[SOUND] Hello,
welcome to this course that will introduce you to
the emerging field of systems biology.
This course can be taken as a stand-alone course.
It will give you a brief overview of this new discipline in biological sciences.
Or it can be taken as a part of a five course series
that over the year can lead to a certificate in systems biology.
So what is systems biology?
Over the past 50 years or so,
we have gained a really good understanding of the various parts or
components of what may for cells, tissues,
and organs startings from genetics and genomics, where the genes of many
organisms including that humans of been fully decoded and sequenced.
To biochemistry, where many proteins have been studied.
For both structure and function and in depth to understand what the proteins
do by themselves, and what they do by interacting with their partners.
To cellamonular biology where we study how proteins are organized
to form sub-cellular systems such as the mitochondria or
nucleus or the cell cytoskeleton to physiology which
can be it is focused on the study of functions of cells,
tissues, organs, and organisms.
There has been a vast amount of knowledge that has been gained or.
This knowledge sort of allows us now to gain a perspective
where one can start a level of genes and
go to the level of functions of the cell tissue and organismal level and
understand how the information in genes are decoded to form proteins.
And how proteins interact with other components of the cellsus lipids and
sugars and nucleotides and so on and
all of these together give rise to cellular tissue in organismal functions.
It is these kind of integrated study that is called systems biology.
So the void itself is a marphis term,
it can mean stems at various levels of biological organization.
So one might have a system at the level of a cell or at the level of a tissue or
organ, or the level of the whole organisms.
We're going back to the other end, it may be sub cellular levels,
like I said before, either mitochondria, or nucleus can also be called systems.
So there isn't one fixed definition of what
systems biology will be or is and
one can,different people can have different perspective of how
the field is growing and
sort of focused on.
In this course I will focus largely on systems biology at the cellular level
in mostly mammalian cells because this provides us a natural segue
into how cells become tissues and organs.
And will allow me towards the end of this course to describe
briefly a systems approach as to the study in medicine and
pharmacology and therapeutics.
So, there are many ways to organize this course and
there is probably is not any one correct or wrong way to do this.
So, I've organized this course to sort of
follow the natural flow in which there really is
biological disciplines sort of grew and came to be.
So the course starts with sort of molecular components by chemistry and
cell biology, how individual components, genes,
proteins, etc., were discovered and their functions analyzed.
And how these were put together to form smaller sub-cellular
systems such as a signalling pathway or a metabolic pathway or
the sort of a cellular machine such as the cytoskeleton or the electrical machinery.
The groups of channels in the cell membrane and how these were studied both
experimentally and how they can be studied computationally using dynamical model.
To sort of both understand function and predict future behavior.
I then move on to developing new, or describing new technologies,
the omics technologies that allow us to study changes in many,
many components at a time.
These kinds of omic technologies, genomic and perdiomics and so
on have become the hallmark or sort of the defining characteristics
of the experimental approaches used in systems biology.
And these technologies allow us to do survey type experiments
where we can study how many components.
So for in the case of gene expression, or
microarray experiments, how levels of many messenger
RNAs change in response to one or more perturbations.
Similarly, perdiomics allows us to study how where many
proteins may change in response to a certain kind of perturbation.
These large numbers and the large datasets that come from
these omix experiments require us to organize these data
in an appropriate manner that can be used for analysis and extract knowledge.
And sometimes there is large data that is now increasingly being called big data.
The field is devoted to their analysis and
organization of such data is called bioinformatics.
These data can be analyzed using other ideas like statistical and
mathematical tools.
And the field of mathematics that deals with this kind of analysis of
large datasets is called graph theory, or network analysis.
And we will study, we will learn how
network analysis has been very useful in understanding
how systems are organized.
Finally, I will end with some brief description of how
system biology, or systems approach is going to
be very useful in the field of medicine and therapeutics.
As this is an online course, there aren't going to be any
experiments to do, and also I will not really have you
run any simulations or do any network or computations right now.
Largely, this is going to be a thinking course rather than a doing course.
But this what I mean is that, I want you to get a sense of how
one can use quantitative reasoning to deal with large data sets.
And how understanding what kinds of mathematical representation
are appropriate for different kinds of biological questions and
subcellular systems.
And how mathematical analysis can provide more deep understanding of how,
Behaviors occur and emerge, and
how one can get predictive value from computational analysis.
This course, of course, will be followed by three other courses,
one focus on experimental technologies in systems biology,
which will really be, if you want to call it, sort of a show and tell
course where we'll describe to you how these various technologies are used and
what they can be used for in the context of systems biology experiments.
And a course on bioinformatics graph the area network analysis and
the course on dynamical modeling.
I hope you will find all of these courses both interesting and useful,
both in understanding contemporary biology and
in bringing in cutting edge technologies to your own
research and caregotes.
So let us go over the key objectives of this course.
As you take the course, you might find
that some part of the course are Simpler or
more difficult depending on what the material is and what your background is.
And you might be wondering why in this sort of material placed
in this particular junction as I said before,
there's no really right wrong way of organizing.
This one can do it in several different ways.
But irrespective of how the material is sequenced, overall
when one puts together the material that I will present to you in this course,
it should satisfy the following course objectives.
One, learn about low-throughput and high throughput ways
by which system components have been identified and studied.
Two, learn about small-scale dynamical models using differential
equations and what these models can tell us about systems behaviors.
Three, learn about network analysis and how it can be used
to obtain an understanding of systems-level organization.
Four, learn how interaction between components
lead to the emergence of systems level properties.
And five, learn about the potential for
systems level reasoning in medicine and pharmacology.
Together I hope this will sort of take you to a understanding of biology or
cell biology moving form components and interactions into
groups are sets of components that are associated with a function and
how these sub cellular functions give rise to cell and tissue level function.