1
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Lawton P, Fahimipour AK, Anderson KE. Interspecific dispersal constraints suppress pattern formation in metacommunities. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230136. [PMID: 38913053 DOI: 10.1098/rstb.2023.0136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/12/2024] [Indexed: 06/25/2024] Open
Abstract
Decisions to disperse from a habitat stand out among organismal behaviours as pivotal drivers of ecosystem dynamics across scales. Encounters with other species are an important component of adaptive decision-making in dispersal, resulting in widespread behaviours like tracking resources or avoiding consumers in space. Despite this, metacommunity models often treat dispersal as a function of intraspecific density alone. We show, focusing initially on three-species network motifs, that interspecific dispersal rules generally drive a transition in metacommunities from homogeneous steady states to self-organized heterogeneous spatial patterns. However, when ecologically realistic constraints reflecting adaptive behaviours are imposed-prey tracking and predator avoidance-a pronounced homogenizing effect emerges where spatial pattern formation is suppressed. We demonstrate this effect for each motif by computing master stability functions that separate the contributions of local and spatial interactions to pattern formation. We extend this result to species-rich food webs using a random matrix approach, where we find that eventually, webs become large enough to override the homogenizing effect of adaptive dispersal behaviours, leading once again to predominately pattern-forming dynamics. Our results emphasize the critical role of interspecific dispersal rules in shaping spatial patterns across landscapes, highlighting the need to incorporate adaptive behavioural constraints in efforts to link local species interactions and metacommunity structure. This article is part of the theme issue 'Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics'.
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Affiliation(s)
- Patrick Lawton
- Biophysics Graduate Program, University of California , Riverside, CA, USA
| | - Ashkaan K Fahimipour
- Department of Biological Sciences, Florida Atlantic University , Boca Raton, FL, USA
- Center for Complex Systems and Brain Sciences, Florida Atlantic University , Boca Raton, FL, USA
| | - Kurt E Anderson
- Department of Evolution, Ecology, & Organismal Biology, University of California , Riverside, CA, USA
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2
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Li Z, Wang S, Sun M, Jin M, Khain D, Yang Q. Comprehensive Parameter Space Mapping of Cell Cycle Dynamics under Network Perturbations. ACS Synth Biol 2024; 13:804-815. [PMID: 38420905 DOI: 10.1021/acssynbio.3c00631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Studies of quantitative systems and synthetic biology have extensively utilized models to interpret data, make predictions, and guide experimental designs. However, models often simplify complex biological systems and lack experimentally validated parameters, making their reliability in perturbed systems unclear. Here, we developed a droplet-based synthetic cell system to continuously tune parameters at the single-cell level in multiple dimensions with full dynamic ranges, providing an experimental framework for global parameter space scans. We systematically perturbed a cell-cycle oscillator centered on cyclin-dependent kinase (Cdk1), enabling comprehensive mapping of period landscapes in response to network perturbations. The data allowed us to challenge existing models and refine a new model that matches the observed response. Our analysis demonstrated that Cdk1 positive feedback inhibition restricts the cell cycle frequency range, confirming model predictions; furthermore, it revealed new cellular responses to the inhibition of the Cdk1-counteracting phosphatase PP2A: monomodal or bimodal distributions across varying inhibition levels, underscoring the complex nature of cell cycle regulation that can be explained by our model. This comprehensive perturbation platform may be generalizable to exploring other complex dynamic systems.
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Affiliation(s)
- Zhengda Li
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California 94305, United States
| | - Shiyuan Wang
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Meng Sun
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Minjun Jin
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Daniel Khain
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Qiong Yang
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan 48109, United States
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3
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Song YM, Campbell S, Shiau L, Kim JK, Ott W. Noisy Delay Denoises Biochemical Oscillators. PHYSICAL REVIEW LETTERS 2024; 132:078402. [PMID: 38427894 DOI: 10.1103/physrevlett.132.078402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 11/17/2023] [Indexed: 03/03/2024]
Abstract
Genetic oscillations are generated by delayed transcriptional negative feedback loops, wherein repressor proteins inhibit their own synthesis after a temporal production delay. This delay is distributed because it arises from a sequence of noisy processes, including transcription, translocation, translation, and folding. Because the delay determines repression timing and, therefore, oscillation period, it has been commonly believed that delay noise weakens oscillatory dynamics. Here, we demonstrate that noisy delay can surprisingly denoise genetic oscillators. Specifically, moderate delay noise improves the signal-to-noise ratio and sharpens oscillation peaks, all without impacting period and amplitude. We show that this denoising phenomenon occurs in a variety of well-studied genetic oscillators, and we use queueing theory to uncover the universal mechanisms that produce it.
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Affiliation(s)
- Yun Min Song
- Department of Mathematical Sciences, KAIST, Daejeon 34141, Republic of Korea
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Republic of Korea
| | - Sean Campbell
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA
| | - LieJune Shiau
- Department of Mathematics and Statistics, University of Houston Clear Lake, Houston, Texas 77058, USA
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon 34141, Republic of Korea
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Republic of Korea
| | - William Ott
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA
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4
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Jin J, Xu F, Liu Z, Qi H, Yao C, Shuai J, Li X. Biphasic amplitude oscillator characterized by distinct dynamics of trough and crest. Phys Rev E 2023; 108:064412. [PMID: 38243441 DOI: 10.1103/physreve.108.064412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/28/2023] [Indexed: 01/21/2024]
Abstract
Biphasic amplitude dynamics (BAD) of oscillation have been observed in many biological systems. However, the specific topology structure and regulatory mechanisms underlying these biphasic amplitude dynamics remain elusive. Here, we searched all possible two-node circuit topologies and identified the core oscillator that enables robust oscillation. This core oscillator consists of a negative feedback loop between two nodes and a self-positive feedback loop of the input node, which result in the fast and slow dynamics of the two nodes, thereby achieving relaxation oscillation. Landscape theory was employed to study the stochastic dynamics and global stability of the system, allowing us to quantitatively describe the diverse positions and sizes of the Mexican hat. With increasing input strength, the size of the Mexican hat exhibits a gradual increase followed by a subsequent decrease. The self-activation of input node and the negative feedback on input node, which dominate the fast dynamics of the input node, were observed to regulate BAD in a bell-shaped manner. Both deterministic and statistical analysis results reveal that BAD is characterized by the linear and nonlinear dependence of the oscillation trough and crest on the input strength. In addition, combining with computational and theoretical analysis, we addressed that the linear response of trough to input is predominantly governed by the negative feedback, while the nonlinear response of crest is jointly regulated by the negative feedback loop and the self-positive feedback loop within the oscillator. Overall, this study provides a natural and physical basis for comprehending the occurrence of BAD in oscillatory systems, yielding guidance for the design of BAD in synthetic biology applications.
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Affiliation(s)
- Jun Jin
- Department of Physics, Xiamen University, Xiamen, Fujian 361005, China
| | - Fei Xu
- Department of Physics, Anhui Normal University, Wuhu, Anhui 241002, China
| | - Zhilong Liu
- Department of Physics, Xiamen University, Xiamen, Fujian 361005, China
| | - Hong Qi
- Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Chenggui Yao
- College of Data Science, Jiaxing University, Jiaxing, Zhejiang 314000, China
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen, Fujian 361005, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Xiang Li
- Department of Physics, Xiamen University, Xiamen, Fujian 361005, China
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5
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Merzbacher C, Mac Aodha O, Oyarzún DA. Bayesian Optimization for Design of Multiscale Biological Circuits. ACS Synth Biol 2023. [PMID: 37339382 PMCID: PMC10367132 DOI: 10.1021/acssynbio.3c00120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.
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Affiliation(s)
| | - Oisin Mac Aodha
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K
- The Alan Turing Institute, London NW1 2DB, U.K
| | - Diego A Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K
- The Alan Turing Institute, London NW1 2DB, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, U.K
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6
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Rombouts J, Verplaetse S, Gelens L. The ups and downs of biological oscillators: a comparison of time-delayed negative feedback mechanisms. J R Soc Interface 2023; 20:20230123. [PMID: 37376871 PMCID: PMC10300510 DOI: 10.1098/rsif.2023.0123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
Many biochemical oscillators are driven by the periodic rise and fall of protein concentrations or activities. A negative feedback loop underlies such oscillations. The feedback can act on different parts of the biochemical network. Here, we mathematically compare time-delay models where the feedback affects production and degradation. We show a mathematical connection between the linear stability of the two models, and derive how both mechanisms impose different constraints on the production and degradation rates that allow oscillations. We show how oscillations are affected by the inclusion of a distributed delay, of double regulation (acting on production and degradation) and of enzymatic degradation.
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Affiliation(s)
- Jan Rombouts
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Developmental Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Department of Cellular and Molecular Medicine, KU Leuven, Belgium
| | - Sarah Verplaetse
- Department of Cellular and Molecular Medicine, KU Leuven, Belgium
| | - Lendert Gelens
- Department of Cellular and Molecular Medicine, KU Leuven, Belgium
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7
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Chen X, Wang T, Guan Y, Ouyang Q, Lou C, Qian L. The Topological Characteristics of Biological Ratio-Sensing Networks. Life (Basel) 2023; 13:life13020351. [PMID: 36836707 PMCID: PMC9965423 DOI: 10.3390/life13020351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/12/2023] [Accepted: 01/21/2023] [Indexed: 01/31/2023] Open
Abstract
Ratio sensing is a fundamental biological function observed in signal transduction and decision making. In the synthetic biology context, ratio sensing presents one of the elementary functions for cellular multi-signal computation. To investigate the mechanism of the ratio-sensing behavior, we explored the topological characteristics of biological ratio-sensing networks. With exhaustive enumeration of three-node enzymatic and transcriptional regulatory networks, we found that robust ratio sensing was highly dependent on network structure rather than network complexity. Specifically, a set of seven minimal core topological structures and four motifs were deduced to be capable of robust ratio sensing. Further investigations on the evolutionary space of robust ratio-sensing networks revealed highly clustered domains surrounding the core motifs which suggested their evolutionary plausibility. Our study revealed the network topological design principles of ratio-sensing behavior and provided a design scheme for constructing regulatory circuits with ratio-sensing behavior in synthetic biology.
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Affiliation(s)
- Xinmao Chen
- School of Physics, Peking University, Beijing 100871, China
| | - Tianze Wang
- Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Ying Guan
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Qi Ouyang
- School of Physics, Peking University, Beijing 100871, China
- Center for Quantitative Biology, Peking University, Beijing 100871, China
- Correspondence: (Q.O.); (C.L.); (L.Q.)
| | - Chunbo Lou
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Correspondence: (Q.O.); (C.L.); (L.Q.)
| | - Long Qian
- Center for Quantitative Biology, Peking University, Beijing 100871, China
- Correspondence: (Q.O.); (C.L.); (L.Q.)
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8
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Maryu G, Yang Q. Nuclear-cytoplasmic compartmentalization of cyclin B1-Cdk1 promotes robust timing of mitotic events. Cell Rep 2022; 41:111870. [PMID: 36577372 DOI: 10.1016/j.celrep.2022.111870] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 11/15/2022] [Accepted: 12/01/2022] [Indexed: 12/28/2022] Open
Abstract
The cyclin-dependent kinase (Cdk1) oscillator is widely characterized in homogenized cytosolic extracts, leaving unclear the impact of nucleocytoplasmic compartmentalization. Here, by developing a Förster resonance energy transfer (FRET) biosensor, we track Cdk1 spatiotemporal dynamics in reconstituted cells with or without side by side and find compartmentalization significantly modulates clock properties previously found in bulk studies. Although nucleus-absent cells display highly tunable frequency, the nucleus-present cells maintain constant frequency against cyclin B1 variations. Despite high expression variability, cyclin degraded within the same duration, enabling a robust mitotic phase. Moreover, Cdk1 and cyclin B1 cycle rigorously out-of-phase, ensuring wide phase-plane orbits, essential for oscillation robustness. Although Cdk1 in homogeneous extracts is well known for delayed switch-like activation, we find active cyclin B1-Cdk1 accumulates in nuclei, without delay, until the nuclear envelope breakdown (NEB) when another abrupt activation triggers anaphase. Cdk1 biphasic activation and spatial compartmentalization may together coordinate the accurate ordering of different downstream events.
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Affiliation(s)
- Gembu Maryu
- Department of Biophysics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Qiong Yang
- Department of Biophysics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA.
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9
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Discovering design principles for biological functionalities: Perspectives from systems biology. J Biosci 2022. [DOI: 10.1007/s12038-022-00293-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Sun Z, Wei W, Zhang M, Shi W, Zong Y, Chen Y, Yang X, Yu B, Tang C, Lou C. Synthetic robust perfect adaptation achieved by negative feedback coupling with linear weak positive feedback. Nucleic Acids Res 2022; 50:2377-2386. [PMID: 35166832 PMCID: PMC8887471 DOI: 10.1093/nar/gkac066] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 01/15/2022] [Accepted: 01/25/2022] [Indexed: 12/21/2022] Open
Abstract
Unlike their natural counterparts, synthetic genetic circuits are usually fragile in the face of environmental perturbations and genetic mutations. Several theoretical robust genetic circuits have been designed, but their performance under real-world conditions has not yet been carefully evaluated. Here, we designed and synthesized a new robust perfect adaptation circuit composed of two-node negative feedback coupling with linear positive feedback on the buffer node. As a key feature, the linear positive feedback was fine-tuned to evaluate its necessity. We found that the desired function was robustly achieved when genetic parameters were varied by systematically perturbing all interacting parts within the topology, and the necessity of the completeness of the topological structures was evaluated by destroying key circuit features. Furthermore, different environmental perturbances were imposed onto the circuit by changing growth rates, carbon metabolic strategies and even chassis cells, and the designed perfect adaptation function was still achieved under all conditions. The successful design of a robust perfect adaptation circuit indicated that the top-down design strategy is capable of predictably guiding bottom-up engineering for robust genetic circuits. This robust adaptation circuit could be integrated as a motif into more complex circuits to robustly implement more sophisticated and critical biological functions.
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Affiliation(s)
- Zhi Sun
- CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100149, China
| | - Weijia Wei
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100149, China
| | - Mingyue Zhang
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China.,School of Physics, Peking University, Beijing 100871, China
| | - Wenjia Shi
- Department of Applied Physics, School of Sciences, Xi'an University of Technology, Xi'an 710048, China
| | | | - Yihua Chen
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100149, China
| | - Xiaojing Yang
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China
| | - Bo Yu
- CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Chao Tang
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China.,School of Physics, Peking University, Beijing 100871, China
| | - Chunbo Lou
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100149, China
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11
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Bhattacharya P, Raman K, Tangirala AK. Discovering adaptation-capable biological network structures using control-theoretic approaches. PLoS Comput Biol 2022; 18:e1009769. [PMID: 35061660 PMCID: PMC8809615 DOI: 10.1371/journal.pcbi.1009769] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 02/02/2022] [Accepted: 12/16/2021] [Indexed: 11/19/2022] Open
Abstract
Constructing biological networks capable of performing specific biological functionalities has been of sustained interest in synthetic biology. Adaptation is one such ubiquitous functional property, which enables every living organism to sense a change in its surroundings and return to its operating condition prior to the disturbance. In this paper, we present a generic systems theory-driven method for designing adaptive protein networks. First, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as ‘design requirements’ for the underlying networks. We go on to prove that a protein network with different input–output nodes (proteins) needs to be at least of third-order in order to provide adaptation. Next, we show that the necessary design principles obtained for a three-node network in adaptation consist of negative feedback or a feed-forward realization. We argue that presence of a particular class of negative feedback or feed-forward realization is necessary for a network of any size to provide adaptation. Further, we claim that the necessary structural conditions derived in this work are the strictest among the ones hitherto existed in the literature. Finally, we prove that the capability of producing adaptation is retained for the admissible motifs even when the output node is connected with a downstream system in a feedback fashion. This result explains how complex biological networks achieve robustness while keeping the core motifs unchanged in the context of a particular functionality. We corroborate our theoretical results with detailed and thorough numerical simulations. Overall, our results present a generic, systematic and robust framework for designing various kinds of biological networks. Biological systems display a remarkable diversity of functionalities, many of which can be conceived as the response of a large network composed of small interconnecting modules. Unravelling the connection pattern, i.e. design principles, behind important biological functionalities is one of the most challenging problems in systems biology. One such phenomenon is perfect adaptation, which merits special attention owing to its universal presence ranging from chemotaxis in bacterial cells to calcium homeostasis in mammalian cells. The present work focuses on finding the design principles for perfect adaptation in the presence of a stair-case type disturbance. To this end, the current work proposes a systems-theoretic approach to deduce precise mathematical (hence structural) conditions that comply with the key performance parameters for adaptation. The approach is agnostic to the particularities of the reaction kinetics, underlining the dominant role of the topological structure on the response of the network. Notably, the design principles obtained in this work serve as the most strict necessary structural conditions for a network of any size to provide perfect adaptation.
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Affiliation(s)
- Priyan Bhattacharya
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
- Initiative for Biological Systems Engineering (IBSE), IIT Madras, Chennai, India
| | - Karthik Raman
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
- Initiative for Biological Systems Engineering (IBSE), IIT Madras, Chennai, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India
- * E-mail: (KR); (AKT)
| | - Arun K. Tangirala
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
- Initiative for Biological Systems Engineering (IBSE), IIT Madras, Chennai, India
- * E-mail: (KR); (AKT)
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12
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Cyclin/Forkhead-mediated coordination of cyclin waves: an autonomous oscillator rationalizing the quantitative model of Cdk control for budding yeast. NPJ Syst Biol Appl 2021; 7:48. [PMID: 34903735 PMCID: PMC8668886 DOI: 10.1038/s41540-021-00201-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 11/01/2021] [Indexed: 01/21/2023] Open
Abstract
Networks of interacting molecules organize topology, amount, and timing of biological functions. Systems biology concepts required to pin down 'network motifs' or 'design principles' for time-dependent processes have been developed for the cell division cycle, through integration of predictive computer modeling with quantitative experimentation. A dynamic coordination of sequential waves of cyclin-dependent kinases (cyclin/Cdk) with the transcription factors network offers insights to investigate how incompatible processes are kept separate in time during the eukaryotic cell cycle. Here this coordination is discussed for the Forkhead transcription factors in light of missing gaps in the current knowledge of cell cycle control in budding yeast. An emergent design principle is proposed where cyclin waves are synchronized by a cyclin/Cdk-mediated feed-forward regulation through the Forkhead as a transcriptional timer. This design is rationalized by the bidirectional interaction between mitotic cyclins and the Forkhead transcriptional timer, resulting in an autonomous oscillator that may be instrumental for a well-timed progression throughout the cell cycle. The regulation centered around the cyclin/Cdk-Forkhead axis can be pivotal to timely coordinate cell cycle dynamics, thereby to actuate the quantitative model of Cdk control.
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13
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Shen J, Liu F, Tu Y, Tang C. Finding gene network topologies for given biological function with recurrent neural network. Nat Commun 2021; 12:3125. [PMID: 34035278 PMCID: PMC8149884 DOI: 10.1038/s41467-021-23420-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 04/28/2021] [Indexed: 11/12/2022] Open
Abstract
Searching for possible biochemical networks that perform a certain function is a challenge in systems biology. For simple functions and small networks, this can be achieved through an exhaustive search of the network topology space. However, it is difficult to scale this approach up to larger networks and more complex functions. Here we tackle this problem by training a recurrent neural network (RNN) to perform the desired function. By developing a systematic perturbative method to interrogate the successfully trained RNNs, we are able to distill the underlying regulatory network among the biological elements (genes, proteins, etc.). Furthermore, we show several cases where the regulation networks found by RNN can achieve the desired biological function when its edges are expressed by more realistic response functions, such as the Hill-function. This method can be used to link topology and function by helping uncover the regulation logic and network topology for complex tasks. Networks are useful ways to describe interactions between molecules in a cell, but predicting the real topology of large networks can be challenging. Here, the authors use deep learning to predict the topology of networks that perform biologically-plausible functions.
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Affiliation(s)
- Jingxiang Shen
- Center for Quantitative Biology, Peking University, Beijing, China.,School of Physics, Peking University, Beijing, China
| | - Feng Liu
- Center for Quantitative Biology, Peking University, Beijing, China.,School of Physics, Peking University, Beijing, China
| | - Yuhai Tu
- IBM T. J. Watson Research Center, Yorktown Heights, New York, USA
| | - Chao Tang
- Center for Quantitative Biology, Peking University, Beijing, China. .,School of Physics, Peking University, Beijing, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
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14
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Kimchi O, Goodrich CP, Courbet A, Curatolo AI, Woodall NB, Baker D, Brenner MP. Self-assembly-based posttranslational protein oscillators. SCIENCE ADVANCES 2020; 6:6/51/eabc1939. [PMID: 33328225 PMCID: PMC7744077 DOI: 10.1126/sciadv.abc1939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 11/03/2020] [Indexed: 06/12/2023]
Abstract
Recent advances in synthetic posttranslational protein circuits are substantially impacting the landscape of cellular engineering and offer several advantages compared to traditional gene circuits. However, engineering dynamic phenomena such as oscillations in protein-level circuits remains an outstanding challenge. Few examples of biological posttranslational oscillators are known, necessitating theoretical progress to determine realizable oscillators. We construct mathematical models for two posttranslational oscillators, using few components that interact only through reversible binding and phosphorylation/dephosphorylation reactions. Our designed oscillators rely on the self-assembly of two protein species into multimeric functional enzymes that respectively inhibit and enhance this self-assembly. We limit our analysis to within experimental constraints, finding (i) significant portions of the restricted parameter space yielding oscillations and (ii) that oscillation periods can be tuned by several orders of magnitude using recent advances in computational protein design. Our work paves the way for the rational design and realization of protein-based dynamic systems.
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Affiliation(s)
- Ofer Kimchi
- Harvard University School of Engineering and Applied Sciences, Cambridge, MA 02138, USA.
| | - Carl P Goodrich
- Harvard University School of Engineering and Applied Sciences, Cambridge, MA 02138, USA
| | - Alexis Courbet
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | - Agnese I Curatolo
- Harvard University School of Engineering and Applied Sciences, Cambridge, MA 02138, USA
| | - Nicholas B Woodall
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | - Michael P Brenner
- Harvard University School of Engineering and Applied Sciences, Cambridge, MA 02138, USA
- Kavli Institute for Bionano Science and Technology Harvard University, Cambridge, MA 02138, USA
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15
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Groaz A, Moghimianavval H, Tavella F, Giessen TW, Vecchiarelli AG, Yang Q, Liu AP. Engineering spatiotemporal organization and dynamics in synthetic cells. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2020; 13:e1685. [PMID: 33219745 DOI: 10.1002/wnan.1685] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 10/13/2020] [Accepted: 10/30/2020] [Indexed: 12/28/2022]
Abstract
Constructing synthetic cells has recently become an appealing area of research. Decades of research in biochemistry and cell biology have amassed detailed part lists of components involved in various cellular processes. Nevertheless, recreating any cellular process in vitro in cell-sized compartments remains ambitious and challenging. Two broad features or principles are key to the development of synthetic cells-compartmentalization and self-organization/spatiotemporal dynamics. In this review article, we discuss the current state of the art and research trends in the engineering of synthetic cell membranes, development of internal compartmentalization, reconstitution of self-organizing dynamics, and integration of activities across scales of space and time. We also identify some research areas that could play a major role in advancing the impact and utility of engineered synthetic cells. This article is categorized under: Biology-Inspired Nanomaterials > Lipid-Based Structures Biology-Inspired Nanomaterials > Protein and Virus-Based Structures.
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Affiliation(s)
| | | | | | | | | | - Qiong Yang
- University of Michigan, Ann Arbor, Michigan, USA
| | - Allen P Liu
- University of Michigan, Ann Arbor, Michigan, USA
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16
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Lafranchi L, Müllers E, Rutishauser D, Lindqvist A. FRET-Based Sorting of Live Cells Reveals Shifted Balance between PLK1 and CDK1 Activities During Checkpoint Recovery. Cells 2020; 9:E2126. [PMID: 32961751 PMCID: PMC7564076 DOI: 10.3390/cells9092126] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/20/2022] Open
Abstract
Cells recovering from the G2/M DNA damage checkpoint rely more on Aurora A-PLK1 signaling than cells progressing through an unperturbed G2 phase, but the reason for this discrepancy is not known. Here, we devised a method based on a FRET reporter for PLK1 activity to sort cells in distinct populations within G2 phase. We employed mass spectroscopy to characterize changes in protein levels through an unperturbed G2 phase and validated that ATAD2 levels decrease in a proteasome-dependent manner. Comparing unperturbed cells with cells recovering from DNA damage, we note that at similar PLK1 activities, recovering cells contain higher levels of Cyclin B1 and increased phosphorylation of CDK1 targets. The increased Cyclin B1 levels are due to continuous Cyclin B1 production during a DNA damage response and are sustained until mitosis. Whereas partial inhibition of PLK1 suppresses mitotic entry more efficiently when cells recover from a checkpoint, partial inhibition of CDK1 suppresses mitotic entry more efficiently in unperturbed cells. Our findings provide a resource for proteome changes during G2 phase, show that the mitotic entry network is rewired during a DNA damage response, and suggest that the bottleneck for mitotic entry shifts from CDK1 to PLK1 after DNA damage.
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Affiliation(s)
- Lorenzo Lafranchi
- Department of Cell and Molecular Biology, Karolinska Institutet, SE-171 77 Stockholm, Sweden; (L.L.); (E.M.)
| | - Erik Müllers
- Department of Cell and Molecular Biology, Karolinska Institutet, SE-171 77 Stockholm, Sweden; (L.L.); (E.M.)
| | - Dorothea Rutishauser
- Division of Physiological Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-171 77 Stockholm, Sweden;
- Science for Life Laboratory, SE-171 65 Stockholm, Sweden
| | - Arne Lindqvist
- Department of Cell and Molecular Biology, Karolinska Institutet, SE-171 77 Stockholm, Sweden; (L.L.); (E.M.)
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17
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Dang Y, Grundel DAJ, Youk H. Cellular Dialogues: Cell-Cell Communication through Diffusible Molecules Yields Dynamic Spatial Patterns. Cell Syst 2020; 10:82-98.e7. [PMID: 31954659 PMCID: PMC6975168 DOI: 10.1016/j.cels.2019.12.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 10/16/2019] [Accepted: 12/04/2019] [Indexed: 02/08/2023]
Abstract
Cells form spatial patterns by coordinating their gene expressions. How a group of mesoscopic numbers (hundreds to thousands) of cells, without pre-existing morphogen gradients and spatial organization, self-organizes spatial patterns remains poorly understood. Of particular importance are dynamic spatial patterns such as spiral waves that perpetually move and transmit information. We developed an open-source software for simulating a field of cells that communicate by secreting any number of molecules. With this software and a theory, we identified all possible "cellular dialogues"-ways of communicating with two diffusing molecules-that yield diverse dynamic spatial patterns. These patterns emerge despite widely varying responses of cells to the molecules, gene-expression noise, spatial arrangements, and cell movements. A three-stage, "order-fluctuate-settle" process forms dynamic spatial patterns: cells form long-lived whirlpools of wavelets that, following erratic dynamics, settle into a dynamic spatial pattern. Our work helps in identifying gene-regulatory networks that underlie dynamic pattern formations.
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Affiliation(s)
- Yiteng Dang
- Kavli Institute of Nanoscience, Delft University of Technology, Delft 2629HZ, the Netherlands; Department of Bionanoscience, Delft University of Technology, Delft 2629HZ, the Netherlands
| | - Douwe A J Grundel
- Kavli Institute of Nanoscience, Delft University of Technology, Delft 2629HZ, the Netherlands; Department of Bionanoscience, Delft University of Technology, Delft 2629HZ, the Netherlands
| | - Hyun Youk
- Kavli Institute of Nanoscience, Delft University of Technology, Delft 2629HZ, the Netherlands; Department of Bionanoscience, Delft University of Technology, Delft 2629HZ, the Netherlands; CIFAR, CIFAR Azrieli Global Scholars Program, Toronto, ON M5G 1M1, Canada.
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18
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Hiscock TW. Adapting machine-learning algorithms to design gene circuits. BMC Bioinformatics 2019; 20:214. [PMID: 31029103 PMCID: PMC6487017 DOI: 10.1186/s12859-019-2788-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 04/02/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Gene circuits are important in many aspects of biology, and perform a wide variety of different functions. For example, some circuits oscillate (e.g. the cell cycle), some are bistable (e.g. as cells differentiate), some respond sharply to environmental signals (e.g. ultrasensitivity), and some pattern multicellular tissues (e.g. Turing's model). Often, one starts from a given circuit, and using simulations, asks what functions it can perform. Here we want to do the opposite: starting from a prescribed function, can we find a circuit that executes this function? Whilst simple in principle, this task is challenging from a computational perspective, since gene circuit models are complex systems with many parameters. In this work, we adapted machine-learning algorithms to significantly accelerate gene circuit discovery. RESULTS We use gradient-descent optimization algorithms from machine learning to rapidly screen and design gene circuits. With this approach, we found that we could rapidly design circuits capable of executing a range of different functions, including those that: (1) recapitulate important in vivo phenomena, such as oscillators, and (2) perform complex tasks for synthetic biology, such as counting noisy biological events. CONCLUSIONS Our computational pipeline will facilitate the systematic study of natural circuits in a range of contexts, and allow the automatic design of circuits for synthetic biology. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.
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Affiliation(s)
- Tom W Hiscock
- Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
- Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, UK.
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