My laboratory is engaged in a long term project to understand how DNA sequence specifies biological form. We are interested not only in the specification of typical form by a typical genome, but also in the effects of variability. Such variability might take the form of genetic variation in a population or intrinsic fluctuations in an individual. These problems touch on issues central to developmental and evolutionary biology, and efforts to solve them have previously led to the development of new branches of mathematics.
We consider these issues in the specific context of segment determination in the fruit fly Drosophila melanogaster, but actively seek collaborations with investigators working on other organisms or with pure theoreticians. The starting point for our own investigations are quantitative data on gene expression, extracted from images of confocally scanned fixed or living embryos. We use this numerical information to find parameter sets for specific models of fundamental processes of gene regulation and pattern formation by means of large scale optimization procedures performed on parallel computers. These models may be specified in terms of DNA sequence or be more coarse-grained. They might take the form of a dynamical system, deterministic or stochastic, or simply be a complex but explicit mathematical function.
Our goal is to use every tool in the toolbox—wet experiments, statistics, computational science, and mathematics—to solve a well focused scientific problem: how does a fly go from DNA sequence to a fate map of presumptive segments at single cell resolution?
Evolution of biological cooperation: an algorithmic approach.
Evolution of biological cooperation: an algorithmic approach. Sci Rep. 2024 01 17; 14(1):1468.
PMID: 38233462
Global repression by tailless during segmentation.
Global repression by tailless during segmentation. Dev Biol. 2024 Jan; 505:11-23.
PMID: 37879494
The effect of mutational robustness on the evolvability of multicellular organisms and eukaryotic cells.
The effect of mutational robustness on the evolvability of multicellular organisms and eukaryotic cells. J Evol Biol. 2023 06; 36(6):906-924.
PMID: 37256290
Robust morphogenesis by chaotic dynamics.
Robust morphogenesis by chaotic dynamics. Sci Rep. 2023 05 09; 13(1):7482.
PMID: 37160971
Fully interpretable deep learning model of transcriptional control.
Fully interpretable deep learning model of transcriptional control. Bioinformatics. 2020 07 01; 36(Suppl_1):i499-i507.
PMID: 32657418
An in silico analysis of robust but fragile gene regulation links enhancer length to robustness.
An in silico analysis of robust but fragile gene regulation links enhancer length to robustness. PLoS Comput Biol. 2019 11; 15(11):e1007497.
PMID: 31730659
Physical implications of so(2, 1) symmetry in exact solutions for a self-repressing gene.
Physical implications of so(2, 1) symmetry in exact solutions for a self-repressing gene. J Chem Phys. 2019 Jul 28; 151(4):041101.
PMID: 31370538
Adaptation, fitness landscape learning and fast evolution.
Adaptation, fitness landscape learning and fast evolution. F1000Res. 2019; 8:358.
PMID: 31656586
Correction: A sequence level model of an intact locus predicts the location and function of nonadditive enhancers.
Correction: A sequence level model of an intact locus predicts the location and function of nonadditive enhancers. PLoS One. 2018; 13(5):e0197211.
PMID: 29734377
Synthetic enhancer design by in silico compensatory evolution reveals flexibility and constraint in cis-regulation.
Synthetic enhancer design by in silico compensatory evolution reveals flexibility and constraint in cis-regulation. BMC Syst Biol. 2017 Nov 29; 11(1):116.
PMID: 29187214