STAT 300 Model building

1
STAT 300Model building

• Andrzej Polanski 
• Duncan Hall, Room 2091
• http//www.stat.rice.edu/mathbio/stat300
• polanski_at_stat.rice.edu

2
Textbook

• D. Brown, P. Rothery
• Models in Biology Mathematics, Statistics and
  Computing
• Wiley, 1994

3
Homework, tests

• (50 of the final score) Project work every two
  weeks. Cooperation concerning understanding and
  discussing the material is encouraged. Some
  individual study and elaborating - testing simple
  computer programs, is expected.
• (50 of the final score) 2 tests one in the
  middle of the semester, one in the end of the
  semester. Closed book, one sheet of notes
  admissible, approximately 4 hours.

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Software

• Matlab
• Simulink
• other ?

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Plan

• Introductory comments
• PART I Models for single populations and
  processes
• PART II Modeling interactions
• PART III Selected advanced topics

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Introductory comments

• What is (mathematical) modeling ?
• How do we derive (elaborate) models ?
• What is the benefit of using mathematical models
  ?
• What is computer simulation and what do we use it
  for ?

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What is (mathematical) modeling ?

• a model is a peculiar blend of fact and
  fantasy, of truth, halftruth and falsehood. In
  some ways a model may be reliable, in other ways
  only helpful and at times and in respects
  thoroughly misleading
• (our textbook)

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How do we elaborate models ?

• Hypotheses, principles of conservation, laws of
  physics, chemistry.
• Empirical observations

9
What is the benefit of using mathematical models
?

• Better understanding and verifying biological
  mechanisms
• Predicting future events
• Optimizing control and design actions

10
Computer simulation

• Setting a computer program that would mimic
  reality
• Deterministic and stochastic
• Easy, modeling drawing block diagrams

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What is specific in modeling in biology ?

• Complexity of modeled phenomena
• Extent of simplifications in the models
• Element of randomness

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Research work in modeling

• Model building
• Model learning
• Model understanding
• Applying a model to data
• Model modification

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Models

• Deterministic and stochastic
• Descriptive and mechanistic
• Dynamic and non-dynamic