1. Systems biology integrates existing knowledge from individual experiments into computational models to understand complex biological interactions and functions that cannot be predicted by studying components alone. 2. Systems biology uses "omics" experiments that simultaneously measure many molecular entities to generate big data, which are then analyzed using statistical tools, bioinformatics, and modeling. 3. The goal of systems biology is to discover mechanisms and functions that emerge from biological systems as a whole rather than from individual parts.

1. Improved Medical Education in Basic Sciences
for Better Medical Practicing
ImproveMEd
Systems biology for medicine
I. Introduction to systems biology – How systems work?

2. You probably know what for is this machine …

3. Depending on cutting
piece being used is
possible to get any of
these cuts.
And just by looking at the
cutting pieces you can
predict what would be the
outcome.

4. Can you guess what for is this
machine?

5. The more complex system gets it’s
harder to predict all the possible
functions performed by it.
Imagine the system made out of 100 000 pieces...
per cell…

6. Systems biology is trying to understand how
molecules interact and come together to give rise
to subcellular machinery that form functional
units capable of operations that are needed for
cell, tissue/organ level physiological functions.
Iyengar, 2000s

7. 20.’st century dealed with individual
molecules ( X-ray crystalography)and their
binary interactions (enzyme kinetics)…
1962.

8. What is purpose of insulin
signaling?
Choi & Kim, 2010, skeletal muscle
- ↑Uptake in skeletal muscle & fat
- ↑Glycogen synthesis in muscle
- ↓ Hepatic glucose production
- ↓ lipolysis in adipocytes
Information outside the cell is converted into
series of reactions with a final outcome –
regulation of intracellular processes.
Purpose of insulin signaling is to enhance glucose
uptake and to stimulate cell metabolism !

9. Can we present transduction process mathematically
and compute final products?
If we know the initial concentration of ligand and
receptor and forward and reverse reaction rates we
can compute how product is formed with respect to
time.
Radioligand studies were used to measure rate constant and
concentration of receptor in individual experiments.

10. The signal transduction process
(entire signaling pathway) can be
written as a series of ordinary
differential equations (ODE) used
to represent enzyme reactions,
starting from receptor and
finishing with end products.

11. Bottom-up approach:
individual experiments and
individual discoveries of binary
molecular interaction.
Hypothesis driven studies!
cAMP – Sutherland (1971.)
cAMP dependant protein kinase A – Krebs & Fisher (1992.)
G-proteins - Gilman & Rodbell (1994.)
Β-adrenergic receptors – Lefkowitz & Kobilka (2012.)

12. If entities from one signaling
pathways interact with
entities of another pathway ,
pathways become networks.

13. 1. Systems biology builds on molecular biology,
biochemistry and cell biology and uses already
collected knowledge about biological interactions.
2. Systems biology integrates existing knowledge from
many experiments in computational models .
Purpose is to find functions coming out from complex
interactions not predicted by looking at individual
components (e. g. presence of switch in a signaling
network, robustness…)

14. A switching behavior was discovered
for the first time in MAP-kinase
network using computational
simulation and experimental prove.
Just if EGF levels were above 5 nM for a
100 min, levels of MAPK didn’t came
back to baseline, but remained high.
The switch turning cell from one mode
of operation to another (resting
state/division) was built in a network of
mutualy interacting signaling pathways
(PLCγ-PKC & Ras-Raf-MAPK) Bhalla & Iyengar (1999) Science 283: 381-7.

15. Signaling pathways have motives that resemble
electronic networks e.g. feedback loops. The
feedback loops with positive and negative
regulators operate under wide range of
physiological conditions – oscillate between two
states- what we recognize as flexibility and
robustness.
Bhalla & Iyengar, 2001.
Positive
regulators
Negative
regulators

16. 3. System biology use experiment that measure many
molecular entities simultaneously.
Top-Down approach – a big data and comprehensive
picture that needs special mathematical models,
statistical tools and bioinformatics.
Hypothesis generating studies!

17. One of the first papers using the big data
approach was:
Iyer et al. (1999) The transcriptional program in
the response of human fibroblasts to serum.
Science. 283:83-7
Microarrays were used to simultaneously
measure 9996 elements, representing 8613
human genes in 8-hour time point after serum
treatment. RNA from serum deprived cells were
used to prepare Cy3-labeled cDNA while RNA
from stimulated cells were used to prepare Cy5
labeled cDNA.
Iyer et al. (1999)

18. Molecules:
• DNAs genes + non-coding sequences
• RNAs mRNA, tRNA, rRNA, miRNA, snRNA, scRNA…
• Proteins
• Lipids
• Metabolites

19. OMICS = experimental approach that simultaneously
measures many individual entities at the same time
point and results with the big data.
Often used to compare in between different time
points or different conditions.
Genomics - genes involved in certain physiological
function.
Proteomics – co-expressed genes.
Metabolomics – many metabolites existing
simultaneously in a cell, tissue or organ.

20. Bioinformatics is discipline dealing
with organizing the big data on a
searchable way and extracting the
key data.
The big data are publicly available at sites like: GEO
(mRNA profiling), Target Scan (microRNA), Swiss-Prot
(proteins), OMIM (disease genes), DbGAP (Genome-
wide association studies).

21. The two main approaches are used for computing
from database:
1. generating list of entities that are statistically co-
related in the same base (at that time point and under
these conditions) – e.g. all co-expressed proteins at
starvation
2. generating list of statistically co-related entities
between different bases – e.g. all expressed mRNAs in
Down syndrome.

22. 1. Systems biology builds on biochemistry, molecular biology
and cell biology.
2. Systems biology uses experiments designed in OMICs style.
3. The further analysis of collected big data uses statistical
methods, bioinformatics approach and different types of
modeling.
4. The main goal of system biology is to discover hidden
mechanisms and functions that derive from the system as a
whole and which can not be detected by observing individual
parts.