Department of Biotechnology
The Department of Biotechnology will introduce students to the fascinating science behind gene editing, medical breakthroughs, and genetic research. Through our resource hub website, students can explore topics like CRISPR, PCR, cloning, GMOs, and synthetic biology, while also learning how biostatistics and bioinformatics help make sense of real-world data from clinical trials and genomics. Whether you're curious about how vaccines are developed or how DNA solves crimes, our site will break it down clearly in a way that is inspiring!
Medical Biotechnology
Medical biotechnology uses living cells and biological systems to develop technologies and products aimed at improving human health. It involves the research and creation of pharmaceuticals, vaccines, diagnostic tools, gene therapy, and regenerative medicine. This field combines principles of biology, medicine, and technology to fight diseases, understand genetic disorders, and develop personalized treatments for patients.
Biostatistics & Bioinformatics
Biostatistics involves designing studies, analyzing data, and drawing conclusions from research in public health, clinical trials, and epidemiology. It’s essential for understanding trends in disease, evaluating treatments, and informing healthcare policy.
Bioinformatics, on the other hand, focuses more on managing and interpreting large-scale biological data, such as DNA sequences or protein structures, using computational algorithms and software.
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What is Biotechnology?
By Melissa Chiang
What is it?
Biotechnology is the use of advances in molecular biology for applications in human and animal health, agriculture, the environment, and specialty biotechnological manufacturing.
What do biotech companies do?
Modern biotechnology provides breakthrough products and technologies to combat debilitating and rare diseases, reduce our environmental footprint, feed the hungry, use less and cleaner energy, and have safer, cleaner and more efficient industrial manufacturing processes.
Skills and understandings needed
Technical Skills
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Data Analytics
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Bioinformatics
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Regulatory Compliance
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Generative AI and Machine Learning
Soft Skills
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Cross-functional collaboration
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Communication
Pathway to Biotech
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A bachelor's degree in biotechnology, biology, or chemistry is usually required for entry-level positions
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A Master's degree or PhD is often required for advanced roles, such as biotechnologies or biomedical scientist
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Internships and hands-on experience as well as professional certifications can be helpful
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For high school: physics, chemistry, biology, algebra and calculus
Career paths
Biotechnology offers many different career paths to choose from. Some of these include...
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Biotechnologist
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Geneticist
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Biomedical Engineer
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Research Scientist
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Biochemist
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Microbiologist
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DNA Analyst
Works Cited
What is Bioinformatics?
By Hasika Chauhan
Definition
The science of collecting and analyzing complex biological data(ex.genetic codes). It ivolves computer technology to store, analyze, and disseminate biological data and evidence.
What does it matter?
Because bioinformatics combines different fields, it can provide a complete view of biological processes. Specifically, this can help identify new targets for drug development and improve disease diagnosis and treatment!
Bioinformatics allows researchers to analyze huge amounts of data that would otherwise be impossible to process, which leads to impressive scientific discoveries!
This field of study can have many applications, including evolutionary biology and genomic epidemiology.
Accomplishments in Innovation
Machine learning and AI in drug discovery:
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By analyzing large sets of data, AI can make accurate predictions that humans might otherwise miss.
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In 2025, AI will be a large part of the drug development process, allowing researchers to identify new drug candidates, predict their effects, and forecast potential side effects before clinical trials.
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This streamlined process makes for less risk, more individualized treatment plans, and therefore more effectiveness.
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Example: AI can help scientists identify which cancer treatments are most likely to work with a particular genetic mutation, leading to personalized and accessible treatments!
Career paths
Graduate High School (4 years) - focusing on science and data-related courses
Earn a Bachelor's degree (4 years) science field with a focus on info science
Gain Industry Experience (1 year or more) - gaining work experience
Earn a Master's Degree (2 years) - degree in any application of bioinformatics
Earn a PhD (optional, 3 years or more) - allows one to teach or lead clinics
Keeping Up - follow the news on the field to keep up with the latest innovations
Works Cited
What is Biostatistics?
By Yixuan Li
What is it?
Biostatistics is the intersection of statistics and biology, biostatistics uses data to solve real-world problems in health, medicine, and environmental science.
Biostatistics applies statistical techniques to analyze and interpret data from biological, medical, and public health research. It helps in making evidence-based decisions for improving healthcare and understanding life sciences.
What do biostatisticians do?
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Design clinical trials to test new medicines
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Analyze disease spread and public health data
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Predict outcomes for patient care
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Develop statistical models for genetics research
Why does biostatistics matter?
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Improves healthcare outcomes
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Advances medical research
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Helps control and prevent disease outbreaks
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Supports policy-making in public health
Pathway to Biostatistics
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Degree: Bachelor's in Math, Statistics, or Biology, then a Master's/PhD in Biostatistics.
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Courses: Calculus, Probability, Epidemiology, Computational Biology.
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Courses that recommend high schoolers to take in high school: AP Calculus, AP Statistics, AP Biology, AP Chemistry, etc.
Career paths
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Clinical Data Analyst: Interpret medical data from clinical trials.
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Epidemiologist: Study disease patterns to improve public health.
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Biostatistician in Pharma: Design and analyze drug development trials.
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Statistical Geneticist: Analyze genetic data to uncover hereditary patterns.
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Health Data Scientist: Use AI and statistical tools to interpret health data.
Key skills
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Proficiency in programming languages like R, Python, and SAS.
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Strong knowledge of probability and statistical modeling.
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Understanding of epidemiology and public health principles.
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Attention to detail for data cleaning and validation.
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Communication skills to explain findings to non-experts.
Most Recent Accomplishments
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COVID-19 Vaccine Development: Biostatisticians designed and analyzed global clinical trials to test the safety and efficacy of vaccines, helping curb the pandemic.
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AI in Disease Prediction: Integration of machine learning with biostatistics is now being used to predict diseases like Alzheimer's early.
Famous innovations or applications
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Human Genome Project: Biostatistics played a key role in mapping all human genes, paving the way for personalized medicine.
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Flu Vaccine Adjustments: Annually, biostatisticians analyze global flu strains to design effective vaccines.
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Public Health Milestones: Tracking disease outbreaks like Ebola and Zika to mitigate global health crises.
Works Cited
How is CRISPR Changing Medicine?
By Jaquline Chi Ching Lio
What is it?
CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats. It's a gene-editing technology that allows scientists to precisely modify DNA of living organisms.
The origin of CRISPR
The origin of CRISPR: Bacteria using it as a defense mechanism against viruses:
When a virus attacks, bacteria can capture a piece of the virus's DNA and store it in their own genome, which helps them recognize and fight off the virus if it attacks again in the future.
Scientists realized its potential for gene editing:
1. Scientists identify the specific DNA sequence they want to edit.
2. A piece of RNA, called guide RNA , is designed to match the target DNA sequence. An enzyme (usually Cas9) follows the guide RNA and acts like molecular scissors to cut the DNA at a specific location.
3. Guide RNA binds to the target DNA sequence and Cas9 cuts the DNA at the targeted spot.
4. After the DNA is cut, the cell tries to repair it. Scientists use this repair process to ass or remove genetic material, or to make changes to the DNA sequence.
Innovations/Applications
CRISPR is used in various applications:
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Creating disease models, identifying pathogenic genes, and developing targeted therapies.
-->It's also being explored for treating genetic disorders, cancers, and other diseases.
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It's also being explored for treating genetic disorders, cancers, and other diseases.
Skills required
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Bachelor's/advanced degree in: Biology / Biochemistry / Genetics
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Teamwork
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Communication
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Analytical and problem solving skills
Recent Accomplishment(s)
In late 2023, CRISPR - based medicine was first approved and produced CASGEVY -- a cure for sickle cell disease (SCD).
--> CASGEVY - a one-time gene therapy treatment for people age 12 years or older with sickle cell disease.
CRISPR-Cas9 was used to modify patients' blood-forming stem cells so that they can begin making new, healthy red blood cells.
Career paths
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Molecular Biologist
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Biomedical Engineer
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Bioinformatician
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Biotechnologist
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Research Scientist
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Pharmacologist
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PhD Student
Works Cited
What is a Clinical Trial?
By Yixuan Li
Overview
Clinical trials are the backbone of medical innovation. They involve carefully planned research on human participants to test new treatments and improve existing ones. These trials ensure that medications, therapies, and medical procedures are safe, effective, and beneficial for patients.
Key Concepts:
Purpose: To evaluate the effectiveness and safety of new treatments.
Process: Divided into 4 phases, each with specific goals:
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Phase I: Focuses on safety and dosage.
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Phase II: Tests effectiveness.
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Phase III: Compares with existing treatments.
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Phase IV: Monitors long-term effects after approval.
Participants: Both healthy volunteers and patients.
Informed Consent: All participants are educated about the study and must agree voluntarily.
Ethics: All trials must be approved by an ethics committee and follow strict protocols.
Real-World Impact:
For example, the rapid development of COVID-19 vaccines involved thousands of volunteers in multiple clinical trials across the world. Their success in these trials saved millions of lives and showed how powerful clinical research can be.
As a Biostatistician in Clinical Trials, You Might:
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Help design the study and determine the trial’s structure (e.g., randomized, double-blind), sample size, and data collection methods to ensure valid, unbiased results.
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Use statistical methods to calculate the required sample size, making sure the trial includes enough participants to detect meaningful effects.
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Decide how participants are assigned to different treatment groups to avoid bias.
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Monitor data as it’s collected, perform interim analyses to ensure safety and determine if early stopping is needed.
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Determine if the treatment had a statistically significant effect, and assess safety, efficacy, and side effects.
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Work with researchers and clinicians to interpret and communicate findings, make the data understandable and useful for healthcare decisions.
Why it matters:
Without biostatisticians, clinical trials wouldn’t have the rigor and reliability they need. They ensure that life-saving treatments are backed by real evidence and that patient safety is always prioritized.
Additional Learning Opportunities:
SIR Model
By Omay Li
What is the SIR Model?
The SIR model is a compartmental model that is an extremely useful framework in epidemiology. It consists of 3 ordinary differential equations, each describing the dynamics of one compartment - the number of susceptible(S), infectious(I), and recovered(R) individuals.
Susceptible - individuals who can get infected
Infectious - individuals who have the disease & are capable of passing it to others
Recovered - individuals who have either recovered from the disease or have died
*Note that the model assumes people recovered from the disease are fully immune to the disease & cannot be infected again & that total population is constant
Why is it important?
Epidemiologists & public health officials can use the parameters from the model to gain a more detailed understanding of the outbreak of a disease by using the model to predict the trend. During this, two really useful parameters are the basic reproduction number & effective reproduction number.
Basic reproduction number - the number of new infections on average caused by a single infected individual in a fully susceptible population. If this parameter is greater than 1, an epidemic will occur.
Effective reproduction number - very similar to the basic reproduction number; however, it measures at a time instant t where the population is no longer fully susceptible. If this drops below 1, herd immunity is achieved.
SIR model provides a rather simple but helpful framework to help researchers develop a better understanding of the disease, and also, note that it can be modified into many other extended forms that takes into account more nuanced situations & variables, e.g, vaccinated population, varied age group and etc.
Applications of the Model
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One of the most impactful applications of the SIR model is modelling the COVID-19 pandemic. The SIR model acted as the base of many other sophisticated epidemiological models that guided public health responses worldwide.
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Other interesting applications aside from COVID - modeling outbreaks of novel pathogens like the Ebola & Zika virus to help allocate medical resources, modelling populations of wildlife that contracted some kind of disease like rabies.