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BCH210H Assignment
Creation date：2024-06-04 15:41:39
Belonging category：生物biology
Tag BCH210H
Assignment

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BCH210H  Assignment

BCH210H Fall 2023 Assignment – 20% of final grade
Chaos at the membrane: protein dysregulation and cancer
As you are learning about the importance of proteins throughout the course, complete
the following assignment and submit a draft electronically on Quercus. Your draft will be
graded, and a TA will provide you with feedback so that you may elaborate on the various
sections and submit a final written assignment by the end of the course.
The purpose of this assignment is to gain experience researching a topic and
communicating your understanding about protein structure and function, including how
they are categorized and interact with different molecules in the body. You will also work
with a variety of computational tools and technologies to access information and
understand a protein at the molecular level. These tools allow biochemists to
demonstrate how a protein works in the cell based on its structure and the interactions
that occur. This assignment is designed to take you through some of the tools that compile
what is known about a protein and be used to analyze a protein’s structure and/or
function based on the amino acids present.
You are expected to complete this assignment independently and are responsible for
maintaining academic integrity in this course. You must reference any primary sources
of information using CSE format (Author Year) and properly paraphrase any ideas that are
not your own original work. Please see the BCH210H Lib guide also posted on Quercus for
a refresher on how to paraphrase ideas and cite your references using CSE format. Provide
an alphabetized list of references at the end of your draft. You may also wish to consult
the writing centre or resources at your college for help if you haven’t written an
assignment at U of T before. All submissions will be screened using the University of
Toronto plagiarism detection tool and any questionable behaviour will be investigated.
Furthermore, the knowing use of generative artificial intelligence tools, include ChatGPT
and other AI writing and coding assistants, for the completion of, or to support the
completion of this assignment, may be considered an academic offence. This course is
designed to promote your learning and intellectual development, and representing an
idea that was AI-generated as one’s own idea does not help you achieve the course
learning objectives.
Please read through the complete instructions before starting the assignment and
speak with Dr. Bathurst during office hours if you have any questions.
BCH210H Fall 2023 Draft Assignment
2
Draft Final
Introduction See instructions below. Use feedback received from draft to
improve your introduction.
Add any more details based on what
you’ve learned later in the course.
Target Protein Structure See instructions below. Use feedback received from draft to
improve this section.
Using what you learn in week 5, elaborate
on your membrane protein’s structure and
function.
Add hydropathy plot figures and
discussion.
Protein Purification and
Characterization
See instructions below.
Write this section as a
table.
Bullet points or full
sentences are fine. But
make sure to include all
details.
Use feedback received from draft to
improve this section.
Write this section in paragraph form.
Ligand Binding Site and
Signaling
See instructions below. Use feedback received from draft to
improve this section.
Elaborate on growth factor interaction
with target protein, including adding a
figure and discussion of amino acid
interactions.
Using what you learn in week 6, discuss
the downstream signaling cascade
resulting from the growth factor binding
its receptor, as well as mechanisms of
hyperactive receptor signaling in cancer.
BCH210H Fall 2023 Draft Assignment
3
Assignment Requirements:
Signaling molecules like hormones or growth factors bind to their receptors, often located
in the cell membrane, to elicit a response in the cell. But what happens when this signaling
goes awry? Dysregulation of membrane proteins is a key hallmark of cancer. Receptors
are often overexpressed and/or hyperactive in cancer cells, leading to continuous
activation of downstream signaling pathways that result in uncontrolled growth,
proliferation, and other events that promote tumour growth. Understanding the
structure and function of these signalling molecules and their receptor proteins is
essential in order to understand how they can drive tumour growth, as well as to design
drugs that can target and bind the receptors to inhibit signaling.
For this assignment you have the choice to research one of two different growth factors:
epidermal growth factor (EGF) or vascular endothelial growth factor A (VEGF-A). Note that
there are different VEGF proteins in the VEGF-family (PGF, VEGF-B, VEGF-C, VEGF-D). This
assignment will specifically use VEGF-A.
Include the following information in your draft using separate section headings and refer
to the rubric on Quercus for how each section will be evaluated. You will need to consult
the literature to address each topic using full sentences. For the final assignment, there is
a maximum of 1 page of text for each of the 5 sections, for a total of ~2000 words.
Therefore, keep the final word count in mind when writing your draft. You’ll need to be
concise, provide the necessary details and avoid irrelevant information. Images, their
associated figure titles and captions, and the reference list are not included in the page
and word limit. Please use 12-point font, single spaced, margins no smaller than 1.5 cm,
and submit your document in a pdf format. Do not include a title page.
Feedback on the draft will be provided in time for the final submission later in the course.
However, all the information you need to complete the final assignment is provided in
the first 6 weeks of the course, therefore you can begin working on the final assignment
immediately.
The final written assignment will be awarded style marks for your writing, so make sure
you leave time to edit your assignment and review it for any grammatical errors. If you
have not yet submitted a written piece of work at U of T, you may want to consider
reaching out to the Writing Centre or your college for assistance prior to the due date.
Draft Submission Details: Save your file as Name.Draft.BCH210H.Fall.2023.pdf (where
‘Name’ is your lastname.firstname). Upload and submit your assignment as a pdf to
Quercus at the latest by Tuesday October 10, 2023, at 5PM ET.
MAKE SURE YOU SUBMIT YOUR ASSIGNMENT AT LEAST 30 MINUTES BEFORE IT IS DUE
AS ASSIGNMENTS THAT ARE SUBMITTED EVEN 1 MINUTE LATE WILL BE MARKED LATE
AND PENALIZED. Late submissions will be penalized -10%/day.
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EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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