PACS/Plant response to stress & Biological Networks - Revision history

Yd: /* The Lechatelier-Braun principle */

2016-04-11T12:47:03Z

‎The Lechatelier-Braun principle

Yd: /* Context */

2016-04-11T12:44:04Z

‎Context

Yd: /* Expected Validations and Outcomes */

2016-04-07T07:48:44Z

‎Expected Validations and Outcomes

Yd: /* A spatio-temporal nodal approach */

2016-04-06T14:12:02Z

‎A spatio-temporal nodal approach

Yd: /* Expected Validations */

2016-04-06T14:10:42Z

‎Expected Validations

Yd: /* "Validations" */

2016-04-06T14:10:09Z

‎"Validations"

Yd: /* A spatio-temporal nodal approach */

2016-04-06T14:09:35Z

‎A spatio-temporal nodal approach

Yd: /* A spatio-temporal nodal approach */

2016-04-06T14:08:04Z

‎A spatio-temporal nodal approach

Yd: /* A spatio-temporal nodal approach */

2016-04-06T14:04:56Z

‎A spatio-temporal nodal approach

Yd: /* A spatio-temporal nodal approach */

2016-04-06T14:04:13Z

‎A spatio-temporal nodal approach

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 ====''The Lechatelier-Braun principle''====
-According to the Lechatelier-Braun principle all the thermodanymic potentials are connected.
+According to the Lechatelier-Braun principle all the thermodynamic potentials are connected.
-For instance, the hydraulic and electric responses of plant are known to be highly correlated (Mancuso 1999) revealing the underlying coupling between P and μ.
+For instance, the hydraulic and electric responses of plant are known to be highly correlated (Mancuso 1999) revealing the underlying coupling between  <math>P</math> and <math>\mu</math>.
-Similar observations of coupling between μ and T have been reported from vegetal thermoelectric responses. All these thermodynamic-type analyses (cf. also e.g. Demirel & Sandler 2002, or Qian & Beard 2005) would benefit from a description of the energetic & material fluxes in order to build up solid knowledge about a plant metabolism. The dynamics energy budget 􏰀(DEB􏰁) theory is the most popular non-species-specific theory of this kind. However, this now quite standard modeling of biochemical networks neglects the spatial structure and the complex transport and allocation processes in the organism.
+Similar observations of coupling between <math>\mu</math> and  <math>T</math> have been reported from vegetal thermoelectric responses. All these thermodynamic-type analyses (cf. also e.g. Demirel & Sandler 2002, or Qian & Beard 2005) would benefit from a description of the energetic & material fluxes in order to build up solid knowledge about a plant metabolism. The dynamics energy budget 􏰀(DEB􏰁) theory is the most popular non-species-specific theory of this kind. However, this now quite standard modeling of biochemical networks neglects the spatial structure and the complex transport and allocation processes in the organism.
 ====''A spatio-temporal nodal approach''====

@@ Line 4: / Line 4: @@
 ==''Context'' ==
 Global agriculture is facing a serious threat from climate change that compromises
-global food security and impact the ecosystem services and biodiversity.
+global food security and impacts the ecosystem services and biodiversity.
 High temperatures affect plant development at the level of seed germination which represents the first step of plant establishment and also reduce the plant growth by affecting the shoot net assimilation rates and thus the total dry weight of the plant (Bita et al 2013). It is the first climatic factor capping yields on a global scale for wheat (Lobell et al 2007) and rice (Peng et al 2004). Projections predict a 9-15% decrease in the production of major cereals by 2020 (Hisas et al. 2011). Climate change will increase the negative effect on crop productivity not only due to heat waves, i.e. an increase of several degrees over the seasonal temperature for a sustained period of days (IPCC 2014), but also by exacerbating broad-spectrum stresses such as drought, cold, salinity, flood, submergence and pests (Kole et al 2015). Moreover, recent studies have revealed that the response of plants to combinations of two or more stress conditions is unique and cannot be directly extrapolated from the response of plants to each of the different stresses applied individually. Indeed the responses to the combined stresses are complex and largely controlled by different, and sometimes opposing, signaling pathways that may interact and inhibit each other (Suzuki et al 2014).

@@ Line 41: / Line 41: @@
 Since the approach is quite versatile, more complex non-homogeneous structures and topologies may also be analyzed, and help possibly encompassing the role of the spatial biological cells distribution for the plant response to stress. Possibly anisotropic, discontinuous transport and thermoelectric properties may also be included, hence mimicking the sharp interface between cells of different kinds. Notice that the numerical strategy is able to enforce the fluxes (of energy, matter, species) continuity at the local (nodal) scale, and at any time. This aspect is of paramount importance, enforcing the compatibility of the numerical modeling with physical first-principles.
-The aim would be to be able to validate the network modeling against the observed data obtained from experiments,. Once thoroughly validated, ons should be able to extrapolate the modeling to situations where no data can be measured (because e.g. of too high spatial and/or temporal resolution requirements), or difficult to measure properties. To our knowledge, this would be the very first spatio-temporal network-based thermodynamic-type modeling of the whole plant, in order to reproduce the plant response to different kinds of simultaneous (and hence interacting) stresses.
+The aim would be to be able to validate the network modeling against the observed data obtained from experiments,. Once thoroughly validated, one should be able to extrapolate the modeling to situations where no data can be measured (because e.g. of too high spatial and/or temporal resolution requirements), or difficult to measure properties. To our knowledge, this would be the very first spatio-temporal network-based thermodynamic-type modeling of the whole plant, in order to reproduce the plant response to different kinds of simultaneous (and hence interacting) stresses.
 == ''Participants'' ==
 François Bouteau, Delphine Bonnin,  [https://www.researchgate.net/profile/Hayat_El-Maarouf-Bouteau Hayat El-Maarouf-Bouteau], Patrice Meimoun, [https://www6.rennes.inra.fr/umreva/Annuaire/INFlux/E.-Le-Deunff Erwan Le Deunff], Eric Herbert, Christophe Goupil, Yves D'Angelo.

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 [[File:Pacs.png|330 px]]
-To fix ideas, each building block of the above kind (the numerical elementary cell) can be connected to other cells of the same type. A locally linearized relationship between the column vector  <math>{\bf X}</math> and the vector of potential output/input differences <math>{\bf \delta \mu}</math> can be proposed, thank to the introduction of the Onsager matrix <math>{\bf Z}</math>, <math>{\bf X}= {\bf Z \mu}</math>. The coefficient of the matrix <math>{\bf Z} </math> can depend on <math>{\bf X}</math> and <math>{\bf \mu}</math> (their local value, and possibly their temporal gradient or history).
+To fix ideas, each building block of the above kind (the numerical elementary cell) can be connected to other cells of the same type. A locally linearized relationship between the column vector  <math>{\bf X}</math> and the vector of potential output/input differences <math>{\large \mu}</math> can be proposed, thank to the introduction of the Onsager matrix <math>{\bf Z}</math>, <math>{\bf X}= {\bf Z}{\large \mu}</math>. The coefficient of the matrix <math>{\bf Z} </math> can depend on <math>{\bf X}</math> and <math>{\large \mu}</math> (their local value, and possibly their temporal gradient or history).
 ===''Expected Validations and Outcomes''===

@@ Line 38: / Line 38: @@
 To fix ideas, each building block of the above kind (the numerical elementary cell) can be connected to other cells of the same type. A locally linearized relationship between the column vector  <math>{\bf X}</math> and the vector of potential output/input differences <math>{\bf \delta \mu}</math> can be proposed, thank to the introduction of the Onsager matrix <math>{\bf Z}</math>, <math>{\bf X}= {\bf Z \mu}</math>. The coefficient of the matrix <math>{\bf Z} </math> can depend on <math>{\bf X}</math> and <math>{\bf \mu}</math> (their local value, and possibly their temporal gradient or history).
-===''Expected Validations''===
+===''Expected Validations and Outcomes''===
 Since the approach is quite versatile, more complex non-homogeneous structures and topologies may also be analyzed, and help possibly encompassing the role of the spatial biological cells distribution for the plant response to stress. Possibly anisotropic, discontinuous transport and thermoelectric properties may also be included, hence mimicking the sharp interface between cells of different kinds. Notice that the numerical strategy is able to enforce the fluxes (of energy, matter, species) continuity at the local (nodal) scale, and at any time. This aspect is of paramount importance, enforcing the compatibility of the numerical modeling with physical first-principles.

@@ Line 34: / Line 34: @@
 The figure below illustrates the envisaged structure for the elementary building block and the way the blocks can be (physically) connected:  generalized fluxes <math>X_i </math>and generalized input and output potentials <math>\mu_j^{in}</math> and  <math>\mu_j^{out}</math> are inter-connected through a (locally linearized) Onsager-type approach.
-[File:Pacs.png|330 px]
+[[File:Pacs.png|330 px]]
-To fix ideas, each building block of the above kind (the numerical elementary cell) can be connected to other cells of the same type. A locally linearized relationship between the column vector  <math>{\bf X}</math> and the vector of potential output/input differences <math>{\bf \delta \mu}</math> can be proposed, thank to the introduction of the Onsager matrix <math>{\bf Z}</math>,  <math>{\bf X}</math>= {\bf Z \mu}</math>. The coefficient of the matrix <math>{\bf Z </math> can depend on <math>{\bf X}</math> and <math>{\bf \mu}</math> (their local value, and possibly their temporal gradient or history).
+To fix ideas, each building block of the above kind (the numerical elementary cell) can be connected to other cells of the same type. A locally linearized relationship between the column vector  <math>{\bf X}</math> and the vector of potential output/input differences <math>{\bf \delta \mu}</math> can be proposed, thank to the introduction of the Onsager matrix <math>{\bf Z}</math>, <math>{\bf X}= {\bf Z \mu}</math>. The coefficient of the matrix <math>{\bf Z} </math> can depend on <math>{\bf X}</math> and <math>{\bf \mu}</math> (their local value, and possibly their temporal gradient or history).
 ==="Validations"===

@@ Line 36: / Line 36: @@
 |[[File:Pacs.png|330 px]]
 |}
 To fix ideas, each building block of the above kind (the numerical elementary cell) can be connected to other cells of the same type. A locally linearized relationship between the column vector  <math>{\bf X}</math> and the vector of potential output/input differences <math>{\bf \delta \mu}</math> can be proposed, thank to the introduction of the Onsager matrix <math>{\bf Z}</math>,  <math>{\bf X}</math>= {\bf Z \mu}</math>. The coefficient of the matrix <math>{\bf Z}</math> can depend on <math>{\bf X}</math> and <math>{\bf \mu}</math> (their local value, and possibly their temporal gradient or history).