Ying Xu, Ph.D. Department of Biochemistry and Molecular Biology and

Institute of Bioinformatics, University of Georgia

Computation vs Biologythe past, now and future

What Computation Has Done for Biology

Genome sequencing technologies have and continue to profoundly change biology and related sciences

Enormous amount of information could be derived through genome analyses and even through visualizing genomes

“Shot-gun” sequencing has made it possible to have genomes sequenced in such efficient manner

The un-sung hero for making “shot-gun” sequencing useful is the computational techniques for genome assembly

similar can be said about the roles played by computational techniques in generation of other omic data

• omic: genomic, transcriptomic, proteomic, metabolomic, …

What Computation Has Done for Biology

Computational techniques have enabled omic data generation and utilization!

Numerous bioinformatics tools have been developed to derive functional/structural/interactional information from omic data

What Computation Has Done for Biology

Gene finding, function prediction

Structure prediction, modeling

Molecular Interaction prediction

Biological network prediction, modeling

Cell-level modeling, simulation

Mechanical stress to cell signaling modeling

Modeling of ecosystems

A key role these computational techniques have played is to rapidly narrow down searches for needle in haystacks

What Computation Has Done for Biology

Computational analysis of Plasmodium falciparummetabolism

computational simulations have helped to identify 216 “chokepoints” in this pathway modelamong all 24 previously suggested drug targets, 21 target at the “chokepointsamong the three popular drugs for malaria, they all targeted at the “chokepoints”

Plasmodium causes human malaria

Modeling and simulation have inspired rapid generation of testable hypotheses

a good example is the discovery of disordered proteins

What Computation Has Done for Biology

Computational techniques have substantially sped up the process and expanded the scope of biological research

The sequence-comparison program BLAST has been cited ~70,000 times in the published literature!

it is rare nowadays to have a biology paper not citing any bioinformatics software tools!

The software MolProbity has transformed the field of X-ray and NMR-based structural solution of bio-molecules since its inception in 2003

the average quality of all the solved protein structures in the PDB database has improved at a constant rate on an annual basis due to its application

What Computation Has Done for Biology

Computation vs Biology: NowBased on omic data of cancer vs normal tissues, people can start to predict cancer diagnostic markers in blood/urine

identification of differentially expressed genes/proteins in cancer vsnormal tissuescomputational prediction of proteins that can secret into blood and/or excrete into urineexperimental validation of the predicted biomarkers in blood/urine

21 urine samples of gastric cancer patients vs 21 samples of healthy people

Endothelial lipase (LIPG)

Computation vs Biology: NowBased on our understanding about DNA replication & DNA repair, one could possibly derive evolutionary history from normal to cancer genome

Hence possibly identify key early mutations in cancer genome

Philadelphia and CML

Gleevec

Tighter integrations of biology and computation allow to tackle substantially more challenging biological problems

Computation vs Biology: NowIntegration of sophisticated modeling capabilities and high-performance computing allows to study complex biological systems and behavior

understanding complex relationship between human microbiome and human health; interactions between microbes and plants

Computation vs Biology: NowLarge amounts of biological data allow elucidation of fundamental rules and principles of biological systems

we discovered genomic locations of genes in prokaryotes follow simple rules: gene locations minimize an energy function

This realization enables a new type of genome analysis capability, making previously challenging problems solvable

prokaryotic genomic contig assembly

Computation vs Biology: Now

Challengesneed to train a new generation of biologists with quantitative skills and capable of utilizing large quantities of biological data

require changes in biological curricula at the level of high-school, college and graduate school

need to let the educational leaders understand and recognize this change and the new needs

Future of Computational BiologyComputational biology could lead the way in elucidation of complex biological systems like cancer formation and progression or biosynthesis of plant cell walls

plant cell wall

cancer pathways

Future of Computational Biology

Modeling of human diseases as systems biology problems by taking into consideration of multiple factors

Personalized medicine will be well developed taking into consideration of genomic data and real time omic data, which will involve sophisticated modeling and decision making

Future of Computational BiologyOmic data will be omnipresent in our daily lives, thanks to the next generations of omic techniques

individual genomesreal-time omic data collected at annual physicals like blood chemistry reports now…

A new professional, omic data analysts, will emerge in a big way like IT professionals today, needed everywhere!

hospitals, biotech companies, insurance industry, genome (omic data)-based counseling of various types……

Future of Computational BiologyElucidation of specific rules and principles about individual biological systems will merge and evolve into “Theories of Biology”, which will fundamentally change the empirical nature of biological sciences today

Biology will become a science like physics as Temple Smith predicted 10 years ago; and biological experiments will be guided by theories rather than through trial-and-error

Take-Home MessageComputational biology, coupled with high-throughput omicdata generation, is revolutionizing biology and associated sciences

New flood of biological data about ourselves (e.g., individual genomes) are coming in a big way

We clearly need to prepare our future students and the society adequately

AcknowledgementMy colleagues at CSBL @ UGA

Dr. Juan CuiDr. Xizeng MaoDr. Yanbin Yin

Funding agenciesNSF, DOE, NIH, Georgia Research Alliance

A special issue on “Computational Challenges from Modern Biology” in JCST (2010)

14 articles by leading researchers like Michael Waterman, David Sankoff, Satoru Miyano, ….