Biocomputation Research Group
Research in the Biocomputation Research Group focuses on the
description and analysis of control networks in biological cells. We
are particularly interested in the way organisms change and adapt to
their environment, and, therefore, in the regulation, coordination, and
evolution of processes such as signal transduction and multi-cellular
embryonic development.
SBML and SBGN
 |
Anyone who wishes to investigate the characteristics of any biological network
needs to express the network structure and the properties of its components in a
precise and unequivocal way. The > (SBML) aims
to provide a free and open computer-readable, XML-based format for
representing models of biochemical reaction networks. Together with
teams at the Control and
Dynamical Systems group at Caltech, the Machine Learning
Systems group at NASA JPL (both in Pasadena, CA), and the Systems Biology Institute (Tokyo,
Japan), we are closely involved in the definition, development, and
dissemination of SBML. Many computational tools that are used in the
study of biochemical reaction networks have already adapted SBML as a
standard language for model exchange, storage, and documentation.
We also support the Systems Biology Graphical Notation
(SBGN) in some of the software that we are developing. SBGN is a graphical
visualisation standard for network diagrams in Molecular and Cellular Biology. |
Genetic Regulatory Networks
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Genetic regulatory networks (GRNs) contain and dispense the
information necessary for embryonic development, the information
required for organisms to adapt to their environment, and for numerous
other processes. GRNs are formed by genes that contain the information
required to form transcription factors, proteins that can initiate or
stop the formation of other protein molecules. Stimulation or
suppression of protein formation is very specific: a certain type of
transcription factor will directly affect transcription of, at most, a
few genes, and often only in a particular combination with
transcription factor molecules of different types. Specific parts of
genes are "recognised" by specific transcription factors, and, in
collaboration with Alistair Rust at the Institute for Systems Biology
(Seattle, WA), we attempt to develop methods for the automated
prediction of transcription factor recognition sites and genetic
regulatory network inference. |
Characteristics of Biological Control Networks
|
The structure of biological regulatory networks is deduced
from observed responses to external stimuli, taking into account prior
knowledge of the properties of system components, in a process
sometimes termed solving the inverse problem, or reverse engineering.
Once a model has been constructed, it can be used to predict observable
responses to new stimuli, and be (provisionally) accepted or refuted.
However, these models typically have many components, even more
interactions, and a considerable amount of feedback, so that the
distinction between cause and effect is blurred, and responses are
not at all obvious. If the predictions of a model do not match the
observations, it is often impossible to decide whether the model
structure or its dynamic parameters are to blame for its failure.
Therefore, it is also necessary to study the models themselves. In
collaboration with the Adaptive
Systems and Artificial
Neural Networks groups at UH, we are trying to find ways to limit
and scrutinize 'model-space'. We are also investigating whether we can
apply some of the principles of biological regulation and coordination
to the design and control of artificial embodied adaptive systems, such
as robots or spatially extended neural networks. |
Other projects
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Alumni of the Biocomputation Research Group have worked on
many different projects, some of which are ongoing. Several of these
projects had dedicated websites, which are no longer maintained, but
which may still be of interest. They can be found by following the
Archive link. |
Contact
Dr Maria Schilstra
STRI
University of Hertfordshire
Hatfield, Herts AL10 9AB
UK
T: +44 (0) 1707 284769
F: +44 (0) 1707 284156
E: m.j.1.schilstra @ herts.ac.uk