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Envoyé par Jeremie Unterberger

Exploration of metabolic reaction networks of carbon fixationmardi 21 mars 2023 16:27:27 |
Membre depuis : 8 mois Posts: 1 |

Supervisors: D. Lacoste and J. Unterberger

Location: Gulliver laboratory, ESPCI, Paris

Contact: david.lacoste@espci.fr, jeremie.unterberger@univ-lorraine.fr

Funding: Appel à projets IMPT (Mathématiques pour la Planète terre) or EDPIF

We are broadly interested in understanding how the laws of thermodynamics

constrain biological processes like metabolism, adaptation and evolution using methods

of non-equilibrium statistical physics.

This exploratory project discusses two types of theoretical approaches for

studying chemical reaction networks, in particular autocatalytic networks, for which a

recent classification has been proposed in the group [1]. These networks play an

essential role in the context of the origin of life, but also for questions concerning carbon

fixation metabolisms, a topic of interest for the energy transition. Previously only a

handful of pathways were thought to exist, but a wealth of new observations and

simulations have recently led to a complete turnaround. A first type of approach relies on

classical methods for studying chemical networks, such as graph theory, statistical

physics and the thermodynamics of non-equilibrium systems [3]. A second approach is

based on the principle that some real systems are too complex to have a sufficient

knowledge of the reactions which are involved (in particular regarding the kinetics). For

this reason, it is more natural to turn to methods specific to high-dimensional systems,

such as the theory of random matrices as done in Ref. [2].

Using both approaches, we want to explore metabolic chemical networks which

can use CO2 as carbon source among a large panel of possible chemical networks, see

e.g. [4] or [5]. We also want to understand how evolution favors certain networks, which

could be classified according to their topological structure and their thermodynamical

properties.

The work will involve a significant amount of analytical calculations and numerical

simulations. A solid knowledge in Statistical Physics and Mathematics is required, a

taste for ambitious projects at the interface between Chemistry and Physics would be a

plus, but no previous knowledge in the field of Origin of Life or in modelling complex

metabolisms is assumed. This project will benefit from interactions with various

colleagues, experimentalists and theoreticians, who are broadly interested in the field of

Systems Biology and its potential use for applications to the energy transition.

References:

[1] Universal motifs and the diversity of autocatalytic systems, A. Blokhuis, D. D.

Lacoste, and P. Nghe, PNAS, 117, 25230 (2020)

[2] Emergence of homochirality in large molecular systems, G. Laurent, D. Lacoste, and

P. Gaspard, PNAS, 118 (2021)

[3] Stoechiometric and dynamical autocatalysis for diluted chemical reaction networks,

P. Nghe and J. Unterberger, J. Math. Biol 85, 26 (2022).

[4] A. Bar-Even, E. Noor, R. Milo (2012). A survey of carbon fixation pathways through a

quantitative lens, J. Experimental Botany 63 (6), 2325–2342.

[5] R. Braakman, E. Smith (2012). The emergence and early evolution of biological

carbon-fixation, PLoS Comput Biol B(4).

Location: Gulliver laboratory, ESPCI, Paris

Contact: david.lacoste@espci.fr, jeremie.unterberger@univ-lorraine.fr

Funding: Appel à projets IMPT (Mathématiques pour la Planète terre) or EDPIF

We are broadly interested in understanding how the laws of thermodynamics

constrain biological processes like metabolism, adaptation and evolution using methods

of non-equilibrium statistical physics.

This exploratory project discusses two types of theoretical approaches for

studying chemical reaction networks, in particular autocatalytic networks, for which a

recent classification has been proposed in the group [1]. These networks play an

essential role in the context of the origin of life, but also for questions concerning carbon

fixation metabolisms, a topic of interest for the energy transition. Previously only a

handful of pathways were thought to exist, but a wealth of new observations and

simulations have recently led to a complete turnaround. A first type of approach relies on

classical methods for studying chemical networks, such as graph theory, statistical

physics and the thermodynamics of non-equilibrium systems [3]. A second approach is

based on the principle that some real systems are too complex to have a sufficient

knowledge of the reactions which are involved (in particular regarding the kinetics). For

this reason, it is more natural to turn to methods specific to high-dimensional systems,

such as the theory of random matrices as done in Ref. [2].

Using both approaches, we want to explore metabolic chemical networks which

can use CO2 as carbon source among a large panel of possible chemical networks, see

e.g. [4] or [5]. We also want to understand how evolution favors certain networks, which

could be classified according to their topological structure and their thermodynamical

properties.

The work will involve a significant amount of analytical calculations and numerical

simulations. A solid knowledge in Statistical Physics and Mathematics is required, a

taste for ambitious projects at the interface between Chemistry and Physics would be a

plus, but no previous knowledge in the field of Origin of Life or in modelling complex

metabolisms is assumed. This project will benefit from interactions with various

colleagues, experimentalists and theoreticians, who are broadly interested in the field of

Systems Biology and its potential use for applications to the energy transition.

References:

[1] Universal motifs and the diversity of autocatalytic systems, A. Blokhuis, D. D.

Lacoste, and P. Nghe, PNAS, 117, 25230 (2020)

[2] Emergence of homochirality in large molecular systems, G. Laurent, D. Lacoste, and

P. Gaspard, PNAS, 118 (2021)

[3] Stoechiometric and dynamical autocatalysis for diluted chemical reaction networks,

P. Nghe and J. Unterberger, J. Math. Biol 85, 26 (2022).

[4] A. Bar-Even, E. Noor, R. Milo (2012). A survey of carbon fixation pathways through a

quantitative lens, J. Experimental Botany 63 (6), 2325–2342.

[5] R. Braakman, E. Smith (2012). The emergence and early evolution of biological

carbon-fixation, PLoS Comput Biol B(4).