Glucose Modelling

Question 1

Model

Answer 1

The complexity of a model is lower than the complexity of the system
A good model represent all features which are relevant for the application
Genome-scale constrained:
Computationally represent metabolites. Can predict fluxed without a lot of info, assume steady state
Machine/Deep Learning:
Massive good quality data needed, lack of interpretation: why is the model doing this? black box, find patterns without human interaction
Dynamic models:
Math, quantify fluxes. Represent our knowledge of the fluxes

Question 2

Metabolism study

Answer 2

Medication don’t work on all individuals -> tailor treatment

Question 3

Dynamic modeling

Answer 3

mechanistic: based on mathematical description of biological phenomenon. ex: Glucose-Insulin system
modeled using differential equations, quantitative information on interactions, dynamics and regulation. equations reflect physiological knowledge. Handled by parameters
Minimal model: parsimonious description of the key components of system functionality.
Too simple: response not accurate
Too complex: requires info not available by studies

Question 4

Glucose regulatory system

Answer 4

Plasma glucose

Paslma Insulin

Question 5

T2DM

Answer 5

Insulin resistance -> less glucose stored/used in/by organs -> increase glucose in blood

Question 6

Glucose minimal model

Answer 6

Predict plasma glucose given insulin concentration following an oral glucose dose
Parameters: insulin sensitivity (amount of insulin to be produced to have certain amount of glucose: sensitivity body cell to insulin)
Input: glucose produce by liver and arrive in plasma from gut
output: glucose leave plasma from uptake of periphery and liver
Insulin action: enhance glucose uptake by periphery and inhibit glucose production by liver

Question 7

Parameter estimation

Answer 7

Interpolation
Simulate glucose
compute error
Fit model to data by changing parameters
reduce error

Question 8

Central dogma

Answer 8

DNA contains instructions for making protein which are copied to RNA and RNA use instruction to make protein.
DNA -> RNA -> Protein
Transcription: DNA to mRNA
Translation: mRNA to protein

Question 9

Genetic Variation

Answer 9

Genomics

difference in DNA among individuals

Question 10

Epigenetics

Answer 10

Heritable changes in gene expression
phenotype not genotype
factors affecting transcription

Question 11

Transcriptomics

Answer 11

mRNA level
expression level genes
microarray: complementary RNA binding to short sequence. Measuring gene expression level

Question 12

Proteomics

Answer 12

study of proteins

post translational modifications, shapes and folding of proteins

Question 13

Metabolomics

Answer 13

study of metabolites

ATP, amino acids: impact