HASTE: Hierarchical Analysis of Spatial and Temporal Data

The HASTE project, a SSF-funded project on computational science and big data, takes a holistic approach to new, intelligent ways of processing and managing very large amounts of microscopy images to leverage the imminent explosion of image data from modern experimental setups in the biosciences. One central idea is to represent datasets as intelligently formed and maintained information hierarchies, and to prioritize data acquisition and analysis to certain regions/sections of data based on automatically obtained metrics for usefulness and interestingness.

The project is a collaboration between the Wählby lab (PI),  Hellander lab (co-PI), both at the Department of Information Technology, Uppsala University, the Spjuth lab (co-PI) at the Department of Pharmaceutical Biosciences, Uppsala University,  the Nilsson lab at the Department of Biochemistry and Biophysics at Stockholm University and SciLifeLab, Vironova AB and AstraZeneca AB.

Read more on the project webpage.

 

HASTE is granted 29 MSEK funding from SSF

Our project Hierarchical Analysis of Spatial and TEmporal Data (HASTE) is granted 29 MSEK funding from SSF. The project, with PI Carolina Wählby  and co-PIs Andreas Hellander, Ola Spjuth and Mats Nilsson, will explore new ways to gain insight from massive amounts of spatial and temoral image data through hierarchical analysis models and smart cloud systems for managing data, see http://haste.research.it.uu.se.

Simulation of Stochastic Multicellular Systems

While we have learned a lot about gene regulation and control from single cell models, there is a limit to what can be understood without considering cell-cell interaction. However, there is a fundamental computational gap between detailed models of single cells and models of multicellular systems comprising of large number of interacting cells such as bacterial colonies, tissue and tumors.

We seek to bridge the vast computational gap between quantitative, stochastic models of intracellular regulatory pathways and coarse-level models of multicellular systems. We also engage in development of simulation methodology for modeling specific biological systems toghether with collaborators.

 

Recent publications:

  • Marketa Kaucka, Evgeny Ivashkin, Daniel Gyllborg, Tomas Zikmund, Marketa Tesarova, Jozef Kaiser, Meng Xie, Julian Petersen, Vassilis Pachnis, Silvia K Nicolis , Tian Yu, Paul Sharpe, Ernest Arenas, Hjalmar Brismar, Hans Blom, Hans Clevers , Ueli Suter, Andrei S Chagin, Kaj Fried, Andreas Hellander and Igor Adameyko, (2016) Analysis of neural crest-derived clones reavals novel aspects of facial development, Science Advances 2(8). 

Collaborators:

 

Smart systems for computational experiments

smart_workflow

The integration between on the one hand data, modeling and algorithms, and on the other hand the specification, coordination and execution of large scale and data-intensive computational experiments poses a fundamental problem in all scientific disciplines relying on modeling and simulation. Today it is largely left to the modeler or engineer to manually tune models to fit data, to choose algorithms, to configure simulation workflows and to analyze simulation result. This is a big burden to place on e.g. a biologist who is mainly interested in how she can use modeling and simulation to learn new things about a biological system of interest. By utilizing machine learning and cloud computing, we are developing smart systems for scalable and efficient model exploration. An example of a workflow is shown in the image below, where a high-dimensional parameter sweep application is augmented with automated feature extraction and clustering, followed by training a model for classification based on user-defined labels (such as interesting or non-interesting realizations). With this model, the smart sweep application will learn to more efficiently explore areas of interestingness in the parameter space.  

Software and applied cloud computing

Open source computational science and engineering (CSE) software is an integral part of methodology-oriented computational research and a priority in the group. Due to the ongoing transformation of e-infrastructure to clouds, methods and workflows that promote horizontal scalability and elasticity for cloud applications are needed, and this may in many cases require re-thinking of how we best make use of computational resources. Other important questions include reproducibility and handling of large and complex data. 

Selected recent publications: 

Multiscale simulations of chemical kinetics

A theme in the last decade of computational systems biology has been how molecular noise is a factor that needs to be acc
ounted for, both to understand how gene regulatory networks are able to operate robustly in a noisy molecular environment and to explain phenotypic variability on both the individual cell and population levels. A particularly intriguing question is the interplay between spatial and temporal aspects of intracellular signaling is organized. Numerically, efficient spatial stochastic methods are needed to study this, but they become much more computationally demanding, largely due to the multiscale nature of the pathways and processes. A central area in the group is have the development of hybrid simulation methods for stochastic reaction-diffusion processes.

Recent publications:

Postdoc in Scientific Computing/Applied Cloud Computing

We are looking for a talented individual to join our efforts on creating smart and scalable cloud services to support simulation-driven scientific discovery via large-scale computational experiments such as parameter sweeps. This is a classic and very important problem that we will approach in new ways, leveraging recent advances in cloud computing, data-intensive computing and machine learning. See the full advertisement here (Deadline Sept. 1):

http://www.uu.se/en/about-uu/join-us/details/?positionId=107117

The successful candidate will contribute to the interdisciplinary research group Distributed Computing Applications (DCA) at the Department of Information Technology, Uppsala University.

 

 

try.stochss.org: Try StochSS as a Service

To make it easy to try StochSS, our software for rapid model development and simulation of stochastic regulatory networks, we are now providing it as a service on http://try.stochss.org. To use it, simply follow the link to set up your account.

Since this only require a modern browser and a valid email address (no software installation), we hope that this service will help experienced modelers evaluate the software, and importantly, that it will reduce the barrier for new modelers to explore the possibilities of stochastic simulations in systems biology.

Screen Shot 2016-06-22 at 10.12.36 AM
Screenshot showing volume rendering of a spatial stochastic simulation of a spatial negative feedback loop modeling the Hes1 regulatory network as described further in http://rsif.royalsocietypublishing.org/content/10/80/20120988

After testing StochSS, if you think it will be useful in your research, there are multiple options for you to use it on your own resources. The simplest way to get started is to download the binary package (uses Docker).

Our trial server is deployed in the SNIC Science Cloud. If you would like to provide StochSS as a service for your reserach group or for a distributed collaboration, you can do this easily on your own servers, or in another cloud infrastructure provider such as Amazon EC2. MOLNs, another member of the StochSS-suite of tools, can help you to configure and deploy an identical setup. Please do not hesitate to reach out to us if you need help with this process.

Many of you also like the possibility to work with solvers in a programming environment. All of the tools that are powering StochSS are also available as stand alone libraries:

  • PyURDME (Python API for spatial stochastic modeling and simulation )
  • Gillespy (Python API for well-mixed simulations, based on StochKit2)

In addition, if you have access to cloud infrastructure, and would like to work in a pre-configured environment powered by a Jupyther Notebook frontend and interactive parallel computing, you should check out MOLNs:

  • MOLNs: Cloud platform/orchestration framework for large-scale computational experiments such as ensembles and parameter sweeps,  backed by Jupyther and Ipython Parallel.