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Printable Handouts
Navigable Slide Index
- Introduction
- Very complex interactions of many players
- Technology development
- Is the Earth or the Sun the center of the Universe?
- What model?
- Observation: set-point viral load
- Viral load at steady state
- A simple model
- The equation of the simple model
- Disrupting equilibrium
- Apheresis experiment
- Extremely fast viral turnover
- T cell dynamics
- Telomeres over time
- Expression of Ki67
- Deuterated glucose
- Assessing T cell dynamics
- Modeling T cell dynamics
- Model equations
- Results: uninfected vs. infected
- Results: untreated vs. treated
- Correlation proliferation vs. %Ki67+
- Interpreting the results
- Explaining conflicting results
- D-glucose labeling revisited
- T cell dynamics
- T cell Receptor Excision Circles (TREC)
- Behavior of TREC with age and HIV infection
- TREC dynamics
- A simple model of TREC dynamics
- What do TREC dynamics tell us?
- In summary
- Further reading
- Acknowledgements
Topics Covered
- The role of modelling in immunology
- A first simple model: viral clearance
- The equation of the simple model
- Apheresis experiment
- Models of T-cell dynamics: proliferation
- Telomeres
- Expression of Ki67
- Deuterated glucose
- T cell Receptor Excision Circles (TREC) dynamics
- Models explain contradictory observations
- Models of T-cell dynamics: thymic production
- Models help design experiments
Links
Series:
- The Immune System - Key Concepts and Questions
- Periodic Reports: Advances in Clinical Interventions and Research Platforms
Categories:
Therapeutic Areas:
Talk Citation
Ribeiro, R.M. (2022, October 16). Mathematical modeling in immunology [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved October 13, 2024, from https://doi.org/10.69645/ISSQ8828.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. Ruy M. Ribeiro has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Other Talks in the Series: The Immune System - Key Concepts and Questions
Other Talks in the Series: Periodic Reports: Advances in Clinical Interventions and Research Platforms
Transcript
Please wait while the transcript is being prepared...
0:00
Welcome all.
My name is Ruy Ribeiro.
Today, I'd like to talk about
the impact of mathematics
in immunology,
which is a subject that has
a longer history than
one might think.
0:12
Perhaps the first
question is why even talk
about mathematics and modeling
in the context of immunology?
While the immune response
is a very complex
process with many
interacting players,
different types of cells,
myriads of molecules,
cytokines, chemokines,
interleukins.
The processes that these
cells and molecules
mediate are very complex,
redundant and dynamic.
We know from the
history of science that
mathematics is an
appropriate framework to
deal with this
complexity in helping to
explore relationships
and mechanisms.
0:46
That's even more the
case due to the recent
technology and
development explosion
that led to a massive influx in
our capacity to gather data,
exposing even more of the
complexity of the immune response.
However, to transform data into
information and knowledge,
we need a way to
analyze the big data.
Again, mathematics is the
appropriate language for that.
1:12
Of course, mathematics has
an ancient history
of doing just that.
Transforming data
into knowledge.
Is the Earth or the sun at
the center of the universe?
As depicted here,
a representation of the
Ptolemaic Universe.
On the right,
a representation of
the Copernican system.
Well, we know the
answer is neither.
But the point is that
even with hundreds of
years of observations
from many places,
so a lot of data,
the model of the
solar system was
wrong for over 1200 years.
Yet, Aristarchus of Samos,
in the third century
Before the Common Era,
using basic trigonometry
in the figure here,
had already inferred in
texts that are mostly lost,
that the sun is at the center
and the Earth moves around it,
and even spins on itself.
This shows that mathematics
has a long history
of translating data
into knowledge.