activity
20052018
most citedA core genetic module : the Mixed Feedback Loop

69 citations · 207 across the 7 of their papers we have counts for

collaborators

7 papers

physics.bio-ph2018

Numerical parameter space compression and its application to microtubule dynamic instability

Chieh-Ting Hsu, Gary J. Brouhard, Paul François

Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e. are sensitiv…

physics.bio-ph2017★ 18 cited

Untangling the hairball: fitness based asymptotic reduction of biological networks

Félix Proulx-Giraldeau, Thomas J. Rademaker, Paul François

Complex mathematical models of interaction networks are routinely used for prediction in systems biology. However, it is difficult to reconcile network complexities with a formal u…

q-bio.QM2015★ 1 cited

Embryonic Elongation controlled by Graded Cell Fate: A Molecular Dynamics Approach

Anna Mkrtchyan, Bertrand Bénazéraf, Olivier Pourquié +1

Regression of Hensen's node from anterior to posterior is driving the elongation and patterning of avian embryo body. Recent experiments link gradient of presomitic mesoderm cell m…

q-bio.MN2015★ 15 cited

Phenotypic spandrel: absolute discrimination and ligand antagonism

Paul François, Mathieu Hemery, Kyle A. Johnson +1

We consider the general problem of sensitive and specific discrimination between biochemical species. An important instance is immune discrimination between self and not-self, wher…

q-bio.CB2015★ 56 cited

The case for absolute ligand discrimination : modeling information processing and decision by immune T cells

Paul François, Grégoire Altan-Bonnet

Some cells have to take decision based on the quality of surroundings ligands, almost irrespective of their quantity, a problem we name "absolute discrimination". An example of abs…

q-bio.MN2013★ 48 cited

Principles of Adaptive Sorting Revealed by In Silico Evolution

Jean-Benoît Lalanne, Paul François

Many biological networks have to filter out useful information from a vast excess of spurious interactions. We use computational evolution to predict design features of networks pr…