Dilution and ELISA model submission
Safety gate · before any work
- Wear the required PPE, keep the bench clear, handle equipment only as directed, and know where the eyewash, sink, and spill kit are before you start.
- Human samples and data stay private: label with a code, never a name, and dispose of materials in the correct waste container, then wash your hands.
Do now
Submit your standard curve, model ELISA data, and interpretation to close the dilution week.
- Hand in
- Standard curve graph, model ELISA data table (colors and concentrations), short interpretation of positive results, and one error sentence.
- Where
- Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
You get two school days for every day you were absent, so this deadline moves with you.
You know which model wells were positive, but a reviewer who was not in the room does not. How do you package your , your data, and your reasoning so they could replicate and trust your call?
Submit your , model data, and interpretation to close the dilution week.
- • You will be able to submit a labeled and data.
- • You will be able to interpret model results against a curve.
- • You will be able to name a source of measurement error.
- Name the three parts a complete lab submission should include so someone else could check your work.
- Why does naming a source of error in your own data make the result more trustworthy, not less?
- 1Finalize your standard-curve graph and label the axes clearly.
- 2Tabulate your model colors and the concentration you assigned each well.
- 3Write a short interpretation naming which samples were positive and how you knew.
- 4Add one sentence on a source of error in reading colors by eye.
- 5Submit your graph, , and interpretation on the class site.
- 6Confirm it is turned in and note one thing you want to do more carefully in the wet lab.
What did this day actually feel like?
Dilution and ELISA model submission
Dilution calculations, standard curve, model diagram.
Turned in: lab report → Lab Reports folder
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
The same day, drawn.
Dilution calculations, standard curve, model diagram.
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
🛠 Get unstuck · pick your level
Lab day: Tier 1 is the whole class at the bench. No extension today.
🔑 Today's words · 5
Tap a word in the lesson for a plain meaning and one example. Recycled into next week's Do-Now.
Do the work · 80-minute blockfirst 5 min = hook▸
💡 Big idea: A result travels with its graph, data, and interpretation and openly names its error, so that another scientist can replicate it and know exactly how far to trust it.
- 0-15 minFinalize the standard-curve graph: check axis labels, units, and best-fit line
- 15-30 minComplete the : well ID, observed color, assigned concentration for every well
- 30-50 minWrite the interpretation paragraph: name positive results and explain the evidence from the curve
- 50-62 minWrite the error sentence: name one specific source of uncertainty in reading colors by eye
- 62-72 minSubmit graph, , and interpretation to the class site
- 72-80 minConfirm all items show as turned in; note one thing to do more carefully in the wet lab next week
- • The goal of any lab exercise is not just to run the procedure; it is to produce a record someone else can evaluate.
- • Today you finalize and submit everything from the dilution week: the , the data, and the interpretation.
- • Next week you run the real wet ; what you notice about your errors today shapes how carefully you work then.
- • Exit goal: all three submission items confirmed as turned in before you leave.
- • A complete submission packages the visual evidence (graph), the raw data (table), and the interpretation together.
- • Naming a source of error is part of good science; it shows you understand the limits of your own data.
- • Reviewing your model data before the wet helps you anticipate where precision matters most.
Concentration, serial dilution, standard curves, antigen–antibody binding, direct vs. indirect ELISA. · Dilution and model submission
Day 5 of this lesson. Open this exact section in myPLTW (find it in Clever, Microsoft sign-in), then do the work below.
Do this: Open the pre-lab submission in myPLTW for Activity 1.1.5 ELISA and confirm your standard-curve work is ready for next week's wet lab.
Submit your complete pre-lab packet: dilution plan, diagram, and standard-curve table.
Standard-curve table should be done (Thursday); full pre-lab packet submitted today.
Pre-lab packet submission visible in the course shell.
The official PLTW activity stays inside myPLTW. If myPLTW will not open, use F1 and E1-E3 on this page to complete today's local evidence decision, then make up the official activity when access returns. Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
Check things off as you work, then submit. This tells Mr. Mendoza how you're doing so he can help the class. It does not replace turning in your producible through the submission route shown below.
Use the code Mr. Mendoza gave you, not your name. Saved on this device.
Concentration, serial dilution, standard curves, antigen–antibody binding, direct vs. indirect ELISA. · Dilution and ELISA model submission
Open the pre-lab submission in myPLTW for Activity 1.1.5 ELISA and confirm your standard-curve work is ready for next week's wet lab.
Standard-curve table should be done (Thursday); full pre-lab packet submitted today.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Submit your , model data, and interpretation to close the dilution week.
- Finalize your standard-curve graph and label the axes clearly.
- Tabulate your model colors and the concentration you assigned each well.
- Write a short interpretation naming which samples were positive and how you knew.
- Add one sentence on a source of error in reading colors by eye.
- Submit your graph, , and interpretation on the class site.
- Confirm it is turned in and note one thing you want to do more carefully in the wet lab.
Lab report: graph, model (colors and concentrations), short interpretation of positive results, and one error sentence.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Use the checklist just below and upload by 11:29 PM for full credit. Absent with an excused absence? You get two school days for every day you were absent, so this deadline moves with you.
| Task | Who |
|---|---|
| Finalize your standard-curve graph and label the axes clearly. | _______ |
| Tabulate your model colors and the concentration you assigned each well. | _______ |
| Write a short interpretation naming which samples were positive and how you knew. | _______ |
| Add one sentence on a source of error in reading colors by eye. | _______ |
| Submit your graph, , and interpretation on the class site. | _______ |
| Confirm it is turned in and note one thing you want to do more carefully in the wet lab. | _______ |
Working solo? Put your own name in "Who" for every row.
- You will be able to submit a labeled and data.
- You will be able to interpret model results against a curve.
- You will be able to name a source of measurement error.
- 1Do thisSubmit your standard curve, model ELISA data, and interpretation to close the dilution week.
- 2Use this resource
- 3Submit thisLab report: Standard curve graph, model ELISA data table (colors and concentrations), short interpretation of positive results, and one error sentence.
- 4Submit it here
- 1Open the drop folder.
- 2Sign in with your district Microsoft account, not a personal one.
- 3Upload the file, named Lastname_Firstname__Assignment Title.
- 4Your own upload panel says Uploaded with a green check: that is your receipt.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Genetics of Disease (Medical Interventions) › Concentration, serial dilution, standard curves, antigen–antibody binding, direct vs. indirect ELISA. › Lab reportOpen the drop folder
Learn it · deck, reading, and vocabulary▸
The deck carries the prior idea forward, lets you inspect an analogy, maps the rule to biology, and ends with the same evidence decision and exit ticket used on this page.
Generated from this lesson's canonical data with a red-team citation check.
An binds only its specific , so the color it produces reliably signals that exact target and how much of it was present, which is what makes the a trustworthy diagnostic.
A result travels with its graph, data, and interpretation and openly names its error, so that another scientist can replicate it and know exactly how far to trust it.
A bridge prototype is tested against a load limit, cost limit, and user need before revision.
- Which requirement is a criterion?
- Which limit is a constraint?
- What test result should trigger a redesign?
A design improves when evidence is compared with explicit criteria and constraints.
Biomedical designs also require , ethics, and biological validation beyond a physical prototype test.
- • Bridge requirements map to design criteria.
- • Load results map to E1-E3.
- • Revision maps to the next evidence-based iteration.
Driving question: You know which model wells were positive, but a reviewer who was not in the room does not. How do you package your , your data, and your reasoning so they could replicate and trust your call?
What you already know: An binds only its specific , so the color it produces reliably signals that exact target and how much of it was present, which is what makes the a trustworthy diagnostic.
New idea: A result travels with its graph, data, and interpretation and openly names its error, so that another scientist can replicate it and know exactly how far to trust it.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Dilution and ELISA model submission. Use it with E1-E3; it is a model or context image, not experimental or patient data. What to notice: Trace the labeled testing, treatment, or biological process and identify where evidence limits the decision.
- Observe or measure the relevant feature in Dilution and model submission.
- Organize the observation with a stable evidence ID.
- Apply this rule: A design improves when evidence is compared with explicit criteria and constraints.
- Choose the option the evidence supports and state the limit of the conclusion.
Real biomedical example: You know which model wells were positive, but a reviewer who was not in the room does not. How do you package your , your data, and your reasoning so they could replicate and trust your call?
What the evidence supports: E1-E3 and F1 support the daily take-home when the response meets the stated success criteria.
What it cannot prove: The package does not support claims beyond this lesson's or any real patient diagnosis.
- • : A molecule, often on a germ's surface, that the immune system recognizes as foreign and responds to by making matching antibodies.
- • : A Y-shaped made by the immune system that binds to a specific foreign target, marking it for destruction or blocking its effect.
- • : A lab test that uses antibodies linked to an to detect and measure a specific , with a color change signaling its presence.
- • : A stepwise process of repeatedly diluting a sample by the same factor to make a range of lower, known concentrations.
- • : A graph made from samples of known concentration, used to read off the unknown concentration of a test sample from its measured signal.
- • : The specific molecule an acts on, fitting into the enzyme's active site so it can be changed into a product.
- • : A measure of how much light a sample blocks at a given wavelength, used to estimate how concentrated a substance is in a solution.
Use it now: Choose one decision option. Cite E1 and E3, then explain how the rule connects the evidence to your choice.
Go further, optional: The source links below are optional enrichment. Every fact required for today's local evidence decision appears in this lesson package.
A complete submission packages the visual evidence (graph), the raw data (table), and the interpretation together.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
A result travels with its graph, data, and interpretation and openly names its error, so that another scientist can replicate it and know exactly how far to trust it.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
You will be able to submit a labeled and data.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-GEND-2026-09-18 · Simulated classroom evidence scenario
Your role: medical interventions team member
Decision: Your team must decide what the evidence from Dilution and model submission supports before submitting the lab report named on the lesson page.
- • Keep the current design.
- • Revise the feature that misses a criterion.
- • Run one more fair test before choosing.
Response: State one choice, cite at least two evidence IDs, explain the rule that connects them, and add one limitation. Submit it as the lab report.
Claim ceiling: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Dilution and model submission. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Reason for review: Your team must decide what the evidence from Dilution and model submission supports before submitting the lab report named on the lesson page.
Context: A result is only as trustworthy as its documentation: the graph, the raw data, and the interpretation have to travel together, and naming your own sources of error is what turns a claim into evidence someone else can check.
- • T1: Finalize your standard-curve graph and label the axes clearly.
- • T2: Tabulate your model colors and the concentration you assigned each well.
- • T3: Write a short interpretation naming which samples were positive and how you knew.
- • T4: Add one sentence on a source of error in reading colors by eye.
- • T5: Submit your graph, , and interpretation on the class site.
- • T6: Confirm it is turned in and note one thing you want to do more carefully in the wet lab.
- • E1: A complete submission packages the visual evidence (graph), the raw data (table), and the interpretation together.
- • E2: A result travels with its graph, data, and interpretation and openly names its error, so that another scientist can replicate it and know exactly how far to trust it.
- • E3: You will be able to submit a labeled and data.
Measurements: Use only the measurements, units, graph, or counts supplied in today's task. No additional patient measurement is implied.
Figure finding: Teaching diagram for Dilution and model submission. Trace the labeled testing, treatment, or biological process and identify where evidence limits the decision. This is a teaching model, not patient or experimental data.
Uncertainty: This is a composite classroom scenario. Missing history, measurements, or confirmation tests remain unknown and limit the conclusion.
For a linear , y = mx + b. To estimate an unknown concentration, use x = (y - b) / m.
A is y = 0.40x + 0.10. An unknown signal is 0.90. x = (0.90 - 0.10) / 0.40 = 2.0 concentration units.
Signal units belong on y. Concentration units belong on x. Confirm the unknown falls inside the standards before interpreting it.
Use the equation or graph supplied today to estimate one unknown. Show the substitution, concentration unit, and range check.
Students often think Students often think admitting a source of error weakens their result, so they leave it out to look more confident.. The trap: Naming a source of error strengthens a result, because it shows you know the limits of your data and where a reader should be cautious. A result with no stated limits looks less trustworthy to a scientist, not more, because every real measurement has error, and hiding it just makes the reader find it for you.
Interpretation: Well 1 was positive because its color matched the most concentrated standard on my curve, while Well 4 was effectively negative. I knew this by reading each well's color signal across to the best-fit line and down to its concentration.
Source of error: reading colors by eye is subjective, so two people might assign slightly different concentrations to a medium-blue well; a plate reader would reduce this error.
For the wet lab I want to: pipette more carefully so my dilution steps are exactly tenfold.
This model shows the level of evidence and organization needed to complete: A packaged submission with the labeled standard curve, the model ELISA data table, a short interpretation of which samples were positive, and one source of error.
- State the question and method.
- Present the observations and data with units.
- Explain the result, limitations, and next investigation.
Keep the structure. Replace the question, facts, measurements, and evidence. Then recheck units, vocabulary, and whether the conclusion goes beyond the evidence.
Also due today: Submit graph, data table, and interpretation on the class site.
- CER:
- Claim, Evidence, Reasoning: make a claim, back it with evidence, explain your reasoning.
- SOP:
- Standard Operating Procedure, the exact steps to follow (especially in a lab).
- Tracker:
- Your PLTW progress log where you record completed evidence.
- myPLTW:
- The PLTW course site where you do the online activities. Find it in Clever with your Microsoft sign-in, right next to Schoology.
Tap the speaker to hear a term. Add two of these to your notebook glossary with a definition and an example in your own words.
Pick just 2 or 3 words from today and make them yours: write what each one means in your own words, name the context clue or evidence that helped, then give one example from what you actually did in Dilution and ELISA model submission. Try your own words first; the glossary is there if you get stuck. This is voluntary and counts as extra credit, so keep it short.
Saved on this device. Show Mr. Mendoza or add these to your notebook glossary to claim the extra credit.
Classroom documents for this lesson are posted in Schoology. Open Clever, then Schoology, and find each one by the name shown on its card.
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Placement rationale
Matched model, dilution, by path:Medical-Interventions/Unit-1_How-to-Fight-Infection/1.1_The-Mystery-Infection; keywords:elisa, , dilution. Score 154. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Open this when the class reaches this activity and use it to complete the required lesson artifact.
Placement rationale
Matched model, dilution, by path:Medical-Interventions/Unit-1_How-to-Fight-Infection/1.1_The-Mystery-Infection; keywords:elisa, . Score 146. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Placement rationale
Matched model, dilution, by path:Medical-Interventions/Unit-1_How-to-Fight-Infection/1.1_The-Mystery-Infection; keywords:, dilution. Score 142. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
How to get there: open Clever and sign in with your Microsoft (district) account. Both myPLTW and Schoology are in Clever. Do the activity in myPLTW. Turn the work in on this site or hand it to Mr. Mendoza, because that is the step that counts as submitted. Schoology only shows your report-card grade later.
Check yourself · commit, then reveal▸
Claim ceiling for this check: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Dilution and model submission. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
A classmate submits an ELISA lab report with a beautiful graph and a data table, but no sentence about sources of error and no written interpretation of which samples were positive. Name two things a reviewer could NOT do with this submission, and why.
Write an answer and pick a confidence to unlock the key.
Fast retrieval with instant answers, not the commit-then-reveal check above. Try each from memory first: write what you remember about the earlier units, then check yourself here.
Go further and get help▸
I can name the procedure's purpose and the evidence I will record. I can identify each named hazard and the control that reduces it: Wear the required PPE, keep the bench clear, handle equipment only as directed, and know where the eyewash, sink, and spill kit are before you start. My data table is ready before materials are handled.
Finish the checklist before you handle any material.
- • Wear the required PPE, keep the bench clear, handle equipment only as directed, and know where the eyewash, sink, and spill kit are before you start.
- • Human samples and data stay private: label with a code, never a name, and dispose of materials in the correct waste container, then wash your hands.
- 1Before materials are handled, identify the purpose, variables or comparison, controls, measurement units, and stop-work condition.
- 2Finalize your standard-curve graph and label the axes clearly.
- 3Tabulate your model ELISA colors and the concentration you assigned each well.
- 4Write a short interpretation naming which samples were positive and how you knew.
- 5Add one sentence on a source of error in reading colors by eye.
- 6Submit your graph, data table, and interpretation on the class site.
- 7Confirm it is turned in and note one thing you want to do more carefully in the wet lab.
- 8Record each result in the prepared table before interpreting it. Mark missing, repeated, or invalid results truthfully.
- 9Complete the named cleanup and waste route, remove PPE safely, wash hands when required, and confirm the station is ready for the next group.
| Trial or sample ID | Independent condition | Measured result with units | Observation before interpretation | Quality-control note |
|---|---|---|---|---|
Before the procedure, predict the result and cite the rule behind the prediction.
After the procedure, compare the result with the prediction and name one limitation or source of uncertainty.
What today's skills lead to. These are real health-science careers this course builds toward. Tap one to see, on the US Department of Labor's O*NET site, what the job actually involves, what it pays, and how fast it is growing.
The bench work needs equipment you do not have at home. Do the thinking half now: read the procedure, write your prediction, and set up your data table so it is ready.
Back in class. Ask Mr. Mendoza for the class data set, or for a bench slot to run it yourself. Do not submit a Lab report with invented numbers.
Class still runs. Complete the online activity above (it's self-guided). Need the concept taught without a teacher? Use this authoritative explainer:
HHMI BioInteractive (preview; use fallback if blocked)- CompleteEvery required part of the artifact is present, nothing left blank.
- AccurateThe science and the data are correct and match the evidence.
- Scientific reasoningYou explain your claim with evidence and reasoning (CER), not just an answer.
- Professional communicationClear, organized, labeled, and written the way a clinician or scientist would.
- SubmittedTurned in the right way, on the class site or handed to Mr. Mendoza in class, and confirmed. Not in Schoology: that is where the report-card grade appears later.
- Error analysis and method · counts doubleName a specific limit of the method and how it moved your result, and compare what you predicted to what happened. "Human error" does not count; say what about the procedure or instrument caused it.