Department ofPhysics
Faculty of Science@Lack of math: Side effects include
dizziness and distrust
CIHR Logo Usage Guidelines

Itʼs important to read the following carefully.
All guidelines must be followed if you are to be allowed to use the Canadian Institutes of Health Research (CIHR)ʼs logo.

• The logo may not be used on any material that infringes on the intellectual property of CIHR or other rights, or that disobeys any
municipal, provincial, federal, or international law.

Nerd Nite Tokyo — 11 May 2018
• The logo may never be used in any way that would defame CIHR.

• The logo must not be distorted in perspective or appearance, or changed in any manner whatsoever.
• The logo may only be used on Web pages that make accurate references to CIHR, and must be displayed on the same page as the reference. The Web
page should be set up so that it is clear to the viewer that the Web page is the companyʼs Web page and not that of CIHR. In particular, on any Web pageon which it uses the logo, the company must also display, in the primary and more prominent position, its own Web page title, trademarks, and logos.
ロゴタイプ＋フルネーム一行組

白バック

Catherine Beauchemin
カトゥリン · ボシゥメン• On the Web, the logo must retain an active link to CIHRʼs homepage at http://www.cihr-irsc.gc.ca/.
CIHR reserves the right to withdraw permission to display its logo, and may request any party that has previously been granted permission to change or
discontinue any use of the logo.
The CIHR logo may never be used except in accordance with what is outlined above.

CIHR logo
CMYK eps

Funded by:
CIHR logo
Black and Pantone 356 eps

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 1/22

Quick intro to me...
I was born/grew up inMontréal, Canada (French)
My research is in a field I dubbed
ViroPhysics(using physics’ approach to study viruses)
1/3 of the time:
Senior Visiting Scientist
Interdisciplinary Theoretical andMathematical Sciences (iTHEMS)
programme at RIKEN
Wako, Japan.
2/3 of the time:
Professor of Physics
Ryerson University
Toronto, Canada.
C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 2/22

There is a crisis of trust in physicians

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 3/22Distrust of clinical practice somewhat justified

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 4/22

Distrust of clinical practice somewhat justified

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 5/22

A problem with the institutional culture
In the shame/blame environment, where errors are seen as a form of personal
moral failure that shatters the culture of infallibility inculcated in physicianssince the first day of professional training, the physician’s ultimate fear —“losing face” in front of one’s peers.
Prof. Albert A. Wu, MD
Medical Error: The Second Victim.
British Medical Journal 320: pp.726–727, 2000
Burnout, compassion fatigue, and mental illness are enormous challenges
to our profession, and working in a culture that demands infallibility is apredisposing factor that we can and should address.
Linda Fineman, DVM, DACVIM (Oncology)
To Err Is Human
American Veterinarian, January 19, 2018

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 6/22

A problem with basic (lab) health research

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 7/22

A problem with basic (lab) health research

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 8/22

Why are health results not reproducible?
reproducible can be predicted to recur even when experimental conditions
may vary to some degree (robust under change). → Scientific req

replicable ability to obtain an identical result when an experiment is performed under precisely identical conditions (requires no change).

A
B

replicated 3x

Replicated #1: A > B

reproduced 3x

Reproduced #1: A ≈ B
C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 9/22

Underestimating variability = bad scienceABreplicated 3x
ABreproduce 2x

reproduced 3x

reproduced 3x

replicated 3x

Replicated #1: A > B
Replicated #2: A < B

Reproduced #1: A ≈ B
Reproduced #2: A ≈ B

Results disagree (wrong)

Results agree!

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 10/22

Health research suffers from lack of MATH!

• Experimental results alone, i.e. in the absence of a mathematical description,
are of limited scope (non-predictive) and open to mis-interpretation.
• The qualitative (rather than quantitative) nature of experimental findings
makes them hard to validate or challenge.
• Both experimental and theoretical investigations together are required torobustly advance a research field.

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 11/22

Virophysics: the laws of virus infectiondT= −βT Vinf
dt
dE1
nE
dEi=2,3,...,nE nEnE
Eclipse= βT Vinf − E1=
Ei−1 − Eidt
τEdt
τE
τEnI
dIj=2,3,...,nInInIdI1 nE
Infectious=EnE − I1= Ij−1 − Ijdt
τEτIdtτI
τInnIIXXdVinfdVtot
Virus= pinf
Ij − c|inf{zVinf}= ptotIj − ctotVtot
dtdt
j=1
j=1
Target|{zproduction}

infectivity decay

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 12/22

Matching math model with experiments

Single-cycle assay (MOI = 3 virus/cell)

Infectious virus (pfu/mL)

10(peak virus)

32

1010

1

Vpeak = p/c

10Infectious virus (pfu/mL)

410-5

Multiple-cycle assay (MOI = 10 virus/cell)

Length of eclipse
phase (τE)

10910810710610510410310210plateau width isinfectious lifespan (τI)
growth rateis dependanton β, τE,τI, c, p

decay rate isclearance rate (c)010 0481216
Time post-infection (h)202402448
72
96Time post-infection (h)120

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 13/22

Differences between wild-type & mutant strain
Eclipse phase, τE (h)Single Cycle (SC)106

Prod. rate (RNA/h/cell)

Virus (PFU/mL)

105
104
103

0 2 4 6 8 10 12 14102

101

102

103

104

Infectious lifespan, τI (h) Infectiousness (infection/RNA)WT-H275 (PFU/mL)
MUT-Y275 (PFU/mL)

101

04121620Multiple Cycle (MC)1012
Virus (PFU/mL or RNA/mL)

81010
0 10 20 30 40 50 60 70 10−2 10−1 100
108

Infecting time, tinf (min)101102

Basic repro. num., R0106
104

WT-H275 (PFU/mL)

102

MUT-Y275 (PFU/mL)

WT-H275 (RNA/mL)
MUT-Y275 (RNA/mL)

1000244872
Time (h)98120

0 5 10 15 20 25 30 35 40 102

103

104

105

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 14/22

Math model predicts experimental outcomes[This is not a fit!]
H:Y = 100:0
1101080.8

60.6

40.4

20.2

0010
10101010

H:Y = 80:20

1210Virus (RNA/mL)

10

H:Y = 0:100

12Infected Cell Fraction

121101080.8

60.6

40.4

20.2

1010
10

100010
H:Y = 50:50

H:Y = 20:80

10110

10

80.8

60.6

40.4

20.2

101010

100

10024

48

72

024

48

72

024

48

72

0

Time post-infection (h)C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 15/22

Yay! Let’s do it again for anew WT and MUT pair... oups

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 16/22

NEW WT params don’t all match OLD ones
Eclipsephase,
phase,ττEE (h)(h)
EclipseSingle Cycle (SC)

106

Prod.
rate
(RNA/h/cell)
Prod.
rate
(RNA/h/cell)

Virus (PFU/mL)

105
104
103
23451 11005 26 47 68 98 10
10 1112 12
14 13 1010
102 10103 10 10410
Infectiouslifespan,
lifespan,ττI I (h)(h)
R(infection/RNA)
0 /burst
Infectious
Basicrepro.
num., R0102
WT-I223 (PFU/mL)

101

MUT-V223 (PFU/mL)
10004121620Multiple Cycle (MC)1012
Virus (PFU/mL or RNA/mL)

81010−101−100 2010 30
20 4030 50
40 60507060 10102 −2 1010
30 10104 10 105 10
Infectingtime,time,ttinfinf (min)(min)
Basicrepro.
num., R0Infecting
Burstsize(RNA/cell)

108
106
WT-I223 (PFU/mL)

104
102100

WT-I223 (RNA/mL)
MUT-V223 (PFU/mL)
MUT-V223 (RNA/mL)
02448
72Time (h)

98

120

225 35304035 1010
300 55 10101515
20 2025 30

31010445610105 1010610

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 17/22

Problems come from experiment, not analysis
Single Cycle: Comparing Data Sets, H275 WT

1012

1010

1010

108108V (PFU/mL or RNA/mL)

V (PFU/mL or RNA/mL)

1012

106
104
102100 048Time (h)12H275 PFU
H275 RNA
I223 WT PFU
I223 WT RNA
1620Multiple Cycle: Comparing Data Sets, H275 WT

106
104102100 02448

Time (h)72

H275, Pizzorno
H275 PFU
H275 RNA
I223 WT PFU
I223 WT RNA
96120

Our math analysis found that between OLD → NEW experiments:A lower virus production rate (p) → lower peak virus in MC.

A shorter infectious lifespan (τI ) → shorter virus plateau width in MC.
A similar eclipse phase length (τE ) → same time of saturation in SC.
The parameter changes we found are all echoed in the data...

If the properties of a strain are experiment-specific, aren’t experiments
just producing random answers? Well... euh... yeah. But...
C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 18/22

Parameters consistent within an experimentInf. clearance, cpfu (1/h)

PFU prod. ratio, pSC /pMC

Infectiousness, pPFU/RNA

(D)

0.05

0.10

0.15

0.20

10−5 10−4 10−3 10−2 10−1 10−3

WT-H275 (Pinilla, 2012)
MUT-H275Y (Pinilla, 2012)10−210−1100

WT-I223 (current)
MUT-I223V (current)• While parameters do vary from experiment-to-experiment (WT 6= WT)• Parameters that are not strain specific are consistent and robust within agiven experiment [(WT = MUT), (WT = MUT)].
Perhaps: the properties of one strain relative to another (A = 3×standard
strain behaviour), rather than the absolute properties (A = 5), are preserved between experiments.
C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 19/22

Where can we go from here?
• Parameter estimation and predictions work well but...

• Inter-experimental variability is often greater than changes studied.
We should:– Express parameters (strain properties) relative to a reference strain.
Absolute parameter values are meaningless; and/or– Isolate main cause(s) of variability (e.g., cytokine-competency ofcells, serum stock) and account for it (i.e., characterize action andincorporate into model).

• Either way, we’ll need math models to do this.
p<0.001p<0.001p=0.20p = 0.58

p=0.76p<0.13

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 20/22

Butting against a flawed institutional culture...
→ Quote from a reviewer (who rejected our paper):There should be little to no inter-experimental variation, if proper techniques
are used. Were two different people performing these experiments?

→ Translation:

variability in biology = bad/not trustworthy → reject paper.

proper techniques = same person, day, equipment → redefining variability= redefine results significance

Wait... WHAT?!
→ But if experimental variability is too heavily controlled (e.g., same person,same day, equipment, reagents), “experimental results” are unlikely to holdwhen repeated, and even less likely to hold in a person = useless!

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 21/22

Take home message...
• Bad “science” in health research gives science a bad reputation!
• You should be skeptical of medical/health research.
• Math description is required to tackle the issues, i.e. address/study repro-ducibility/variability; quantify info burried in data.
• Solid medical results exist, e.g. many vaccines (mumps, rubella, etc.) andantibiotics can save your life, limb re-attachment and cast for broken bones
are awesome!
• Messaging in health research must improve. E.g., not all vaccine are created equal (e.g., mumps vs influenza); Drs should communicate degree ofuncertainty in treatment with patient and involve them in decision-making.

The END.
Lack of math: Side effects include dizziness and distrust
Catherine Beauchemin (cbeau@ryerson.ca)[URL: http://phymbie.physics.ryerson.ca/˜cbeau]
C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 22/22

Another application: human flu vs bird flu
Degradation rate (1/h)

TCID50Eclipse phase duration (h) Prod. infectious virus ( cell·h )

sH1N1
pH1N1
H5N1
H7N910−3

10−210−1100

4 5 6 7 8 9 10 11 12mL
Virus effectiveness ( TCID)
50 ·h

Rate of infection (1/h)

100

101

102

Simulated MC108
107
106
105
104
103
102
101

10−8 10−7 10−6 10−5 10−4 100101102

103

104

0103
TCID50mL

24 48 72 96 120 144
Time (h)• Seasonal H1N1 (A/New Caledonia/20/1999-like) has slowest infection rate.
• Pandemic H1N1 (A/Mexico/INDRE4487/2009) has a faster rate of infection, but a much longer eclipse phase → similar to sH1N1.
• Avian (H5N1 and H7N9) have shorter eclipse phase and faster infection rate.
• For H5N1 (A/Indonesia/05/2005) it is due to higher virus production rate;for H7N9 (A/Anhui/1/2013) it is due to higher virus infectivity.

C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 23/22

But this is a dangerous position to take...
Quotes from a reviewer:
Further, a significant portion of the manuscript examines the issue of interexperimental variability. I find this to be a major limitation of this work since
this type of variability should not exist if proper techniques are used. In
general, variability of this nature in biological systems makes it difficult tobelieve the results.
There should be little to no inter-experimental variation, if proper techniques
are used. Were two different people performing these experiments? Was
the virus stock the same? Were each identical in their sequences? Thesequestions and others about the experimental setup (reagents, cell types,etc.) should be controlled for and detailed included in this manuscript. Itis unlikely that the variation is true or biologically interesting. Repeatedexperiments with each virus are necessary to distinguish between poor experimental techniques and biological relevance.
But if results are not robust (e.g., within error/variability, both A > B andA < B is possible) to the use of a different experimentor or batch of cells orreagents between 2 experiments, then is it really a result? And how can we
expect it to translate in vivo in a person?
C.Beauchemin (ボシゥメン) — RIKEN/RyersonU — Slide 24/22