Biocomputing with synthetic biological systems

“We aim at engineering information-processing devices with living matter that outperform traditional silicon-based computers at specific tasks—using maths and synthetic biology”

Group leader: Angel Goñi Moreno - Young Investigator Researcher (YIR)

This email address is being protected from spambots. You need JavaScript enabled to view it. 910679186 Ext: 79186 / 910679186 Ext: 79186 ( Lab Ninguno )


Our research activities revolve around the understanding of how information is processed in living cells, such as bacteria, and the rational modification of information flows to engineer novel biocomputations. To this end, we combine the use of mathematical modelling, computational simulations and molecular biology and microbiology experimental methods. An important aspect of our work involves designing—and implementing—models of computation that mimic those found in traditional computers (Figure 1). For example, current efforts from our group deal with engineer Boolean combinatorial logic systems into novel gene regulatory networks—the so-called genetic circuits. We design the circuits using current mathematical models and predictions; and usually develop new ones to include molecular mechanistic details of interest. We then build those circuits using molecular biology techniques, and characterise their performance, thus following the design-build-test synthetic biology lifecycle. Moreover, we incorporate core cellular mechanisms such as metabolism, or processes such as evolution, to come up with more powerful, yet unknown, living models of computation.

Figure 1. Could we build powerful cellular computers? Theoretical computer (above) science has developed increasingly powerful models of computation, from combinatorial logic, to finite-state machines and Turing machines, all of which process information in different ways. Living systems (below), like bacterial cells, also process information following evolutionary solutions and mechanisms. Gene regulatory networks can be engineerd to receive inputs and generate outputs according to combinatorial logic rules, thus building genetic logic gates. Core cellular mechanisms such as metabolism, or processes such as evolution may be exploited to design more powerful, yet unknown, biocomputations. Taken from Grozinger et al. Nat. Comms. (2019).

Research topics:
  • Predictable mathematical modelling and model-based design. We use deterministic, stochastic and agent-based approaches to design and predict the performance of molecular systems, from gene regulation to population formation.
  • Genetic logic gates and circuits. We use the bacteria E. coli and P. putida to engineer combinatorial information-processing networks: from the digitalisation of molecular analogue signals, to the implementation of logic gates.
  • Gene expression noise. We analyse the stochastic nature of molecular signals and their origins. This leads not only to biophysical studies, but also to biocomputing endeavours where stochastic processing is a bonus (Figure 2).
  • Multicellular distributed computing. We design bacterial consortia where cells communicate the output of their internal molecular computation to other cells. As a result, a new computation emerges (Figure 2).
  • Standards for synthetic biology. We are part of the Synthetic Biology Open Language (SBOL) community and contributors to the Standard European Vector Architecture (SEVA). We are interested in implementing standards all along the synthetic biology lifecycle: design, build, test, learn.

Figure 2. Research areas for improving the genetic logic gate paradigm. One of our main interests lies in finding ways to upgrade the current combinatorial design ambition. There are several mechanisms that help us move beyond this point, and could be used for the rational design of biocomputations: a) merged transcriptional and metabolic networks, since both types have different dynamics and input/output domains, b) distributed computations with social interactions, aiming at engineer robust biocomputations, c) gene expression noise, to implement stochastic processes, and d) evolution, as a powerful source of living information-processing. Taken from Grozinger et al. Nat. Comms. (2019).

Representative Publications

Tas, H., Goñi-Moreno, Á., Lorenzo, V. de 2021. A Standardized Inverter Package Borne by Broad Host Range Plasmids for Genetic Circuit Design in Gram-Negative Bacteria. ACS Synthetic Biology 10, 213–217. DOI: 10.1021/acssynbio.0c00529

Tas, H., Grozinger, L., Stoof, R., de Lorenzo, V., Goñi-Moreno, Á. 2021. Contextual dependencies expand the re-usability of genetic inverters. Nature Communications 12, 355. DOI: 10.1038/s41467-020-20656-5

Stoof, R., Goñi-Moreno, Á. 2020. Modelling co-translational dimerization for programmable nonlinearity in synthetic biology. Journal of The Royal Society Interface 17, 20200561. DOI: 10.1098/rsif.2020.0561

Brown, B., Bartley, B., Beal, J., Bird, J.E., Goñi-Moreno, Á., McLaughlin, J.A., Mısırlı, G., Roehner, N., Skelton, D.J., Poh, C.L., Ofiteru, I.D., James, K., Wipat, A. 2020. Capturing Multicellular System Designs Using Synthetic Biology Open Language (SBOL). ACS synthetic biology 9, 2410–2417. DOI: 10.1021/acssynbio.0c00176

Beal, J., Goñi-Moreno, A., Myers, C., Hecht, A., de Vicente, M. del C., Parco, M., Schmidt, M., Timmis, K., Baldwin, G., Friedrichs, S., Freemont, P., Kiga, D., Ordozgoiti, E., Rennig, M., Rios, L., Tanner, K., de Lorenzo, V., Porcar, M. 2020. The long journey towards standards for engineering biosystems. EMBO reports 21, e50521. DOI: 10.15252/embr.202050521

Crowther, M., Grozinger, L., Pocock, M., Taylor, C.P.D., McLaughlin, J.A., Mısırlı, G., Bartley, B.A., Beal, J., Goñi-Moreno, A., Wipat, A. 2020. ShortBOL: A Language for Scripting Designs for Engineered Biological Systems Using Synthetic Biology Open Language (SBOL). ACS Synthetic Biology 9, 962–966. DOI: 10.1021/acssynbio.9b00470

Martínez-García, E., Goñi-Moreno, A., Bartley, B., McLaughlin, J., Sánchez-Sampedro, L., Pascual del Pozo, H., Prieto Hernández, C., Marletta, A.S., De Lucrezia, D., Sánchez-Fernández, G., Fraile, S., de Lorenzo, V. 2020. SEVA 3.0: an update of the Standard European Vector Architecture for enabling portability of genetic constructs among diverse bacterial hosts. Nucleic Acids Research 48, D1164–D1170. DOI: 10.1093/nar/gkz1024

Calles, B., Goñi-Moreno, Á., de Lorenzo, V. 2019. Digitalizing heterologous gene expression in Gram-negative bacteria with a portable ON/OFF module. Molecular Systems Biology 15, e8777. DOI: 10.15252/msb.20188777

Grozinger, L., Amos, M., Gorochowski, T.E., Carbonell, P., Oyarzún, D.A., Stoof, R., Fellermann, H., Zuliani, P., Tas, H., Goñi-Moreno, A. 2019. Pathways to cellular supremacy in biocomputing. Nature Communications 10, 5250. DOI: 10.1038/s41467-019-13232-z

Stoof, R., Wood, A., Goñi-Moreno, Á. 2019. A Model for the Spatiotemporal Design of Gene Regulatory Circuits. ACS Synthetic Biology 8, 2007–2016. DOI: 10.1021/acssynbio.9b00022

Beal, J., Nguyen, T., Gorochowski, T.E., Goñi-Moreno, A., Scott-Brown, J., McLaughlin, J.A., Madsen, C., Aleritsch, B., Bartley, B., Bhakta, S., Bissell, M., Castillo Hair, S., Clancy, K., Luna, A., Le Novère, N., Palchick, Z., Pocock, M., Sauro, H., Sexton, J.T., Tabor, J.J., Voigt, C.A., Zundel, Z., Myers, C., Wipat, A. 2019. Communicating Structure and Function in Synthetic Biology Diagrams. ACS Synthetic Biology 8, 1818–1825. DOI: 10.1021/acssynbio.9b00139

Fellermann, H., Penn, A.S., Füchslin, R.M., Bacardit, J., Goñi-Moreno, A. 2019. Towards Low-Carbon Conferencing: Acceptance of Virtual Conferencing Solutions and Other Sustainability Measures in the ALIFE Community. Artificial Life Conference Proceedings 31, 21–27. DOI: 10.1162/isal_a_00133

Hallinan, J.S., Wipat, A., Kitney, R., Woods, S., Taylor, K., Goñi-Moreno, A. 2019. Future-proofing synthetic biology: educating the next generation. Engineering Biology 3, 25–31. DOI: 10.1049/enb.2019.0001

Mısırlı, G., Taylor, R., Goñi-Moreno, A., McLaughlin, J.A., Myers, C., Gennari, J.H., Lord, P., Wipat, A. 2019. SBOL-OWL: An Ontological Approach for Formal and Semantic Representation of Synthetic Biology Information. ACS Synthetic Biology 8, 1498–1514. DOI: 10.1021/acssynbio.8b00532

Kitney, R., Adeogun, M., Fujishima, Y., Goñi-Moreno, Á., Johnson, R., Maxon, M., Steedman, S., Ward, S., Winickoff, D., Philp, J. 2019. Enabling the Advanced Bioeconomy through Public Policy Supporting Biofoundries and Engineering Biology. Trends in Biotechnology 37, 917–920. DOI: 10.1016/j.tibtech.2019.03.017

Madsen, C., Moreno, A.G., P, U., Palchick, Z., Roehner, N., Atallah, C., Bartley, B., Choi, K., Cox, R.S., Gorochowski, T., Grünberg, R., Macklin, C., McLaughlin, J., Meng, X., Nguyen, T., Pocock, M., Samineni, M., Scott-Brown, J., Tarter, Y., Zhang, M., Zhang, Z., Zundel, Z., Beal, J., Bissell, M., Clancy, K., Gennari, J.H., Misirli, G., Myers, C., Oberortner, E., Sauro, H., Wipat, A. 2019. Synthetic Biology Open Language (SBOL) Version 2.3. Journal of Integrative Bioinformatics 16. DOI: 10.1515/jib-2019-0025

Goñi-Moreno, A., Nikel, P.I. 2019. High-Performance Biocomputing in Synthetic Biology–Integrated Transcriptional and Metabolic Circuits. Frontiers in Bioengineering and Biotechnology 7. DOI: 10.3389/fbioe.2019.00040

Kim, J., Goñi‐Moreno, A., Calles, B., Lorenzo, V. de 2019. Spatial organization of the gene expression hardware in Pseudomonas putida. Environmental Microbiology 21, 1645–1658. DOI: 10.1111/1462-2920.14544

Goñi-Moreno, A., de la Cruz, F., Rodríguez-Patón, A., Amos, M. 2019. Dynamical Task Switching in Cellular Computers. Life 9, 14. DOI: 10.3390/life9010014

Amos, M., Goñi-Moreno, A. 2018. Cellular Computing and Synthetic Biology, in: Stepney, S., Rasmussen, S., Amos, M. (Eds.), Computational Matter. Springer International Publishing, Cham, pp. 93–110. DOI: 10.1007/978-3-319-65826-1_7

Goñi-Moreno, A., de Lorenzo, V. 2018. Bio-Algorithmic Workflows for Standardized Synthetic Biology Constructs, in: Braman, J.C. (Ed.), Synthetic Biology: Methods and Protocols, Methods in Molecular Biology. Springer, New York, NY, pp. 363–372. DOI: 10.1007/978-1-4939-7795-6_20

McLaughlin, J.A., Myers, C.J., Zundel, Z., Mısırlı, G., Zhang, M., Ofiteru, I.D., Goñi-Moreno, A., Wipat, A. 2018. SynBioHub: A Standards-Enabled Design Repository for Synthetic Biology. ACS Synthetic Biology 7, 682–688. DOI: 10.1021/acssynbio.7b00403

García-Betancur, J.-C., Goñi-Moreno, A., Horger, T., Schott, M., Sharan, M., Eikmeier, J., Wohlmuth, B., Zernecke, A., Ohlsen, K., Kuttler, C., Lopez, D. 2017. Cell differentiation defines acute and chronic infection cell types in Staphylococcus aureus. eLife 6, e28023. DOI: 10.7554/eLife.28023

Goñi-Moreno, A., Wipat, A., Krasnogor, N. 2017. CSBB: synthetic biology research at Newcastle University. Biochemical Society Transactions 45, 781–783. DOI: 10.1042/BST20160437

Goñi-Moreno, Á., Benedetti, I., Kim, J., de Lorenzo, V. 2017. Deconvolution of Gene Expression Noise into Spatial Dynamics of Transcription Factor–Promoter Interplay. ACS Synthetic Biology 6, 1359–1369. DOI: 10.1021/acssynbio.6b00397

Goñi‐Moreno, Á., Kim, J., Lorenzo, V. de 2017. CellShape: A user-friendly image analysis tool for quantitative visualization of bacterial cell factories inside. Biotechnology Journal 12, 1600323. DOI: 10.1002/biot.201600323

Chavarría, M., Goñi-Moreno, Á., de Lorenzo, V., Nikel, P.I. 2016. A Metabolic Widget Adjusts the Phosphoenolpyruvate-Dependent Fructose Influx in Pseudomonas putida. mSystems 1. DOI: 10.1128/mSystems.00154-16

Espeso, D.R., Martínez-García, E., de Lorenzo, V., Goñi-Moreno, Á. 2016. Physical Forces Shape Group Identity of Swimming Pseudomonas putida Cells. Frontiers in Microbiology 7. DOI: 10.3389/fmicb.2016.01437

Goñi-Moreno, A., Carcajona, M., Kim, J., Martínez-García, E., Amos, M., de Lorenzo, V. 2016. An Implementation-Focused Bio/Algorithmic Workflow for Synthetic Biology. ACS Synthetic Biology 5, 1127–1135. DOI: 10.1021/acssynbio.6b00029


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