“Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological systems” (David Baltimore, Nobel Laureate)

2. 
“ Organisms function in an integrated manner-our
senses, our muscles, our metabolism and our minds
work together seamlessly. But biologists have
historically studied organisms part by part and
celebrated the modern ability to study them molecule
by molecule, gene by gene. Systems biology is critical
science of future that seeks to understand the
integration of the pieces to form biological systems”
(David Baltimore, Nobel Laureate)

3. Biology
Biomolecules
RNA
Chemistry
Medicine
DNA
Cells
Networks
Proteins
Engineering
Computers
Mathematics

4. Variomics
Transcriptomics
Genomics
Phenomics
Proteomics
Metagenomics
Metatranscriptomics

5. biome, CHOmics, cellome, cellomics, chronomics, clinomics,
complexome, crystallomics, cytomics, cytoskeleton, degradomics,
diagnomicsTM, enzymome, epigenome, expressome, fluxome, foldome,
secretome, functome, functomics, genomics, glycomics, immunome,
transcriptomics, integromics, interactome, kinome, ligandomics,
lipoproteomics,
localizome,
phenomics,
metabolome,
pharmacometabonomics,
methylome,
microbiome,
morphome,
neurogenomics, nucleome, secretome, oncogenomics, operome,
transcriptomics,
ORFeome, parasitome, pathome, peptidome,
pharmacogenome, pharmacomethylomics, phenomics, phylome,
physiogenomics, postgenomics, predictome, promoterome, proteomics,
pseudogenome, secretome, regulome, resistome, ribonome, ribonomics,
riboproteomics, saccharomics, secretome, somatonome, systeome,
toxicomics, transcriptome, transcriptomics, translatome, secretome,
unknome, vaccinome, variomics.............
(http://www.genomicglossaries.com/content/omes.asp)

6. Any FOOD for thoughts………..

7. Complicated!!…….Why?
How can we make sense of this complexity?
Can we convey our understanding of this
complexity?

8. Molecular biologybiomolecule structure and
function is studied at the
molecular level
Systems biologyspecific interactions of
components
in
the
biological system are
studied
–
cells, tissues, organs, and
ecological webs
◦ Integrative
approach
in
which
scientists
study
pathways and networks, will
touch
all
areas
of
biology,
including
drug
discovery
Era of Molecular Biology (1953 –2001)
Era of Systems Biology (2001 – ??)

9. Genomics
Transcriptomics
Proteomics
Systems Biology
1990
1995
2000
2005
2010
2015
2020
(http://www.biomedcentral.com/bmcsystbiol/)

10. study
of
an
organism,
viewed
as
an integrated and interacting network of
genes, proteins and biochemical reactions which give
rise to life.
Looking at the whole system rather than at
components, such as sugar metabolism or a cell
nucleus
Completeness is a recent aspect
Mathematics/modelling-essential
Whole>Sum of parts: Give Rise Emerging properties
Properties ‘arise’ from components interaction
(Interactomics!!)

12. 
Details are now clearer…….

13. Top-down and bottom-up
approach
(Bruggeman and Westerhoff, 2006)

14. Challenged Vs. Control
B
O
T
T
O
M
T
O
P
D
O
W
N
U
P
Deductive: From known properties of
components, system functions deduced.
Properties emerge from interaction of the
components.
Inductive: From how the system reacts to
the perturbations. One infers which
components are critical and how the system
may function

15. Omics (the bottom-up approach) focuses on the identification and global measurement of molecular
components. Modeling (the top-down approach) attempts to form integrative (across scales) models of
animal physiology and disease, although with current technologies, such modeling focuses on relatively
specific questions at particular scales, e.g., at the pathway or organ levels. An intermediate
approach, with the potential to bridge the two, is to generate profiling data from high-throughput
assays for biological complexity, interacting active pathways, intercommunicating cell types and
different environments at multiple levels
(Butcher et al., 2004)

16. Basic Science/”Understanding Life”
Predicting Phenotype from
Genotype
Understanding/Predicting
Metabolism
Understanding Cellular Networks
Understanding Cell-Cell
Communication
Understanding
Pathogenicity/Toxicity
“Raising the Bar” for Biologists
“Making Biology a Predictive
Science”

17. 100’s of completed genomes
1000’s of known reactions
10,000’s of known 3D structures
100,000’s of protein-ligand interactions
1,000,000’s of known proteins & enzymes
Decades of biological/chemical know-how
Computational & Mathematical resources
“The Push to Systems Biology”

18. Genomics
Proteomics
Metabolomics
Phenomics
Bioinformatics
Genometrics
Proteometrics
Metabometrics
Phenometrics
Biosimulation

19. Genomics (HT-DNA sequencing)
Mutation detection (SNP methods)
Transcriptomics
(Gene/Transcript
measurement, SAGE, gene chips, microarrays)
Proteomics (MS, 2D-PAGE, protein chips, Yeast-2hybrid, X-ray, NMR)
Metabolomics
(NMR,
X-ray,
capillary
electrophoresis)

20. study of organisms in terms of their
DNA sequences, or 'genomes'

22. Study of total set of transcripts in a
given organism, or to the specific
subset of transcripts present in a
particular cell type

23. (Soares and Valcárcel, 2006)

24. Large-scale
study
of
proteins, particularly their structures
and functions

27. Bioinformatics
Data
Research,
development,
or
application of computational tools
and approaches for expanding the
use
of
biological, medical, behavioural or
health data, including those to
acquire, store, organize, archive, ana
lyze, or visualize such data

28. systematic study of the unique
chemical fingerprints that specific
cellular processes leave behind" specifically, the study of their smallmolecule metabolite profiles.

29. ( Bionaz and Loor, 2008)

30. Genome-wide reconstruction of the regulatory and metabolic
network in a sequenced organism….. Gives only static
information
(Weckwerth, 2011)

31. Sequencing capacity ↑
Number of sequenced genomes↑
Number of SNPs identified ↑
SNP typing capacity ↑
Microarray data ↑
Proteomics data ↑
Metabolomics data ↑
Data in databases ↑
Sequencing costs ↓
SNP genotyping costs ↓
Profiling costs ↓

32. Integromics

34. Information
Omics classes
Genomics
Transcriptomics
Proteomics
Metabolomics
Phenomics
Sequence
Pathways (Reactome)
Structure
APIaPIAPI
Data / DBMS
Expression
Pathways
Bioinformatics
Portal/Publication

36. Integromics approach in the Systems Roadmap
(Sauer et al., 2007)

38. Accessing information using ontologies and web databases that contain models
encoded in ML
The ML ensure that
models are encoded
in a consistent form
and allow simulation
packages to import
the models in a
standard format

39. CGH

40. The functional sequelae of SNPs and the consequences
of alterations in transcript levels can not be routinely
predicted in a comprehensive manner
The amount of protein does not necessarily correlate
with enzyme activity or protein function due to
multiple
posttranslational
modifications
and
compartmentation
Examination of whole cell or tissue extracts are not
necessarily indicative of the physical interactions
between moieties

41. Data management:
Clear indication of the source and context of the
data
Meaningful identifiers (everybody’s proud of their
clever system that nobody else uses)
Accessible data sources
Models / Methods to interpret the data
An honest assessment of the benefits and limits of
various modeling approaches
A realistic assessment of the near-term capabilities of
current modeling approaches.

42. The ability to understand the limits of the
data and models
Complexity of mammalian systems
"As the complexity of the variable increases, it
becomes more important to have a solid model
of what you think you can predict and to then
test it explicitly, rather than less important as
the machine learning enthusiasts would have it"
(Michael Bittner, Tgen)

43. Atomic Scale
0.1 - 1.0 nm
Coordinate data
Dynamic data
0.1 - 10 ns
Molecular dynamics
Molecular Scale
1.0 - 10 nm
Interaction data
10 ns - 10 ms
Interactions
Cellular Scale
10 - 100 nm
Concentrations
Diffusion rates
10 ms - 1000 s
Fluid dynamics

44. Tissue Scale
0.01m - 1.0 m
Metabolic input
Metabolic output
1 s – 1 hr
Process flow
Organism scale
0.01m – 4.0 m
Behaviors
Habitats
1 hr – 100 yrs
Mechanics
Ecosystem scale
1 km – 1000 km
Environmental impact
Nutrient flow
1 yr – 1000 yrs
Network Dynamics

45. If one scale (e.g., protein-protein
interactions) behaves deterministically and
with isolated components, then we can use
plug-n-play approaches
 If
it
behaves
chaotically
or
stochastically, then we cannot
 Most biological systems lie between this
deterministic order and chaos: Complex
systems


46. 



High level of biological organization.
Traits
broader
than
in
human
medicine:
Productivity,
product
quality,
disease
resistance, fertility, behaviour, welfare, footprint
Divergently selected lines that differ quantitatively in
specific traits.
Samples from tissues, blood or other body fluids
(milk) from a large number of animals with welldocumented
management,
and
performance
recordings are available

47. 
Understand underlying mechanisms of complex
traits, and genotype environment-phenotype
relationships

Fill the gap between genotype and phenotype:
‘Deep’ phenotyping.

“predictive biology”; Biomarkers for product
quality or health issues

48. Technology development
Nanotechnology and microfluidic devices
High thoroughput and inexpensive genome sequencing tech.
Improved computational approaches to modeling and
simulation
Advances in basic biological concept
elucidate a catalogue, or “periodic chart” of modules that
cells typically use to perform basic biological processes

49. Practical Applications: targeted prediction and control
P4 Medicine (predictive, preventive,
participatory medicine) : goals-
personalized
and
Stratification of diseases and patient populations for
specific diagnosis and more effective treatment
More rational drug design for improved efficacy and
decreased side effects
Use of genetic information to determine probable
health history and blood biomarker diagnostic tests.
“The blood will become a window into health and
disease”
Restoration of a disease-perturbed network to its
normal state by genetic or pharmacological
intervention

50. Understanding of genes and mechanisms involved in
estrous behavior, estrus regulation and milk or meat
production
Can unveil dynamic cellular networks that provide an
important framework for drug discovery and design. The
future of drug target discovery is going to be
understanding the dynamics of disease-perturbed
networks

51. Targeting bacterial protective pathways that are induced to
remediate reactive oxygen species damage, and in particular
manipulating
the
DNA
damage
repair
pathways, becomes, therefore, one potential approach to
potentiate the effect of antibiotics.
small molecules could be produced that would lead to the
creation of super-Cipro, super-Gentamicin, or super-Ampicillin
Insight into bacterial cell death pathways and protective
mechanisms induced by antibiotics. Network-based analyses
will lead to the development of novel, more effective
antibiotics, as well as ways to enhance existing antibacterial
drugs. These efforts will be critical in our ongoing fight
against antibiotic resistance

52. “Solving the puzzle of complex diseases, from
obesity to cancer, will require holistic
understanding of the interplay between factors
such
as
genetics,
diet,
infectious
agents, environment, behavior, and social
structures.”
(Elias Zerhouni, The NIH Roadmap, Science2003, 302:63- 64)

53. THANK
YOU
“Our brains are wired for narrative,
not statistical uncertainty.”
(Francis Bacon)