domain: sdv shs

text:
Camille Clément

Septembre

Clermont-Ferrand And

« Terre en jeu »: a role-play to understand the social pressure in farmland access

1. An ANR project  JASMINN : Protection of peri-urban agricultural land in the Mediterranean: justice issues and land innovations 2. A postdoctoral position about land arrangements between landowners and farmers near Montpellier 3. Build a presentation of my findings to a broader audience, including the uninitiated

Presentation of the game

How is it played?

General rules

1. Four farmer profiles (a winegrower, cereal farmer, livestock breeder and market gardener), four landowner profiles, a mayor and a property developer.

2. Four rounds, each lasting seven minutes and three inter-rounds each lasting around 5-6 minutes 3. Players must attain the individual objectives they have been set on their player cards.

4. For attain their objectives, players can sell or purchase plots of land or rent them in the long term or in the short term.

5. Events are taking place during inter-rounds (general events and events by players)

Game context

1. A role game organised around a board which represents a municipality with a village at its center and farmland around it.

first approach of a complex issue Professional public : a desire to learn more

	Pictures
	Debriefing They have tested it for you Mixed results according to audiences
	2. Peri-urban context 3. The PLU (Local Planning Regulations) is not voted 4. The urbanization can be made only in the continuity of village JASMINN seminar Terre de Liens association TOTAL : 22 games The board 3 exemples de plateaux finaux In three times Two audiences : formation and no formation Students  very good feedback, a regular public for game 1. The facilitator gathers impressions, feelings, expectations and actions of the players during researchers card citizens One example of a player Plateau de départ the game 2. The facilitator present scientific results, objectives of game and ideas for future (specific for any game session) 3. A large discussion take place PolyTech 2 Master geography 1 Total 6 Licence level Oasisdici association Association of consumers in Carcassonne citizens Technicians  bad feedback, what results? citizens 6 Master level Ardennes team) diversified public Elected people  bad feedback, a game too simple technicians and 14 Rural Foundation for Wallonia (Samois-2 games : Internship students 2 Monts) "Pays Serre-Ponçon, Ubaye, Durance" of the Southern Alps Diversified public Diversified public  mixed feedback, it deepend of cases For who? FORMATION Number of parts SupAgro 9 No formation Public Association of municipalities (Avants-Elected people Non professional public : a Citizens  very good feedback, a fun game discovery, a
	Total	8

An all-public and broad-cast version : a) No facilitator, b) For citizens or students, c) Awareness of the problem of urban sprawl and the protection of local agriculture

A reinforcement version (actual version) : a) Needing a facilitator, b) Public more informed but not specialists in the subject (students, new elected officials, territorial technicians).

A professional version : a) Needing a facilitator, b) Public : urban planning and agricultural development specialists, c) Identify relevant land tools and find development solutions to combine housing and agricultural activity.


domain: sdv info

text:
Motivation BD Coarse-graining SBD Variability

§ Purely kinetic model (law of mass-action) : no space, no polymer structure (but size-dependent kinetic rates). § Indirect interaction between polymer C i , i ě 2 via the available number of monomers C 1 .

Becker-Döring model

Reversible one-step coagulation-fragmentation

C i `C1 p i ÝÝá âÝÝ q i`1 C i`1
Ball, Carr, Penrose, Comm. Math. Phys 104(4), 1986 § § Typical coefficient are derived from physical principles p i " i α , q i " p i ´zs `q i γ ¯.

Motivation BD Coarse-graining SBD Variability

Becker-Döring model

Reversible one-step coagulation-fragmentation

Set of kinetic reactions :

C i `C1 p i
ÝÝá âÝÝ

q i`1 C i`1 , i ě 1 . §
In spontaneous polymerization experiment, § Initial condition given by c i pt " 0q " 0 @i ě 2. § Measured variable : ř iěn iC i (n is an unknown parameter) § The (observed) nucleation time is given by inftt ě 0 :

ÿ iěn iC i ptq ě δm | C i pt " 0q " mδ i"1 u .
Another quantity of interest is the following First Passage Time, inftt ě 0 : C N ptq ě 1 | C i pt " 0q " mδ i"1 u .

Motivation BD Coarse-graining SBD Variability

Deterministic Becker-Döring model

Reversible one-step coagulation-fragmentation

C i `C1 p i
ÝÝá âÝÝ

q i`1 C i`1 $ ' ' ' & ' ' ' % dc i dt " J i´1 ´Ji , i ě 2 , J i " p i c 1 c i ´qi`1 c i`1 , i ě 1 , dc 1 dt " ´J1 ´ř8
i"1 J i . § Deterministic version : infinite system of ODEs.

Motivation BD Coarse-graining SBD Variability

Deterministic Becker-Döring model

Reversible one-step coagulation-fragmentation

C i `C1 p i
ÝÝá âÝÝ

q i`1 C i`1 $ ' ' ' & ' ' ' % dc i dt " J i´1 ´Ji , i ě 2 , J i " p i c 1 c i ´qi`1 c i`1 , i ě 1 , dc 1 dt " ´J1 ´ř8 i"1 J i .
§ Deterministic version : infinite system of ODEs. § Well-posedness theory for sublinear coefficients in X "

# pc i q iě1 P R N `: ÿ iě1 ic i ă 8 + Deterministic Becker-Döring model Reversible one-step coagulation-fragmentation C i `C1 p i
ÝÝá âÝÝ

q i`1 C i`1 $ ' ' ' & ' ' ' % dc i dt " J i´1 ´Ji , i ě 2 , J i " p i c 1 c i ´qi`1 c i`1 , i ě 1 , dc 1 dt " ´J1 ´ř8 i"1 J i .
§ Deterministic version : infinite system of ODEs. § Well-posedness theory for sublinear coefficients in X "

# pc i q iě1 P R N `: ÿ iě1 ic i ă 8
+ § Preserves mass for all times

8 ÿ i"1 ic i ptq " 8 ÿ i"1 ic i p0q ": m . $ ' ' ' & ' ' ' % dc i dt " J i´1 ´Ji , i ě 2 , J i " p i c 1 c i ´qi`1 c i`1 , i ě 1 , dc 1 dt " ´J1 ´ř8 i"1 J i .
Ball, Carr, Penrose, Comm. Math. Phys 104(4), 1986

Equilibrium is given by J i " J " 0, which implies

c i " Q i z i , Q i "
p 1 p 2 ¨¨¨p i´1 q 2 q 3 ¨¨¨q i z is given by the mass at equilibrium, mpzq :"

ÿ iě1 iQ i z i
Is there a solution of mpzq " ? mp"

ÿ iě1 ic i ptqq $ ' ' ' & ' ' ' % dc i dt " J i´1 ´Ji , i ě 2 , J i " p i c 1 c i ´qi`1 c i`1 , i ě 1 , dc 1 dt " ´J1 ´ř8 i"1 J i .
Ball, Carr, Penrose, Comm. Math. Phys 104(4), 1986

If the serie mpzq " ř iě1 iQ i z i has a finite radius of convergence z s and if suptmpzq , z ă z s u ": m s ă 8 , then there is a critical mass such that there is no equilibrium with mass m ą m s .

$ ' ' ' & ' ' ' % dc i dt " J i´1 ´Ji , i ě 2 , J i " p i c 1 c i ´qi`1 c i`1 , i ě 1 , dc 1 dt " ´J1 ´ř8 i"1 J i . Ball,

Remark

There is a Lyapounov function, given by Hpcq "

ÿ c i ˆln ˆci Q i ˙´1 ˙. $ ' ' ' & ' ' ' % dc i dt " J i´1 ´Ji , i ě 2 , J i " p i c 1 c i ´qi`1 c i`1 , i ě 1 , dc 1 dt " ´J1 ´ř8 i"1 J i .
Penrose, Comm. Math. Phys 124, 1989

There exist "almost steady-states", for which J i " J ˚pmq ‰ 0. As m OE m s , if such steady-states are used as initial condition, then the solution § (for finite t) c i ptq ´ci p0q is exponentially small § lim tÑ8 c i ptq ´ci p0q is not exponentially small Moreover J ˚pmq is exponentially small § The new phase is being formed extremely slowly, after a long metastable period. § For constant or linear kinetic rates p i , q i , one can reduce the system to 1 or 2 ODEs on

c 1 , ÿ iě2 c i , ÿ iě2 ic i . § Based on scaling arguments, one can show that for q i " 0 (irreversible nucleation), inftt ě 0 : c n ptq ě δm | c i pt " 0q " mδ i"1 u » 1 m . while for "q i Ñ 8 2 (pre-equilibrium nucleation), inftt ě 0 : c n ptq ě δm | c i pt " 0q " mδ i"1 u » 1 m n .
Use of BD-like model in protein polymerization models pre-equilibrium nucleation step, constant rates

$ ' ' ' ' & ' ' ' ' % dc 1 dt " ´ppc 1 ´qqy , . dy dt " Kc n 1 p`Qpm ´c1 ptqqq , dz dt
" ppc 1 ´qqy , . ppx 0 qf px 0 , tq " ppx 0 q p N c 1 ptq n q N `ppx 0 qc 1 ptq , We start from a rescaled model (ε " 1{n, ε 2 " 1{m)

ř iěn c i z " ř iěn ic i ppiq " p, qpiq " q Q " secondary nucleation mechanism (fragmentation, heterogeneous nucleation...)

Continuous approximation, nucleation as a boundary condition

$ ' ' ' & ' ' ' % Bf px, tq Bt `c1 ptq Bppxqf px, tq Bx " r¨¨¨s . c 1 ptqppx 0 qf px 0 , tq " Npc 1 ptqq , dc 1 dt " λ ´γc 1 ´nNpc 1 q ´c1 ż 8 x 0 ppxqf px,
Prigent
$ ' & ' % dc ε i dt " 1 ε " J ε i´1 ´Jε i ‰ , i ě 2 , m ε " c ε 1 ptq `ε2 ÿ iě2 ic ε i ptq .
Scaling idea : excess of monomer, time scale " 1{ε

c ε 1 ptq :" ε 2 c 1 pt{εq , c ε i ptq :" c i pt{εq
Compensated aggregation / fragmentation

p ε i :" p i ε 2 , q ε i :" q i , J ε i " p ε i c ε 1 c ε i ´qε i`1 c ε i`1
and slow first step : p ε 1 :"

p 1 ε 4 ,
Large Size, Excess of monomer

We start from a rescaled model (ε " 1{n, ε 2 " 1{m)

$ ' & ' % dc ε i dt " 1 ε " J ε i´1 ´Jε i ‰ , i ě 2 , m ε " c ε 1 ptq `ε2 ÿ iě2 ic ε i ptq .
From the polymer point of view, we have accelerated fluxes, all of the same order :

1 ε p ε 1 C ε 1 C ε 1 Ý ÝÝÝÝÝ á â ÝÝÝÝÝ Ý 1 ε q ε 2 C ε 2 C ε 2 C ε i´1 1 ε p ε pεpi´1qqC ε 1 C ε i´1 Ý ÝÝÝÝÝÝÝÝÝÝÝ á â ÝÝÝÝÝÝÝÝÝÝÝ Ý 1 ε q ε pεiqC ε i C ε i 1 ε p ε pεiqC ε 1 C ε i Ý ÝÝÝÝÝÝÝÝÝ á â ÝÝÝÝÝÝÝÝÝ Ý 1 ε q ε pεpi`1qqC ε i`1 C ε i`1 ,
We start from a rescaled model (ε " 1{n, ε 2 " 1{m)

$ ' & ' % dc ε i dt " 1 ε " J ε i´1 ´Jε i ‰ , i ě 2 , m ε " c ε 1 ptq `ε2 ÿ iě2 ic ε i ptq .
Weak form : for any test function (ϕ i ),

d dt ÿ iě2 c ε i ϕ i " 1 ε J ε 2 ϕ 2 `ÿ iě3 J ε i " ϕ i`1 ´ϕi ε  .
Large Size, Excess of monomer

We start from a rescaled model (ε " 1{n, ε 2 " 1{m)

$ ' & ' % dc ε i dt " 1 ε " J ε i´1 ´Jε i ‰ , i ě 2 , m ε " c ε 1 ptq `ε2 ÿ iě2 ic ε i ptq . f ε pt, xq " ř iě2 c ε i ptq1 rpi´1{2qε,pi`1{2qεq pxq, ϕ i " ş pi`1{2qε pi´1{2qε ϕpxqdx, $ ' ' ' ' ' ' ' & ' ' ' ' ' ' ' % d dt ż `8 0 f ε pt, xqϕpxq dx " " p ε 1 c ε 1 ptq 2 ´qε 2 c ε 2 ptq ‰ ˜1 ε ż 5{2ε 3{2ε ϕpxq dx ż `8 0 J ε pt, xq∆ ε ϕpxq dx , m ε " c ε 1 ptq `ż `8 0 xf ε pt, xq dx .
where ∆ ε ϕpxq " ϕpx`εq´ϕpxq ε and J ε pt, xq " .p ε pxqc ε

1 ptqf ε pt, xq ´qε px ὲqf ε pt, x `εq d dt ż `8 0 f ε pt, xqϕpxq dx " " p ε 1 c ε 1 ptq 2 ´qε 2 c ε 2 ptq ‰ ˜1 ε ż 5{2ε 3{2ε ϕpxq dx ż `8 0 rp ε pxqc ε 1 ptqf ε pt, xq∆ ε ϕpxq ´qε pxqf ε pt, xq∆ ´εϕpx qs dx ,
Theorem (Deschamps, Hingant, Y. ( 2016))

We suppose : § Control and convergence of rate functions § Control and convergence of initial condition § ppxq " px rp , qpxq " qx rq near x " 0, and r q ě r p . § c 1 p0q ą ρ :" lim xÑ0 qpxq{ppxq d dt

ż `8 0 f ε pt, xqϕpxq dx " " p ε 1 c ε 1 ptq 2 ´qε 2 c ε 2 ptq ‰ ˜1 ε ż 5{2ε 3{2ε ϕpxq dx ż `8 0 rp ε pxqc ε 1 ptqf ε pt, xq∆ ε ϕpxq ´qε pxqf ε pt, xq∆ ´εϕpx qs dx ,
Theorem (Deschamps, Hingant, Y. ( 2016))

we have f ε Ñ f (in C pr0, T s; w ´˚´Mpr0,
f ε pt, xqϕpxq dx " " p ε 1 c ε 1 ptq 2 ´qε 2 c ε 2 ptq ‰ ˜1 ε ż 5{2ε 3{2ε ϕpxq dx ż `8 0 rp ε pxqc ε 1 ptqf ε pt, xq∆ ε ϕpxq ´qε pxqf ε pt, xq∆ ´εϕpx qs dx ,
Theorem (Deschamps, Hingant, Y. ( 2016))

Nptq is an explicit function of c 1 ptq, and is given by a quasi steadystate approximation of c ε 2 " f ε pt, 2εq, given by the solution of

$ ' & ' %
0 " rJ i´1 pc 1 q ´Ji pc 1 qs , i ě 2 , c 1 ptq " c 1 .

J i pc 1 q " pi rp c 1 ´qpi `1q rq 1 rp"rq .

When c 1 ą lim xÑ0 qpxq ppxq , the solution of J i " J ‰ 0 is linked to the loss of mass in the classical BD theory.

Exemples § For r p ă r q , we get Npc 1 q " αc 2 1 , and

lim xÑ0 `x rp f pt, xq " α p c 1 ptq .
Motivation BD Coarse-graining SBD Variability

Exemples § For r p ă r q , we get Npc 1 q " αc 2 1 , and lim xÑ0 `x rp f pt, xq " α p c 1 ptq . § For r p " r q , we get Npc 1 q " α p c 1 ppc 1 ´qq, and

lim xÑ0 `x rp f pt, xq " α p c 1 ptq .
Exemples § For r p ă r q , we get Npc 1 q " αc 2 1 , and lim xÑ0 `x rp f pt, xq " α p c 1 ptq . § For r p " r q , we get Npc 1 q " α p c 1 ppc 1 ´qq, and lim

C i `C1 p i
ÝÝá âÝÝ

q i`1 C i`1
Transitions are given by ÝÝá âÝÝ

P " C 1 pt `dtq " C 1 ptq ´2 C 2 pt `dtq " C 2 ptq `1 * " p 1 C 1 ptqpC 1 ptq ´1qdt `opdtq
q i`1 C i`1
Transitions are given by

P $ & % C 1 pt `dtq " C 1 ptq ´1 C i pt `dtq " C i ptq ´1 C i`1 pt `dtq " C i`1 ptq `1 , .
- ÝÝá âÝÝ

" p i C 1 ptqC i ptqdt `opdtq
q i`1 C i`1
Transitions are given by

P $ & % C 1 pt `dtq " C 1 ptq `1 C i pt `dtq " C i ptq `1 C i`1 pt `dtq " C i`1 ptq ´1 , .
-

" q i`1 C i`1 ptqdt `opdtq
Stochastic Becker-Döring model Reversible one-step coag.-frag.

C i `C1 p i
ÝÝá âÝÝ

q i`1 C i`1
Due to detailed-balance, the asymptotic prob. distribution is

ΠpC q " B M M ź i"1 pQ i q C i C i ! , Q i " p 1 p 2 ¨¨¨p i´1 q 2 q 3 ¨¨¨q i .
The expected number of clusters of size i is

E Π C i " Q i B M {B M´i , and MB ´1 M " M ÿ i"1 iQ i B ´1 M´i .
Moreover, analogy with supercritical case in BD holds :

ˆlim iÑ8 p i q i`1 " z s ą 0 ˙ñ ˆlim MÑ8 E Π C i " Q i z i s ˙ §
With the large volume scaling : c ε i " εC i , and p i " εp i , q i " q i : Law of large numbers as M Ñ 8 [Jeon, CMP (1998)] § Any macroscopic quantity like inftt ě 0 :

ÿ iěN iC i ptq ě ρM | C i pt " 0q " Mδ i"1 u .
converges (in standard scaling) to a finite deterministic value as M Ñ 8. § With the large volume scaling : c ε i " εC i , and p i " εp i , q i " q i : Law of large numbers as M Ñ 8 [Jeon, CMP (1998)] § Any macroscopic quantity like inftt ě 0 :

ÿ iěN iC i ptq ě ρM | C i pt " 0q " Mδ i"1 u .
converges (in standard scaling) to a finite deterministic value as M Ñ 8. § This may not be true for microscopic quantity, for instance.

inftt ě 0 : How to explain large variability in M Ñ 8 ?

C N ptq ě 1 | C i pt " 0q " Mδ i"1 u .
Roughly speaking, due to the law of large number (+CLT), in order to obtain a positive variance in a continuous settings, one needs to avoid that the nucleation occurs in finite time in the limit M Ñ 8. § We seek situations (model, scaling) where the nucleation is a rare event, that do not occurs in the deterministic limit M Ñ 8.

Unfavorable aggregation in SBD

Reversible one-step coag.-frag.

C i `C1 p i Ý ÝÝÝ á â ÝÝÝ Ý q i`1 {ε C i`1
Y . et al., JCP, 144, 2016 Using pre-equilibrium hypothesis, in the unfavorable aggregation limit, the leading order of the first assembly time of a cluster of size N is

ă T ą« εÑ0 1 ε N´2 ś N´1 k"2 q k ś N´1 k"1 p k ś N´1 k"0 pM ´kq .
Also, in the asymptotic ε Ñ 0 the first assembly time T is an exponential distribution. § This behavior can be used to couple a first part, very unfavorable, to a second part, favorable or irreversible

Unfavorable aggregation in SBD

Reversible one-step coag.-frag.

C i `C1 p i Ý ÝÝÝ á â ÝÝÝ Ý q i`1 {ε C i`1
Y . et al., JCP, 144, 2016 Unfavorable aggregation in SBD Reversible one-step coag.-frag. Large nucleus scaling Reversible one-step coag.-frag.

C i `C1 p i Ý ÝÝÝ á â ÝÝÝ Ý q i`1 {ε C i`1 Y. et
C i `C1 p i ÝÝá âÝÝ q i`1 C i`1 f ε pt, xq " ř iě2 C ε i ptq1 rpi´1{2qε,

Stochastic view of Classical Nucleation Theory

In the classical scaling from SBD to BD, with gelation coefficients (sup `ř iQ i z i ˘" ρ s ă 8), there is a phase transition in finite random time

The transition phase is abrupt and corresponds to the rapid formation of a single large cluster

Stochastic view of Classical Nucleation Theory

The transition phase is abrupt, occurs at a random time and corresponds to the rapid formation of a single large cluster n cluster models Can we perform LDP calculations with n clusters ? pk 0 , k 1 q p k 0 pm´pk0`k1qεq

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ q k 0 `1 pk 0 `1, k 1 q , pk 0 , k 1 q p k 1 pm´pk0`k1qεq

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ q k 1 `1 pk 0 , k 1 `1q , which converges (with time rescaling) to dx dt " ppxqpm ´x ´y q ´qpxq dy dt " ppy qpm ´x ´y q ´qpy q n cluster models

Can we perform LDP calculations with n clusters ? pk 0 , k 1 q p k 0 pm´pk0`k1qεq

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ q k 0 `1 pk 0 `1, k 1 q , pk 0 , k 1 q p k 1 pm´pk0`k1qεq

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ q k 1 `1 pk 0 , k 1 `1q , which converges (with time rescaling) to dx dt " ppxqpm ´x ´y q ´qpxq dy dt " ppy qpm ´x ´y q ´qpy q

xÑ0`x

  rp f pt, xq " α p c 1 ptq . § For faster fragmentation rate q ε 2 , we may get Npc 1 q " f pt, xq " αc 1 ptq c 1 ptq pc 1 ptq `q2 ,or Npc 1 q " 0, and lim xÑ0 `x rp f pt, xq " 0 .

  reaction rates with M Re-scaling nucleus size with M Back to classical nucleation theory

  tqd

					Helal et al., J. Math.
	Continuous approximation, nucleation as a	Biol., 2013
	boundary condition		f pt, xq=number of polymer size x
					Npc 1 q " αc n
	$ ' &	Bf px, tq Bt	`Bpppxqc 1 ptq ´qpxqqf px, tq Bx	" r¨¨¨s .
	'
	%

§

  Second case (pp0qM ă qp0q) : Exponentially large time and 'translated' trajectory. ( ppxq " x, qpxq " 0.1.) MFPT is explicit and is exponentially large in ε § The "rate" is exponentially small

	Quantifying the rare event in a toy model
	A much simpler version of this model consider that a single
	aggregate may be formed at a time :	pi`1{2qεq pxq
				converges towards solution of
		i	p i pm´iεq ÝÝÝÝÝá âÝÝÝÝÝ q i`1	i `1 ,	Bf Bt § To leading order the stationary `BpJpx, tqf pt, xqq Bx " 0 , prob. density is
	which converges (with time rescaling) to	u ˚px q " C	ş x log ´qpyq ppy qpm´y q ¯dy ppxqpm ´xqqpxq e ´1 ε a	.
	dx dt	" ppxqpm ´xq ´qpxq

(+boundary condition, if needed) and Jpx, tq " ppxqc 1 ptq ´qpxq . § How can we obtain large assembly time in this scaling ?

Large nucleus scaling § First case (pp0qm ą qp0q) : Convergence towards a deterministic value. § case A § case B

Large nucleus N " ? M §

Deterministic BD model -Some remarks § For constant or linear kinetic rates p i , q i , one can reduce the system to 1 or 2 ODEs on c 1 ,

Use of BD-like model in protein polymerization models Irreversible nucleation step, "Heaviside" rates

Powers & Powers, Biophys. J. 91, 2006

For b " c : pre-equilibrium hypothesis.

Use of BD-like model in protein polymerization models pre-equilibrium nucleation, polymerization-fragmentation, "oligomers at 0"

dc i dt " pc 1 pc i´1 ´ci q ´qpi ´1qc i `2q

Stochastic Becker Döring model

Reversible one-step coag.-frag.

Stochastic Becker Döring model

Reversible one-step coag.-frag.

Preserves mass for all times

Stochastic Becker-Döring model Reversible one-step coag.-frag.

ÝÝá âÝÝ

Time interval between transition

given transition is selected at random according to its weight.

Stochastic Becker-Döring model

Reversible one-step coag.-frag.

ÝÝá âÝÝ

Time interval between transition

given transition is selected at random according to its weight.

Stochastic Becker-Döring model

Reversible one-step coag.-frag.

ÝÝá âÝÝ

Time interval between transition

given transition is selected at random according to its weight.

Stochastic Becker-Döring model

Reversible one-step coag.-frag.

ÝÝá âÝÝ

Time interval between transition

given transition is selected at random according to its weight.

Stochastic Becker-Döring model

Reversible one-step coag.-frag.

ÝÝá âÝÝ

Time interval between transition

given transition is selected at random according to its weight.

Stochastic Becker-Döring model

Reversible one-step coag.-frag.

ÝÝá âÝÝ

Time interval between transition

given transition is selected at random according to its weight. ÝÝá âÝÝ

Drawback : exponential increase of the size of the state-space !

where σpiq is the sum of the divisors of i ÝÝá âÝÝ

Due to detailed-balance, the asymptotic prob. distribution is

Stochastic Becker-Döring model

Reversible one-step coag.-frag.

ÝÝá âÝÝ

Due to detailed-balance, the asymptotic prob. distribution is

The expected number of clusters of size i is

Coarse-Grained model

Then, for "small α", and large volume, the lag time is composed of the convolution of an Exponential variable of rate α and a deterministic time given by the ODE

Coarse-Grained model

Then, for ν ą 1, and ε Ñ 0, the lag time converges "essentially" to an exponential distribution (in the time scale

Coarse-Grained model Then, for the rescaled process εpC 1 ptq, C 1 ptq, Z ptqq we observ "translated trajectory" as ε Ñ 0

Quantifying the rare event in a toy model

A much simpler version of this model consider that a single aggregate may be formed at a time :

ÝÝÝÝÝá âÝÝÝÝÝ Thanks for your attention ! n cluster models

Can we perform LDP calculations with n clusters ?

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ

which converges (with time rescaling) to dx dt " ppxqpm ´x ´y q ´qpxq dy dt " ppy qpm ´x ´y q ´qpy q n cluster models

Can we perform LDP calculations with n clusters ?

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ

which converges (with time rescaling) to dx dt " ppxqpm ´x ´y q ´qpxq dy dt " ppy qpm ´x ´y q ´qpy q n cluster models

Can we perform LDP calculations with n clusters ?

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ

which converges (with time rescaling) to dx dt " ppxqpm ´x ´y q ´qpxq dy dt " ppy qpm ´x ´y q ´qpy q n cluster models

Can we perform LDP calculations with n clusters ?

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ

which converges (with time rescaling) to dx dt " ppxqpm ´x ´y q ´qpxq dy dt " ppy qpm ´x ´y q ´qpy q n cluster models

Can we perform LDP calculations with n clusters ? pk 0 , k 1 q p k 0 pm´pk0`k1qεq

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ q k 0 `1 pk 0 `1, k 1 q , pk 0 , k 1 q p k 1 pm´pk0`k1qεq

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ q k 1 `1 pk 0 , k 1 `1q , which converges (with time rescaling) to dx dt " ppxqpm ´x ´y q ´qpxq dy dt " ppy qpm ´x ´y q ´qpy q n cluster models

Can we perform LDP calculations with n clusters ? pk 0 , k 1 q p k 0 pm´pk0`k1qεq

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ q k 0 `1 pk 0 `1, k 1 q , pk 0 , k 1 q p k 1 pm´pk0`k1qεq

ÝÝÝÝÝÝÝÝÝÝá âÝÝÝÝÝÝÝÝÝÝ q k 1 `1 pk 0 , k 1 `1q , which converges (with time rescaling) to dx dt " ppxqpm ´x ´y q ´qpxq dy dt " ppy qpm ´x ´y q ´qpy q


domain: math info sdv

text:
Véronique Martin

The 3D template « bank » is critical Bank of cholesterol oxidases EC 1.1.3.6

Cholesterol oxidase:

• monomeric flavoenzyme catalyzes oxidation and isomerization into cholest-4-en-3-one.

• two folds exist, cofactor-FAD-dependent, covalently bound or not.

--Annotation comparator's analysis

-nadB : L-aspartate oxidase also in 28 other orthologs (some well conserved) -BdD8_00254 : FAD dependant oxidoreductase also in 6 other orthologs (some well conserved) -sdhA : succinate dehydrogenase also in 19 other orthologs (some well conserved) -BdD8_03122 : pyridine nucleotide-disulfide oxidoreductase also in 17 other orthologs (some well conserved) -murB : UDP-N-acetylenolpyruvoylglucosamine reductase also in 30 other orthologs (some well conserved) --Genomic organisation's analysis -nadB : 1 interesting genes in 5' ? : lpdA (Dihydrolipoyl dehydrogenase) ; synteny of ~192 in other B. dorei, ~6 in other B., no synteny with further species : -BdD8_00254 : 2 interesting genes in 3' ? : choloylglycine hydrolase, phenylacetate--CoA ligase ; synteny of ~192 in other B. dorei, ~6 in other B., no synteny with further species -sdhA : nothing interesting -BdD8_03122 : nothing interesting -murB : no interesting keyword in neighbhor CDS ; synteny of 14-26 with other B. dorei, 3-11 with other B no synteny with further species Analysis of the 5 genes from 3D, using Insyght Thomas Lacroix

  1-Split of each fasta sequence in the genome -> more than 3,400 for each genome of B. dorei 2-Multiple sequence alignment for each fasta: 'HHblits' > PSI-Blast 3-Prediction & addition of SS elements: 'adssl.pl' 4-Profiling & comparaison with the 3D template « bank »:'HHBlitsdb.pl' & 'HHsearch' 5-Extraction, ranking and analyse of scores. 6-Analyse of selected sequences of proteins. Annotation? Fonction? 7-In fine, a list of cholesterol oxidase putative proteins to test in vitro / in vivo Inverse strategy: setting up of standalone commands • Prediction of MSA profile with SS for each protein of B. Dorei 175 T & B. dorei D8 genomes • Compare each protein with a bank of cholesterol oxidase « Enzyme A » to be assessed ✪ B. dorei 175 T 99.9 % aa bacdor_03524.fasta.a3m.hhm.out.hhmakeBK2 àUDP-N-acetyl muramate dehydrogenase Sorted by Prob > 95 and size > 50 amino acid residues Results for B. dorei 175 T with Bank1 and Bank2 Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 99.4 % aa bacdor_01751.fasta.a3m.hhm.out.hhmakeBK1 à Hypothetical protein 99.1 % aa bacdor_01928.fasta.a3m.hhm.out.hhmakeBK1 à L-aspartate oxidase 99.1 % aa bacdor_02089.fasta.a3m.hhm.out.hhmakeBK1 à Succinate Dehydrogenase flavoprotein 97.5 % aa bacdor_03886.fasta.a3m.hhm.out.hhmakeBK1 à Pyridine nucleotide oxido reductase ✪ B. dorei 175 T 99.9 % aa bacdor_03524.fasta.a3m.hhm.out.hhmakeBK2 àUDP-N-acetyl muramate dehydrogenase Sorted by Prob > 95 and size > 50 amino acid residues Results for B. dorei 175 T with Bank1 and Bank2 99.9 % 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase = Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase FAD 1I19 Cholesterol oxidase from bank2 UDP-N-acetylenolpyruvoylglucosamine reductase 1I19 Bank2 1MBB E.coli Gram-Oxidoreductase MurB Results for B. dorei D8 with Bank2 Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase FAD 1I19 Cholesterol oxidase from bank2 UDP-N-acetylenolpyruvoylglucosamine reductase 1I19 Bank2 1MBB E.coli Gram-Oxidoreductase MurB Results for B. dorei D8 with Bank2 FAD Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase Results for B. dorei D8 with Bank2 Active site cholesterol oxidase FAD binding FAD binding Active site oxido-reductase Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 FAD binding covalent H 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase FAD > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 for B. dorei D8 with Bank2 No His covalent complex 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 B. dorei D8 with Bank2 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase FAD Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 99.4 % aa bacdor_01751.fasta.a3m.hhm.out.hhmakeBK1 à Hypothetical protein 99.1 % aa bacdor_01928.fasta.a3m.hhm.out.hhmakeBK1 à L-aspartate oxidase 99.1 % aa bacdor_02089.fasta.a3m.hhm.out.hhmakeBK1 à Succinate Dehydrogenase flavoprotein 97.5 % aa bacdor_03886.fasta.a3m.hhm.out.hhmakeBK1 à Pyridine nucleotide oxido reductase ✪ B. dorei 175 T 99.9 % aa bacdor_03524.fasta.a3m.hhm.out.hhmakeBK2 àUDP-N-acetyl muramate dehydrogenase Sorted by Prob > 95 and size > 50 amino acid residues Results for B. dorei 175 T with Bank1 and Bank2 99.9 % 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase = Gene ordered at Genscript. Cloning and expression tests in progress. Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 Then, purification, activity test and crystallization assays Cholesterol oxidases are secreted. This contains a signal peptide.

  99.4 % aa bacdor_01751.fasta.a3m.hhm.out.hhmakeBK1 à Hypothetical protein 99.1 % aa bacdor_01928.fasta.a3m.hhm.out.hhmakeBK1 à L-aspartate oxidase 99.1 % aa bacdor_02089.fasta.a3m.hhm.out.hhmakeBK1 à Succinate Dehydrogenase flavoprotein 97.5 % aa bacdor_03886.fasta.a3m.hhm.out.hhmakeBK1 à Pyridine nucleotide oxido reductase ✪ B. dorei 175 T 99.9 % aa bacdor_03524.fasta.a3m.hhm.out.hhmakeBK2 àUDP-N-acetyl muramate dehydrogenase Sorted by Prob > 95 and size > 50 amino acid residues Results for B. dorei 175 T with Bank1 and Bank2 99.9 % 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase Analysis of candidates-Comparison within cholesterol degrading genomes and others Annotation comparator's analysis Genomic organisation's analysis Analysis of the 5 genes from 3D, using Insyght ✪ ✪ ✪ No significant hits with clear syntheny Need of transcriptomic analysis

•Fragments

  Ions detection corresponding to COPROSTANOL • DIMS-MS/MS method in positive ionisation mode culture in progress ✪ E. Coprostanoligenes samples : Genome sequencing, assembling and analysis No significant difference between the two genomes. Similar enzymes are found. Transcriptomic analysis of both chromosome and plasmids of B. dorei 175 T & B. dorei D8? Genomic & transcriptomic analysis of both chromosome & plasmids of E. coprostanoligenes. Can we rule out some synergy among bacteria of the microbiota? Activity test and 3D structure validation of our cholesterol-oxidase hit in bank2 Cholesterol conversion in the gut microbiota -Cholesterol conversion in the gut microbiota: the mystery enzyme(s) ? Outline Towards the Structural Screening of Microbial Ecosystems -MetaFoldScan project : 3D screening of gut microbiota -Conclusions and perspectives ✔ -More than 10 millions prokaryotic genes -40% protein sequences of unknown function -Assessing the fold is possible -Function can be inferred from structure, only 1393 unique 3D fold -Fishing out the right fold is crucial ! MetaFoldScan : a pluridisciplinary challenge -More than 10 millions prokaryotic genes -40% protein sequences of unknown function -Assessing the fold is possible -Function can be inferred from structure, only 1393 unique 3D fold -Fishing out the right fold is crucial ! MetaFoldScan : a pluridisciplinary challenge 3D Fold ✪ Reveals evolutionary relationship ✪ Helps to understand the mechanisms of function ✪ Deciphers its biological role within the organism { -More than 10 millions prokaryotic genes -40% protein sequences of unknown function -Assessing the fold is possible -Function can be inferred from structure, only 1393 unique 3D fold -Fishing out the right fold is crucial ! MetaFoldScan : a pluridisciplinary challenge 1-Capitalize on MetaOmic datasets that are underexploited 2-Develop a versatile bioinformatics tool to scan 3D fold in MetaOmics 3-Identify hits from 3D Targets 4-Validate hits and pathways involved as pro-or antibiotic markers Objectives of structural metagenomics Structural screening of Microbial Ecosystems 1-Develop & integrate cutting-edge tools in bioinformatics î Identify Hits from 3D targets within MetaHit î Increase the functional annotation î Biologist friendly Web user 2-Achieve 3D-MetaOmics. î Intensive modeling of entire proteome î Distribution, diversity of folds î Identify proteins with new functions/ fold 3-Validate Hits impacting Health î Pro and anti-biotic biomarkers î Universal antibiotic î Identify homeostasis marker Gut microbiota Beyond the description of Metagenomics MetaFoldScan objectives Analysis of candidates-Comparison within cholesterol degrading genomes and other => all 5 present in other B. dorei : very well conserved (id 100% length 100%) , synteny BdD8_00352 and BdD8_00254 (only one on same contig) but far apart => + B vulgatus, intestinalis, ovatus, plebeius : well conserved (id 60-80% length 100%) ; no synteny => 4/5 (mostly BdD8_00254 missing) in B. massiliensis, acidifaciens, fragilis, uniformis, eggerthii, caccae, pyogenes, thetaiotaomicron, helcogenes : well conserved (id 60-80% lenght 100%) ; no synteny => 1-3/5 in other, less well conserved (id 20-40% lenght 60-100%) ; no synteny

✪

  Zoom out analysis using InsyghtAnalyse of interesting genes from other species known to reduce cholesterol Do they have orthologs in D8? keyword related to cholesterol degradation : 5-cholesten-3-one; 4cholesten-3-one; cholestenone beta reductase; EC1.1.3.6; EC1.3.1.3; cholesterol oxidase and FAD dependent oxidoreductase (POSIX Regex : .*FAD.*oxidoreductase.*) Set of -20 bacteroides anaerobie (included 6 B dorei) which do not degrade cholesterol -3 Mycobacterium aerobie lifestyle which degrade cholesterol -4 Rhodococcus aerobie lifestyle which degrade cholesterol -Mycobacterium tuberculosis H37Rv, taxo_id = 83332 [NC_000962] -1 * cholesterol oxidase : choD, Rv3409c: cholesterol oxidase => Close to oxidoreductase, in conserved synteny with all bacteria cholesterol catabolism except Schizophyllum commune (eukaryotes) Þ no interest for D8 -0 * .*FAD.*oxidoreductase.* -0 * EC1.3.1.3 -Mycobacterium smegmatis str. MC2 -0 * cholest -3 * EC1.3.1.3

•

  Ions detection corresponding to COPROSTANOL • DIMS-MS/MS method in positive ionisation mode Ions detection corresponding to COPROSTANOL • DIMS-MS/MS method in positive ionisation mode

										E. Coprostanoligenes
																			Catherine. Juste
	•																		Fabienne Beguet-Crespel
	Samples resuspended in 500 µL of Chloroform/ Methanol (50/50)
	Diluted to 1/100eme before analysis
			95.08580														Coprostanol
																			[M+H-H2O]+
																				371.36
	100
															?
				109.10130								283.26259
																	[M+H-H2O]+ : 371.3677
		81.07027
	Relative Abundance Relative Abundance	163.14780 Fragments typical of cholesterol derivatives 135.11664 123.11678 149.13224					388.39 [M+NH4-H2O]+ 371.36634 389.34 [M+NH4]+ [M+H-NH3]+ [M+H-H2O]+
																				406.40
	69.07038							177.16344	203.17909
													245.22597
	57.07051
											205.19482 219.21040
	71.08600 57.07050	95.08579 85.10152	111.11694 125.13244 135.11669	151.14791 165.16356 191.17912 177.16352	217.19461 231.21034 247.24136 259.24156 261.25695 275.27276 275.27247	315.30409 289.28800 315.30361 329.31939 353.31943 353.28353 301.28882	388.15745	416.80756 408.02114
	60 60		80 80	100 100	120 120	140 140	160 160	180 180	200 200	220 220	240 240	260 260	280 280	300 300	320 320	340 340	360 360	380 380	400 400	420 420
													m/z m/z

✪ Direct introduction of the COPROSTANOL standard by DIMS-MS/MS

Fragments typical of cholesterol derivatives

						E. Coprostanoligenes samples
																Catherine. Juste
																Fabienne Beguet-Crespel
		95.08580
			109.10130
														[M+H-H2O]+ : 371.3677
	81.07027
	Relative Abundance			149.13224 135.11664 123.11678										371.36634
						163.14780
	69.07038					177.16344	203.17909
	57.07051
								217.19461		261.25695
										247.24136	275.27247
												289.28800 315.30361 329.31939 353.31943	388.15745	408.02114
	60	80	100	120	140	160	180	200	220	240	260	280	300	320	340	360	380	400	420
										m/z

Acknowledgments Catherine Juste Philippe Gérard Pedro Alzari ✔ Fabienn Béguet-Crespel Jean-François Gibrat Acknowledgments To my dear friends… Andrea Villarino Mabel Berois Maria Natalia Lísa

Strategy

Score analysis

Hits prediction

Coarse-grained filtering of sequence candidates. What are the genes induced by this motif?

• cluster of genes necessary for microbial steroid degradation.

• more than 83 genes in M.smeg and 74 M.tb, 18 are essential.

Evidence of a Palindromic motif :

Palindromic motif TnnAACnnGTTnnA

• Transcriptomic analysis:

• Identification of a conserved motif within its own promoter (TnnAACnnGTTnnA)


text:
1-

  Split of each fasta sequence in the genome -> more than 3,400 for each genome of B. dorei 2-Multiple sequence alignment for each fasta: 'HHblits' > PSI-Blast 3-Prediction & addition of SS elements: 'adssl.pl' 4-Profiling & comparaison with the 3D template « bank »:'HHBlitsdb.pl' & 'HHsearch' 5-Extraction, ranking and analyse of scores. 6-Analyse of selected sequences of proteins. Annotation? Fonction? 7-In fine, a list of cholesterol oxidase putative proteins to test in vitro / in vivo Inverse strategy: setting up of standalone commands • Prediction of MSA profile with SS for each protein of B. Dorei 175 T & B. dorei D8 genomes • Compare each protein with a bank of cholesterol oxidase « Enzyme A » to be assessed Véronique Martin The 3D template « bank » is critical Choosing the bank of cholesterol oxidases -Bank 1 Cholesterol oxidase:• monomeric flavoenzyme catalyzes oxidation and isomerization into cholest-4-en-3-one.• two folds exist, cofactor-FAD-dependent, covalently bound or not.

✪FADFAD

  1B4V. pdb & fasta 1COY.pdb & fasta Cholesterol oxydase from Streptomyces Yue et al, 1999 -1.5 Å Cholesterol oxydase from R. hoagii ex B. steroliticum Li et al, 1993 -1• monomeric flavoenzyme catalyzes oxidation and isomerization into cholest-4-en-3-one. • two folds exist, cofactor-dependent, covalent or not. 3JS8. pdb & fasta 1I19.pdb & fasta Cholesterol oxydase from Chromobacterium sp. DS-1 Sagermann et al, 2010 -1.5 Å Cholesterol oxydase from B. steroliticum Coulombe et al, 2001 -1.7 Å Choosing the bank of cholesterol oxidases -Bank 2 3D structures distinct ✪ Catalytic sites different ✪ Binding patches divergent ✪ FAD binding topologies contrasting despite same redox role C Choosing the bank of cholesterol oxidases -taking one ? Which ? Both ? 99.4 % aa bacdor_01751.fasta.a3m.hhm.out.hhmakeBK1 à Hypothetical protein 99.1 % aa bacdor_01928.fasta.a3m.hhm.out.hhmakeBK1 à L-aspartate oxidase 99.1 % aa bacdor_02089.fasta.a3m.hhm.out.hhmakeBK1 à Succinate Dehydrogenase flavoprotein 97.5 % aa bacdor_03886.fasta.a3m.hhm.out.hhmakeBK1 à Pyridine nucleotide oxido reductase ✪ B. dorei T 99.9 % 172 aa bacdor_03524.fasta.a3m.hhm.out.hhmakeBK2 àUDP-N-acetyl muramate dehydrogenase Sorted by Prob > 95 and size > 50 amino acid residues Identification of hits for B. dorei 175 T with Bank1 and Bank2 Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei 175 T Véronique Martin 99.4 % aa bacdor_01751.fasta.a3m.hhm.out.hhmakeBK1 à Hypothetical protein 99.1 % aa bacdor_01928.fasta.a3m.hhm.out.hhmakeBK1 à L-aspartate oxidase 99.1 % aa bacdor_02089.fasta.a3m.hhm.out.hhmakeBK1 à Succinate Dehydrogenase flavoprotein 97.5 % aa bacdor_03886.fasta.a3m.hhm.out.hhmakeBK1 à Pyridine nucleotide oxido reductase ✪ B. dorei T 99.9 % aa bacdor_03524.fasta.a3m.hhm.out.hhmakeBK2 àUDP-N-acetyl muramate dehydrogenase Sorted by Prob > 95 and size > 50 amino acid residues Comparison of hits for B. dorei 175 T and D8 with Bank1 and Bank2 99.9 % 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase = Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei 175 T 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase FAD 1I19 Cholesterol oxidase from bank2 UDP-N-acetylenolpyruvoylglucosamine reductase 1I19 Bank2 1MBB E.coli Gram-Oxidoreductase MurB Analysis of hits with bank2 two for the two strains Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 Analysis of hits with bank2 two for the two strains 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase One major hit for B. dorei D8 and D 175T with Bank2 : possibly an enzyme Active site cholesterol oxidase FAD binding FAD binding Active site oxido-reductase Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 The hit for B. dorei D8 and D 175T with Bank2 : possibly a flavo-enzyme 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 The hit for B. dorei D8 and D 175T with Bank2 : possibly a flavo-enzyme 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 The hit for B. dorei D8 and D 175T with Bank2 : possibly a flavo-enzyme 99.9 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase 99.4 % aa bacdor_01751.fasta.a3m.hhm.out.hhmakeBK1 à Hypothetical protein 99.1 % aa bacdor_01928.fasta.a3m.hhm.out.hhmakeBK1 à L-aspartate oxidase 99.1 % aa bacdor_02089.fasta.a3m.hhm.out.hhmakeBK1 à Succinate Dehydrogenase flavoprotein 97.5 % aa bacdor_03886.fasta.a3m.hhm.out.hhmakeBK1 à Pyridine nucleotide oxido reductase ✪ B. dorei T 99.9 % 172 aa bacdor_03524.fasta.a3m.hhm.out.hhmakeBK2 àUDP-N-acetyl muramate dehydrogenase Sorted by Prob > 95 and size > 50 amino acid residues Conclusion from screening of B. dorei 175 T with Bank1 and Bank2 99.9 % 172 aa bdd8_003618.fasta.a3m.hhm.out.hhmakeBK2 à UDP-N-acetylenolpyruvoylglucosamine reductase = Cloning and expression tests in progress. Sorted by Prob > 95 and size cut off > 50 amino acid residues ✪ B. dorei D8 Then, purification, activity test and crystallization assays Cholesterol oxidases are secreted. This contains a signal peptide.

•

  Ions detection corresponding to COPROSTANOL • DIMS-MS/MS method in positive ionisation mode Fragments typical of cholesterol derivatives E. Coprostanoligenes samples Coprostanol is present & preferentially detected Coprostanol Catherine. Juste Fabienne Beguet-Crespel

•

  Ions detection corresponding to COPROSTANOL • DIMS-MS/MS method in positive ionisation mode Direct introduction of the COPROSTANOL standard by DIMS-MS/MS

										E. Coprostanoligenes
	✪ Samples resuspended in 500 mL of Chloroform/ Methanol (50/50)				Catherine. Juste Fabienne Beguet-Crespel
	Diluted to 1/100eme before analysis
		95.08580														Coprostanol
																		[M+H-H2O]+
																			371.36
	100													?
			109.10130									283.26259
																[M+H-H2O]+ : 371.3677
	81.07027
	Relative Abundance Relative Abundance	163.14780 Fragments typical of cholesterol derivatives 135.11664 123.11678 149.13224						388.39 [M+NH4-H2O]+ 371.36634 389.34 [M+NH4]+ [M+H-NH3]+ [M+H-H2O]+
																				406.40
	69.07038								177.16344	203.17909
												245.22597
	57.07051
										205.19482 219.21040
	71.08600 57.07050	95.08579 85.10152	111.11694 125.13244 135.11669	151.14791 165.16356 191.17912 177.16352	217.19461 231.21034 247.24136 259.24156 261.25695 275.27276 275.27247	315.30409 289.28800 315.30361 329.31939 353.31943 353.28353 301.28882	388.15745	416.80756 408.02114
	60 60	80 80	100 100	120 120	140 140	160 160	180 180	200 200	220 220	240 240	260 260	280 280	300 300	320 320	340 340	360 360	380 380	400 400	420 420
												m/z m/z

•

  Ions detection corresponding to COPROSTANOL • DIMS-MS/MS method in positive ionisation mode

							E. Coprostanoligenes samples
																Catherine. Juste
																Fabienne Beguet-Crespel
		95.08580
			109.10130
														[M+H-H2O]+ : 371.3677
	81.07027
	Relative Abundance			149.13224 135.11664 123.11678	Fragments typical of cholesterol derivatives		371.36634
						163.14780
	69.07038					177.16344	203.17909
	57.07051
								217.19461		261.25695
										247.24136	275.27247
													289.28800 315.30361 329.31939 353.31943	388.15745	408.02114
	60	80	100	120	140	160	180	200	220	240	260	280	300	320	340	360	380	400	420
										m/z

Acknowledgments

Catherine Juste Philippe Gérard Pedro Alzari Fabienne Béguet-Crespel Jean-François Gibrat Surbhi Dingra Bernard Offmann María Natalia Lisa Andrea Villarino Mabel Berois Danilo Segovia Dario Porley Véronique Martin Thomas Lacroix Valentin Loux Sandra Derozier Mariano Martinez Nalini Rama Rao Jean-Marc Chatel MEM Metaprogram Acknowledgments Catherine Juste Philippe Gérard Pedro Alzari Fabienne Béguet-Crespel Jean-François Gibrat Surbhi Dingra Bernard Offmann María Natalia Lisa Andrea Villarino Mabel Berois Danilo Segovia Dario Porley A n d yo u fo r yo u r ki n d a tt e n ti o n …

Analysis of candidates-Comparison within cholesterol degrading genomes and others

Comparison between E. coprostanoligenes ( 2017

Score analysis

Hits prediction

Coarse-grained filtering of sequence candidates.


text:
Ludovic Calandreau

B Mulot

Elodie Chaillou

C Poupon

A novel 11.7T ultra-high field dmri connectivity atlas of the japanese quail

Raïssa Yebga Hot, Marine Siwiaszczyk, J. Beaujoin, D. A. Barrière, I.

#Th749 : A novel 11.7T ultra-high field diffusion MRI structural connectivity atlas of the Japanese quail

R. Yebga Hot 1,2,3 , M. Siwiaszczyk 4 , J. Beaujoin 1,3 , D. A. Barrière 1,3

Material and methods

The cohort : 21 male and sexually mature Japanese quails including 11 LTI and 10 STI scanned post mortem MRI system : Preclinical Bruker 11.7T MRI scanner (G max =780mT/m) Protocol scan : 16 hours of acquisition per sample including:

• Anatomical MRI: 2D T 2 -weighted spin echo,150µm, TE/TR=16ms/9s • Diffusion MRI: 3D segmented EPI using PGSE, 75 directions, 200µm, TE/TR=23.88ms/250ms, 18 segments, b=4500s/mm² Post-processing :

• No preprocessing required (high SNR level and very few artifacts) • Definition of a template space using the subject that is the closest to the others as the reference (affine registrations and a scaling criterion) • Reorientation of the scans in AC-PC frame (anterior and posterior commissures) • Manual segmentation of the brain masks • Local modeling of the diffusion process with DTI and analytical QBI [2] (SH=8; λ=0.006) • Streamline regularized deterministic tractography in Connectomist [3] • Fiber clustering at the individual and population scales [4] • Labeling of the inter-subject centroids to create an atlas

Prospects

• Methodological optimization (diffeomorphic registration) • Combining this atlas with a 3D anatomical atlas of the Japanese quail to understand how its neuroanatomical structures communicate

  Université Paris-Saclay, Orsay, France; 3 FLI/Noeud Paris-Sud, Orsay, France;4 INRA CNRS IFCE Université François Rabelais Tours, Nouzilly, France;

I. Uszynski 1 , S. Love 4 , L. Calendreau 4 , B. Mulot 5 , E. Chaillou 4 , C. Poupon 1,2,3 1 CEA/Joliot/NeuroSpin/UNIRS, Gif-sur-Yvette, France; 2 5 Beauval Nature (ZooParc de Beauval), Saint-Aignan, France This work received funding from the Centre-Val de Loire regional council (Neuro2Co, France) and from the European Union's Horizon 2020 Framework Program for Research and Innovation (Grant Agreement No 720270, Human Brain Project SGA2)

Contact : raissa.yegbahot@cea.fr

From sample preparation to anatomical and diffusion data at 11.7T

Inter-subject fiber clustering

Intra-subject fiber clustering Connectogram

From connectograms to inter-subject fiber clustering

clusters found

The structural connectivity atlas of the Japanese quail showing 8 major white matter bundles:

The posterior commissure (PC), the corticospinal tract (CST), the cerebellar peduncles (Cb Ped), the optic tract (OT), the septopallio-mesenphalic tract (TSM), the isthmocerebellaris tract (TIC), the cerebellum white matter (Cb WM), and the quintofrontal tract (QF).

The connections of the emotional fear circuit of the Japanese quail. Two structures specifically involved: the arcopallium and the nucleus taenia of amygdala (TnA). [5] A Japanese quail showing tonic immobility

The Japanese quail (Coturnix japonica)

This little nidifugous bird presents a quantifiable response to fear : tonic immobility [1] . This behavioral characteristic leads to two breeding lines of quails :

• The short time immobility (STI)

• The long time immobility (LTI)

Research hypothesis: The two lines do not show the same cerebral organization, and more specifically for the emotional fear circuit.

Goal: Establishing a structural connectivity atlas of the Japanese quail using diffusion magnetic resonance imaging (dMRI) to compare lineages.

The structural connectivity atlas of the Japanese quail and the connections of its emotional fear circuit


Socio-economic and scientific contexts

  impacts of heat treatment on the structure and digestion kinetic of proteins of infant milk formulas mimicking the protein profile of human milk ?


Saïd Bouhallab -Stlo

Saïd B Valérie

L C Florence

R Pascaline

H Stéphane

P Marie-Hélène

F Valérie

B B Thomas

C Fanny

Interactions-Structures Functionalities team objectives

Research context

Our research strategy: Two round-trip approaches



  Industrial partnership -BBA, The consortium of ten French dairy companies (pre-competitive) -French national inter-professional center for the dairy industry (CNIEL) -Chr. Hansen, bioscience company  15-20 publications/year (Food / Dairy science; Interfaces; Physico-chemistry of colloids, polymers)



  Mechanisms behind heteroprotein complex coacervation (liq/liq) and subsequent application for encapsulation of small bioactives  Understanding interactions between caseins and carotenoids at molecular level  Swelling of dry heated protein microparticles in different physico-chemical conditions  Heat treatment -physicochemical properties and nutritional quality of proteins (coll. BN team)  Balance of inter and intra casein micelle interactions during enzymatic coagulation of milk  Changes of the physicochemical characteristics of the casein micelles and related -acid/rennet-gelling properties upon successive thermal treatments

Relationship interactions -assemblies -functionalities of proteins (caseins, globular proteins)

	design	Motivations of studying food protein interactions and	Improve/create
	(protection/vectorization) Nano-micro biomaterials	assemblies?	products) (assembly/disassembly of food Functional properties
	1. Process-induced protein assemblies
	Generation of ingredients with various functional properties
	Irreversible associations (covalent interactions)
	Aggregation mechanisms and kinetics are well-described in model systems
	2. Spontaneous proteins assemblies
	Fundamental characteristic of biological systems (regulation, activity,…)
	Reversible associations (non-covalent interactions)
	Important field in biology. Increasing interest in food science
			7
	 Objectives
	deliver fundamental knowledge, new functional structures and
	application concepts for academic and industrial partners;
	obtain new knowledge on the interactions and assemblies of proteins, to
	enable more effective innovations;
	elaborate type of assemblies that provide optimal properties at
	macroscopic scale (product);
	relate the macroscopic to the molecular scale. A scientifically challenging
	objective from a physics point of view.


• Sylvie Beaufils (IPR)

• Arnaud Saint-Jalmes (IPR)

• Cécile Le Floch-Fouéré

• Alexia Audebert (PhD)

• Coralie Pasquier (PhD)

• Yann Desfougères (PhD)

• Antoine Bouchoux (LISBP)

• Bernard Cabane (CBI)

• Mikael Lund (Lund univ.)

• Simon J. Cox (Aberystwyth univ.)

• Lay-Theng Lee (LLB) -Fouéré, C. et al. (2010) Food Hydrocoll., 24, 275-284 • Le Floch-Fouéré, C. et al. (2009) Food Hydrocoll., 23, 352-365

Protein functionality 2 •••

 2 relation of technological aptitudes to intrinsic features • 'biochemical' viewpoint (amino-acid composition, sequence, structure...) • colloidal viewpoint (size, charge, anisotropy, pressure, transport, concentration...) • the two worlds meet at interfaces Boire et al., 2019 (doi:10/c3jt) proteins as surfactants: adsorption to hydrophobic interfaces • irreversible adsorption • unwanted consequences (loss of enzymatic activity, fouling...) • target effects: stabilization of dispersed systems • our focus: air-water interface, foams film dynamics as an intermediate scale between interface and foam • kinetics, early steps of interfacial film formation • foam rheology as a functional criterion • sensitivity to protein structure and stability • food proteins: open questions in foam / colloid physics • interdisciplinarity as a necessity Zaccagnino et al., 2018 (doi:10/cw2k)

THANK YOU FOR YOUR ATTENTION MERCI Please visit http://www.rennes.inra.fr/stlo_eng STLOpen Days 19-21 March 2019 STLOpen Days 19-21 March 2019

  Annu. Rev. Food Sci. Technol. 10, 14.1-14.19 • Pasquier, C. et al. (2016) Phys. Chem. Chem. Phys., 18, 28458-28465 • Desfougères, Y. et al. (2011) Biomacromolecules, 12, 156-166 • Desfougères, Y. et al. (2011) Langmuir, 27, 14947-14957 • Le Floch

	Key publications
	• Audebert, A. et al. (2019) J. Food Eng. 242, 153-162
	• Audebert, A. et al. (2019) J. Colloid Interface Sci. 542, 222-232
	• Boire, A. et al. (2019)
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