Quantitative Biology:

The new Quantitative Biology (QBio) undergraduate major will be a Biology major for students with quantitative minds

The QBio major (development supported by the Howard Hughes Medical Institute, HHMI) will be one of only several such programs nationwide and the first in the University of California system. The interdisciplinary quantitative biology major will be unique in the value placed on seamlessly combining and applying quantitative approaches and skills to biological questions.

Graduating QBio students will be equipped with the rigorous, quantitatively based foundation required of modern biology including mathematical and computational tools and skills that they can apply to solving real-world, open-ended questions in the biological sciences.

They will be able to interpret a mathematical representation of a biological system, evaluate how well a model represents data, create mathematical models of a biological system, critically evaluate mathematical models, and use models to make experimental predictions.

These skills should prepare our graduates to become leaders in cutting edge fields of biology, with applications ranging from medicine to the environment.

QBio Approaches emphasized:

  • How to ask interesting questions of biological relevance from quantitative or computational viewpoints
  • How to address interesting questions of biological relevance utilizing a suite of quantitative and computational approaches
    in conjunction with experimental or empirical approaches.
  • How to merge theory and experiment in biology

QBio Skills emphasized:

  • Quantitative intuition
  • Analytic skills
  • Logic skills
  • Modeling skills
  • Experimental skills
  • Programming skills

Modeling Covid-19 and Other Pandemics:

  • Module 1 – Lecture 1: Quantitative Approaches for Modeling COVID-19 and Other Pandemics
  • Module 1 – Lecture 2: Spatial Models of Disease Spread & Public Health Control Measures
  • Disease Spread Models_Lecture1(video file)

  • Tutorials:

    1. Introduction to MATLAB: MATLABTutorial
    2. Single-neuron models:
      1. Integrate-and-Fire Model Tutorial
      2. Hodgkin & Huxley Model Tutorial
    3. NEURON graphical interface:
      1. Simple Multi-Compartment Neuron Tutorial
      2. Simple Network Tutorial
    4. Network models: Hopfield Network Tutorial
    5. Receptive field analysis: Space Time Receptive Fields Tutorial
    6. Statistical analyses:
      1. Spike Train Statistics Tutorial
        1. Sur_Orientation_SpikeData.mat
        2. Sur_Orientation_Annotation.m
        3. smooth.m
    7. Principal Components Analysis Tutorial