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A Thing or Two about Systems Biology

A Thing or Two about Systems Biology

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A Thing or Two about Systems Biology

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  1. A Thing or Two about Systems Biology Divya Mistry BCBLab Seminar 10.2.2013

  2. What is Systems Biology? • A systematic study of complex interactions in biological system (wiktionary) • Study of self-organizing behavior • Study of system at cellular level, taking into account interactions between key elements such as DNA and RNA (Qiagen) • Holistic approach to study of biology where we simultaneously monitor all processes as an integrated unit/system (EPA.gov) • There are as many definitions as ways of drawing a border around your “system”

  3. Biology as Information Technology • Biology as observational science • Linear growth • Number of people observing vs. number of things being observed • Biology as information science • Exponential growth • Following the technology curve, with lower-bound on Moore’s law. • Do NOT forget • This is for data collection and processing, NOT data comprehension or our understanding of nature. • Comprehension is still linear; bounded by human observation and verification.

  4. Biology as Engineering • Biology deals with uncertainty • someValue ± someUncertainty • Uncertainty comes from system’s evolution and dynamics • An analog to engineering • Uncertainty has to be so small that it doesn’t matter • The individual parts of system can deteriorate at some rate, but they didn’t self-modify… until recently!

  5. Applications from Both Approaches • BioBricks approach • Take units, and build systems from scratch • Just like Lego®! • Novelty through replacement • JCV’s synthetic organism • M. capricolum to M. mycoides

  6. Systems Biology, Hybrid Approach • Parts that are relatively constant • BioBricks • Parts that are always changing • Novelty by replacement • Dynamic changes in defined boundary A D γ Aγ α C Cβα β B

  7. Systems Biology • Challenge • Given a cell, we can get all the omics • Given all the omics, can we get cell? • Solution step 1 • Interactions between various units of omes. • Defining connections and relationship between layers of omics data.

  8. Metabolome Proteome Infectome Image source: An Introduction to Bioinformatics Algorithms by Jones and Pevzner

  9. Beginnings of Step One • Compare flux changes to expression changes • Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes (Bordel et.al. 2010) • DNA biochem + small DNA templates => de novo chemical oscillator. • Programming an in vitro DNA oscillator using a molecular networking strategy (Montagne et.al. 2011)

  10. Theoretical Work • Cellular Automata (CA) • Simple rules can give rise to extremely complex system (e.g. Conway’s Game, Wolfram’s Rule 30) x(n+1,i) = x(n,i-1) xor [x(n,i) or x(n,i+1)]

  11. Theoretical Work • π-Calculus • Type of process calculus • “Process calculi provide a tool for the high-level description of interactions, communications, and synchronizations between a collection of independent agents or processes” (Wikipedia) • Specifically designed to allow description of concurrent computations where configuration changes during the computation

  12. My Dream for Systems Biology • SimPoint equivalent for Biological systems • “SimSystemsBiology” v0.1alpha • Simulating the interactions between all the omics layers • CA + pi-calculi?

  13. Unique Opportunities for SysBio • Emergent properties • Simple units and interactions give complex systems • We need our termite cathedrals • Digging into mathematics • Founding principles • Find new ways to create better models

  14. Sneak Peek

  15. Sneak Peek

  16. References • DharP.K. (2011) Biology by Design. Nature India Commentary, pub. 28 Feb 2011 • Neumann H, Wang K, Davis L, Garcia-Alai M, Chin J.W. (2010) Encoding multiple unnatural amino acids via evolution of a quadruplet-decoding ribosome. Nature. 18 Mar 2010 • Bordel S, Agren R, Nielsen J (2010) Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes.PLoSComputBiol 6(7). • Montagne K, Plasson R, Sakai Y, Fujii T, Rondelez Y (2011) Programming an in vitro DNA oscillator using a molecular networking strategy. Molecular Systems Biology 7:466 • Gibson D.G. et.al. (2010) Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome. Science 329:5987, pp. 52-56 • Futures in Biotech, Episode 60. http://twit.tv/fib60 • Triangulation, Episodes 7, 9. http://twit.tv/tri • Stephen Wolfram. A New Kind of Science. http://www.wolframscience.com/