improving human health through computational grand...
TRANSCRIPT
Improving human health through computational d h llgrand challenges
• Cellular circuit approaches for diseaseCellular circuit approaches for disease therapeutics
BROADInstitute
• Solving the riddle of genotype to phenotypeg g yp p yp
Cellular circuit approaches for disease therapeutics
Reprogramming
Creating one cell type from another
Exocrine
β‐cell
Motor neuron
Dopa+ neuroniPS cell
Cardiomyocyte
Cortical neuron
Fibroblast
y y
Myoblast
FibroblastOct4, Sox2, Klf4, c‐MycYamanaka, 2006
Cellular (re)programming: Creating one cell type from another
TransdifferentiationExocrine
β‐cell
Motor neuron
Dopa+ neuronES cell
Ascl1, Nurr1, Lmx1a
Ngn2, Isl1, Lhx3Eggan, 2011
Cardiomyocyte
Cortical neuronAscl1, Brn2, Myt1lWernig, 2010 (iN)
Broccoli, 2011
Fibroblast
y y
Myoblast Gata4, Mef2c, Tbx5Srivastava, 2010
Wernig, 2010 (iN)
FibroblastFibro Blood progenitorOct4, CytokinesBhatia, 2010
Fibro HepatocyteHnf4alpha, Foxa1/2/3Suzuki, 2011
Reprogramming factors, orTerminal selectors
Computational challenge: resolve the structure f i TF bi diof programming TF binding events
How many binding events are here?
How close to the actual bound bases are event predictions?
Discovering genome grammars that direct cell fate
A DIFFICULT TIME FOR THE HUMAN GENOME
New high‐throughput methods for the genotype to phenotype bl h l l t di i t ti
• Analyze genetic basis of l d l
problem help locate disease causing mutations
complex traits in a model system
• Collect high‐throughput genetic data from experimentaldata from experimental populations with novel pooled sequencing designs
• Use computational methods pthat adapt to changing sequencing depth and divergence and optimally combine information across locicombine information across loci
• Refine computational predictions with known annotations, targeted validationannotations, targeted validation experiments, and individual genotypes
What is computational biology?
• The development of novelmethods and algorithms that
What is computational biology?
p gcan solve key open questions in biology; It is not data wrangling or routine statistics.It i d t i b t b b th• It is good computer science, because we must be both creative and principled in the way we solve new computational problems. We do work in algorithms, machine learning, and systems.
• It is good biology, because we discover new model structures that match biological mechanismsstructures that match biological mechanisms.
• We are scientific equals in highly collaborative projects.
Who does computational biology at CSAIL?
• Prof. Bonnie Berger
Who does computational biology at CSAIL?
– Networks, genomics, structural biology• Prof. David Gifford
– Regulatory networks and development; genotype‐to‐phenotype• Prof. Polina Golland
– Imaging of biological processes• Prof. Tommi JaakkolaProf. Tommi Jaakkola
– New machine learning approaches to biological questions• Prof. Manolis Kellis
– Genomics and regulatory networks– Genomics and regulatory networks• Prof. Peter Szolovits
– Clinical decision making• Prof Bruce Tidor• Prof. Bruce Tidor
– Molecular and systems analysis of complex biological systems