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[SOUND] Hello,

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welcome to this course
that will introduce you to

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the emerging field of systems biology.

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This course can be taken
as a stand-alone course.

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It will give you a brief overview of this
new discipline in biological sciences.

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Or it can be taken as a part
of a five course series

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that over the year can lead to
a certificate in systems biology.

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So what is systems biology?

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Over the past 50 years or so,

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we have gained a really good
understanding of the various parts or

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components of what may for cells, tissues,

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and organs startings from genetics and
genomics, where the genes of many

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organisms including that humans of
been fully decoded and sequenced.

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To biochemistry,
where many proteins have been studied.

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For both structure and function and
in depth to understand what the proteins

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do by themselves, and what they do
by interacting with their partners.

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To cellamonular biology where we
study how proteins are organized

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to form sub-cellular systems
such as the mitochondria or

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nucleus or
the cell cytoskeleton to physiology which

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can be it is focused on
the study of functions of cells,

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tissues, organs, and organisms.

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There has been a vast amount of
knowledge that has been gained or.

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This knowledge sort of allows
us now to gain a perspective

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where one can start a level of genes and

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go to the level of functions of the cell
tissue and organismal level and

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understand how the information in
genes are decoded to form proteins.

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And how proteins interact with other
components of the cellsus lipids and

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sugars and nucleotides and so on and

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all of these together give rise to
cellular tissue in organismal functions.

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It is these kind of integrated study
that is called systems biology.

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So the void itself is a marphis term,

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it can mean stems at various
levels of biological organization.

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So one might have a system at the level
of a cell or at the level of a tissue or

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organ, or
the level of the whole organisms.

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We're going back to the other end,
it may be sub cellular levels,

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like I said before, either mitochondria,
or nucleus can also be called systems.

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So there isn't one fixed
definition of what

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systems biology will be or is and

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one can,different people can have
different perspective of how

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the field is growing and

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sort of focused on.

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In this course I will focus largely on
systems biology at the cellular level

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in mostly mammalian cells because
this provides us a natural segue

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into how cells become tissues and organs.

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And will allow me towards the end
of this course to describe

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briefly a systems approach as
to the study in medicine and

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pharmacology and therapeutics.

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So, there are many ways to
organize this course and

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there is probably is not any one
correct or wrong way to do this.

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So, I've organized this course to sort of

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follow the natural flow
in which there really is

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biological disciplines sort of grew and
came to be.

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So the course starts with sort of
molecular components by chemistry and

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cell biology,
how individual components, genes,

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proteins, etc., were discovered and
their functions analyzed.

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And how these were put together
to form smaller sub-cellular

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systems such as a signalling pathway or
a metabolic pathway or

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the sort of a cellular machine such as the
cytoskeleton or the electrical machinery.

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The groups of channels in the cell
membrane and how these were studied both

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experimentally and how they can be studied
computationally using dynamical model.

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To sort of both understand function and
predict future behavior.

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I then move on to developing new,
or describing new technologies,

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the omics technologies that allow
us to study changes in many,

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many components at a time.

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These kinds of omic technologies,
genomic and perdiomics and so

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on have become the hallmark or
sort of the defining characteristics

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of the experimental approaches
used in systems biology.

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And these technologies allow us
to do survey type experiments

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where we can study how many components.

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So for in the case of gene expression, or

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microarray experiments,
how levels of many messenger

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RNAs change in response to one or
more perturbations.

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Similarly, perdiomics allows
us to study how where many

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proteins may change in response to
a certain kind of perturbation.

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These large numbers and
the large datasets that come from

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these omix experiments require
us to organize these data

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in an appropriate manner that can be
used for analysis and extract knowledge.

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And sometimes there is large data that is
now increasingly being called big data.

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The field is devoted to their analysis and

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organization of such data
is called bioinformatics.

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These data can be analyzed using
other ideas like statistical and

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mathematical tools.

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And the field of mathematics that
deals with this kind of analysis of

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large datasets is called graph theory,
or network analysis.

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And we will study, we will learn how

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network analysis has been
very useful in understanding

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how systems are organized.

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Finally, I will end with some
brief description of how

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system biology, or
systems approach is going to

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be very useful in the field
of medicine and therapeutics.

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As this is an online course,
there aren't going to be any

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experiments to do, and
also I will not really have you

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run any simulations or do any network or
computations right now.

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Largely, this is going to be a thinking
course rather than a doing course.

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But this what I mean is that,
I want you to get a sense of how

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one can use quantitative reasoning
to deal with large data sets.

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And how understanding what kinds
of mathematical representation

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are appropriate for different
kinds of biological questions and

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subcellular systems.

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And how mathematical analysis can
provide more deep understanding of how,

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Behaviors occur and emerge, and

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how one can get predictive value
from computational analysis.

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This course, of course,
will be followed by three other courses,

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one focus on experimental
technologies in systems biology,

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which will really be, if you want
to call it, sort of a show and tell

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course where we'll describe to you how
these various technologies are used and

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what they can be used for in the context
of systems biology experiments.

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And a course on bioinformatics
graph the area network analysis and

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the course on dynamical modeling.

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I hope you will find all of these
courses both interesting and useful,

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both in understanding
contemporary biology and

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in bringing in cutting edge
technologies to your own

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research and caregotes.

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So let us go over the key
objectives of this course.

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As you take the course, you might find

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that some part of
the course are Simpler or

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more difficult depending on what the
material is and what your background is.

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And you might be wondering why
in this sort of material placed

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in this particular
junction as I said before,

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there's no really right
wrong way of organizing.

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This one can do it in
several different ways.

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But irrespective of how
the material is sequenced, overall

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when one puts together the material that
I will present to you in this course,

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it should satisfy the following
course objectives.

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One, learn about low-throughput and
high throughput ways

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by which system components have
been identified and studied.

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Two, learn about small-scale
dynamical models using differential

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equations and what these models can
tell us about systems behaviors.

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Three, learn about network analysis and
how it can be used

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to obtain an understanding of
systems-level organization.

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Four, learn how interaction
between components

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lead to the emergence of
systems level properties.

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And five, learn about the potential for

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systems level reasoning in medicine and
pharmacology.

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Together I hope this will sort of take
you to a understanding of biology or

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cell biology moving form components and
interactions into

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groups are sets of components that
are associated with a function and

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how these sub cellular functions give
rise to cell and tissue level function.