The use of foundational mathematical modeling techniques to inform understanding & design of complex biological systems

Synthetic biology is a relatively new and diverse field with the potential to revolutionize our command over biological systems via the modification or de novo construction of biological networks and tools. Precise and predictable control over the foundational properties of gene expression and genetic circuit behavior will be critical to the application of synthetic biology in the relevant contexts (for instance, in vivo for therapeutic applications). This level of control can be achieved via the interplay between mathematical modeling and empirical observation. The following work will highlight not only the massive potential of synthetic biology in both bacterial and mammalian systems, but the essential role of mathematical modeling in the field to understand existing biological systems and inform the design of novel systems to control biology. I will also outline my efforts to expand the capabilities of synthetic biology research at William & Mary to include work in mammalian systems, creating a sustainable and accessible framework to enable future students to delve into fundamental control of biological systems on the cutting edge of mammalian synthetic biology research

TESTING USED ROLLER BEARINGS FOR QUALITY AND SERVICE LIFE

Synthetic biology is a rapidly expanding field at the interface of the engineering and biological sciences which aims to apply rational design principles in biological contexts. Many natural processes utilise regulatory architectures that parallel those found in control and electrical engineering, which has motivated their implementation as part of synthetic biological constructs. Tools based upon control theoretical concepts can be used to design such systems, as well as to guide their experimental realisation. In this paper we provide examples of biological implementations of negative feedback systems, and discuss progress made toward realisation of other feedback and control architectures. We then outline major challenges posed by the design of such systems, particularly focusing on those which are specific to biological contexts and on which feedback control can have a significant impact. We explore future directions for work in the field, including new approaches for theoretical design of biological control systems, the utilisation of novel components for their implementation, and the potential for application of automation and machine-learning approaches to accelerate synthetic biological research

The sound of silence:Transgene silencing in mammalian cell engineering

To elucidate principles operating in native biological systems and to develop novel biotechnologies, synthetic biology aims to build and integrate synthetic gene circuits within native transcriptional networks. The utility of synthetic gene circuits for cell engineering relies on the ability to control the expression of all constituent transgene components. Transgene silencing, defined as the loss of expression over time, persists as an obstacle for engineering primary cells and stem cells with transgenic cargos. In this review, we highlight the challenge that transgene silencing poses to the robust engineering of mammalian cells, outline potential molecular mechanisms of silencing, and present approaches for preventing transgene silencing. We conclude with a perspective identifying future research directions for improving the performance of synthetic gene circuits.ISSN:2405-472

Using synthetic biological parts and microbioreactors to explore the protein expression characteristics of Escherichia coli

Synthetic biology has developed numerous parts for the precise control of protein expression. However, relatively little is known about the burden these place on a host, or their reliability under varying environmental conditions. To address this, we made use of synthetic transcriptional and translational elements to create a combinatorial library of constructs that modulated expression strength of a green fluorescent protein. Combining this library with a microbioreactor platform, we were able to perform a detailed large-scale assessment of transient expression and growth characteristics of two <i>Escherichia coli</i> strains across several temperatures. This revealed significant differences in the robustness of both strains to differing types of protein expression, and a complex response of transcriptional and translational elements to differing temperatures. This study supports the development of reliable synthetic biological systems capable of working across different hosts and environmental contexts. Plasmids developed during this work have been made publicly available to act as a reference set for future research

Genetically modified organisms for the environment: stories of success and failure and what we have learned from them

The expectations raised in the mid-1980s on the potential of genetic engineering for in situ remediation of environmental pollution have not been entirely fulfilled. Yet, we have learned a good deal about the expression of catabolic pathways by bacteria in their natural habitats, and how environmental conditions dictate the expression of desired catalytic activities. The many different choices between nutrients and responses to stresses form a network of transcriptional switches which, given the redundance and robustness of the regulatory circuits involved, can be neither unraveled through standard genetic analysis nor artificially programmed in a simple manner. Available data suggest that population dynamics and physiological control of catabolic gene expression prevail over any artificial attempt to engineer an optimal performance of the wanted catalytic activities. In this review, several valuable spin-offs of past research into genetically modified organisms with environmental applications are discussed, along with the impact of Systems Biology and Synthetic Biology in the future of environmental biotechnology. [Int Microbiol 2005; 8(3):213-222

Rational design of a genetic finite state machine: Combining biology, engineering, and mathematics for bio-computer research

[EN] The recent success of biological engineering is due to a tremendous amount of research effort and the increasing number of market opportunities. Indeed, this has been partially possible due to the contribution of advanced mathematical tools and the application of engineering principles in genetic-circuit development. In this work, we use a rationally designed genetic circuit to show how models can support research and motivate students to apply mathematics in their future careers. A genetic four-state machine is analyzed using three frameworks: Deterministic and stochastic modeling through di erential and master equations, and a spatial approach via a cellular automaton. Each theoretical framework sheds light on the problem in a complementary way. It helps in understanding basic concepts of modeling and engineering, such as noise, robustness, and reaction¿di usion systems. The designed automaton could be part of a more complex system of modules conforming future bio-computers and it is a paradigmatic example of how models can assist teachers in multidisciplinary education.D.F. was supported by an internal grant from Palacky University Olomouc (no. IGA_PrF_2020_028) and J.A.C. by MEC, grant number MTM2016-75963-P.Fuente, D.; Garibo I Orts, Ó.; Conejero, JA.; Urchueguía Schölzel, JF. (2020). Rational design of a genetic finite state machine: Combining biology, engineering, and mathematics for bio-computer research. Mathematics. 8(8):1-20. https://doi.org/10.3390/math8081362S12088Khalil, A. S., & Collins, J. J. (2010). Synthetic biology: applications come of age. Nature Reviews Genetics, 11(5), 367-379. doi:10.1038/nrg2775Jullesson, D., David, F., Pfleger, B., & Nielsen, J. (2015). Impact of synthetic biology and metabolic engineering on industrial production of fine chemicals. 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Extended metabolic biosensor design for dynamic pathway regulation of cell factories

[EN] Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctua-tions. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implement-ing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feed-back controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.This work is partially supported by grant MINECO/AEI and EU DPI2017-82896-C2-1-R. P.C. acknowledges support from the Universitat Politecnica de Valencia Talento Programme.Boada-Acosta, YF.; Vignoni, A.; Picó, J.; Carbonell, P. (2020). Extended metabolic biosensor design for dynamic pathway regulation of cell factories. iScience. 23(7):1-25. https://doi.org/10.1016/j.isci.2020.101305S125237Agrawal, D. K., Dolan, E. M., Hernandez, N. E., Blacklock, K. M., Khare, S. D., & Sontag, E. D. (2020). Mathematical Models of Protease-Based Enzymatic Biosensors. ACS Synthetic Biology, 9(2), 198-208. doi:10.1021/acssynbio.9b00279Arnold, F. H. (2017). Directed Evolution: Bringing New Chemistry to Life. Angewandte Chemie International Edition, 57(16), 4143-4148. doi:10.1002/anie.201708408Boada, Y., Vignoni, A., & Picó, J. (2017). Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise. ACS Synthetic Biology, 6(10), 1903-1912. doi:10.1021/acssynbio.7b00087Boada, Y., Vignoni, A., & Picó, J. (2017). Multi-objective optimization for gene expression noise reduction in a synthetic gene circuit * *This work is partially supported by Spanish government and European Union (FEDER-CICYT DPI2014-55276-C5-1). Y.B. thanks grant FPI/2013-3242 of Universitat Politècnica de València, and also thanks the support from the Ayudas para movilidad dentro del Programa para la Formación de Personal Investigador (FPI) de la UPV para estancias 2016. A.V. thanks the Max Planck Society, the CSBD and the MPI-CBG. The authors are grateful to Prof. Dr. Ivo F. Sbalzarini for hosting Y.B in the MOSAIC Group for a research stay, also to Pietro Incadorna from the MOSAIC Group at CSBD for his help in the parallel algorithm implementation, and to Dr. Gilberto Reynoso-Meza from the PPGEPS at Pontifícia Universidade Católica do Paraná for his always helpful comments regarding the MOOD. IFAC-PapersOnLine, 50(1), 4472-4477. doi:10.1016/j.ifacol.2017.08.376Boada, Y., Vignoni, A., & Pico, J. (2020). Multiobjective Identification of a Feedback Synthetic Gene Circuit. IEEE Transactions on Control Systems Technology, 28(1), 208-223. doi:10.1109/tcst.2018.2885694Briat, C., Gupta, A., & Khammash, M. (2016). Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks. Cell Systems, 2(1), 15-26. doi:10.1016/j.cels.2016.01.004Briat, C., & Khammash, M. (2018). Perfect Adaptation and Optimal Equilibrium Productivity in a Simple Microbial Biofuel Metabolic Pathway Using Dynamic Integral Control. 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Biology, ecology, distribution and control of the invasive weed, lactuca serriola l. (wild lettuce) : a global review

Lactuca serriola L. (wild lettuce) is a highly invasive C3 weed in many countries, including Australia, Canada, and the USA. This weed is a severe threat to agricultural systems, especially in crops grown with reduced or no‐tillage approaches, which commonly include wheat, cereals and pulses. Owing to the vertical orientation of its leaves in the north‐south plane and its root architec-ture, L. serriola can maintain high water use efficiency under drought conditions, giving it the ability to expand its range under a drying climate. Each plant can produce up to 100,000 seeds which have no primary dormancy and form a short‐term seedbank lasting up to three years. Most seedlings emerge in autumn and overwinter as a rosette, with a small flush of emergence in spring depicting staggered germination. Research into control methods for this weed has been performed, and these methods include chemical herbicides applied alone and in combination, the establishment of plant competition, tillage, mowing and bioherbicide. Herbicides can provide effective control when applied in the seedling or rosette stage; however, spring germination is difficult to control, as it skips the rosette stage. Some biotypes are now resistant to ALS inhibitor and synthetic auxins, causing concern regarding using herbicides. A dedicated integrated management plan for 3–4 years is recommended for the control of this troublesome species. This review will explore the biology, ecology, distribution, current control techniques and previous research on this weed, allowing us to make recommendations for its future research and management. © 2021 by the authors. Licensee MDPI, Basel, Switzerland