Evolution and design of metabolic networks using Quality-Diversity optimization algorithms

Shauny Van Hoye
Persbericht

Evolutie van metabole netwerken van micro-organismen via Quality-Diversity optimalisatie algoritmen.

In de masterthesis getiteld "Evolution and design of metabolic networks using Quality-Diversity optimization algorithms", werd een nieuw computationeel framework genaamd OptMAP ontwikkeld om strategieën te voorspellen die de groei van micro-organismen en de productie van metabolieten maximaliseren. De karakteristieken van een micro-organisme worden bepaald door zowel de genen van het micro-organisme als zijn omgevingsfactoren. De extreem grote hoeveelheid aan combinaties van mogelijke modificaties van het genoom van het micro-organisme en zijn omgevingsfactoren, maakt het op punt stellen van micro-organismen in het laboratorium zeer uitdagend. Het optimaliseren van de groei van micro-organismen en de productie van metabolieten in een laboratorium kan daarom veel tijd en middelen kosten. In silico optimalisatie van het organisme voordat het opgegroeid en getest wordt in het laboratorium kan daarom helpen om de zoekruimte, gecreëerd door de genen en de omgevingsfactoren, te exploreren.

In deze thesis worden Quality-Diversity evolutionaire algoritmen gebruikt om metabole netwerken van organismen in silico te evolueren met als doel de organismen te kunnen groeien op bepaalde substraten, specifieke metabolieten te produceren enz. We focussen ons op het gebruik van één bepaald Quality-Diversity Evolutionaire Algoritme genaamd Multi-dimensional Archive of Phenotypic (MAP)-Elites. Quality-Diversity Evolutionaire Algoritmen hebben als voordeel dat ze zoeken naar oplossingen die zowel goed presteren als divers zijn. Dit zorgt ervoor dat er een evenwicht bewaard wordt tussen exploratie om lokale minima te vermijden en convergentie tijdens het zoeken naar een set van oplossingen die verspreid is over de zoekruimte. Doorheen de thesis worden genome-scale metabole modellen gebruikt om de genen en het metabolisme van organismen in silico voor te stellen. Het MAP-Elites algoritme maakt het mogelijk om de zoekruimte intelligent te verkennen door het proces van evolutie in silico te simuleren.

Terwijl traditionele Evolutionaire Algoritmen zoeken naar één optimale oplossing, maakt OptMAP gebruik van een Quality-Diversity genaamd Multi-dimensional Archive of Phenotypic (MAP) Elites, dat zowel zoekt naar goed presterende oplossingen alsook expliciet naar diverse oplossingen (Novelty Search). Dit vermogen om verschillende sets van goed presterende en diverse oplossingen te vinden is vooral interessant in de synthetische biologie en metabolic engineering, omdat dit het mogelijk maakt om deze sets van diverse oplossingen in het laboratorium te testen om te zien welke het beste presteren in reële omstandigheden, in plaats van te hopen op een enkele optimale in silico oplossing even goed presteert in het laboratorium als in silico. Bovendien creëert OptMAP ook een archief dat de relaties tussen verschillende dimensies van de ruimte gecreëerd door de karakteristieken van de micro-organismen en het bijbehorende fitnesspotentieel in kaart te brengen om nieuwe wetenschappelijke inzichten te bieden die van belang kunnen zijn in metabolic engineering. OptMAP werd met succes gevalideerd door gen-knockouts en mediumsamenstellingen voor de productie van meerdere metabolieten in Escherichia coli te voorspellen. De resultaten van OptMAP zijn beter dan vergelijkbare bestaande frameworks om gen-knockouts en mediumsamenstellingen voor de productie van metabolieten in micro-organismen te voorspellen.

Hoewel OptMAP zijn nut heeft bewezen voor de voorspelling van gen-knockouts en mediumsamenstellingen, is er nog steeds potentieel voor uitbreiding van OptMAP voor de voorspelling van genregulatie targets, de toevoeging van heterologe routes via knock-ins en andere metabolic engineering technieken die in het laboratorium worden gebruikt, waardoor OptMAP alle nieuwe ontwikkelingen of ontdekkingen op het gebied van metabolic engineering kan bijhouden.

Bibliografie

Adami, C., Ofria, C., and Collier, T. C. (2000). Evolution of biological complexity. Proceedings of the National Academy of Sciences, 97(9):4463–4468.

Amann, T., Schmieder, V., Faustrup Kildegaard, H., Borth, N., and Andersen, M. R. (2019). Genetic en- gineering approaches to improve posttranslational modification of biopharmaceuticals in different production platforms. Biotechnology and Bioengineering, 116(10):2778–2796.

Asadollahi, M. A., Maury, J., Patil, K. R., Schalk, M., Clark, A., and Nielsen, J. (2009). Enhancing sesquiterpene production in saccharomyces cerevisiae through in silico driven metabolic engineer- ing. Metabolic Engineering, 11(6):328–334.

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000). Gene ontology: tool for the unification of biology. Nature Genetics, 25(1):25–29.

Back, T. (1996). Evolutionary algorithms in theory and practice: evolution strategies, evolutionary pro- gramming, genetic algorithms. Oxford university press.

Baquero, F., Coque, T. M., Galán, J. C., and Martinez, J. L. (2021). The origin of niches and species in the bacterial world. Frontiers in Microbiology, 12:566.

Bard, J. (2016). Principles of evolution: Systems, species, and the history of life. Garland Science. Berkner, L. V. and Marshall, L. (1965). On the origin and rise of oxygen concentration in the earth’s

atmosphere. Journal of Atmospheric Sciences, 22(3):225–261.
Blum, C. and Dorigo, M. (2004). Deception in ant colony optimization. In International Workshop on

Ant Colony Optimization and Swarm Intelligence, pages 118–129. Springer.
Brochado, A. R., Matos, C., Møller, B. L., Hansen, J., Mortensen, U. H., and Patil, K. R. (2010). Improved

vanillin production in baker’s yeast through in silico design. Microbial cell factories, 9(1):1–15.

Brownlee, J. (2020). How to choose a feature selection method for machine learning.

Burgard, A. P., Pharkya, P., and Maranas, C. D. (2003). Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and bioengi- neering, 84(6):647–657.

Burns, J. R. (2021). Introducing bacteria and synthetic biomolecules along engineered dna fibers. Small, 17(25):2100136.

Campodonico, M. A., Sukumara, S., Feist, A. M., and Herrgård, M. J. (2018). Computational methods to assess the production potential of bio-based chemicals. In Synthetic Metabolic Pathways, pages 97–116. Springer.

Cardoso, J. G., Jensen, K., Lieven, C., Lærke Hansen, A. S., Galkina, S., Beber, M., Ozdemir, E., Her- rgård, M. J., Redestig, H., and Sonnenschein, N. (2018). Cameo: a python library for computer aided metabolic engineering and optimization of cell factories. ACS synthetic biology, 7(4):1163–1166.

Castle, S. D., Grierson, C. S., and Gorochowski, T. E. (2021). Towards an engineering theory of evolution. Nature Communications, 12(1):1–12.

Chalancon, G., Kruse, K., and Babu, M. M. (2013). Metabolic networks, structure and dynamics. Ency- clopedia of Systems Biology. Springer, New York.

Chandra, N. and Kumar, S. (2017). Antibiotics producing soil microorganisms. In Antibiotics and antibi- otics resistance genes in soils, pages 1–18. Springer.

Choon, Y. W., Mohamad, M. S., Deris, S., Illias, R. M., Chong, C. K., Chai, L. E., Omatu, S., and Corchado, J. M. (2014). Differential bees flux balance analysis with optknock for in silico microbial strains opti- mization. PloS one, 9(7):e102744.

Chowdhury, A., Zomorrodi, A. R., and Maranas, C. D. (2014). k-optforce: integrating kinetics with flux balance analysis for strain design. PLoS computational biology, 10(2):e1003487.

Christensen, B. and Nielsen, J. (1999). Metabolic network analysis. Bioanalysis and Biosensors for Bio- process Monitoring, pages 209–231.

Clune J, Lehman J, S. K. (2019). Icml 2019 tutorial: Recent advances in population-based search for deep neural networks.

Cornejo, E., Abreu, N., and Komeili, A. (2014). Compartmentalization and organelle formation in bacteria. Current opinion in cell biology, 26:132–138.

Courtot, M., Juty, N., Knüpfer, C., Waltemath, D., Zhukova, A., Dräger, A., Dumontier, M., Finney, A., Golebiewski, M., Hastings, J., et al. (2011). Controlled vocabularies and semantics in systems biology. Molecular systems biology, 7(1):543.

Cully, A., Clune, J., Tarapore, D., and Mouret, J.-B. (2015). Robots that can adapt like animals. Nature, 521(7553):503–507.

Cully, A. and Demiris, Y. (2017). Quality and diversity optimization: A unifying modular framework. IEEE Transactions on Evolutionary Computation, 22(2):245–259.

Dahal, S., Zhao, J., and Yang, L. (2020). Genome-scale modeling of metabolism and macromolecular expression and their applications. Biotechnology and Bioprocess Engineering, 25(6):931–943.

Dahal, S., Zhao, J., and Yang, L. (2021). Recent advances in genome-scale modeling of proteome alloca- tion. Current Opinion in Systems Biology, 26:39–45.

de Oliveira Dal’Molin, C. G., Quek, L.-E., Palfreyman, R. W., Brumbley, S. M., and Nielsen, L. K. (2010). Aragem, a genome-scale reconstruction of the primary metabolic network in arabidopsis. Plant phys- iology, 152(2):579–589.

Deb, K. and Goldberg, D. E. (1993). Analyzing deception in trap functions. In Foundations of genetic algorithms, volume 2, pages 93–108. Elsevier.

Deng, W., Shang, S., Cai, X., Zhao, H., Song, Y., and Xu, J. (2021). An improved differential evolution algorithm and its application in optimization problem. Soft Computing, 25(7):5277–5298.

Dua, D. and Graff, C. (2017). UCI machine learning repository.

Dubitzky, W., Wolkenhauer, O., Cho, K.-H., and Yokota, H. (2013). Encyclopedia of systems biology, vol- ume 402. Springer New York.

Ebrahim, A., Lerman, J. A., Palsson, B. O., and Hyduke, D. R. (2013). Cobrapy: constraints-based recon- struction and analysis for python. BMC systems biology, 7(1):1–6.

Education, I. C. (2022). What are neural networks?
Edwards, J. S. and Palsson, B. O. (1999). Systems properties of the haemophilus influenzaerd metabolic

genotype. Journal of Biological Chemistry, 274(25):17410–17416.

FabricNano (2022). Dna nanotechnology.

Fang, X., Lloyd, C. J., and Palsson, B. O. (2020). Reconstructing organisms in silico: genome-scale models and their emerging applications. Nature Reviews Microbiology, 18(12):731–743.

Fani, R. (2012). The origin and evolution of metabolic pathways: why and how did primordial cells construct metabolic routes? Evolution: Education and Outreach, 5(3):367–381.

Federoff, H. J. and Gostin, L. O. (2009). Evolving from reductionism to holism: is there a future for systems medicine? Jama, 302(9):994–996.

Fontaine, M. C., Togelius, J., Nikolaidis, S., and Hoover, A. K. (2020). Covariance matrix adaptation for the rapid illumination of behavior space. In Proceedings of the 2020 genetic and evolutionary com- putation conference, pages 94–102.

Fowler, Z. L., Gikandi, W. W., and Koffas, M. A. (2009). Increased malonyl coenzyme a biosynthesis by tuning the escherichia coli metabolic network and its application to flavanone production. Applied and environmental microbiology, 75(18):5831–5839.

Francke, C., Siezen, R. J., and Teusink, B. (2005). Reconstructing the metabolic network of a bacterium from its genome. Trends in microbiology, 13(11):550–558.

Gabaldón, T. and Pittis, A. A. (2015). Origin and evolution of metabolic sub-cellular compartmentalization in eukaryotes. Biochimie, 119:262–268.

Gaier, A., Asteroth, A., and Mouret, J.-B. (2018). Data-efficient design exploration through surrogate- assisted illumination. Evolutionary computation, 26(3):381–410.

Gleizer, S., Ben-Nissan, R., Bar-On, Y. M., Antonovsky, N., Noor, E., Zohar, Y., Jona, G., Krieger, E., Shamshoum, M., Bar-Even, A., et al. (2019). Conversion of escherichia coli to generate all biomass carbon from co2. Cell, 179(6):1255–1263.

Gomes, J., Urbano, P., and Christensen, A. L. (2013). Evolution of swarm robotics systems with novelty search. Swarm Intelligence, 7(2):115–144.

Gu, C., Kim, G. B., Kim, W. J., Kim, H. U., and Lee, S. Y. (2019). Current status and applications of genome- scale metabolic models. Genome biology, 20(1):1–18.

Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S. N., Richelle, A., Heinken, A., Haraldsdóttir, H. S., Wa- chowiak, J., Keating, S. M., Vlasov, V., et al. (2019). Creation and analysis of biochemical constraint- based models using the cobra toolbox v. 3.0. Nature protocols, 14(3):639–702.

Henry, C. S., DeJongh, M., Best, A. A., Frybarger, P. M., Linsay, B., and Stevens, R. L. (2010). High- throughput generation, optimization and analysis of genome-scale metabolic models. Nature biotech- nology, 28(9):977–982.

Herrgård, M. J., Lee, B.-S., Portnoy, V., and Palsson, B. Ø. (2006). Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in saccharomyces cerevisiae. Genome re- search, 16(5):627–635.

Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8):2554–2558.

Huber, C. and Wachtershauser, G. (1997). Activated acetic acid by carbon fixation on (fe, ni) s under primordial conditions. Science, 276(5310):245–247.

Hussein, F., Kharma, N., and Ward, R. (2001). Genetic algorithms for feature selection and weighting, a review and study. In Proceedings of Sixth International Conference on Document Analysis and Recog- nition, pages 1240–1244.

Iranmanesh, E., Asadollahi, M. A., and Biria, D. (2020). Improving l-phenylacetylcarbinol production in saccharomyces cerevisiae by in silico aided metabolic engineering. Journal of biotechnology, 308:27– 34.

Iyer, M. S., Pal, A., Srinivasan, S., Somvanshi, P. R., and Venkatesh, K. (2020). Global transcriptional regulators fine-tune the translational and metabolic machinery in escherichia coli under anaerobic fermentation. bioRxiv.

Jalali, S. M. J., Ahmadian, S., Khosravi, A., Mirjalili, S., Mahmoudi, M. R., and Nahavandi, S. (2020). Neuroevolution-based autonomous robot navigation: a comparative study. Cognitive Systems Re- search, 62:35–43.

Jiang, S. (2021). Optdesign: Identifying optimum design strategies in strain engineering for biochemical production. bioRxiv.

Joyce, A. R. and Palsson, B. Ø. (2008). Predicting gene essentiality using genome-scale in silico models. In Microbial Gene Essentiality: Protocols and Bioinformatics, pages 433–457. Springer.

Kaggle (2022). Kaggle. https://www.kaggle.com.

Kanehisa, M. and Goto, S. (2000). Kegg: kyoto encyclopedia of genes and genomes. Nucleic acids research, 28(1):27–30.

Kang, M.-J., Baek, K.-R., Lee, Y.-R., Kim, G.-H., and Seo, S.-O. (2022). Production of vitamin k by wild-type and engineered microorganisms. Microorganisms, 10(3):554.

Karlsen, E., Schulz, C., and Almaas, E. (2018). Automated generation of genome-scale metabolic draft reconstructions based on kegg. BMC bioinformatics, 19(1):1–11.

Kawai, F. (1995). Breakdown of plastics and polymers by microorganisms. Microbial and enzymatic bioproducts, pages 151–194.

Keseler, I. M., Mackie, A., Santos-Zavaleta, A., Billington, R., Bonavides-Martínez, C., Caspi, R., Fulcher, C., Gama-Castro, S., Kothari, A., Krummenacker, M., et al. (2017). The ecocyc database: reflecting new knowledge about escherichia coli k-12. Nucleic acids research, 45(D1):D543–D550.

Kim, B., Kim, W. J., Kim, D. I., and Lee, S. Y. (2015). Applications of genome-scale metabolic network model in metabolic engineering. Journal of industrial microbiology and biotechnology, 42(3):339–348.

Kim, W. J., Kim, H. U., and Lee, S. Y. (2017). Current state and applications of microbial genome-scale metabolic models. Current Opinion in Systems Biology, 2:10–18.

Kim, Y., Gu, C., Kim, H. U., and Lee, S. Y. (2020). Current status of pan-genome analysis for pathogenic bacteria. Current opinion in biotechnology, 63:54–62.

Kimura, M. (2020). Diversity of organisms and views on evolution. In My Thoughts on Biological Evolu- tion, pages 1–13. Springer.

King, Z. A., Dräger, A., Ebrahim, A., Sonnenschein, N., Lewis, N. E., and Palsson, B. O. (2015). Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS computational biology, 11(8):e1004321.

King, Z. A. and Feist, A. M. (2013). Optimizing cofactor specificity of oxidoreductase enzymes for the generation of microbial production strains—optswap. Industrial Biotechnology, 9(4):236–246.

King, Z. A., Lu, J., Dräger, A., Miller, P., Federowicz, S., Lerman, J. A., Ebrahim, A., Palsson, B. O., and Lewis, N. E. (2016). Bigg models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic acids research, 44(D1):D515–D522.

knowledgebase, G. O. (2021). The gene ontology resource: enriching a gold mine. Nucleic acids research, 49(D1):D325–D334.

Komosinski, M. and Ulatowski, S. (1998). Framsticks-artificial life. ECML’98 Demonstration and Poster Papers, Chemnitzer Informatik Berichte, pages 7–9.

Kovačič, M. and Župerl, U. (2020). Genetic programming in the steelmaking industry. Genetic Program- ming and Evolvable Machines, 21(1):99–128.

Koziel, S. and Yang, X.-S. (2011). Computational optimization, methods and algorithms, volume 356. Springer.

Kuhn, M., Johnson, K., et al. (2013). Applied predictive modeling, volume 26. Springer.
Lane, N. (2015). The vital question: energy, evolution, and the origins of complex life. WW Norton &

Company.

Lawson, C. E., Martí, J. M., Radivojevic, T., Jonnalagadda, S. V. R., Gentz, R., Hillson, N. J., Peisert, S., Kim, J., Simmons, B. A., Petzold, C. J., et al. (2021). Machine learning for metabolic engineering: A review. Metabolic Engineering, 63:34–60.

Lecointre, G. and Le Guyader, H. (2006). The tree of life: a phylogenetic classification. Harvard University Press.

Lehman, J. and Stanley, K. O. (2011a). Abandoning objectives: Evolution through the search for novelty alone. Evolutionary computation, 19(2):189–223.

Lehman, J. and Stanley, K. O. (2011b). Evolving a diversity of virtual creatures through novelty search and local competition. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, pages 211–218.

Lehman, J. and Stanley, K. O. (2011c). Novelty search and the problem with objectives. In Genetic programming theory and practice IX, pages 37–56. Springer.

Lehman, J., Stanley, K. O., et al. (2008). Exploiting open-endedness to solve problems through the search for novelty. In ALIFE, pages 329–336. Citeseer.

Liapis, A., Yannakakis, G. N., and Togelius, J. (2013). Enhancements to constrained novelty search: Two- population novelty search for generating game content. In Proceedings of the 15th annual conference on Genetic and evolutionary computation, pages 343–350.

Liew, M. J., Salleh, A. H. M., Mohamad, M. S., Choon, Y. W., Deris, S., Samah, A. A., and Majid, H. A. (2016). In silico gene deletion of escherichia coli for optimal ethanol production using a hybrid algorithm of particle swarm optimization and flux balance analysis. Jurnal Teknologi, 78(12-3).

Long, C. P. and Antoniewicz, M. R. (2019). Metabolic flux responses to deletion of 20 core enzymes reveal flexibility and limits of e. coli metabolism. Metabolic Engineering, 55:249–257.

López, F. G., Torres, M. G., Batista, B. M., Pérez, J. A. M., and Moreno-Vega, J. M. (2006). Solving feature subset selection problem by a parallel scatter search. European Journal of Operational Research, 169(2):477–489.

Marashi, S.-A., Kouhestani, H., and Mahdavi, M. (2013). Studying the relationship between robustness against mutations in metabolic networks and lifestyle of organisms. The Scientific World Journal, 2013.

Mardinoglu, A., Gatto, F., and Nielsen, J. (2013). Genome-scale modeling of human metabolism–a sys- tems biology approach. Biotechnology journal, 8(9):985–996.

Martin, W. (2010). Evolutionary origins of metabolic compartmentalization in eukaryotes. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1541):847–855.

Mienda, B. S., Salihu, R., Adamu, A., and Idris, S. (2018). Genome-scale metabolic models as platforms

for identification of novel genes as antimicrobial drug targets. Future Microbiology, 13(4):455–467.

Mienda, B. S. and Shamsir, M. S. (2015). Bioscience and bioengineering communications.

Mienda, B. S., Shamsir, M. S., and Illias, R. M. (2016). Model-guided metabolic gene knockout of gnd for enhanced succinate production in escherichia coli from glucose and glycerol substrates. Compu- tational biology and chemistry, 61:130–137.

Monmarché, N., Guinand, F., and Siarry, P. (2010). Artificial ants. Wiley-iste Hoboken.
Mouret, J.-B. (2020). Evolving the behavior of machines: from micro to macroevolution. Iscience,

23(11):101731.
Mouret, J.-B. and Clune, J. (2015). Illuminating search spaces by mapping elites. arXiv preprint

arXiv:1504.04909.
Mustafa, M. G., Khan, M. G. M., Nguyen, D., and Iqbal, S. (2018). Techniques in biotechnology: Essential

for industry. In Omics Technologies and Bio-Engineering, pages 233–249. Elsevier.
Neveu, M., Kim, H.-J., and Benner, S. A. (2013). The “strong” rna world hypothesis: Fifty years old.

Astrobiology, 13(4):391–403.
Nordmoen, J., Veenstra, F., Ellefsen, K. O., and Glette, K. (2021). Map-elites enables powerful stepping

stones and diversity for modular robotics. Frontiers in Robotics and AI, 8.

Oberhardt, M. A., Zarecki, R., Reshef, L., Xia, F., Duran-Frigola, M., Schreiber, R., Henry, C. S., Ben-Tal, N., Dwyer, D. J., Gophna, U., et al. (2016). Systems-wide prediction of enzyme promiscuity reveals a new underground alternative route for pyridoxal 5’-phosphate production in e. coli. PLoS computational biology, 12(1):e1004705.

Oh, Y.-G., Lee, D.-Y., Lee, S. Y., and Park, S. (2009). Multiobjective flux balancing using the nise method for metabolic network analysis. Biotechnology progress, 25(4):999–1008.

Orth, J. D., Thiele, I., and Palsson, B. (2010). What is flux balance analysis? Nature biotechnology, 28(3):245–248.

Pharkya, P., Burgard, A. P., and Maranas, C. D. (2004). Optstrain: a computational framework for redesign of microbial production systems. Genome research, 14(11):2367–2376.

Pugh, J. K., Soros, L. B., and Stanley, K. O. (2016). Quality diversity: A new frontier for evolutionary computation. Frontiers in Robotics and AI, 3:40.

Quinonez, B., Pinto-Roa, D. P., García-Torres, M., García-Díaz, M. E., Núnez-Castillo, C., and Divina, F. (2019). Map-elites algorithm for features selection problem. In AMW.

Renz, A., Mostolizadeh, R., and Dräger, A. (2021). Clinical applications of metabolic models in sbml format. Systems Medicine.

Richelle, A., David, B., Demaegd, D., Dewerchin, M., Kinet, R., Morreale, A., Portela, R., Zune, Q., and von Stosch, M. (2020). Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective. NPJ systems biology and applications, 6(1):1–5.

Risi, S., Vanderbleek, S. D., Hughes, C. E., and Stanley, K. O. (2009). How novelty search escapes the deceptive trap of learning to learn. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 153–160.

Rocha, I., Maia, P., Rocha, M., and Ferreira, E. C. (2008). Optgene: a framework for in silico metabolic engineering.

Ruckerbauer, D. E., Jungreuthmayer, C., and Zanghellini, J. (2014). Design of optimally constructed metabolic networks of minimal functionality. PLoS One, 9(3):e92583.

Schellenberger, J., Park, J. O., Conrad, T. M., and Palsson, B. Ø. (2010). Bigg: a biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions. BMC bioinformatics, 11(1):1–10.

Schopf, J. W. and Packer, B. M. (1987). Early archean (3.3-billion to 3.5-billion-year-old) microfossils from warrawoona group, australia. Science, 237(4810):70–73.

Scossa, F. and Fernie, A. R. (2020). The evolution of metabolism: How to test evolutionary hypotheses at the genomic level. Computational and Structural Biotechnology Journal, 18:482–500.

Seckbach, J. (2012). Genesis-in the beginning: precursors of life, chemical models and early biological evolution, volume 22. Springer Science & Business Media.

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W., Bridgland, A., et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792):706–710.

Shabestary, K. and Hudson, E. P. (2016). Computational metabolic engineering strategies for growth- coupled biofuel production by synechocystis. Metabolic engineering communications, 3:216–226.

Slowik, A. and Kwasnicka, H. (2020). Evolutionary algorithms and their applications to engineering prob- lems. Neural Computing and Applications, 32(16):12363–12379.

Stanley, K. O. and Lehman, J. (2015). Why greatness cannot be planned: The myth of the objective. Springer.

Stanley, S. M. (1975). A theory of evolution above the species level. Proceedings of the National Academy of Sciences, 72(2):646–650.

Stephanopoulos, G. (2012). Synthetic biology and metabolic engineering. ACS synthetic biology, 1(11):514–525.

Systems, F. (2017). Genetic algorithms and evolutionary algorithms - introduction.
Tavassoly, I., Goldfarb, J., and Iyengar, R. (2018). Systems biology primer: the basic methods and ap-

proaches. Essays in biochemistry, 62(4):487–500.

Tian, H., Chen, S.-C., and Shyu, M.-L. (2020). Evolutionary programming based deep learning fea- ture selection and network construction for visual data classification. Information Systems Frontiers, 22(5):1053–1066.

Tomar, N. and De, R. K. (2013). Comparing methods for metabolic network analysis and an application to metabolic engineering. Gene, 521(1):1–14.

Tsompanas, M.-A., Bull, L., Adamatzky, A., and Balaz, I. (2020). Novelty search employed into the devel- opment of cancer treatment simulations. Informatics in Medicine Unlocked, 19:100347.

Vikhar, P. A. (2016). Evolutionary algorithms: A critical review and its future prospects. In 2016 Interna- tional conference on global trends in signal processing, information computing and communication (ICGTSPICC), pages 261–265. IEEE.

Wahid, N. S. A., Mohamad, M. S., Salleh, A. H. M., Deris, S., Chan, W. H., Omatu, S., Corchado, J. M., Sjaugi, M. F., Ibrahim, Z., and Yusof, Z. M. (2016). A hybrid of harmony search and minimization of metabolic adjustment for optimization of succinic acid production. In International Conference on Practical Applications of Computational Biology & Bioinformatics, pages 183–191. Springer.

Waldner, J.-B. (2013). Nanocomputers and swarm intelligence. John Wiley & Sons.
Walhout, M., Vidal, M., and Dekker, J. (2012). Handbook of systems biology: concepts and insights.

Academic Press.

Wang, H., Robinson, J. L., Kocabas, P., Gustafsson, J., Anton, M., Cholley, P.-E., Huang, S., Gobom, J., Svensson, T., Uhlen, M., et al. (2021). Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proceedings of the National Academy of Sciences, 118(30).

Yang, X.-S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.
Yang, Y.-T., Bennett, G. N., and San, K.-Y. (1998). Genetic and metabolic engineering. Electronic Journal

of Biotechnology, 1(3):20–21.
Youssef, H., Sait, S. M., and Adiche, H. (2001). Evolutionary algorithms, simulated annealing and tabu

search: a comparative study. Engineering Applications of Artificial Intelligence, 14(2):167–181.
Zhang, C. and Hua, Q. (2016). Applications of genome-scale metabolic models in biotechnology and

systems medicine. Frontiers in physiology, 6:413.

Zhi, H. and Liu, S. (2019). Face recognition based on genetic algorithm. Journal of Visual Communication and Image Representation, 58:495–502.

Universiteit of Hogeschool
Master of Science in Bioscience Engineering: Cell and Gene Biotechnology
Publicatiejaar
2022
Promotor(en)
Prof. dr. Bernard De Baets, dr. ir. Michiel Stock
Kernwoorden
https://twitter.com/shaunyvanhoye
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