Laboratory
of Systems Biology
Systems biology
is a term used very widely in the biosciences and in a variety of contexts.
This variety of viewpoints is illustrative of the fact that systems
biology refers to a cluster of peripherally overlapping concepts rather
than a single well-delineated field. Today the term has widespread currency
and popularity as of 2007. Systems biology can be considered as the
operational protocols used for performing computational modelling to
propose specific testable hypotheses about a biological system, experimental
validation, and then using the newly acquired quantitative description
of cells or cell processes to refine the computational model or theory.
Since the objective is a model of the interactions in a system, the
experimental techniques that most suit systems biology are those that
are system-wide and attempt to be as complete as possible. Therefore,
transcriptomics, metabolomics, proteomics and high-throughput techniques
are used to collect quantitative data for the construction and validation
of models.
The systems
biology approach often involves the development of mechanistic models,
such as the reconstruction of dynamic systems from the quantitative
properties of their elementary building blocks. For instance, a cellular
network can be modelled mathematically using methods coming from chemical
kinetics and control theory. Due to the large number of parameters,
variables and constraints in cellular networks, numerical and computational
techniques are often used. Other aspects of computer science and informatics
are also used in systems biology. These include new forms of computational
model, such as the use of process calculi to model biological processes,
the integration of information from the literature, using techniques
of information extraction and text mining, the development of online
databases and repositories for sharing data and models, approaches to
database integration and software interoperability via loose coupling
of software, databases, and the development of syntactically and semantically
sound ways of representing biological models. (such as the SBML).
Biological
system are multiscale, hybrid dynamical systems; they have both deterministic
and stochastic aspects and they contain discrete and continuous processes.
They are most often only partially observable and controllable. Further,
different parts of the system have different levels of knowledge and
description. These sorts of systems are difficult to simulate and statistically
analyze. The applied mathematical research in the laboratory in keyed
to creating general mathematical tools and algorithms for the analysis
of data and models for such systems. In this context, may be thought
of as the specialization of the applied math research to understanding
the particular structures that turn up in cell biological and biological
network modeling. These range from physical chemical theories for intracellular
transport, to cell mechanical modeling, to stoichiometric network analysis.
This is where the general engineering design principles for cellular
regulation are developed. Computation: When the system dynamics to be
analyzed are too complicated it proves necessary to use computer simulation,
numerical continuation, optimization and estimatation to get results.
Implementing algorithms from the applied math efforts and following
the developed theories, programs and tools may be be created for the
analysis of biological data and static/dynamic analysis of network models.
Centro
Regional de Estudios Genómicos
AUGM-UNLP