Table of Contents
Bphys/Biol E-101 = HST 508 = GEN224
Bio 101: Genomics & Computational Biology
Intro 1: Today's story, logic & goals
101
acgt
Post- 300 genomes & 3D structures
Discrete Continuous
Bits (discrete)
Defined quantitative measures
Quantitative definition of life?
Complexity definitions
Complexity & Entropy/Information
Why Model?
Which models will we search, merge & check in this course?
Intro 1: Today's story, logic & goals
Elements
Minimal self-replicating units
Self-replication of complementary nucleotide-based oligomers
Why Perl & Mathmatica?
Facts of Life 101
Conceptual connections
Transistors > inverters > registers > binary adders > compilers > application programs
Self-compiling & self-assembling
Minimal Life: Self-assembly, Catalysis, Replication, Mutation, Selection
Replicator diversity
Maximal Life:
Rorschach Test
Growth & decay
What limits exponential growth?
Solving differential equations
(Hyper)exponential growth
Computational power of neural systems
Post-exponential growth & chaos
Intro 1: Today's story, logic & goals
Inherited Mutations & Graphs
Directed Graphs
System models Feature attractions
Intro 1: Today's story, logic & goals
Bionano-machines
Types of Systems Interaction Models
How to do single DNA molecule manipulations?
One DNA molecule per cell
Most RNAs < 1 molecule per cell.
Mean, variance, & linear correlation coefficient
Mutations happen
Binomial frequency distribution as a function of X Î {int 0 ... n}
Poisson frequency distribution as a function of X Î {int 0 ...¥}
Normal frequency distribution as a function of X Î {-¥... ¥}
One DNA molecule per cell
What are random numbers good for?
Where do random numbers come from?
Where do random numbers come from really?
Mutations happen
Intro 1: Summary
PPT Slide
|