Medicine is undergoing tremendous change. Unlike today, medicine of tomorrow would be pro-active rather than reactive.Medicine would be personalized to individual patient's genome. It would predict, and hence prevent, diseases even before they manifest. Also, this medicine would require active societal participation to bring it from labs to clinics.

1. Sachin Singh Rawat
School of Biotech, GGS IP University
A VISION FOR YOUR MOLECULAR HEALTH

2. Better
measurement
and visualization
Better mathematical
tools to interpret data
Systems
approach
to biology
Systems medicine
P4 Medicine
Predictive
medicine
Preventive
medicine
Personalized
medicine
Participatory
medicine

3. biology as an information science
 Software generates the hardware !!
 Only portion of the program is executed in a
particular cell at a given time. How??
 Two kinds of information characterize all central
mechanisms of life
 Digital information of the genome +
environmental signals
 Information handling structures: DNA, RNA
 Hierarchical levels of information: genes,
proteins, individual phenotype, gene pools,
biome etc

4. systems approach to disease
 The whole is more than the sum of its parts
 Whole - ∑ (parts) = emergent properties
 Diseases are just network perturbations
 Dynamic alteration of expression of
information
 Interactome = Genome + Epigenome +
Transcriptome + Proteome + Metabolome + ….

5. reductionism holism
(systems approach)

6. emerging technologies
 Family genome sequencing
 Proteomics
 Metabolomics
 Single cell analysis
 Imaging
 Induced pluripotent stem cells

7. family genome sequencing
 In the next 10 years or so, individual genome
sequencing would be routine clinical practice
 70% sequencing errors could be identified by using
plausability checks based on Mendelian genetics
 Rare variants would be identified immediately
 Accelerate search for disease-causing genes
 Actionable gene variants would allow specification
of how a patient can improve his health
 Get SNP’ed: know your pre-disposition to diseases

9. proteomics
 20,000 human genes code for millions of
proteins
 Comparing proteome across space and time
would enable new markers for diagnostics and
drug design
 Targeted proteomics: democratization of human
proteins
 Powerful tools in analyzing disease
mechanisms, approaches to identification of
biomarkers

10. metabolomics
 Using stable isotopes to follow the fate and rate of
individual metabolites, the measurement of
metabolite fluxes
 More information on disease progression and
potential adaptive, compensatory physiological and
patho-physiological mechanisms
 MS-based methods to be complemented by pattern-
recognition array-sensors to capture volatile
metabolites in exhaled breath, providing disease-
specific molecular signatures
 E-noses: Non-invasive diagnostic devices

11. single cell analysis
 Microfluidic technologies to study individual
cells at the genomic and the proteomic levels
 Number of discrete cell populations in a
particular tissue or organ
 Identification of cell surface molecules to
categorize discrete populations
 Characterization of disease states by cell
surface molecules

12. imaging
 Spatial and temporal information is critical for
developing reliable disease models
 Would allow identification of actionable
network components
 Need to develop advance high-resolution, high-
content imaging
 Would enable reliable interpretation of
molecular processes in health and disease
 Integrated into molecular diagnostics, medical
decision support systems

13. induced pluripotent stem cells
 First derived by Takahashi and Yamanaka (2006)
 Is a technique for cellular reprogramming
 Will be useful in exploring mechanisms of
disease initiation and progression
 Would reveal many secrets of development
 Will be crucial for curing antigen-specific
autoimmunity and allergies
 Accelerator for regenerative medicine
 Minimal immune response

14. the promise of p4 medicine
 Will quantify wellness and demystify disease
 Each individual will be associated with a virtual
data cloud of billions of data points
 Data cloud will be reduced to simple
hypotheses on health and disease for each
individual
 Quantized self will provide real-time, readily-
digestible insights into optimizing wellness
 Stratification of diseases will revolutionize drug
discovery

15. predictive medicine
 Blood as a window into disease
 Molecular fingerprints of diseases
 Assessment of susceptibility to diseases based
on the genetic makeup (SNPs, ethnicity etc)
 HLA complex is associated with susceptibility
to >50 diseases
 Relative risk = % affected among those with allele
% affected in the general population
 DR2  130X prone to nacrolepsy

16. preventive medicine
 Designing therapeutic, preventive drugs via
systems approaches
 Systems approaches to create vaccines will
transform prevention against infectious
diseases
 Reengineering of disease perturbed networks
at the earliest
 Shift from focus on disease to focus on
wellness

17. personalized medicine
 Different drugs for different genetic subsets of
the population
 Each person would be his/her own control
 Real-time monitoring of single cells to suggest
changes in the therapy being provided
 Need to safeguard against misuse by employers
or insurance companies

18. participatory medicine
 Patient-driven social networks
 Cloud computing and crowd sourcing of medical
data
 Radical changes in medical school curricullum
 Big changes in the Big Pharma
 Emergence of new companies for development
and transfer of technologies involved
 Democratization of medicine
 Adequate healthcare affordable to all

19. systems approach to the prion disease
 To identify DEGs, diseased brain transcriptomes
were subtracted from control transcriptomes at
10 time points across disease progression.
 7400 RNA transcripts, encoded by 1/3rd of mice
genes were altered
 4 major networks, involving 300 DEGs, appeared
to participate in prion disease and the networks
were altered sequentially
 Dynamics of transcriptome alterations explained
pathophysiology of disease

20.  >100 brain-specific mouse transcripts could
be identified, many of them encoding
proteins secreted into blood
 A brain-specific blood fingerprint that could
distinguish for the brain –health from
disease, and in case of disease, the type of
disease– can be developed
“ ….In next 10-15 years, we will have a hand-
held device which would measure around 50
proteins of each of the about 50 major organs
from just nanolitres of blood…. ”

21. reference
Hood, L., Balling, R., Auffray, C., Revolutionizing medicine
in the 21st century through systems approaches,
Biotechnol. J. 2012, 7, 992-1001
TED and other talks by Dr Leroy Hood

22. Thank you 
Google Map of Human Metabolism