Antti Niemisto
Postdoctoral Fellow
Institute for Systems Biology
Biography:
Antti Niemisto is a Postdoctoral Fellow at the Institute for Systems
Biology. He received the degree of Master of Science (Engineering)
with Distinction in information technology in 2002 and the degree of
Doctor of Science (Technology) in signal processing in 2006 from
Tampere University of Technology, Tampere, Finland. He has been with
the Department of Signal Processing at Tampere University of
Technology from 1999. He visited The University of Texas M. D.
Anderson Cancer Center in Houston, Texas, USA for six months in
2003-2004, and in the summer of 2006 he visited the Institute for
Systems Biology (ISB) in Seattle, Washington, USA for three months.
Since February 2007 he has held a postdoctoral fellowship at the ISB.
In 2007 the Academy of Finland awarded him a postdoctoral researcher's
project for the years 2008-2010. His research interests include
biomedical image analysis and nonlinear signal and image processing.
He has authored 34 scientific publications, seven of which are in
refereed scientific journals.
Talk Title: Supporting diverse cellular imaging experiments and analyses through the provision of adaptable enterprise software -part II
Abstract: The majority of scientific information systems are targeted towards the end results of research, rather than aiding in the progression of scientific understanding. The reason is that there is a dichotomy that exists between scientific understanding and scientific elucidation: understanding requires the presentation of a formalization of ideas while the elucidation requires the application of intuition and reasoning to extract meaning from a quandary of data. Therefore, to support elucidation, we must develop flexible systems which can be rapidly adapted for new usage, rather than trying to adopt a "one size fits all" solution. Within this talk we illustrate the requirements for such flexibility by discussing examples of systems biology work that has made extensive use of cellular microscopy. This work involves a large number of imaging platforms (including microfluidic devices and confocal microscopy) which were used to study a number of biological phenomena (including peroxisome biogenesis, p-body dynamics, and protein signaling). Automated image analysis is a key component of all these studies. The high-throughput nature of the imaging platforms sets substantial requirements for both the image analysis itself as well as to the collection, storage, and annotation of the image data. To support this work we are developing a loosely coupled enterprise system which is designed to be: adaptable, so that it can support the diverse analyses that commonly undertaken; flexible, to allow for the rapid integration of new (typically high throughput) data sources; and maintainable, as it is built on standards to support interoperability (with different environments, applications, instruments and programming languages). This system uses a middle-out approach, where instead of focusing on interfaces and data models, a strong identity system with associated life cycle services is used. With such a 'middle-out' approach "top-down" data models are not tightly bound to the individual data stores, so that scientists are free to independently develop the required analysis tools ("bottom-up" development).