Living organisms are complex systems that emerge
from the fundamental building blocks of life. Systems
Biology is a recent field of science that studies these
complex phenomena at the cellular level (Kitano 2002).
Understanding the mechanisms of the cell is essential
for research and development in several areas such as
drug discovery and biotechnological production. In the
latter, metabolic engineering is used for building mutant
microbial strains with increased productivity of
compounds with industrial interest, such as biofuels
(Stephanopoulos 1998). Using computational models of
cellular metabolism, it is possible to systematically test
and predict the optimal manipulations, such as gene
knockouts, that produce the ideal phenotype for a
specific application. These models are typically built in
an iterative cycle of experiment and refinement, by
multidisciplinary research teams that include biologists,
engineers and computer scientists.
The interconnection between different cellular
processes, such as metabolism and genetic regulation,
reflects the importance of the holistic approach claimed
by the Systems Biology paradigm in replacement of
traditional reductionist methods. Although most cellular
components have been studied individually, the
behavior of the cell emerges from the network-level
interaction and requires an integrative analysis. Recent
high–throughput methods have generated the so- called
omics data (e.g.: genomics, transcriptomics, proteomics,
metabolomics, fluxomics) that have allowed the
reconstruction of biological networks (Palsson 2006).
However, despite the great advances in the area, we are
still far from a whole-cell computational model that is
able to simulate all the components of a living cell. Due
to the enormous size and complexity of intracellular
biological networks, computational cell models tend to
be partial and focused on the application of interest.
Also, due to the multidisciplinarity of the field, these
models are based on several different kinds of
formalisms. Therefore, it is important to develop a
framework with common modeling formalisms, analysis
and simulation methods, that is able to accommodate
different kinds biological networks, with different types
of entities and their interactions, into genome-scale
integrated models. Cells are composed by thousands of
components that interact in myriad ways. Despite this
intricate interconnection it is usual to divide and classify
these networks according to biological function. The
main types of networks are signaling, gene regulatory
and metabolic. Signal transduction is a process for
cellular communication where the cell receives and
responds to external stimuli through signaling cascades
(Gomperts et al. 2009; Albert and Wang 2009). These
cascades affect gene regulation, which is the method for
controlling gene expression, and consequently several
cellular functions (Schlittand and Brazma 2007;
Karlebach and Sgamir 2008). Many genes encode
enzymes which are responsible for catalyzing
biochemical reactions. The complex network of these
reactions forms the cellular metabolism that sustains the
cell’s growth and energy requirements (Steuer and
Junker 2009; Palsson 2006).
The objectives of this work, in the context of a PhD
thesis, consist in re-search and selection of an
appropriate modeling formalism to develop a
framework for integration of different biological
networks, with focus on regulatory and metabolic
networks, and the implementation of suitable analysis,
simulation and optimization methods. To achieve these
goals, it is necessary to resolve many modeling issues,
such as the integration of discrete and continuous
events, representation of network topology, support for
different levels of abstraction, lack of parameters and
model complexity. This framework will be used for the
implementation of an integrated model of E. coli, a
widely used organism for industrial application