Systems biology and networks
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Systems biology and networks


Welcome to mooc interactomics course In this
module you have gone through the details of protein microarray platform its work flow
and applications In today’s lecture we will first go through
some of the challenges associated with protein microarray platforms It is essential to get
a perspective of how this high throughput data set can be used to obtain a holistic
understanding of biological systems In today’s lecture we will therefore move from protein
microarrays and see how any proteomics data be it from microarray or other platforms like
mass spectrometry gel based proteomics can be used in a discipline known as systems biology Let us begin with the challenges of array
based proteomics protein and antibody microarrays Protein microarrays is a platform on which
thousands of proteins can be printed As you have seen in the last previous lectures it
provides an important platform for large scale functional analysis of proteins although due
to the high throughput capabilities array based proteomics attracted tremendous attention
in clinical research It has quite a few technological challenges
as well protein array designing difficulties include acquisition arraying and stable attachments
of proteins to array surfaces and detection of interacting proteins Further miniaturization
of assays protein dehydration nonspecific binding unavailability of highly specific
antibodies against all the proteins that comprise the complex proteome and lack of direct correlation
between protein abundance and its activities The complex nature of proteins has posed many
hurdles in the area of array fabrication printing scanning and data analysis DNA microarrays
had laid the foundation in developing experimental and analysis strategies However there is an
enormous scope for innovation and making protein microarrays more robust and user friendly Diversity in protein size spatial location
of proteins in cell post transition modifications and hydrophobic nature of the proteins post
challenges for protein purification maintenance of activity and orientation for functional
assays using protein microarrays Technologies like cell free expression system
have helped to overcome the challenges of high throughput expression and purification
for diverse class of proteins Cell free expression aids in generation of near native configuration
of proteins nucleic acid probable protein arrays and halotag technologies allow the
presentation of hidden or buried epitopes in a protein for development of better assays Many commercial biochips are now available
for the study of post translationally modified proteins Thus protein microarrays have overcome
many of the inherent challenges through innovation in last few years However data analysis continues
to be an area which could be worked on further to establish novel methods for stringent data
analysis There are several challenges associated with
these high throughput technologies however the data from such platforms are indispensable
resource for integrative understanding of living systems This brings us to the last
segment of this module which is known as systems biology System biology is an examination of a biological
entity as an integrated system rather than study of its individual characteristic reactions
and components which is termed as systems biology It is a study of all the mechanism
underlying complex biological processes in the form of its interacting components Systems level understanding require information
from different levels at the gnome transcriptome or proteome level which can then be applied
to understand the complex system and also be applied to different organisms The biological
information is represented by the network of interacting elements and dynamic response
to the perturbations These networks provide insights which cannot be analyzed from the
isolated components of the system The common elements of systems biology include
networks modeling computation and dynamics properties There are different types of biological networks
such as protein protein interaction networks gene regulatory networks protein DNA interaction
networks protein lipid protein other biomolecule network and metabolic networks If you are studying a cell and its systemic
behavior you need to look at the genome its transcriptome profile proteome profile how
protein DNA and different transcription networks are altered protein protein signaling networks
multimeric complexes etc We need to know how they are formed how protein is localized in
the intracellular dynamics and metabolic networks these are the vital entities of studying systems
biology Now systems biology study can be done at different
levels For example to study the complex physiology of human one could look at individual systems
such as respiratory nervous system or other physiological systems Studies can be done
at the intracellular or intercellular level and finally at the molecular level involving
genomics transcriptomics and proteomics So why there is need for systems biology The
study of biology at the system or subsystem level for understanding the biological processes
and network is very much required As you can see in this slide to understand even simpler
system of a cell how it is regulated with the extra cellular space and the cytoplasm
and different other components Examination of structure and dynamics of cellular and
organism function is very much required for understanding of systems rather than characteristics
of isolated parts of the cell or the organism So what is the aim of systems biology To understand
the biology in holistic approach rather than the reductionist approach The systems biology
aims to quantitate the qualitative biological data and provide some level of prediction
by applying different types of computational methods The systems biology approach involves first
of all collection of large experimental data sets and then mathematical models to provide
insight of some significant aspects of data set The simple system biology approach would
involve experiments by adding new data sets which will be used for model constructions
and model analysis and the biological insight derived from these models could be used to
propose new hypothesis The properties of systems are probably more
than just the sum of all of its individual components therefore it is possible that system
may have its own property by applying all the components So what are the different approaches which
have been taken to study systems biology The different approaches of systems biology includes
the model based and data based methods The model based approach involves some prior information
which can be implemented in the models whereas the data based the objective is to find a
new phenomenon The model based relies on computational modeling
and simulation tools whereas the data based methods rely on the omics data sets In model
based systems biology approach it is difficult to build detailed kinetic models but in data
based system the complex relationship among various types of omics information metabolic
path ways and networks can be created Studying systems biology is very challenging
systems biology and biological networks modeling aim to understand the system structure and
function for better understanding of system properties like its robustness as well as
its use for prediction of system behavior in response to the perturbations The reductionist approach involves disintegrating
the system into its component and studying them whereas the integrative approach involves
integrating the study of individual components to form conclusions about the system Even simpler systems such as cell can be linked
with various properties its genome sequences of different molecules intracellular signals
transcription factors different type of sis binding activities the expression profiling
of RNA and proteins and different type of cellular processes What is system biology triangle First of all
the systems information is generated at various level as we have discussed starting from genes
to mRNA to proteins to metabolites or even identifying regulatory motives metabolic path
ways functional modules and different large scale organizations This information has to
be stored and processed and further executed to identify the system level information The systems biology triangle as can been seen
here involves the experimental data sets which could be derived from different types of omic
technologies How the computational analysis can be performed different type of bioinformatics
software and tools and then computational modeling by obtaining some theoretical knowledge The synergistic application of the experiment
theory and technology with modeling to enhance the understanding of biological processes
as whole system rather than the isolated components is termed as systems biology triangle A systems biology triangle the wet lab experiments
or bioinformatics based data analysis could be used to propose a model The model building
as an aid to understand complex system and some hypothesis could be generated which could
be used further to propose more quantitative models or predictive models and also it can
be used for independent techniques for model validation What is systems study First of all the difference
between system study and component should be understood as discussed earlier After generating
the data set and creating the systems biology triangle this information could be used for
understanding the systems in more complex and mechanistic level So what approach one
could take to study about the system extracting and mining the complex and quantitative biological
data Integration and analysis of these data sets
for development of mechanistic mathematical and computational models And validation of
these models by retesting and refining after proposing some hypoxias Different online databases
and repositories are nowadays developed and available for sharing large datasets and various
systems model The systematic approach how molecules act together within the network
of interaction that make up life is defiantly going to be useful to understand the systems
biology Systems study and model building the systems
science includes synthesis modeling concepts analysis Life science disciplines provide
quantative measurements genetic modifications and deriving some hypoxias The information
science enables the visualization the modeling tools and different databases This model building
as an aid to understand the complex system is very useful for system level investigation There are different technologies which have
been employed to study the systems biology obviously we need high throughput data set
which could be derived from microarray platform or RNA deep sequencing different configurations
of mass spectrometry different type of proteomic tools and protein interaction datasets Some of the technologies which are commonly
employed in systems biology could be classified broadly under the following techniques For
genomics the high throughput DNA sequencing methodologies mutation detection using SNP
method For transcriptomics the transcript measurements cam include serial analysis of
gene expression gene chips microarrays and RNA sequencing For proteomics mass spectrometry
two dimension electrophoresis protein chips and yeast 2 hybrids and different structure
proteomic tools such as x ray and nuclear magnetic resonance X ray and NMR are mainly
employed for metabolomic analysis in the field of metabolomics So as you can see here to generate the systems
level information the systems study requires different technologies which could be employed
at different levels in biological systems At genome level by studying different type
of technologies such as high throughput sequencing high throughput arrays transcriptomics transcript
analysis using RNA sequencing and microarrays proteome we have discussed many methodologies Metabolome could be performed using either
by NMR or .. And in phenome studying the images by using NMR method each level of this omic
technologies could be useful for studying systems biology Let us now talk about how to model biological
networks To build a model in systems biology first of all all parts can be generated by
the datasets derived from systems biology approach This system or sub system model can
be generated which could be used for systems model analysis Now this could be applied for
real systems and by applying knowledge using bioinformatics tool it could be applied again
back to the original components which can be used to derive some hypothesis and validation
of these datasets could be performed It will work like a closed loop To build the
models in systems biology information is generated at different level level 1 such as DNA and
gene expression level 2 the intracellular networks level 3 cell cell and transmembrane
signals and level 4 integrated organ level information What are the frameworks required for modeling
schemes Different types of deterministic or stochastic models have been repost The compartmental
variables or individuals or function variables have been studied especially homogenous or
especially explicit models are generated which could be applied in the uniform time scale
or separate time scale This framework could involve single scale
entities or cross scale entities As you can see in this slide this framework requires
different level of information in very complex manner whether it is curation of the databases
how to align this information using bioinformatics tools to generate the predictive models which
could be also developed by using the literature curated datasets or experimental datasets
And finally it could be used to study the system level properties Let us discuss the work flow of mathematical
modeling A paradigm can be proposed based on modify model measure mine Systematic experiments
different type of molecular genetics chemical genetics and cell engineering approaches could
be used to modify and different level of measurements by applying microarrays spectroscopy imaging
microfluidics based approaches from proteomics and genomics which could be used further for
mining which involves bioinformatics databases and data semantics Now these datasets could be used to derive
the model which could be reaction mechanistic statistical or stochastic models Staring from
systematic experiments to reaching and deriving the quantitve models this work flow can be
applied The modeling of probabilistic processes involves
let us say you want to study a biological system so some experiments has to performed
The experimental dataset will be generated from which some statistics could be applied
which can be used for comparison Now different type of models can be generated using simulations
and simulation datasets which can be used for intermediate statistics By comparing these
two types of information and adjusting the parameters one can study the systems and derive
probabilistic processes What are ordinary differential equations and
stochiometric models The quantitive analysis measures and names to make models for precise
kinetic parameters of a systems network component It also uses properties of network connectivity
ODE is a mathematical relation that can be used for modeling biological systems The quantitive
models mostly use ODE to link reactants and products concentrations through the reaction
rate constant To develop the computationally efficient and
reliable models of the underlying gene regulatory networks these ODE models could be used The
stoichiometric model it is a modeling a biological network based on its stoichiometric coefficients
reaction rates and metabolic concentrations So in this lecture we understood the various
challenges associated with the protein array work flow you were introduced to the basic
concept of systems biology which are helping new age researchers to integrate data from
the multi omics resources and pure sciences to understand biological organism systematically
With this foundation concepts we will look at some advanced skills of systems biology
in the next lecture thank you

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