Systems biology proceeds through repeated cycles of experiment and modeling. One way to implement this is reverse engineering, where models are fit to data to infer and analyse regulatory mechanisms. This requires rigorous methods to determine whether model parameters can be properly identified. Applying such methods in a complex biological context remains challenging. We use reverse engineering to study post-transcriptional regulation in pattern formation. As a case study, we analyse expression of the gap genes Krüppel, knirps, and giant in Drosophila melanogaster. We use detailed, quantitative datasets of gap gene mRNA and protein expression to solve and fit a model of post-transcriptional regulation, and establish its structural and practical identifiability. Our results demonstrate that post-transcriptional regulation is not required for patterning in this system, but is necessary for proper control of protein levels. Our work demonstrates that the uniqueness and specificity of a fitted model can be rigorously determined in the context of spatio-temporal pattern formation. This greatly increases the potential of reverse engineering for the study of development and other, similarly complex, biological processes.

SUMMARY: MDWeb and MDMoby constitute a web-based platform to help access to molecular dynamics (MD) in the standard and high-throughput regime. The platform provides tools to prepare systems from PDB structures mimicking the procedures followed by human experts. It provides inputs and can send simulations for three of the most popular MD packages (Amber, NAMD and Gromacs). Tools for analysis of trajectories, either provided by the user or retrieved from our MoDEL database (http://mmb.pcb.ub.es/MoDEL) are also incorporated. The platform has two ways of access, a set of web-services based on the BioMoby framework (MDMoby), programmatically accessible and a web portal (MDWeb). AVAILABILITY: http://mmb.irbbarcelona.org/MDWeb; additional information and methodology details can be found at the web site ( http://mmb.irbbarcelona.org/MDWeb/help.php)

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.

Abstract Summary: FlexServ is a web-based tool for the analysis of protein flexibility. The server incorporates powerful protocols for the coarse-grained determination of protein dynamics using different versions of Normal Mode Analysis (NMA), Brownian dynamics (BD) and Discrete Dynamics (DMD). It can also analyze user provided trajectories. The server allows a complete analysis of flexibility using a large variety of metrics, including basic geometrical analysis, B-factors, essential dynamics, stiffness analysis, collectivity measures, Lindemann's indexes, residue correlation, chain-correlations, dynamic domain determination, hinge point detections, etc. Data is presented through a web interface as plain text, 2D and 3D graphics. Availability: http://mmb.pcb.ub.es/FlexServ; http://www.inab.org Contact: [email protected] or [email protected] Supplementary information: Additional information and methodology details can be found at http://mmb.pcb.ub.es/FlexServ/help.