I am a Biomedical Engineering undergraduate student at the National University of San Marcos in Peru. My main interests lie at the intersection of biotechnology, neuroscience, and medical device development. I am particularly motivated by projects that combine biology, electronics, and computational methods to solve real clinical problems.
During my studies, I have been involved in research projects related to neuroengineering, biosignal processing, and machine learning applications in healthcare. I have also participated in student engineering organizations, where I coordinated research projects and promoted scientific activities among students.
My goal is to contribute to the development of accessible biomedical technologies, especially for low-resource settings in Latin America. Through this course, I hope to strengthen my understanding of biological engineering principles and explore how synthetic biology and biotechnology can be translated into real-world applications.
Biological engineering application I would like to work on a portable, non-invasive glucose monitoring biosensor based on enzymatic detection. The idea is to use enzymes such as glucose oxidase to detect glucose levels from alternative body fluids like sweat or saliva, instead of traditional finger-prick blood tests.
Week 2 Lecture Preparation Professor Jacobson 1. What is the error rate of polymerase and how does biology handle it?
DNA polymerases typically have an error rate of about 10⁻⁷ to 10⁻⁸ per base after proofreading. The human genome has around 3 billion base pairs, so without correction there would be many mutations each replication cycle. Biology solves this discrepancy through proofreading mechanisms in polymerases and additional mismatch repair systems, which reduce the effective mutation rate.
I would like to work on a portable, non-invasive glucose monitoring biosensor based on enzymatic detection. The idea is to use enzymes such as glucose oxidase to detect glucose levels from alternative body fluids like sweat or saliva, instead of traditional finger-prick blood tests.
Conventional glucose monitoring can be painful and inconvenient, especially for patients who need to test multiple times a day. Because of this, many people do not monitor their glucose regularly. A non-invasive and affordable device could make monitoring easier and more frequent, helping prevent long-term complications.
As a biomedical engineering student, I am interested in technologies that improve daily life for patients with chronic diseases. Diabetes is very common in many countries, including Peru, and access to continuous monitoring devices is still limited. A portable enzymatic biosensor could help improve adherence to treatment and overall quality of life.
2. Governance and policy goals
The main goal is to ensure that this technology is safe, reliable, and accessible, while also protecting patient data.
Sub-goals
Protect patient data and privacy
Ensure clinical accuracy and safety
Promote equitable access
3. Governance actions
Option 1: Clinical validation before commercialization
Purpose: Require proper clinical testing before the device enters the market.
Design:
Approval by national health authorities
Mandatory clinical trials
Assumptions:
Regulators have enough resources
Trials reflect real-world conditions
Risks:
Higher development costs
Slower innovation
Option 2: Strong data protection and user control
Purpose: Protect sensitive health data collected by the biosensor.
Design:
Encrypted data transmission
Clear consent for data sharing
User ownership of personal data
Assumptions:
Companies comply with regulations
Users understand consent options
Risks:
Increased system complexity
Higher development costs
Option 3: Public health subsidies for access
Purpose: Make the biosensor accessible to more patients.
Design:
Government or insurance subsidies
Integration into public healthcare programs
Assumptions:
Sufficient funding is available
Distribution systems are effective
Risks:
Budget limitations
Unequal access in remote areas
4. Scoring governance options
Policy Goal
Option 1
Option 2
Option 3
Prevent incidents
1
2
2
Help respond to incidents
2
2
1
Device safety
1
2
2
Environmental protection
2
2
2
Minimize costs
3
2
1
Feasibility
2
2
2
Not impede research
2
2
1
Promote constructive use
2
1
1
5. Recommended strategy
I would prioritize a combination of Option 2 and Option 3.
Protecting patient data is essential, especially in continuous monitoring devices. At the same time, these technologies should be accessible to patients from different socioeconomic backgrounds. Public health programs and subsidies could help ensure broader access.
Option 1 is also important, but regulations should be balanced so they do not unnecessarily slow down innovation.
6. Ethical reflection
One important ethical concern is the handling of sensitive health data. Continuous monitoring systems generate large amounts of personal medical information, which could be misused if not properly protected.
Another issue is inequality. If non-invasive biosensors remain expensive, only certain populations will benefit from them, which could increase health disparities.
Possible governance actions:
Strong data protection policies
Transparent consent mechanisms
Subsidies for low-income patients
Integration into public healthcare systems
Week 2 HW: DNA Read, Write, and Edit
Week 2 Lecture Preparation
Professor Jacobson
1. What is the error rate of polymerase and how does biology handle it? DNA polymerases typically have an error rate of about 10⁻⁷ to 10⁻⁸ per base after proofreading. The human genome has around 3 billion base pairs, so without correction there would be many mutations each replication cycle. Biology solves this discrepancy through proofreading mechanisms in polymerases and additional mismatch repair systems, which reduce the effective mutation rate.
2. How many ways are there to code for an average human protein? Why don’t all of them work? Because of the degeneracy of the genetic code, many amino acids are encoded by multiple codons. For an average protein of a few hundred amino acids, there are astronomically many possible DNA sequences that could encode it. However, not all sequences work well because of factors like codon bias, mRNA secondary structure, GC content, regulatory sequences, and effects on translation efficiency and protein folding.
Dr. LeProust
1. Most commonly used method for oligo synthesis The most common method is phosphoramidite solid-phase chemical synthesis.
2. Why is it difficult to make oligos longer than 200 nt? Each synthesis step has a small error rate. As the oligo length increases, these errors accumulate, leading to a low proportion of full-length, correct sequences.
3. Why can’t you make a 2000 bp gene directly? Because the cumulative error rate would be extremely high. Instead, shorter oligos are synthesized and then assembled into longer DNA fragments using techniques like PCR assembly or Gibson assembly.
George Church Question
Essential amino acids and the lysine contingency
The ten essential amino acids in animals are: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and (in some contexts) arginine.
The lysine contingency refers to engineering organisms that depend on an external supply of lysine to survive. Since lysine is essential and cannot be synthesized by animals, this creates a biological containment strategy. It reduces the risk of engineered organisms surviving outside controlled environments.
