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Computational Biology

Purpose of Course  showclose

The advent of computers transformed science.  Large, complicated datasets that once took researchers years to manually analyze could suddenly be analyzed within a week using computer software.  Nowadays, scientists can use computers to produce several hypotheses as to how a particular phenomenon works, create computer models using the parameters of each hypothesis, input data, and see which hypothetical model produces an output that most closely mirrors reality.

Computational biology refers to the use of computers to automate data analysis or model hypotheses in the field of biology.  With computational biology, researchers apply mathematics to biological phenomena, use computer programming and algorithms to artificially create or model the phenomena, and draw from statistics in order to interpret the findings.  In this course, you will learn the basic principles and procedures of computational biology.  You will also learn various ways in which you can apply computational biology to molecular and cellular biology, biochemistry, neuroscience, evolution, population biology, and behavior.

This course will prepare students in all subfields of biology for future research and data analysis opportunities.  Computer science students interested in biological applications will also find it useful.

Learning Outcomes  showclose

Upon completion of this course, students will be able to:
  • Define computational biology and provide examples of how it is used.
  • Describe how networks, algorithms, and models are employed in biology.
  • Describe how DNA and proteins are manipulated to generate information from sequences and whole genomes.
  • Describe how biological processes can be modeled using computer programming.
  • Identify techniques for gathering information on proteins and their interactions.
  • Provide examples of the use of mathematics in evolution and behavior.
  • Describe the current applications of computational biology.

Course Requirements  showclose

In order to take this course you must:

√    Have access to a computer.

√    Have continuous broadband Internet access.

√    Have the ability/permission to install plug-ins or software (e.g., Adobe Reader or Flash).

√    Have the ability to download and save files and documents to a computer.

√    Have the ability to open Microsoft files and documents (.doc, .ppt, .xls, etc.).

√    Be competent in the English language.

√    Have read the Saylor Student Handbook.

Unit Outline show close


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  • Unit 1: Introduction to Computational Biology  

    This unit will serve as your introduction to the basic principles and procedures of computational biology.  We will begin by discussing the application of mathematics to biological phenomena and then learn in detail how computers can be used to create and manipulate mathematical models.

    How does a scientist extract variables from a natural process in order to create a predictive mathematical formula and then use that formula to create a computer program through which he can quickly manipulate variables to simulate a variety of circumstances within a complex environment?  You will learn each step of that process here.  Upon completion of this unit, you should have a clear understanding of the process of creating a computational model in biology.

    Unit 1 Time Advisory   show close
    Unit 1 Learning Outcomes   show close
  • 1.1 What is Computational Biology?  
  • 1.1.1 Biology  
  • 1.1.2 Applied Mathematics  
  • 1.1.3 Computer Science  
  • 1.1.4 Statistics  
  • 1.2 Mathematical Treatment of Biology  
  • 1.2.1 Creating a Mathematical Equation  
  • 1.3 Networks in Biology  
    • Reading: MIT: Dr. George Church’s “Networks 1-3”

      Link: MIT: Dr. George Church’s “Networks 1-3” (JWPlayer)

      Instructions: Listen to the three audio lectures titled Networks 1, 2, and 3 from this course on Genomics and Computational Biology from MIT’s OpenCourseWare initiative.  You should download and the view the “Lecture Slides” PDF that accompanies each audio lecture and view it while listening.

      Viewing these lectures should take approximately 4.45 hours.

      Terms of Use: George Church, HST.508, Fall 2002.  (Massachusetts Institute of Technology: MIT OpenCourseWare), http://ocw.mit.edu (Accessed August 28, 2012).  License: Creative Commons BY-NC-SA 3.0.

      See a broken link? Please let us know!

  • 1.3.1 Random Networks  
    • Reading: University of Arizona: Robert May’s “Networks”

      Link: University of Arizona: Robert May’s “Networks” (PDF)

      Instructions: Scroll down the page to the section on “Papers” and look for May: “Network structure and the biology of populations” TREE 21: 394.  This paper discusses several types of networks, including random networks.

      Reading this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 1.3.2 Small-World Phenomenon  
  • 1.3.3 Scale-Free Network Model  
  • 1.4 Algorithms  
  • 1.4.1 Classes of Algorithms  
    • Reading: Connexions’ “Introduction to Algorithms”

      Link: Connexions’ “Introduction to Algorithms” (HTML)

      Instructions: This reading covers subunits 1.4.1.1-1.4.1.4.  Please read the linked material, which describes the different algorithms and how they are classified.  This page also includes a number of links that you should explore to enhance your understanding of the different types of algorithms.

      Reading this material should take approximately 2 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 1.4.1.1 Recursive  

    Note: The reading for this subunit is covered by the material under subunit 1.4.1, “Algorithms.”

  • 1.4.1.2 Logical  

    Note: The reading for this subunit is covered by the material under subunit 1.4.1, “Algorithms.”

  • 1.4.1.3 Distributed  

    Note: The reading for this subunit is covered by the material under subunit 1.4.1, “Algorithms.”

  • 1.4.1.4 Approximated  

    Note: The reading for this subunit is covered by the material under subunit 1.4.1, “Algorithms.”

  • 1.4.2 Graph Drawing  
  • 1.4.2.1 Dijkstra’s Algorithm  
  • 1.4.2.2 Kruskal’s Algorithm  
  • 1.5 Dynamic Programming  
  • 1.5.1 Principle of Optimality  
  • 1.5.2 Optimal Substructure  
  • 1.5.2.1 Recursion  
    • Reading: Wikipedia’s “Recursion”

      Link: Wikipedia’s “Recursion” (HTML)

      Instructions: The article defines recursion and discusses recursion data, programs, and algorithms.

      Reading this material should take approximately 1.15 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 1.5.2.2 Bellman-Ford Algorithm  
    • Reading: CSAnimated’s “Bellman-Ford Algorithm”

      Link: CSAnimated’s “Bellman-Ford Algorithm” (Flash)

      Instructions: This is an animated slide show that describes the algorithm.  It has an audio component associated with it so make sure you are on a computer where you can hear it while viewing the animated slide show.

      Viewing this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 1.5.3 Overlapping Problems  
  • 1.5.3.1 Top-Down Approach  
    • Reading: wordIQ’s “Top Down”

      Link: wordIQ’s “Top Down” (HTML)

      Instructions: Read this page on the top down approach to programming and how it contrasts with a bottom up approach, which is covered in the subsequent unit.

      Reading this material should take approximately 15 minutes.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

      Submit Materials

  • 1.5.3.2 Bottom-Up Approach  
    • Reading: wordIQ’s “Bottom-up”

      Link: wordIQ’s “Bottom-up” (HTML)

      Instructions: Read this page on the bottom-up approach in programming and how it contrasts with a top down approach.

      Reading this material should take approximately 15 minutes.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

      Submit Materials

  • Unit 2: Computational Molecular Biology  

    This unit will deal with molecular biology.  From determining the genetic sequence in a scrap of DNA to creating a 3-D virtual model of a protein, scientists use computer modeling in this field to help us better understand a world far beyond the reaches of the microscope.  Study in molecular biology yields vast amounts of data that must be carefully analyzed and interconnected to construct a realistic picture of this world.  Computers greatly reduce the manual workload and error associated with complex analysis.  In this unit, you will learn how to use computers in order to better understand DNA sequencing, transcriptional regulation, protein structure, and protein interactions.  Upon completion of this unit, you should understand each process and be able to create your own models based upon the principles and procedures studied.

    Unit 2 Time Advisory   show close
    Unit 2 Learning Outcomes   show close
  • 2.1 Mathematical Treatment of Molecular Biology  
  • 2.1.1 Principles of Molecular Biology  
    • Reading: PDFgeni.org’s “Basic Principles of Molecular Biology 1”

      Link: PDFgeni.org’s “Basic Principles of Molecular Biology 1” (PDF)

      Instructions: The document provides a brief summary of a variety of topics in molecular biology.  Click on the link titled “Basic Principles of Molecular Biology 1.”  Pay particular attention to the sections on the nature of the genetic material and the central dogma.

      Reading this material should take approximately 45 minutes.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

      Submit Materials

  • 2.2 DNA Sequencing  
  • 2.2.1 DNA Structure and Classification  
  • 2.2.2 Data Collection  
  • 2.2.2.1 DNA Microarrays  
    • Web Media: YouTube: Proneural’s “DNA Microarrays”

      Link: YouTube: Proneural’s “DNA Microarrays” (YouTube)

      Instructions: Please watch this video, which provides an example of DNA microarrays in plants.  Try and break down the material in to steps involved in the process.

      Watching this video and taking notes should take approximately 15 minutes.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.2.2.2 DNA Sequencing Process  
  • 2.2.2.2.1 Manual  
    • Reading: National Diagnostics’ “Manual Sequencing”

      Link: National Diagnostics’ “Manual Sequencing” (HTML)

      Instructions: This is a brief introduction to manual sequencing.  Two versions of sequencing (Sanger Sequencing and Maxam & Gilbert Sequencing) are presented on the pages following the links provided.  Take note of the process and the differences between the two methods.

      Reading this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.2.2.2.2 Automated  
  • 2.2.2.3 Alignment  
  • 2.2.2.3.1 Pairwise Alignment  
    • Reading: University of Wisconsin: Mark Craven’s “Pairwise Alignment”

      Link: University of Wisconsin: Mark Craven’s “Pairwise Alignment” (PDF)

      Instructions: Click on the “pairwise-alignment-1.pdf” link to download the PDF.  This PowerPoint will introduce you to alignments, edits, and gaps and define the concept of pairwise alignment.  The PowerPoint also connects sequence alignment to dynamic computer programming.

      Reading this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.2.2.3.2 Multiple Sequence Alignment  
  • 2.2.2.3.3 Whole Genome Alignment  
    • Reading: Universität des Saarlandes: Center for Bioinformatics’ “Whole Genome Alignment”

      Link: Universität des Saarlandes: Center for Bioinformatics’ “Whole Genome Alignment” (PDF or PPT)

      Instructions: Click on the “V4-Alignment.pdf” or “V4-Alignment.ppt” link to download the presentation.  This presentation explains why whole genome alignments are used, presents a few different ways of aligning sequences, and addresses local versus global alignments.  For information in the presentation that needs more clarification, refer directly to the citations at the bottom of each slide.

      Reading this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.2.3 Comparisons  
  • 2.2.3.1 Comparison with Sequence Databases  
  • 2.2.3.1.1 FASTA  
    • Reading: State University of New York – Stony Brook: Steven Skiena’s “FASTA”

      Link: State University of New York – Stony Brook: Steven Skiena’s “FASTA” (HTML)

      Instructions: The resource describes FASTA and presents a step-by-step process for using it.  FASTA is the first widely used method of using a known sequence to search a database for similar sequences.  The best approach for understanding this is to actually create a fictitious sequence and use the search engine to analyze it.

      Reading this material should take approximately 30 minutes.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.2.3.1.2 BLAST  
    • Reading: National Center for Biotechnology Information’s “BLAST”

      Link: National Center for Biotechnology Information’s “BLAST” (HTML)

      Instructions: The link will take you to the BLAST website, where you will find a description of its capabilities and be able to use this search tool.  Please try the search tool to see how it performs.

      Studying this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.2.3.1.3 Finding Sequence Motifs  
  • 2.2.3.2 Genome Comparison  
  • 2.2.3.3 Molecular Phylogenetic Analysis  
  • 2.2.3.3.1 Principles of Molecular Evolution  
  • 2.2.3.3.2 Methods for Determining Distance  
  • 2.2.3.3.2.1 Average Linkage Clustering  
    • Reading: Statistics.com’s “Average Linkage Clustering”

      Link: Statistics.com’s “Average Linkage Clustering” (HTML)

      Instructions: This page provides a short paragraph about linkage clustering and a link for the linkage function that you should click on and view that information.  Use the additional links to provide a more complete understanding of how this function is used and where it can be applied.

      This reading should take approximately 30 minutes.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.2.3.3.2.2 Neighbor-Joining  
  • 2.2.3.3.3 Maximum Parsimony  
  • 2.2.3.3.4 Maximum Likelihood  
    • Reading: University of Alaska Fairbanks: Mark Lindberg’s “Maximum Likelihood Estimation”

      Link: University of Alaska Fairbanks: Mark Lindberg’s “Maximum Likelihood Estimation” (PDF)

      Instructions: From this webpage, click on the link for the reading labeled “Maximum Likelihood Estimation.”  The math may be a little advanced, so try and learn the advantages and disadvantages of this method as well as the reasoning behind why it is used instead of other methods of creating phylogenetic trees.

      Reading this material should take approximately 45 minutes.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

      Submit Materials

  • 2.2.4 Statistical Modeling  
  • 2.2.4.1 Markov Models  
    • Reading: Simon Fraser University: Bertille Antoine’s “Markov Chains”

      Link: Simon Fraser University: Bertille Antoine’s “Markov Chains” (PDF)

      Instructions: View the Markov chain PDF on the page.  This PDF describes Markov chain analysis using transition matrices and a specific example.

      Reading this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

      Submit Materials

  • 2.2.4.2 Hidden Markov Models  
    • Reading: Monash University: Lloyd Allison’s “Hidden Markov Models”

      Link: Monash University: Lloyd Allison’s “Hidden Markov Models” (HTML)

      Instructions: This reading covers different types of Markov models and provides an example of a Hidden Markov model.  This reading will help reinforce what you learned in subunit 2.2.4.1.  How are hidden Markov models different from standard Markov models?  Where would you apply them?

      Reading this material should take approximately 45 minutes.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.2.4.3 Bootstrap Analysis  
  • 2.3 Transcriptional Regulation  
  • 2.3.1 Transcriptional Regulation Process  
  • 2.3.1.1 Simple Regulation  
  • 2.3.1.2 Regulatory Networks  
  • 2.3.2 Data Collection  
  • 2.3.2.1 Finding Regulatory Sequence in DNA  
  • 2.3.3 Statistical Modeling  
  • 2.3.3.1 Bayesian Networks  
  • 2.3.3.2 Boolean Networks  
    • Reading: Connexions: Ewa Paszek’s “Boolean Networks”

      Link: Connexions: Ewa Paszek’s “Boolean Networks” (HTML)

      Introductions: The reading relates Boolean networks to molecular biology and provides an example of how they are used in biology.  You should able to describe what a Boolean network is and apply to the example given of cell cycle regulation.

      Reading this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.3.3.3 Dynamic Modeling  
  • 2.3.3.4 Using Differential Equations  
  • 2.4 Proteins  
  • 2.4.1 Protein Structure  
  • 2.4.1.1 Protein Structure and Classification  
  • 2.4.1.2 Data Collection  
  • 2.4.1.2.1 Protein Preparation  
    • Reading: Wikipedia’s “Protein Purification”

      Link: Wikipedia’s “Protein Purification” (HTML)

      Instructions: The page covers methods of extraction and outlines the steps of protein purification.  After reading this, you should be able to describe the steps necessary for extracting and purifying proteins.

      Reading this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.4.1.2.2 Protein Imaging  
  • 2.4.1.2.2.1 X-Ray Crystallography  
  • 2.4.1.2.2.2 NMR  
  • 2.4.1.3 Structure Modeling  
  • 2.4.1.3.1 Structure Prediction  
  • 2.4.1.3.1.1 Homology Modeling  

    Note: This subunit is covered by the reading under subunit 2.4.1.3: Structure Modeling.

  • 2.4.1.3.1.2 Fold Recognition  

    Note: This subunit is covered by the reading under subunit 2.4.1.3: Structure Modeling.

  • 2.4.1.3.1.3 Threading  

    Note: This subunit is covered by the reading under subunit 2.4.1.3: Structure Modeling.

  • 2.4.1.3.1.4 Ab Initio  

    Note: This subunit is covered by the reading under subunit 2.4.1.3: Structure Modeling.

  • 2.4.1.3.2 Computational Protein Design  
  • 2.4.1.3.3 Statistical Tests of Structure Accuracy  
  • 2.4.2 Protein-Protein Interactions  
  • 2.4.2.1 Principles of Protein Interactions  
  • 2.4.2.2 Methods for Detecting Interactions  
  • 2.4.2.2.1 Gel Electrophoresis  
  • 2.4.2.2.2 Affinity Chromatography  
    • Reading: Amersham Pharmacia Biotech’s “Affinity Chromatography”

      Link: Amersham Pharmacia Biotech’s “Affinity Chromatography” (PDF)

      Instructions: Click the first link under “Affinity Chromatography” to download the PDF.  This is a large and comprehensive manual on the process of Affinity Chromatography.  You should be able to describe the general method including how it works and any drawbacks to the method.

      Reading this material should take approximately 2 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 2.4.2.2.3 Mass Spectroscopy  
  • 2.4.2.3 Modeling  
  • 2.4.2.3.1 Bayes’ Theorem  
  • 2.4.2.3.2 Bayesian Networks  
  • 2.4.2.3.3 Likelihood Ratios  
  • 2.4.2.3.4 Clustering Coefficients  
  • Unit 3: Modeling Biological Processes  

    Nearly any biological process can be reduced to a mathematical formula and modeled on a computer.  The large, multivariable datasets involved in the study of complex processes can in fact be understood much more quickly and easily with the help of computer modeling.  An animal behaviorist can use mathematical prediction models to instantaneously predict the reaction that a subject will have to a stimulus, though the spectrum of possible outcomes and the complexity of the mathematical model might appear quite daunting to someone attempting to model it manually.  Alternately, using computational modeling, a biochemist could quickly predict the variety of outcomes that a cellular cascade reaction might have based upon the amount of substrate introduced to the cell and, in this way, make a better estimate as to the amount that should be used in a particular experiment or in order to achieve a particular desired outcome.   In this unit, we will look at examples of computational modeling in biochemistry, cell biology, neuroscience, population biology, evolution, and behavior.  You will learn the basic procedures for computational modeling in each of these fields and will gain a fuller understanding of the flexibility and universal applicability of mathematical modeling.

    Unit 3 Time Advisory   show close
    Unit 3 Learning Outcomes   show close
  • 3.1 Biochemistry Techniques  

    Note: Biochemistry is the study of the chemical reactions that occur in biological settings.  In this section, we will learn how to model the behavior of biologically-relevant molecules and the reaction rates of chemical reactions catalyzed by enzymes.

  • 3.1.1 Mathematical Treatment of Biochemistry  
  • 3.1.1.1 Molecular Dynamics  
    • Reading: Wikipedia’s “Molecular Dynamics”

      Link: Wikipedia’s “Molecular Dynamics” (HTML)

      Instructions: The page defines molecular dynamics and presents many of the details of the simulations and algorithms involved.  How can molecular dynamics be used to understand the movements of atoms or even proteins?

      Reading this material should take approximately 1.3 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 3.1.1.2 Enzymes and Kinetics  
    • Reading: Wikipedia’s “Enzyme Kinetics”

      Link: Wikipedia’s “Enzyme Kinetics” (HTML)

      Instructions: This entry introduces you to enzyme kinetics, including single and multiple substrate assays and Michaelis-Menten and non-Michaelis-Menten kinetics.  You should be able to describe the factors that influence enzyme kinetics and use the Michaelis-Menten equation.

      Reading this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 3.2 Cellular Pathways  

    Note: Chemical reactions within cells often depend upon intricate cascades of reactions, where an interruption of any step in the sequence can completely skew the final product by either preventing the production of an important protein or inhibiting the breakdown and removal of a detrimental compound from tissue.  In this subunit, we will learn how cellular processes can be modeled and predicted using the computer.

  • 3.2.1 Mathematical Treatment of Cellular Pathways  
  • 3.2.1.1 Cellular Pathways  
    • Reading: Nature Cell Biology: Bree B. Aldridge, John M. Burke, Douglas A. Lauffenburger, and Peter K. Sorger’s “Physiochemical Modeling of Cell Signaling Pathways”

      Link: Nature Cell Biology: Bree B. Aldridge, John M. Burke, Douglas A. Lauffenburger, and Peter K. Sorger’s “Physiochemical Modeling of Cell Signaling Pathways” (PDF)

      Instructions: The article discusses branches of biology where physiochemical models are used and the steps involved in creating and using models.  How do we know which models are correct and how do we identify the challenges involved in using these types of models?

      Reading this material should take approximately 1.3 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

      Submit Materials

  • 3.2.2 Prediction Modeling  
  • 3.2.2.1 Ordinary Differential Equation Modeling  
  • 3.2.2.2 Partial Differential Equations  
  • 3.2.2.3 Flux-Balance Analysis  
    • Reading: Nature.com’s “Flux Balance Analysis Primer”

      Link: Nature.com’s “Flux Balance Analysis Primer” (HTML)

      Instructions: This link will introduce the basic concepts of Flux-Balance Analysis and the complete mathematical background behind it.  It includes information about metabolic pathway construction as well.  You may want to search for articles listed in the literature cited section to gain more insight into the method of flux-balance analysis.

      Reading this material should take approximately 1.3 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 3.2.2.4 Analysis of Extreme Pathways  
  • 3.2.4 Statistical Testing for Accuracy  
  • 3.3 Neuronal Signaling  

    Note: The process of thinking and doing is so complex, it is hard to understand how it could be duplicated in any way.  However, it all comes down to individual action potentials, the firing of individual neurons within a network, and the ways in which those firings affect other neutrons within the network.  Here, we will learn about the mathematical modeling of action potentials (and changes in membrane potential that may or may not create an action potential) and discover how we to predict the behavior of a single neuron or a simple neural network based on these models.

  • 3.3.1 Mathematical Treatment of Neuronal Signaling  
  • 3.3.1.1 The Action Potential  
    • Reading: Bryn Mawr College: Rebecca Vandiver’s “Hodgkin-Huxley Model”

      Link: Bryn Mawr College: Rebecca Vandiver’s “Hodgkin-Huxley Model” (PDF)

      Instructions: Click on the link entitled “Hodgkin-Huxley Model” to download the PDF.  This presentation covers the Hodgkin-Huxley model of action potentials and includes a mathematical treatment.  You should be able to qualitatively describe the model and how it is applied to action potentials.

      Reading this material should take approximately 1 hour.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 3.3.1.2 Changes in Membrane Potential  
  • 3.3.2 Prediction Modeling  
  • 3.3.2.1 Single-Neuron Modeling  
  • 3.3.2.2 Artificial Neural Networks  
  • 3.4 Population Biology  

    Note: Predicting population viability and growth based upon multivariable data sets that contain information on the age and sex of individuals, the predation rate, and limiting resource factors is done much more efficiently on a computer than it is manually.  Here we will learn to predict population growth in a simple population using computer modeling.

  • 3.4.1 Mathematical Treatment of Population Biology  
  • 3.4.2 Predicting Population Growth  
  • 3.5 Evolution  

    Note: Computer modeling comes in handy when studying the evolution of a population, species, or group of species.  Using allelic frequency (the frequency with which a particular gene sequence appears at a given locus) and a mathematical calculation of the Hardy-Weinberg Equilibrium, we can quickly see whether a population is currently evolving or not.  By comparing the physical or molecular characteristics of different species and mathematically sorting out the species with highest numbers of similarities, we can create phylogenies, or “family trees” of species, which estimate the process of evolution and divergence among those species over millions of years.  In this section, we will learn how to predict evolutionary change within a population and evolutionary connection between populations using computer modeling methods.

  • 3.5.1 Mathematical Treatment of Evolution  
  • 3.5.1.1 Hardy-Weinberg Equilibrium  
  • 3.5.1.2 Creating Phylogenies  
    • Reading: Indiana University: ENSI/SENSI’s “Making Cladograms”

      Link: Indiana University: ENSI/SENSI’s “Making Cladograms” (HTML and PDF)

      Instructions: This is a well-developed lesson plan that guides you through the creation of a cladogram.  After reading the page, scroll to the bottom and view the 6 page PDF on creating a cladogram.  Work through this as it is the best way to learn how cladograms are developed.

      Reading this material should take approximately 2 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 3.5.2 Modeling Evolutionary Change  
  • 3.5.3 Modeling Evolutionary Connections  
  • 3.6 Behavior  

    Note: To think that behavior can be mathematically predicted seems counter-intuitive, yet a number of mathematical formulas and matrices have been created and successfully deployed in order to predict the outcomes of conflicts between animal behavior and psychology.  The study of behavior through mathematics is famously known as “Game Theory.”  In this section, we will learn about a few standard methods of predicting behavior through Game Theory and discover how to model them on the computer.

  • 3.6.1 Mathematical Treatment of Behavior  
  • 3.6.1.1 Game Theory  
  • 3.6.1.2 Prisoner's Dilemma  
    • Reading: University of Iowa Math Club: Erin Pearse’s “The Prisoner’s Dilemma”

      Link: University of Iowa Math Club: Erin Pearse’s “The Prisoner’s Dilemma” (PDF)

      Instructions: Click on the link entitled “The Prisoner's Dilemma. Invited speaker. April 23, 2009. Grinnell College.” to download the PDF.  This is a presentation that covers the game, the mathematics behind the game, and variations on the theme.  Pay attention to definitions and to real world application of the Prisoner’s Dilemma.

      Reading this material should take approximately 1.3 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 3.6.2 Prediction Modeling  
  • 3.6.2.1 Collective Behavior  
  • Unit 4: Current Topics in Computational Biology  

    In this final unit, we will shift our focus from theoretical generalities in computational procedure to specific applications of Computational Biology in current research.   This unit should provide you with a better understanding of the ways in which Computational Biology can applied in research.  We will look at topics in Computational Biology that have come up in the mainstream media and study papers that have recently been published in scientific journals.

    Note: The content of this unit will change over time and is dependent on the contents of the current issue of the journal, or current topics in the news.   This unit will likely be more of an overview than an in-depth study; it should expose you to various research methods and approaches to drawing conclusions from data. 

    Unit 4 Time Advisory   show close
    Unit 4 Learning Outcomes   show close
  • 4.1 Current Research  
  • 4.1.1 In the News  
  • 4.1.1.1 Molecular Biology  
    • Reading: ScienceDaily News’ “Molecular Biology Articles”

      Link: ScienceDaily News’ “Molecular Biology Articles” (HTML)

      Instructions: At this page, click on the ‘Articles’ tab and then click on the molecular biology link.  Try using either the search option to look for articles related to what you have covered in other units or take a different approach and look for articles that give you a sense of what recent discoveries have taken place within areas you have covered in the course.

      Studying these materials should take approximately 3 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 4.1.1.2 Biological Processes  
    • Reading: e! Science News’ “Fundamental Biological Processes”

      Link: e! Science News’ “Fundamental Biological Processes” (HTML)

      Instructions: This site allows you to choose from a variety of articles related to biological processes.  Search under Computational Biology to locate articles related to material in the course.  This could simply be an opportunity to late articles that cover an area of personal interest within computational biology.

      Studying these materials should take approximately 3 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

  • 4.1.2 In Scientific Journals  
  • 4.1.2.1 Molecular Biology  
  • 4.1.2.2 Biological Processes  

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