 0.9,
            color="l2fc",
            lowCol = "deepskyblue4",
            midCol = "#DDDDDD",
            highCol = "firebrick",
            extrawidth=3,
            centered=FALSE,
            leghoffset=-0.5,
            legvoffset=1.5,
            label = "description",
            scale = 0.7,
            maxFDRLab = 1e-12,
            minZscoreLab = 2.5,
            wrap = 15)

Now we can export an SVG version and play with the labels in Inkscape. This part is unfortunately the most demanding …