151. Modeling of a Cell-Free Synthetic System for Biohydrogen Production
- Author
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Frédéric Cadet, Nicolas Fontaine, Bernard Offmann, Brigitte Grondin-Perez, GARDEBIEN, Fabrice, Dynamique des Structures et Interactions des Macromolécules Biologiques - Pôle de La Réunion (DSIMB Réunion), Biologie Intégrée du Globule Rouge (BIGR (UMR_S_1134 / U1134)), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA), Energy Lab (ENERGY Lab), Université de La Réunion (UR), Unité de fonctionnalité et ingénierie de protéines (UFIP), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Université des Antilles (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Université des Antilles (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Laboratoire d'Energétique, d'Electronique et Procédés (LE2P)
- Subjects
Artificial neural network ,[SDV.BIO]Life Sciences [q-bio]/Biotechnology ,Hydrogen ,Computer science ,[SPI.GPROC] Engineering Sciences [physics]/Chemical and Process Engineering ,chemistry.chemical_element ,02 engineering and technology ,Cellobiose ,01 natural sciences ,Synthetic biology ,chemistry.chemical_compound ,Component (UML) ,Production (economics) ,Biohydrogen ,[SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering ,Hydrogen production ,010405 organic chemistry ,System optimization ,021001 nanoscience & nanotechnology ,0104 chemical sciences ,[SDV.BIO] Life Sciences [q-bio]/Biotechnology ,Biochemical engineering ,Cell-Free synthetic system ,chemistry ,Yield (chemistry) ,Mathematical modeling ,0210 nano-technology ,Simulation - Abstract
International audience; Hydrogen is a good candidate for the next generation fuel with a high energy density and an environment friendly behavior in the energy production phase. Micro-organism based biological production of hydrogen currently suffers low hydrogen production yields because the living cells must sustain different cellular activities other than the hydrogen production to survive. To circumvent this, teams have explored the synthetic assembly of enzymes in-vitro in cell-free systems with specific functions. Such a synthetic cell-free system was recently devised by combining 13 different enzymes to synthesize hydrogen from cellulose or cellobiose with better yield than microorganism-based systems. We used methods based on differential equations calculations to investigate how the initial conditions and the kinetic parameters of the enzymes influenced the productivity of a such system and, through simulations, identified those conditions that would optimize hydrogen production starting with cellobiose as substrate. Further, if the kinetic parameters of the component enzymes of such a system are not known, we showed how, using artificial neural network, it is possible to identify alternative models that account for the rate of production of hydrogen. This work demonstrates how modeling can help in designing and characterizing cell-free systems in synthetic biology. A web-based simulator implementing our differential equations based model is provided freely as a service for noncommercial usage at http://www.bo-protscience.fr/h2.
- Published
- 2015