About
Articles by Julien
-
Top 3 Takeaways from HFMA 2024 Annual Conference: The Future of Revenue Cycle Automation
Top 3 Takeaways from HFMA 2024 Annual Conference: The Future of Revenue Cycle Automation
By Julien Dubuis
Activity
-
Who doesn't love a custom cocktail? 🥂 Join us steps away from #HFMA2024 at Nym's Fire and Ice Cocktail Party to connect with leaders from across…
Who doesn't love a custom cocktail? 🥂 Join us steps away from #HFMA2024 at Nym's Fire and Ice Cocktail Party to connect with leaders from across…
Liked by Julien Dubuis
-
We're excited to be a sponsor of the #HFMA2024 Annual Conference! Are you attending? The Nym team will be at Booth #800, so be sure to stop by to…
We're excited to be a sponsor of the #HFMA2024 Annual Conference! Are you attending? The Nym team will be at Booth #800, so be sure to stop by to…
Liked by Julien Dubuis
-
Check out Nym's latest whitepaper on the use of Clinical Language Understanding to power autonomous medical coding 💡 This is the most up-to-date…
Check out Nym's latest whitepaper on the use of Clinical Language Understanding to power autonomous medical coding 💡 This is the most up-to-date…
Shared by Julien Dubuis
Experience & Education
Volunteer Experience
-
Director
Club Praxis
- Present 8 years 7 months
Politics
Club Praxis is an independent non-partisan think tank whose mission is to formulate and promote concrete policy reforms for France. Its scope of expertise covers education, research, economy, finance and law. It consists primarily of French expatriates living in New York City. Our recent publications have covered the reform of the French pension system, the reform of French democratic institutions, global financial regulation, the European gas market, and complex issues of diversity in France.
-
Publications
-
Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework
Genetics
The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We…
The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail.
Other authors -
-
Positional information, in bits
PNAS
In a developing embryo, individual cells need to “know” where they are to do the right thing. How much do they know, and where is this knowledge written down? Here, we show that these questions can be made mathematically precise. In the fruit fly embryo, information about position is thought to be encoded by the concentration of particular protein molecules, and we measure this information, in bits. Just four different kinds of molecules are almost enough to specify the identity of every cell…
In a developing embryo, individual cells need to “know” where they are to do the right thing. How much do they know, and where is this knowledge written down? Here, we show that these questions can be made mathematically precise. In the fruit fly embryo, information about position is thought to be encoded by the concentration of particular protein molecules, and we measure this information, in bits. Just four different kinds of molecules are almost enough to specify the identity of every cell along the long axis of the embryo, and we argue that the way in which this information is distributed reflects an optimization principle, maximizing the information available from a limited number of molecules.
Other authors -
-
A simple method for estimating the entropy of neural activity
Journal of Statistical Mechanics Theory and Experiment
The number of possible activity patterns in a population of neurons grows exponentially with the size of the population. Typical experiments explore only a tiny fraction of the large space of possible activity patterns in the case of populations with more than 10 or 20 neurons. It is thus impossible, in this undersampled regime, to estimate the probabilities with which most of the activity patterns occur. As a result, the corresponding entropy—which is a measure of the computational power of…
The number of possible activity patterns in a population of neurons grows exponentially with the size of the population. Typical experiments explore only a tiny fraction of the large space of possible activity patterns in the case of populations with more than 10 or 20 neurons. It is thus impossible, in this undersampled regime, to estimate the probabilities with which most of the activity patterns occur. As a result, the corresponding entropy—which is a measure of the computational power of the neural population—cannot be estimated directly. We propose a simple scheme for estimating the entropy in the undersampled regime, which bounds its value from both below and above. The lower bound is the usual 'naive' entropy of the experimental frequencies. The upper bound results from a hybrid approximation of the entropy which makes use of the naive estimate, a maximum entropy fit, and a coverage adjustment. We apply our simple scheme to artificial data, in order to check their accuracy; we also compare its performance to those of several previously defined entropy estimators. We then apply it to actual measurements of neural activity in populations with up to 100 cells. Finally, we discuss the similarities and differences between the proposed simple estimation scheme and various earlier methods.
Other authors -
-
Accurate measurements of dynamics and reproducibility in small genetic networks
Molecular Systems Biology
Quantification of gene expression has become a central tool for understanding genetic networks. In many systems, the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four…
Quantification of gene expression has become a central tool for understanding genetic networks. In many systems, the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ∼20% of the observed embryo-to-embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross-correlations. We use this analysis to extract gap gene profile dynamics with ∼1 min accuracy. The combination of these new measurements and analysis techniques reveals a twofold increase in profile reproducibility owing to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair-rule genes
Other authors -
Honors & Awards
-
Normalien fellowship
French Ministry of Education
Four year stipend of ~$20,000 / year (awarded to the top 2% of students that take the ENS competitive entrance exam)
Languages
-
French
Native or bilingual proficiency
-
English
Native or bilingual proficiency
-
German
Limited working proficiency
-
Spanish
Elementary proficiency
Other similar profiles
Explore collaborative articles
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
Explore More