Biology Modules
Using AgentSheets to Visualize Molecular Behavior, Action Potentials, and the Neuromuscular Junction
Module Author: Karl Romstedt
Author Contact: kromsted@capital.edu
Funded By: National Science Foundation
This tutorial describes how to use Agent Sheets® as a tool for modeling molecular events. The agents will represent molecules in random motion and each will have unique characteristics so that it can interact with other molecular agents in specific ways. The molecular components represented here are modular in that one or more of them can be easily incorporated to make a variety of different models. The easiest model shows simple diffusion. Other elements demonstrated here will include membranes, acetylcholine receptors, voltage-gated channels and the paralytic poison, curare. Ultimately, these can be used together to model and visualize a nerve action potential and the neuromuscular junction.
This exercise is intended for a college audience or advanced high school students. Another learning module written by the author may be helpful for reinforcing similar learning objectives. This refers to "Modeling Neurophysiology using STELLA®." This previously written module investigates nerve action potentials and is available on Capital University’s CSAC website or by contacting the author.
Sequence Alignment using Align
Module Author: Richard M. Salter
Author Contact: rms@cs.oberlin.edu
Funded By: The National Science Foundation
Align is a Java application that presents 3 essential alignment algorithms. In this module students will learn about the algorithms used in pairwise sequence alignment. Students use the Align tool to help them understand how these algorithms work, and to experiment with sequence matching under various constraints. This module consists of 4 laboratory exercises distributed as PDF files. Part I introduces sequence alignment. Part II describes the basic dynamic programming algorithm. Part III discusses the scoring schemes used in sequence alignment. Part IV considers semi-global and local variations of the basic algorithm. Each part contains worked-out examples and several exercises with solutions.
Multiple Sequence Alignment
Module Author: Helen Piontkivska
Author Contact: opiontki@kent.edu
Funded By: The National Science Foundation
Multiple sequence alignment is a critical part of almost any bioinformatics sequence analyses. This module introduces essential concepts and tools used in reconstruction of multiple sequence alignments. Commonly used tools such as ClustalW, DiAlign and BLAST are introduced, together with tool descriptions and usage examples. Among other topics covered: dot-plots, scoring matrices and sequence logo tools. The goal of supplied exercises and final project ideas is to introduce students to multiple sequence alignment tools, and to inform them about different options available and how they may affect the obtained results. The importance of using different tools and selection of proper scoring matrices and other parameters is emphasized. The module includes multiple (1) example in-class and homework exercises, (2) and ideas for a final project, that gradually “immerse” students into the alignment reconstruction process. Over the course of these hands-on exercises, students are expected to learn about essential concepts of alignment reconstruction and to familiarize themselves with several alignment tools. Following the completion of the module, students are expected to be able to reconstruct multiple sequence alignments on their own, by selecting relevant sequences, choosing proper scoring matrices and generally determining which of the alignment approaches (global or local) should be used.
Logarithms, Magnitude, and Regression for Biological Scaling: A Computational Science Module
Logarithms, Magnitude, and Regression for Biological Scaling: A Computational Science Module Class Data
Module Author: Andrew J. Kerkhoff
Author Contact: kerkhoffa@kenyon.edu
Funded By: National Science Foundation
This computational science module covers the basics of logarithms and exponents (i.e., magnitude scales) and the statistical technique of least-squares regression in the context of biological scaling, or allometry. Allometry is the study of how various aspects of life change quantitatively with organism size. The idea of magnitude (and thus logarithms and exponentiation) is essential in this context because organisms span an astounding 21 orders of magnitude in mass. That is, the largest organisms, like blue whales and giant sequoias, are approximately 10,000,000,000,000,000,000,000 (or ten-thousand-million-million-million) times heavier than the smallest bacteria, yet all organisms must abide by the same laws of physics, biochemistry, genetics, physiology, and evolution. Comparing organisms on magnitude or logarithmic scales allows us to better understand how the various aspects of biological form, function, and diversity change with organism size.
Modeling Populations and Habitats for Kirtland’s Warbler
Modeling Populations and Habitats for Kirtland’s Warbler: Zipped Files
Modeling Populations and Habitats for Kirtland’s Warbler: Supporting Documents
Module Author: Timothy L. Lewis
Author Contact: tlewis@wittenberg.edu
Funded By: W. M. Keck Foundation
Conserving endangered species often requires a balancing of the biological needs of endangered populations against the human desires for economic and recreational opportunities. At least some biological aspects of every species are poorly understood, some do have large data sets that can be difficult to interpret. All are confounded by human interactions. Computational science allows visualization, analysis, and interpretation of large data sets in ways that can inform these complex biological and environmental problems. This module will allow students to explore one of the fundamental paradigms of conservation biology, island biogeography, and apply that theoretical ecological concept to a real-world problem by creating models for habitat management. Specifically this module reviews island biogeography as it applies to forest fragmentation in northern Michigan and uses the related concepts to explore applications to preservation of the endangered Kirtland’s warbler (Dendroica kirtlandii). Students will learn fundamental ecological concepts, visualize and analyze large spatial data sets of an endangered species using a free but sophisticated geographic information system (GIS), and develop an environmental impact statement formatted output to explain recommendations based on their analysis of the data and developed models.
Classification of Hybrids Using Genetic Markers and Maximum-Likelihood Estimates
Module Authors: Andrew T. Phillips, C. Alex Buerkle, Marc R. Goulet, Alexander J. Smith, Paul J. Thomas
Author Contact: gouletmr@uwec.edu
Funded By: Battelle
Organisms exist as distinct species, or groups of individuals that are genetically, and often reproductively, isolated from other groups. Such species harbor distinct features and retain these features over long periods of time. However, many species are also capable of reproducing with members of other species, resulting in hybrid individuals. As an example, when horses and donkeys reproduce, a hybrid offspring commonly known as the mule results. Generally though, mules are unable to reproduce, and hence they do not lead to mixing of the genes of the parental species. The fitness of such hybrid individuals varies, but in many cases some fraction of these hybrids are capable of reproduction and could potentially act as a conduit for genes to flow back and forth between species. The ubiquity of hybridization in the natural world has led biologists to study the corresponding genetic and ecological circumstances under which species retain their unique identities, and those which lead to an erosion of such differences. In other words, hybridization between species represents an opportunity for biologists to study reproductive and ecological barriers to the exchange of genes between species. The study of barriers between species has advanced our understanding of speciation and evolution. It has also has played a role in modern agriculture, including our understanding of the genetics of domestication and the evaluation of the risk that transgenes (a small fragment of genetic DNA that is injected into a developing embryo) will move from crop plants into wild relatives. Thus, in a wide variety of research settings it is often necessary to quantify the affinity of a hybrid individual with each of the parental species involved in hybridization. This module investigates just such a measure of affinity, called the hybrid index, and presents just one such approach, based on the use of genetic markers, for doing so.
Tumor Dynamics
Tumor Dynamics: Introduction
Tumor Dynamics: Introduction Slides
Tumor Dynamics: Zipped Files
Module Authors: Lisette de Pillis, Ami Radunskaya
Author Contact: depillis@hmc.edu
Funded By: W. M. Keck Foundation
The main goal of this super-Module is to give the students an understanding of the steps involved in creating a continuous time deterministic model, from understanding the underlying biology of the system to developing the mathematical model itself to evaluating the model both analytically and numerically. We make heavy use of the research paper by Kuznetsov [KMTP94] as a guide for the students. If all three Modules are used, then by the end of the super-Module, the students should be able to understand, and in most cases even recreate, the processes and results described in Kuznetsov's article.
Gene Identification
Gene Identification: Supporting Documents
Module Author: Chuck Daniels
Author Contact: daniel.7@osu.edu
Funded By: W. M. Keck Foundation
The advent of rapid DNA sequencing methods has revolutionized molecular life sciences. Within the last decade the genomes, or genetic blueprints, of nearly 200 organisms have been completed and the stream of information continues to grow. For the first time, modern biologists have the opportunity to predict all of the capabilities of an organism based on its gene content. Despite its promise, deciphering these genomes is a problem not yet within the reach of current experimental techniques and the biologists have turned to computer scientists and mathematicians to help in developing new methods for analysis and storage of genome data. This has led to formation of a new discipline, Bioinformatics. This module examines the "language" of genes and illustrates how basic statistical methods can be applied to the problem of gene prediction. The merger of computational sciences with biology, and the challenges facing Bioinformatics, is explored through the use of analysis tools available at the National Center for Biotechnology Information (NCBI).
Protein Identification
Module Author: Chuck Daniels
Author Contact: daniel.7@osu.edu
Funded By: W. M. Keck Foundation
How is the biological identity of a potential gene product predicted? This is one of the primary problems faced by bioinformaticians. The genetic code provides rules for the prediction of open reading frames; however, these data do not allow assignment of a function to the gene product. Current predictive methods depend on the identification of homologs or related sequences that have already been identified and are present in the NCBI databases. The problem is reduced to the questions: Are there any related sequences present in the databases? And if so, is this relationship sufficiently significant that an assignment of function can be made? Methods for the identification of gene products are based on a series of tools that measure the relatedness of nucleic acid or protein sequences. This is achieved by finding the best alignment between two macromolecules. The alignment problem will be present to the students as a progression of methods starting with simple matrix comparisons visualized as dot plots, then extended to introduce global alignments, which utilized dynamic programming methods. The dynamic programming approach is similar to the familiar “traveling salesman” problem. The final step is to apply these methods to query a large database, such as NCBI, searching for related molecules. In this later step we will also address the problem of determining the significance of the match. For this we will introduce concepts of probability. Biological science students will see the difficulties and the limitations in assigning gene identities based solely on sequence information. CS/Math students will see the applications of statistical tools to the problem of pattern matching. The importance of data management and the visualization of sequence alignment will also be emphasized. The problems will be designed to illustrate the basic concepts of alignment and then extended to searching larger databases. The NCBI site provides a search tool, Basic Local Alignment Search Tool or BLAST, which allows users to query the NCBI databases. This is the commonly used method. There are numerous options for homework and project assignments, and many of these can be built to be extensions of the assignments from the preceding module.
Diffusion in Biology: A Mathematical Modeling Approach
Module Author: Ignatios Vakalis
Author Contact: ivakalis@csc.calpoly.edu
Funded By: W. M. Keck Foundation
The module is intended as a stand alone component of a second course in Computational Science or as a component of a Mathematical/Computational Biology course. It examines the formulation and solution processes of mathematical models that describe diffusion for the 1-D and 3-D cases. Diffusion is the process by which matter is transported form one part of a system (i.e., biological system) to another as a result of random molecular motion. Diffusion processes play a significant role in many biological phenomena. For example, through diffusion many metabolites are exchanged between a cell and its environment of between the blood stream and tissues. The learning goals of the module are: (1) To study the process of developing mathematical models for biological diffusion, for 1-D and 3-D cases; (2) To study the solution process of the above models analytically and numerically; and (3) To model diffusion over a thin membrane and solve the model.
Modeling the Cardiovascular System using STELLA®
Module Author: Karl Romstedt
Author Contact: kromsted@capital.edu
Funded By: W. M. Keck Foundation
This module teaches concepts regarding cardiovascular function and modeling. It is intended for a college audience but requires only basic skills in biology, mathematics and programming. The module includes lecture/discussion periods, computer laboratories and hands-on experimentation. It is designed to be a stand-alone unit that can be integrated anywhere into the course on Computational Biology. If it is used after the section on statistics and curve-fitting, however, these concepts can be incorporated into group projects involving physiological experimentation and data collection. Little in the way of explicit prerequisites are required since the intention is to cover the required biology and computational science during the module. Some questions may require using the web or other references for investigation of advanced topics. The central focus is on a model for blood flow that uses Poisseuille’s Law to explain the relationship between blood vessel diameter, blood viscosity and blood pressure.
Modeling Mushroom Fairy Rings
Modeling Mushroom Fairy Rings Mathematical Tutorial
Module Author: Angela B. Shiflet & George W. Shiflet
Author Contact: shifletab@wofford.edu
Funded By: W. M. Keck Foundation
Sometimes in a forest or yard, mushrooms seem magically to grow in circles, which we call fairy rings. In this module, we develop simulations for the expansion and interactions of such mushroom fairy rings. After analyzing the system, formulating the model, and considering appropriate rules for the spreading of mushrooms, we create a simulation using the graphical computer algebra system Mathematica. Projects involve various refinements of the model. We have designed the module for science and mathematics majors with the following goals: 1. To study the modeling process, 2. To study the simulation process, 3. To apply the graphical computer algebra system Mathematica in simulations, and 4. To investigate model refinement.
Modeling Malaria
Module Author: Angela B. Shiflet & George W. Shiflet
Author Contact: shifletab@wofford.edu
Funded By: W. M. Keck Foundation
The ancient disease of malaria still is having devastating consequences for individuals and societies. Caused by a protozoan belonging to the genus Plasmodium and transmitted by the Anopheles mosquito, malaria infects millions human beings each year. In this module, we develop models of the effects of malaria on various populations of humans and mosquitoes. After analyzing the system, formulating the model, and considering appropriate differential equations, we create a model using the systems modeling tool STELLA. Projects involve various refinements of the model. We have designed the module for science and mathematics majors with the following goals: 1. To study the modeling process, 2. To study various differential equations for modeling population dynamics, 3. To apply the systems dynamics tool STELLA in modeling, and 4. To investigate model refinement.
Spread of SARS
Spread of SARS: STELLA Model SIR
Spread of SARS: STELLA Model Relationships
Module Author: Angela B. Shiflet & George W. Shiflet
Author Contact: shifletab@wofford.edu
Funded By: W. M. Keck Foundation
Severe Acute Respiratory Syndrome (SARS), which emerged in 2003, is a highly infectious disease caused by a coronavirus. The high infection and mortality rates and the lack of treatment alarmed health officials throughout the world. Quick action by organizations, like WHO and CDC, to contain the outbreak averted a public health catastrophe. In this module, we develop a simplified model (SIR) of the spread of an infectious disease before considering a more involved model of SARS. For the former, after analyzing the system and formulating the model with appropriate differential equations, we create a model using the systems modeling tool STELLA. For the latter, we build on the earlier model to perform the analysis and much of the model formulation, but leave the completion of the model to the student. Projects involve various refinements of the models along with additional problems.
Pharmacokinetics: Mathematical Analysis of Drug Distribution in Living Organisms
Module Author: Igantios Vakalis
Author Contact: ivakalis@capital.edu
Funded By: W. M. Keck Foundation
Pharmacokinetics is the study of time course of a drug or a metabolite in different fluids and tissues and of the mathematical relationships required to develop models to interpret such data. The mathematical theory of drug phenomena is a branch of the mathematical theory of metabolism. Even though drugs are not normal metabolites they do affect different metabolic processes. While the drug is acting, it does take part in some phases of metabolism. The theory of drug phenomena can be subdivided into two categories: (1) The modeling of distribution of drugs in the organism, and (2) The biochemical kinetics of the interaction of the drug with different components of the organism. The first category will be used as the background info, while the module focuses on the mathematical treatment of the second category.
Sequence Alignment
Module Author: Wayne Becktel
Author Contact: wbecktel@capital.edu
Funded By: National Science Foundation (9952806)
Sequence alignment and homology are two tools of Bioinformatics and Proteomics. As such, they are used with Sequence Analysis and empirical, structural information to classify and investigate DNA, RNA, and structural proteins. The problems associated with Sequence Analysis can also be used in this module. This is to say that, by their very nature, the problems are open-ended. The two classes of gene sequences considered are those for the d-endotoxins from various strains of B. thuringeinisis and the histone-like proteins from various Archeons. In examining these sequences, the student will compare the different methods of statistical analysis and examine to what extent each technique does or does not accurately align the DNA sequence. In addition, attention will be paid to the nature of the changes of coded amino acid and how these changes might influence the behavior of the structural proteins.
Biological Curve Fitting
Module Author: Karl Romstedt
Author Contact: kromsted@capital.edu
Funded By: National Science Foundation (9952806)
Although students will have had previous experiences with a variety of mathematical relationships, these relationships are typically studied in a way that is different from their applications in a real science environment. In particular, students are usually presented with a formula which they graph and investigate by changing parameters within the equation. They may also be asked to determine values of the dependent variables at different points along the graphs. In this module, students will do the reverse. That is, they will start with a series of points on a graph and use them to determine the formula that would be graphed through the points rather than vice-versa. This requires an understanding of error terms (ε) which add increased complexity to the underlying relationships to be graphed.
Modeling Neurophysiology using STELLA®
Module Author: Karl Romstedt
Author Contact: kromsted@capital.edu
Funded By: National Science Foundation (9952806)
This module teaches basic concepts about the functions of neurons. The central focus will be on a STELLA® model of resting and action potentials using the Goldman equation for the contributions of multiple ions to membrane voltage. It is intended for a college audience. Basic skills in biology, mathematics, physics, chemistry and programming are helpful but little in the way of explicit prerequisites are required since the intention is to cover the needed science during the module. The module will be taught using lecture/discussion periods and computer laboratories. It is designed to be a stand-alone unit that can be integrated anywhere into a course on Computational Biology.
Phylogeny Project
Module Author: Karl Romstedt
Author Contact: kromsted@capital.edu
Funded By: National Science Foundation (9952806)
The goal for this module is to show how students can access on-line DNA databases and then use the data for constructing phylogenetic trees. The databases contain the real genetic codes for many organisms although it is often incomplete. Most of the code has now been read for a variety of species including humans. The sequences are sometimes read from the genomic DNA but another form is derived from messenger RNA (mRNA).
Sequence Analysis
Module Author: Wayne J. Becktel
Author Contact: wbecktel@capital.edu
Funded By: National Science Foundation (9952806)
Sequence analysis of biopolymers constitutes the first step in connecting the chemical composition of a molecule with its final function. The concept that the function of a molecule derives from its chemical composition and sequence is fundamental to modern biology. This module presents introductory sequence analysis of nucleic acids and proteins in a series of six lectures and laboratory experiences. Students completing this module will understand basic concepts of analysis and pattern recognition in genomics and proteomics, construction of a molecular sequence dictionary, and the use of histograms in the visualization of sequences. Students should have a background of biology and computer science at the level of understanding polynucleotide and polypeptide composition and an introductory course in Computational Science. This module may either be used as a stand-alone introduction or in combination with the Alignment and Comparison module.
Protein Sequence Comparisons
Protein Sequence Comparisons: Supporting Documents
Module Author: Margaret Goodman
Author Contact: mgoodman@wittenberg.edu
Funded By: National Science Foundation
Sequence Comparison is a useful tool when investigating protein structure and function. In particular, sequence comparisons can help to identify key functional domains and/or relatedness among proteins with similar structure and function. The wealth of sequence information in databases and sequence comparison tools (BLASTA, FASTA, etc.) available on line makes this investigation accessible. However, at the merger between biological function and computational sequence comparisons, an understanding of the strategies and calculations as well as the fundamental biology involved are essential for developing biologically relevant interpretations. This module addresses fundamental computational strategies for investigating sequence similarity of the human alpha 2 and beta 2 adrenergic receptors, identifying correlations between conserved and divergent domains and the physiological function of the receptors.