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Halužan Vasle A, Moškon M. Synthetic biological neural networks: From current implementations to future perspectives. Biosystems 2024; 237:105164. [PMID: 38402944 DOI: 10.1016/j.biosystems.2024.105164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 01/03/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024]
Abstract
Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.
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Affiliation(s)
- Ana Halužan Vasle
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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Turpin B, Bijman EY, Kaltenbach HM, Stelling J. Efficient design of synthetic gene circuits under cell-to-cell variability. BMC Bioinformatics 2023; 24:460. [PMID: 38062373 PMCID: PMC10701960 DOI: 10.1186/s12859-023-05538-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 10/18/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Synthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. RESULTS Here, we present a computational framework and an efficient algorithm to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a Non-linear Mixed-Effects (NLME) framework into a Markov Chain Monte-Carlo (MCMC) algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. CONCLUSION We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.
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Affiliation(s)
- Baptiste Turpin
- Department of Biosystems Science and Engineering (D-BSSE) and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland
| | - Eline Y Bijman
- Department of Biosystems Science and Engineering (D-BSSE) and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland
| | - Hans-Michael Kaltenbach
- Department of Biosystems Science and Engineering (D-BSSE) and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland
| | - Jörg Stelling
- Department of Biosystems Science and Engineering (D-BSSE) and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland.
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Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression. Cell Syst 2022; 13:353-364.e6. [PMID: 35298924 DOI: 10.1016/j.cels.2022.02.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 11/18/2021] [Accepted: 02/17/2022] [Indexed: 12/27/2022]
Abstract
Cells live in constantly changing environments and employ dynamic signaling pathways to transduce information about the signals they encounter. However, the mechanisms by which dynamic signals are decoded into appropriate gene expression patterns remain poorly understood. Here, we devise networked optogenetic pathways that achieve dynamic signal processing functions that recapitulate cellular information processing. Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling edge pulse detector and show that this circuit can be employed to demultiplex dynamically encoded signals. We combine this demultiplexer with dCas9-based gene networks to construct pulsatile signal filters and decoders. Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state. Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway. Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
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A synthetic gene circuit for imaging-free detection of signaling pulses. Cell Syst 2022; 13:131-142.e13. [PMID: 34739875 PMCID: PMC8857027 DOI: 10.1016/j.cels.2021.10.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/20/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
Cells employ intracellular signaling pathways to sense and respond to changes in their external environment. In recent years, live-cell biosensors have revealed complex pulsatile dynamics in many pathways, but studies of these signaling dynamics are limited by the necessity of live-cell imaging at high spatiotemporal resolution. Here, we describe an approach to infer pulsatile signaling dynamics from a single measurement in fixed cells using a pulse-detecting gene circuit. We computationally screened for circuits with the capability to selectively detect signaling pulses, revealing an incoherent feedforward topology that robustly performs this computation. We implemented the motif experimentally for the Erk signaling pathway using a single engineered transcription factor and fluorescent protein reporter. Our "recorder of Erk activity dynamics" (READer) responds sensitively to spontaneous and stimulus-driven Erk pulses. READer circuits open the door to permanently labeling transient, dynamic cell populations to elucidate the mechanistic underpinnings and biological consequences of signaling dynamics.
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Yuan W, Xu Y, Zheng K, Fu S, Wang Y, Qin Y. Experimental generation of perfect optical vortices through strongly scattering media. OPTICS LETTERS 2021; 46:4156-4159. [PMID: 34469963 DOI: 10.1364/ol.435636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
Perfect optical vortices enable the unprecedented optical multiplexing utilizing orbital angular momentum of light, which, however, suffer from distortion when they propagate in inhomogeneous media. Herein, we report on the experimental demonstration of perfect optical vortice generation through strongly scattering media. The transmission-matrix-based point-spread-function engineering is applied to encode the targeted mask in the Fourier domain before focusing. We experimentally demonstrate the perfect optical vortice generation either through a multimode fiber or a ground glass, where the numerical results agree well with the measured one. Our results might facilitate the manipulation of orbital angular momentum of light through disordered scattering media and shed new light on the optical multiplexing utilizing perfect optical vortices.
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Nakamura A, Goto Y, Kondo Y, Aoki K. Shedding light on developmental ERK signaling with genetically encoded biosensors. Development 2021; 148:271153. [PMID: 34338283 DOI: 10.1242/dev.199767] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The extracellular signal-regulated kinase (ERK) pathway governs cell proliferation, differentiation and migration, and therefore plays key roles in various developmental and regenerative processes. Recent advances in genetically encoded fluorescent biosensors have unveiled hitherto unrecognized ERK activation dynamics in space and time and their functional importance mainly in cultured cells. However, ERK dynamics during embryonic development have still only been visualized in limited numbers of model organisms, and we are far from a sufficient understanding of the roles played by developmental ERK dynamics. In this Review, we first provide an overview of the biosensors used for visualization of ERK activity in live cells. Second, we highlight the applications of the biosensors to developmental studies of model organisms and discuss the current understanding of how ERK dynamics are encoded and decoded for cell fate decision-making.
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Affiliation(s)
- Akinobu Nakamura
- Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan.,Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan
| | - Yuhei Goto
- Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan.,Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan.,Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan
| | - Yohei Kondo
- Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan.,Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan.,Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan
| | - Kazuhiro Aoki
- Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan.,Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan.,Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan.,IRCC International Research Collaboration Center, National Institutes of Natural Sciences, 4-3-13 Toranomon, Minato-ku, Tokyo 105-0001, Japan
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