May 17, 2019
Deriving the Drosophila gap gene system ab-initio from an optimization principle
Date: May 17, 2019 |
3:00 pm –
4:00 pm
Speaker:
Thomas Sokolowski
Location: Mondi Seminar Room 3, Central Building
Early embryogenesis is driven by complex spatio-temporal patterns that
specify distinct cell identities according to their locations in the
embryo. This process is remarkably reproducible, even though it results
from regulatory interactions that are individually noisy. Despite
intense study, we still lack a comprehensive, biophysically realistic
model for at least one biological system that could simultaneously
reproduce quantitative data and rigorously explain the emergence of
developmental precision. Moreover, traditional approaches fail to
provide any insight as to why certain patterning mechanisms (and not
others) evolved, and why they favor some (but not other) parameter
values. We address both questions during Drosophila early development.
Recent work has shown that the gap gene expression patterns in
Drosophila optimally encode positional information. We asked whether one
can mathematically derive the gap gene networkwithout any fitting to
databy maximizing the encoded positional information. We constructed a
generic, biophysically accurate spatial-stochastic model of gene
expression dynamics, where genes respond to morphogen signals and
mutually interact in an arbitrary pattern, and optimized its parameters
for positional information. Firstly, our results show how experimentally
observed precision can be achieved with simple biochemical processes and
within known constraints, such as developmental time or protein/mRNA
numbers. Secondly, we show that multiple optimal solutions exist and
identify their characteristics. Finally, we show that one of the optimal
solutions closely corresponds to the real Drosophila gap gene expression
pattern. To our knowledge this is the first successful ab-initio
derivation of any biological network in a biophysically realistic
setting. Our results suggest that even though real biological networks
are hard to intuit, they may represent optimal solutions to optimization
problems which evolution can find.
specify distinct cell identities according to their locations in the
embryo. This process is remarkably reproducible, even though it results
from regulatory interactions that are individually noisy. Despite
intense study, we still lack a comprehensive, biophysically realistic
model for at least one biological system that could simultaneously
reproduce quantitative data and rigorously explain the emergence of
developmental precision. Moreover, traditional approaches fail to
provide any insight as to why certain patterning mechanisms (and not
others) evolved, and why they favor some (but not other) parameter
values. We address both questions during Drosophila early development.
Recent work has shown that the gap gene expression patterns in
Drosophila optimally encode positional information. We asked whether one
can mathematically derive the gap gene networkwithout any fitting to
databy maximizing the encoded positional information. We constructed a
generic, biophysically accurate spatial-stochastic model of gene
expression dynamics, where genes respond to morphogen signals and
mutually interact in an arbitrary pattern, and optimized its parameters
for positional information. Firstly, our results show how experimentally
observed precision can be achieved with simple biochemical processes and
within known constraints, such as developmental time or protein/mRNA
numbers. Secondly, we show that multiple optimal solutions exist and
identify their characteristics. Finally, we show that one of the optimal
solutions closely corresponds to the real Drosophila gap gene expression
pattern. To our knowledge this is the first successful ab-initio
derivation of any biological network in a biophysically realistic
setting. Our results suggest that even though real biological networks
are hard to intuit, they may represent optimal solutions to optimization
problems which evolution can find.
