Computational Systems Biology TUM WS 2011/12 Lecture 1: Overview 2011-10-20 Dr. Arthur Dong
Things Good To Know Math, Physics, Computer Science (Statistics and Programming) Life Sciences (Biochemistry and Molecular Biology) Bioinformatics (sequence, structure, system) Three pillars roughly equal weight; adjustment possible. Synergistic collaboration among SEM faculties / schools / universities. Appreciate complementary strengths + Acquire complementary ways of thinking. Technical competence + Ability to ask the right questions. All About You What's your background, and why are you here? If you study bioinformatics, ... For those from physical sciences: Where else can you do cutting-edge research that matters so early?! Risk not taking the time to truly understand biology. For those from life sciences: How about finishing an experiment in days rather than months?! Could be a steep learning curve at the beginning.
Course Philosophy and Content Bio or informatics? Computational or biology? Exciting new field (yeast genome 1996) From modeling with DE's to mining patient data – pick your shades of gray! Focus on significant questions in biology and medicine. Theory/tools as means rather than ends. Network-based systems biology. Course Format and Requirement Weekly lecture: cutting-edge research rather than “closed” subject Critical reading of seminal/representative papers on discussed topics Papers != textbooks != Bible Look for both strengths and weaknesses Go beyond – Open questions? Next steps? Apply elsewhere? Final presentation (30-min, in groups of 2-3 students)
“Classical” Biology (up to 1950s) Anatomy – Organs, tissues, cells Mendelian Genetics Evolution of species Then came triumph of reductionism...
“Modern” Biology – Molecular Biology of The Cell Cell Biology Biochemistry Molecular Genetics Molecular Evolution Where does bioinformatics come into the picture? Classically: Protein structure prediction Genomics: Sequence search and comparison Functional genomics and proteomics: Networks and systems
Biology/Chemistry Information AGAGCATGTTGGCCTGGTCCTTT GCTAGGTACTGTAGAGCAGGTGA GAGAGTGAGGGGGAAGGACTCCA AATTAGACCAGTTCTTAGCCATGA AGCAGAGACTCTGAAGCCAGACT ACCTGGGTCCCAATCTTGGGCTT GGTATTTCCTCGCTGTGTGACTCT GGGTAAGTTACTTAACTTCTCTGT GCCTCAGTTCTCTCAAGTGTAAAG TGACGCTTGTAAAAGTGTCTCCTG CAAAAGAAAGGGCTGCTGGGAGG AGGGGTGTCCCTGGTGTGCACTA AGTACAATATGAGTTTGT … … … Genetic Code MGLSDGEWQLVLNVWGKVEADIP GHGQEVLIRLFKGHPETLEKFDKFK HLKSEDEMKASEDLKKHGATVLTA LGGILKKKGHHEAEIKPLAQSHATK HKIPVKYLEFISECIIQVLQSKHPGD FGADAQGAMNKALELFRKDMASN YKELGFQG
Protein Folding and Structure Prediction Sequence determines structure and structure determines function (roughly!) Challenge: Given target sequence, predict target structure Homology Modelling: Target sequence has a homologous sequence with solved structure 1. Align the two sequences (crucial step) 2. Put target sequence onto homologous structure and “massage” Need at least 40% homology Target Template
What if no close homologous structure? …QNVERLSLRKNHLTSLPASFKRLSRLQYLDLHNNNFKEIPYILT… ?? Threading: •Inverse folding problem •3D profile and pairwise contact potential •Difficulty with multi-domain proteins or those with no clear domain structures
Ab Initio Prediction Molecular Dynamics Physics – throw in electric charge, solvent etc. and minimize the energy function “Logo” Method Assemble library fragments Partial success on small proteins In general computationally prohibitive How does nature work? Bradley et al , Science 2005
(Traditional) Tenets of Molecular Biology One gene, one protein, one function (or disease) Protein sequence determines structure, structure determines function 2 nd . str. pred. Success! Coiled coils Beta barrels ? Protein Folding Intrinsic disorders … … Partial success on small proteins, but dead end? How does nature work?
Genomics – Producing the “Parts List” Large-scale sequencing of genomes and the resulting data explosion Sequence Comparison: Given a query, find “similar” sequences among tens of millions in databases – fast! Align a group of related sequences; identify conserved residues or regions for structure or function prediction. Cluster sequences according to different features. String comparison algorithms and machine learning (regression, clustering, hidden Markov models, neural networks, etc.)
Functional Genomics and Proteomics Understanding How Parts Work Individually and Together Genome-wide mRNA expression profiling; synthetic lethal screening Proteome-wide Yeast-2-hybrid screening and co-AP/MS
Protein-Protein Interactions: Stable Complexes
Transient Protein-Protein Interactions Yeast-two-hybrid PPI reconstitutes TF to turn on reporter gene that enables growth on selective media RF A D O RF A D O A D B B B O RF D BD D BD D BD HIS 3 H IS 3 HIS 3
Systems Biology in The “Omics” Era GENOME & EPIGENOME protein-gene interactions PROTEOME protein-protein interactions METABOLOME Biochemical reactions Citrate Cycle
Networks – the central platform of Systems Biology Protein-protein interaction networks Gene-regulatory networks Metabolic pathways Graph theory Statistical mechanics Vertical and Horizontal Data Integration! Differential equations
From Regular Graphs to Complex Networks Favorite graphs • Cliques and bipartites • Trees • Cycles • Lattices Favorite problems • Euler/Hamil. paths • Chromaticity ??? • Graph isomorphism
Random Graphs and the Erdös-Rényi model Construction • Start with N nodes (>>1) • Connect each pair with probability p (<<1) Properties • Node degree k follows Poisson distribution • Short average path length • Low clustering coefficient (=p) Poisson distribution N = 10 p = 0.2 <k> = 1.8
Real-world Complex Networks Communication networks • Telephone lines • Internet • WWW Social networks • Co-authorship • Actor Biological networks • Gene-regulatory • Protein-protein interaction • Metabolic Are real-world complex networks really random? What are the organizing principles behind such networks? How could such networks have evolved?
Network Parameters and Types
Distinct Topology of Viral and Cellular Systems Small power coefficient Low local clustering
Viral Networks Exhibit Much Higher Attack Tolerance Than Cellular Networks Yeast KSHV
Combining Multiple Systems Big Picture Combined viral-host analysis: Need known viral-host interactions! Stelzl et al , Cell 2005 Rual et al , Nature 2005
The Systems Biology of Pathogen-Host Interactions Uetz / Dong et al , Science , 2006 Virus Adopts Cellular Features upon “Infection”
Develop new tools for network and system analysis Severe lack of tools, even for a single complex network! Network Alignment Network Docking Assess impact of coverage and noise on network topology Topology should reflect biology Statics should reflect dynamics
Systems Biology in Medicine – Disease Networks Goh et al , PNAS 2007
Cancer Classification Set Marker (Leukemia) Network Marker (Breast Cancer)
Looking Ahead Classical Biology Molecular Biology Systems Biology Whole Parts Whole Understand how life works Mechanism (and hopefully) treatment for cancer and other diseases Synthetic biology, new materials, new energies Genome Phenome Computational Systems Biology What questions to ask? What stories to tell?
How To Read A Paper Focus: Technical details or the big picture? Within the paper: What's the whole point, the take-home lesson? Why did they do what they did? (historical perspective) Any parts problematic and could be improved? Expected versus unexpected Go beyond the paper: Observation – Question – Hypothesis – Investigation – Application What's the next obvious step? Can I apply the same ideas/techniques in other areas? Turn any question into a project (and possibly a paper)!
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