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The Essential Tools for Computational Biology: Bridging Data and Discovery

In today’s life sciences, computation has become pivotal, spurred by the influx of vast biological data across scales. Bob Elde, a professor at the College of Biological Sciences, emphasizes that open access to biological data such as DNA sequences and protein structures propels research and innovation. Historical examples, from Hodgkin-Huxley modeling to ecosystem sciences, illustrate the evolution of computation in biology. This field encompasses various models and techniques, including stochastic processes and simulations, essential for understanding complex biological phenomena.

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The Essential Tools for Computational Biology: Bridging Data and Discovery

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  1. What do biologists need to compute? Bob Elde, Dean College of Biological Sciences and Professor, Department of Neuroscience

  2. “Computation, driven in part by the influx of large amounts of data at all biological scales, has become a central feature of research and discovery in the life sciences.” Bourne, Brenner & Eisen, PLoS Computational Biology 1:1, 2005

  3. “Computational biology thrives on open access to DNA sequences, protein structures and other types of biological data. . .” ibid

  4. Historical examples of computation in biology • Hodgkin-Huxley modeling of membrane potential and action potential • Kinetic analysis of enzymes, receptor/ligand intereactions • Development as the French flag problem • Ecosystem sciences

  5. Converging opportunities • “. . .from molecules to ecosystems.”

  6. Computer Simulation in Biology, Keen & Spain, 1992Table of Contents - Simple Model Equations • Analytical models based on differential equations • Analytical models based on stable states • Estimating model coefficients from experimental data • Planning and problems of programming • Numerical solution of rate equations

  7. Models with mulitiple components, Keen & Spain, 1992 • Kinetics of biochemical reactions • Models of homogeneous populations of organisms • Simple models of microbial growth • Population modles based on age-specific events • Simulations of populution genetics • Models of light and photosynthesis • Temperature and biological activity

  8. Multiple components, Keen & Spain, 1992 - continued • Compartmental models of biogeochemical cycling • Diffusion models • Compartmental models in physiology • Application of matrix methods to simulations • Physiological control systems

  9. Probabilistic models, Keen & Spain, 1992 • Monte Carlo modeling of simple stochastic processes • Modeling of sampling processes • Random walks and related stochastic processes • Markov chain simulations in biology

  10. Supplementary models, Keen & Spain, 1992 • Models of cellular function • Models of development and morphogenesis • Models of epidemics

  11. Computation in the curriculum • Take calculus • Statistics for the disinterested scientist

  12. Genomics - Bioinformatics • Data mining • Pattern recognition

  13. Proteomics • Data mining • Higher order modeling of structures • Pattern recognition

  14. Metabolomics • Flux through all pathways under all conditions

  15. Cellomics • Higher order function • Systems biology

  16. Examples • Center for Cell Dynamics http://raven.zoology.washington.edu/celldynamics/ • Bacterial chemotaxis http://flash.uchicago.edu/~emonet/biology/agentcell/ • Imaging http://images2.aperio.com/aperio/view.apml?cwidth=852&cheight=535&chost=images2.aperio.com&returnurl=http://www.aperio.com/&csis=0

  17. What do we need, here and now? Put the needs of our students first, and everything else will fall into place, almost naturally (after Donald Kennedy, Academic Duty)

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