Veranstaltungsort: Frankfurt Institute for Advanced Studies, Ruth-Moufang-Str. 1, 60438 Frankfurt am Main, Lecture Hall
Am 23.06.2022 um 14 Uhr halten Dr. Thomas Sokolowski (Nachwuchsgruppenleiter CMMS) und CMMS-Doktorand Michael Ramirez Sierra (Arbeitsgruppe Sokolowski) Vorträge zum Thema "Modellierung von Developmental Biology".
Dr. Thomas Sokolowski
Modelling of Developmental Biology
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 particular sets of parameter values.
We address both questions during early fly embryo development. Previous work has shown that the gap gene expression patterns in Drosophila optimally encode positional information. We therefore asked whether one can mathematically derive the gap gene network--without any fitting to data--by maximizing the encoded positional information. To this end, we extended our previous models into a generic, biophysically accurate spatial-stochastic model of gene expression dynamics, where gap genes respond to morphogen input signals and mutually interact in an arbitrary fashion, and optimized its parameters for positional information.
Firstly, our results show how the experimentally observed precision can be achieved with basic biochemical processes and within known resource and time constraints. Secondly, we show that a rich ensemble of optimal solutions exists and systematically analyse its characteristics, finding that some of the optimal solutions closely correspond to the real gap gene expression pattern. While numerous, optimal solutions still constitute a small subset of all possible solutions, and feature common properties.
Finally, we explore a broad range of "mutated" optimal ensembles in which relevant components of the wild-type setting are altered or completely discarded, and systematically map out how this affects the encoded positional information and other relevant pattern properties; this allows us to rationalize the design of the wild-type gap gene system and the possible roles of its specific components. To our knowledge our work provides the first successful ab-initio derivation of a nontrivial 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.
Spatial-Stochastic Model of Cell Fate Decisions in Early Mouse Development
The delicate balance necessary for ensuring reliable specification of cell lineages is an intriguing problem in developmental biology. As an important paradigm in tissue development, the early mouse embryo cell fate decisions have been extensively researched, but the underlying mechanisms remain poorly understood. Current approaches to this problem still primarily rely on deterministic modeling techniques, although stochasticity is an inherent feature of this biological process.
As such, we are developing a multi-scale event-driven spatial-stochastic simulator for emerging-tissue development. We build up new simulation schemes for incorporating suitable tissue-scale phenomena, and we fix
important parameters by using experimental values or numerical optimization to infer biophysically-feasible regimes. We first explore the characteristics of this system in a single-cell setting. We then extend the study to a multi-cellular setting in order to understand how positional information is robustly achieved and preserved. Our latest results indicate a potential signaling mechanism for reliable patterning emergence, despite strong constraints imposed by cell cycles. We are closely exploring how these signals redefine cell fates through the action of auto- and paracrine feedbacks.