Teddy Graham Lab - Biology

Teddy Graham Lab - Biology

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You are a bear-eating monster. Because of this, you eat only happy bears.

New bears are born every 'year' (during hibernation) and the birth rate is one new bear for every old bear left from the last year. The happy trait is recessive, so the happy bears are homozygous recessive. In addition, because the sad trait is dominant, the sad bears are either homozygous or heterozygous dominant.

Make a prediction about what will happen to the phenotypic and genotypic frequencies in the population after a few generations. Explain your reasoning.


1. Obtain a population of 10 bears and record he number of happy and sad bears and the total population number. Assume that the genotypes in your beginning population are homozygous dominant or recessive (there are no heterozygotes).

Using the equation for Hardy-Weinberg equilibrium, calculate the frequencies of both the dominant and recessive alleles and the genotypes that are represented in the population.

p2 + 2pq + q2 = 1 p + q = 1

Example: If 5 of the 10 bears are happy, then 10 out of 20 alleles would be happy alleles. Therefore the q2 number would be 0.5. You must then determine the q number by taking the square of 0.5.

2. Eat three happy bears. (If you do not have three happy bears, then eat the difference in sad bears.) You will use the remaining bears to produce offspring during breeding season. Each remaining bear bear will produce one new bear.

3. Repeat this process for four generations of bears and construct a data table to show how many of happy and sad bears are in the population for each generation. Data should reflect the frequency of each type of bear.

Generationp2 (sad)2pq (sad)q2 (happy)pq
1 (initial)

4. Using your data, construct a graph that will show what happens to the bear population over time. Use percentages of happy and sad bears for each generation. Changes in frequencies should be shown on the same graph.


Prepare a short summary of what you observed in this activity that addresses the following:

  • What is happening to the genotype and allele frequencies in the population of Teddy Grahams?

  • What would you expect to happen if you continued the selection process for additional generations?

  • How would the frequencies change if you were to now select for the sad bears?

  • Why doesn’t the recessive allele disappear from the population? How is it protected?

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As a junior player Graham played for the Owen Sound Greys, helping them win the 1924 Memorial Cup as Canadian junior champions. [1] He played two seasons of senior hockey before he turning professional in 1926, signing with the Chicago Cardinals of the American Hockey Association. [2] He played for the team for one year, and after they folded Graham moved cross-town to the Chicago Black Hawks of the National Hockey League (NHL). Graham's NHL debut came on November 15, 1927 against the Boston Bruins, and his first goal, and only point of the season, was on January 4, 1928 against the Montreal Canadiens. [3]

Partway through the season Graham was traded to the Moose Jaw Warriors of the Prairie Hockey League in January 1928, though Moose Jaw traded him the same day to the Saskatoon Sheiks, where Graham finished the 1927–28 season with. He then signed with the Tulsa Oilers of the AHA, and spent the 1928–29 and most of the 1929–30 seasons there before being traded back to the Black Hawks. [2] Graham remained with Chicago until 1933 when he was traded to the Montreal Maroons, where he played 19 games before being traded in January 1934 to the Detroit Red Wings. With the Red Wings he played 52 games over two seasons, as well as 7 games for their International Hockey League affiliate, the Detroit Olympics, before being traded again, this time to the St. Louis Eagles, where he played the last 13 games of the 1934–35 season.

The Eagles folded after the season and the players were dispersed to the other NHL teams, with Graham being selected by the Boston Bruins. [4] He would play the 1935–36 season and the first game of the 1936–37 season with the Bruins, scoring four goals and one assist in 49 games, before being traded to the New York Americans, where he finished the 1936–37 season with, playing 31 games. Graham played a further season in the International American Hockey League before retiring in 1938. Graham subsequently became an ice hockey referee. [2]

Grey played in two Stanley Cup Finals during his career. The first was in 1931 Stanley Cup Finals with Chicago, who lost to the Montreal Canadiens. He reached the finals again in 1934 with Detroit, joined by former Owen Sound teammate Cooney Weiland, but lost to the Black Hawks. [2]


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Understanding Dominant And Recessive Alleles

You should talk about genetics and alleles before introducing the Punnett square worksheet in your classroom. Students should ideally also have a good understanding of how to calculate probabilities.

Students should be familiar with genes and understand that genes are a unit of hereditary information while an allele is a possible sequence or variant of a gene.

You should also talk about observable genetic traits, also known as phenotypes. Students should understand that there are dominant and recessive alleles that won’t become phenotypes unless they are combined with another recessive allele. You can introduce the notion of codominant alleles with high school students.

Make sure the Punnett square activities are connected to lessons about genetics, inheritance, and alleles. You can use these activities to introduce these concepts or to help students get a more thorough understanding of genetics and probabilities.

Barney Graham, M.D., Ph.D.

The goal of the Viral Pathogenesis Laboratory (VPL) in the Vaccine Research Center (VRC) is to better understand basic aspects of viral pathogenesis and apply that knowledge toward development of safer and more effective vaccines. Many aspects of prior VPL work were instrumental in the rapid response to the COVID-19 pandemic. Under Dr. Graham’s direction, the VPL designed and developed the first COVID-19 vaccine candidate and helped discover the first SARS-CoV-2 neutralizing monoclonal antibody to enter human clinical trials. This was achieved by implementing a plan outlined for rapid pandemic response, based on prototype pathogen preparedness. Guided by structures of HKU1-CoV, MERS-CoV, and SARS-CoV combined with a generalizable spike antigen design solution established for betacoronaviruses, technological tools developed by the VRC for precision vaccinology, and rapid platform manufacturing in collaboration with Moderna, Inc., the first clinical trial participant was injected with a candidate mRNA vaccine expressing stabilized SARS-CoV-2 spike protein 65 days from the time of sequence release. Preclinical testing and assay development were performed in parallel to support advanced clinical trials for mRNA-1273. The VPL’s prior work on the pathogenesis of respiratory syncytial virus (RSV) vaccine-associated enhanced respiratory disease (VAERD) also provided an essential framework for safety evaluations and regulatory decisions required for accelerated vaccine development (Graham, Science 2020).

To define how coronaviruses and other viruses cause disease, the VPL investigates functional and structural features of viral pathogens as well as mechanisms for regulating the composition and timing of host immune responses using in vitro systems, animal models, and clinical trials. Understanding RSV biology and pathogenesis has been a central theme of the VPL. Studying VAERD was the basis for work on T cell function and regulation (reviewed in Ruckwardt et al. Immunity 2019). Solving the structure of the RSV prefusion F (McLellan et al., Science 2013a) and identifying stabilizing mutations (McLellan et al. Science 2013b) led to demonstrating that prefusion specific antibodies were more potent than antibodies to postfusion surfaces (Ngwuta et al., Science Translational Medicine 2015). Those studies led to development of a stabilized prefusion F protein trimer (DS-Cav1) candidate vaccine (Crank et al. Science 2019) that is now in advanced clinical development. This work provided a clinical proof-of concept for structure-based vaccine design (Graham et al., Annual Review of Medicine 2019) and informed the subsequent structural work on coronavirus spike proteins and fusion proteins from Nipah and other paramyxoviruses.

The VPL emphasizes the use of new technologies that have evolved over the last decade driven largely from efforts to develop an HIV vaccine. Some of these include structure-based antigen design, self-assembling nanoparticle display, single-cell analysis for assessing T cell function and discovering monoclonal antibodies, defining antibody repertoires and lineages, and nucleic acid and vector-based vaccine antigen delivery vehicles (Graham, Immunological Reviews 2013). All of the programs have used some of these tools, and the universal influenza vaccine development program has used them all. There are several aims to achieve the objective of durable universal influenza immunity against both seasonal and pandemic strains of influenza virus (Kanekiyo and Graham, Cold Spring Harbor Press, 2020) that include strategies for improved supraseasonal vaccines and those for pandemic preparedness and response. Major approaches include targeting the conserved hemagglutinin (HA) stem domain for both group 1 (Yassine et al., Nature Medicine, 2015) and group 2 (Corbett et al., mBio, 2019) influenza A by displaying structurally-defined headless HA stem trimers on self-assembling ferritin nanoparticles. The immunological intent is to induce defined antibody lineages known to have cross-neutralizing activity. Another approach using self-assembling nanoparticle display of structurally-defined antigens seeks to avoid immunodominant strain-specific HA head responses by a heterotypic mosaic array of antigens on each particle (Kanekiyo et al., Nature Immunology, 2019).

Under Dr. Graham, the VPL addresses the following pathogens: 1) coronavirus (MERS-CoV, SARS-CoV-1, and SARS-CoV-2), 2) influenza (flu), 3) respiratory syncytial virus (RSV) , 4) EV-D68, 5) Nipah and other paramyxoviruses, 6) Zika, and 7) Ebola. This work is performed within the two Sections, the Viral Pathogenesis Section (VPS) and the Biodefense Research Section (BRS). The VPS is organized in three units which operate under Dr. Graham’s leadership assisted by Karin Bok, MS, Ph.D., Senior Advisor for Vaccine Development. The BRS is directed by Dr. Nancy Sullivan.

Viral Pathogenesis Section

The objectives of the VPS directed by Dr. Graham are to: 1) study basic virological and immunological determinants of viral diseases 2) define immunological correlates of protection from virus infection and disease 3) develop animal models and immunological assays to support the development of vaccines and monoclonal antibodies for prevention and treatment of viral diseases and 4) develop vaccines and monoclonal antibodies to prevent viral diseases and to establish a framework for prototype pathogen preparedness.

In anticipation of future pandemic threats, Dr. Graham was part of a multi-divisional NIAID effort that developed the strategy for implementing a Prototype Pathogen Preparedness Plan (P4) over the last few years. This approach involves leveraging in-depth knowledge of selected prototypic viruses within the 25 virus families known to infect humans to inform and accelerate the development of medical countermeasures for new or re-emerging pathogens, recently exemplified by the response to COVID-19.

Vaccine Immunobiology Unit

Research Group

Tracy Ruckwardt, Ph.D., Staff Scientist and Unit Head Man Chen, Ph.D., Staff Scientist Core Rebecca Loomis, Ph.D., Staff Scientist core Tony DiPiazza, Ph.D., IRTA Postdoctoral Fellow Azad Kumar, Ph.D., Scientist Deepika Nair, Biologist Alexandrine Derrien-Colemyn, Biologist Kizzmekia Corbett, Ph.D., Research Fellow and Team Lead for Coronavirus Research Olu Abiona, IRTA Post-Baccalaureate Anne Werner, IRTA Post-Baccalaureate

The Vaccine Immunobiology Unit (VIU) studies basic aspects T cell biology with an emphasis on neonatal immune responses and understanding determinants of adaptive responses following respiratory virus infections. This unit seeks to understand factors that contribute to age-dependent differences in immune responses to design vaccines that elicit precisely targeted and balanced adaptive immune responses. The VIU directs work on antigen design, animal models, and immune assays to support vaccine development for RSV and other emerging viral infections such as SARS-CoV-2, Zika, EV-D68, Nipah, and other paramyxoviruses.

Molecular Immunoengineering Unit

Research Group

Masaru Kanekiyo, DVM, Ph.D., Staff Scientist and Unit Head Seyhan Boyoglu-Barnum, Ph.D., Staff Scientist (core) Adrian Creanga, Ph.D., Staff Scientist (core) Brian Fisher, Ph.D., Biologist, and VPL laboratory manager Rebeca Gillespie, Biologist Julia Lederhoffer, Ph.D., IRTA Postdoctoral Fellow Syed Moin, Ph.D., Scientist Geoffrey Hutchinson, IRTA Post-Baccalaureate

The Molecular Immunoengineering Unit (MIU) designs and tests new vaccine strategies using a technology-driven approach to address unmet challenges in vaccinology. This unit: 1) investigates basic aspects of protein antigenicity and immunogenicity 2) uses molecular and single-cell technologies to define mechanistic basis for protective immunity that guides antigen design with an emphasis on influenza 3) uses structural biology and protein engineering to design immunogens that improve the quality, breadth, and durability of immune responses with an emphasis on influenza and 4) develops assays and tools that inform vaccine development.

VPL Translational Science Core

Research Group: Karin Bok, M.S., Ph.D. Senior Advisor Vaccine Development and Unit Head Gabriela Alvarado, Ph.D., Scientific Program Manager

The VPL Translational Science Core (VPTS) provides scientific advice and scientific program management support for pre-clinical product development and their transition to clinical testing and is essential for maintaining the VRC vaccine development pipeline through the Vaccine Production Program (VPP). The VPTS is also the main liaison to external stakeholders and scientific collaborators. This team also manages the collaborative interactions among the different VRC teams and directly liaises VPL with VRC’s Program Management and Strategy teams.

A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence

The emergence of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome (MERS)-CoV underscores the threat of cross-species transmission events leading to outbreaks in humans. Here we examine the disease potential of a SARS-like virus, SHC014-CoV, which is currently circulating in Chinese horseshoe bat populations. Using the SARS-CoV reverse genetics system, we generated and characterized a chimeric virus expressing the spike of bat coronavirus SHC014 in a mouse-adapted SARS-CoV backbone. The results indicate that group 2b viruses encoding the SHC014 spike in a wild-type backbone can efficiently use multiple orthologs of the SARS receptor human angiotensin converting enzyme II (ACE2), replicate efficiently in primary human airway cells and achieve in vitro titers equivalent to epidemic strains of SARS-CoV. Additionally, in vivo experiments demonstrate replication of the chimeric virus in mouse lung with notable pathogenesis. Evaluation of available SARS-based immune-therapeutic and prophylactic modalities revealed poor efficacy both monoclonal antibody and vaccine approaches failed to neutralize and protect from infection with CoVs using the novel spike protein. On the basis of these findings, we synthetically re-derived an infectious full-length SHC014 recombinant virus and demonstrate robust viral replication both in vitro and in vivo. Our work suggests a potential risk of SARS-CoV re-emergence from viruses currently circulating in bat populations.

Conflict of interest statement

The authors declare no competing financial interests.


Figure 1. SARS-like viruses replicate in human…

Figure 1. SARS-like viruses replicate in human airway cells and produce in vivo pathogenesis.

Figure 2. SARS-CoV monoclonal antibodies have marginal…

Figure 2. SARS-CoV monoclonal antibodies have marginal efficacy against SARS-like CoVs.

Figure 3. Full-length SHC014-CoV replicates in human…

Figure 3. Full-length SHC014-CoV replicates in human airways but lacks the virulence of epidemic SARS-CoV.

Figure 4. Emergence paradigms for coronaviruses.

Figure 4. Emergence paradigms for coronaviruses.

Coronavirus strains are maintained in quasi-species pools circulating in…

Teddy Graham Lab - Biology

Let’s look at our pea example from lecture: Recall that there were 100 diploid plants. Flower color was controlled by a single gene with two alleles. The dominant allele (A) codes for purple flowers, and the recessive allele (a) codes for white flowers. Assume that we have been able to determine the genotypes of each and every plant and that we found:

60 plants have the genotype AA.

20 plants have the genotype Aa.

20 plants have the genotype aa.

60 plants have the genotype AA so the frequency of the AA genotype is 60/100 = 0.6

20 plants have the genotype Aa so the frequency of the Aa genotype is 20/100 = 0.2

20 plants have the genotype aa so the frequency of the aa genotype is 20/100 = 0.2

Notice that these genotype frequencies add up to 1. Explain why this must be so. Note that the fact they sum to 1 has NOTHING to do with the Hardy-Weinberg equilibrium.

20 plants have the genotype Aa. Each has a copy of the A allele for a total of 20 A alleles in the population.

Therefore, the number of A alleles: 120 + 20 = 140. The frequency of the A allele (p) = the number of A alleles (140) divided by the total number of alleles (200).

p = (120 + 20)/200 = 140/200 = 0.7
Calculate the frequency of the recessive allele, a, in the same manner: 20 plants have the genotype Aa. They each have a single copy of the a allele for a total of 20 a alleles.

20 plants have the genotype aa. They each have two copies of the a allele for a total of 40 a alleles. Therefore, the total number of a alleles: 20 + 40 = 60. The frequency of the a allele (q) = the number of a alleles (60) divided by the total number of alleles (200).

q = (20 + 40)/200 = 60/200 = 0.3
Notice that p and q sum to 1 -> 0.7 + 0.3 = 1. This is always true if there are only two alleles. A good check on your math is to calculate these independently of each other and check that they sum to 1.

A population in genetic equilibrium after a generation of mating will have the following genotype frequencies: p 2 2pq q 2



The mission of the the Duke University Program in Computational Biology and Bioinformatics is to train predoctoral students to become leaders at the interdisciplinary intersection of quantitative and biomedical sciences, using sophisticated computational methods to address contemporary challenges across biology and medicine.

Message from the Director

We're excited to offer graduate training in cutting-edge computational biology and bioinformatics. We encourage students take full advantage of the creativity-driven, highly interdisciplinary research environment at Duke. With participating faculty from Engineering, Medicine, and Arts & Sciences, students can choose from a wide range of research and training opportunities. I encourage you to explore our website for more information and to contact individual faculty members whose research interests you.

Wang Lab

Research in the Wang Lab focuses on the biogenesis, function, and defects of the Golgi in diseases. The Golgi apparatus is a central membrane organelle for protein trafficking and secretion in all eukaryotic cells. A unique feature of this organelle is a stack of flattened cisternae. Our research aims to understand how this structure is formed and why its formation is important for Golgi function under normal and disease conditions.

In vitro
reconstituted Golgi membranes (Learn more about our Research).

Research Areas of Interest
1. Golgi biogenesis, function, and defects in diseases
2. Membrane trafficking
3. Cell cycle regulation
4. Cell biology and diseases, including cancer, asthma and Alzheimer’s disease
5. Post-translational modifications, including phosphorylation, ubiquitination, N- and O-glycosylation

Watch the video: How to Stock a BiologyGenetics Lab (September 2022).

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    I confirm. And I ran into this. Let's discuss this issue. Here or at PM.

  7. Al-Asfan

    I'm sorry, I can't help you, but I am sure that they will help you find the right solution. Do not despair.

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