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This presentation will
examine systems biology with
a focus on the
foundational principles
of systems biology,
highlighting its
holistic approach to
understanding complex
biological networks.
We will discuss the integration
of diverse OMIS data,
such as genomics,
transcriptomics,
proteomics and metabolomics, and
the computational
modeling techniques
used to interpret
these datasets.
The lecture will contrast
systems biology with
reductionist approaches,
emphasizing the importance of
data integration and
current challenges.
Finally, we will explore
real world applications from
disease mechanism discovery to
precision medicine and
metabolic engineering.
Today, we explore
systems biology,
a field at the
crossroads of biology,
mathematics, engineering,
and computational science.
Unlike the traditional
reductionist approach,
systems biology adopts
a holistic perspective,
examining how
biological components
interact within
complex networks.
By integrating large scale
data with predictive models,
researchers move beyond
cataloging genes and proteins,
aiming to understand
emergent properties
like cellular behavior
and physiological functions that
arise from these interactions.
A cornerstone of
systems biology is
its capacity to integrate
diverse Omix data.
Genomics provides the
blueprint of life with
DNA sequences offering insight
into gene content and structure.
A cell's activity depends on
interpreting this blueprint,
