1
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Maroudas-Sklare N, Goren N, Yochelis S, Jung G, Keren N, Paltiel Y. Probing the design principles of photosynthetic systems through fluorescence noise measurement. Sci Rep 2024; 14:13877. [PMID: 38880795 DOI: 10.1038/s41598-024-64068-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 06/05/2024] [Indexed: 06/18/2024] Open
Abstract
Elucidating the energetic processes which govern photosynthesis, the engine of life on earth, are an essential goal both for fundamental research and for cutting-edge biotechnological applications. Fluorescent signal of photosynthetic markers has long been utilised in this endeavour. In this research we demonstrate the use of fluorescent noise analysis to reveal further layers of intricacy in photosynthetic energy transfer. While noise is a common tool analysing dynamics in physics and engineering, its application in biology has thus far been limited. Here, a distinct behaviour in photosynthetic pigments across various chemical and biological environments is measured. These changes seem to elucidate quantum effects governing the generation of oxidative radicals. Although our method offers insights, it is important to note that the interpretation should be further validated expertly to support as conclusive theory. This innovative method is simple, non-invasive, and immediate, making it a promising tool to uncover further, more complex energetic events in photosynthesis, with potential uses in environmental monitoring, agriculture, and food-tech.
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Affiliation(s)
- Naama Maroudas-Sklare
- Department of Applied Physics, Hebrew University of Jerusalem, 91904, Jerusalem, Israel
- Department of Plant & Environmental Sciences, The Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Naama Goren
- Department of Applied Physics, Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Shira Yochelis
- Department of Applied Physics, Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Grzegorz Jung
- Department of Physics, Ben Gurion University of the Negev, 84105, Beer Sheva, Israel
- Instytut Fizyki PAN, 02668, Warszawa, Poland
| | - Nir Keren
- Department of Plant & Environmental Sciences, The Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yossi Paltiel
- Department of Applied Physics, Hebrew University of Jerusalem, 91904, Jerusalem, Israel.
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2
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Yang Y, Li S, Luo L. Responses of organ precursors to correct and incorrect inductive signals. Trends Cell Biol 2024; 34:484-495. [PMID: 37739814 DOI: 10.1016/j.tcb.2023.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/14/2023] [Accepted: 08/31/2023] [Indexed: 09/24/2023]
Abstract
During embryonic development, the inductive molecules produced by local origins normally arrive at their target tissues in a nondirectional, diffusion manner. The target organ precursor cells must correctly interpret these inductive signals to ensure proper specification/differentiation, which is dependent on two prerequisites: (i) obtaining cell-intrinsic competence; and (ii) receiving correct inductive signals while resisting incorrect ones. Gain of intrinsic competence could avoid a large number of misinductions because the incompetent cells are nonresponsive to inductive signals. However, in cases of different precursor cells with similar competence and located in close proximity, resistance to incorrect inductive signals is essential for accurate determination of cell fate. Here we outline the mechanisms of how organ precursors respond to correct and incorrect inductive signals.
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Affiliation(s)
- Yun Yang
- Institute of Development Biology and Regenerative Medicine, Southwest University, Chongqing, China
| | - Shuang Li
- Institute of Development Biology and Regenerative Medicine, Southwest University, Chongqing, China
| | - Lingfei Luo
- Institute of Development Biology and Regenerative Medicine, Southwest University, Chongqing, China; School of Life Sciences, Fudan University, Shanghai, China.
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3
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Burger PB, Hu X, Balabin I, Muller M, Stanley M, Joubert F, Kaiser TM. FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology. J Chem Inf Model 2024; 64:3812-3825. [PMID: 38651738 PMCID: PMC11094716 DOI: 10.1021/acs.jcim.4c00071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024]
Abstract
In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.
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Affiliation(s)
- Pieter B. Burger
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Xiaohu Hu
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ilya Balabin
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Morné Muller
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Megan Stanley
- Microsoft
Research AI4Science, 21 Station Road, Cambridge CB1 2FB, U.K.
| | - Fourie Joubert
- Centre
for Bioinformatics and Computational Biology, Department of Biochemistry,
Genetics and Microbiology, University of
Pretoria, Pretoria 0001, South Africa
| | - Thomas M. Kaiser
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
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4
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Miller WB, Baluška F, Reber AS, Slijepčević P. Biology in the 21st century: Natural selection is cognitive selection. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2024; 190:170-184. [PMID: 38740143 DOI: 10.1016/j.pbiomolbio.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 05/02/2024] [Accepted: 05/10/2024] [Indexed: 05/16/2024]
Abstract
Natural selection has a formal definition as the natural process that results in the survival and reproductive success of individuals or groups best adjusted to their environment, leading to the perpetuation of those genetic qualities best suited to that organism's environmental niche. Within conventional Neo-Darwinism, the largest source of those variations that can be selected is presumed to be secondary to random genetic mutations. As these arise, natural selection sustains adaptive traits in the context of a 'struggle for existence'. Consequently, in the 20th century, natural selection was generally portrayed as the primary evolutionary driver. The 21st century offers a comprehensive alternative to Neo-Darwinian dogma within Cognition-Based Evolution. The substantial differences between these respective evolutionary frameworks have been most recently articulated in a revision of Crick's Central Dogma, a former centerpiece of Neo-Darwinism. The argument is now advanced that the concept of natural selection should also be comprehensively reappraised. Cognitive selection is presented as a more precise term better suited to 21st century biology. Since cognition began with life's origin, natural selection represents cognitive selection.
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Affiliation(s)
| | - František Baluška
- Institute of Cellular and Molecular Botany, University of Bonn, Germany.
| | - Arthur S Reber
- Department of Psychology, University of British Columbia, Vancouver, BC, Canada.
| | - Predrag Slijepčević
- Department of Life Sciences College of Health, Medicine and Life Sciences, University of Brunel, UK.
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5
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Vedel S, Košmrlj A, Nunns H, Trusina A. Synergistic and antagonistic effects of deterministic and stochastic cell-cell variations. Phys Rev E 2024; 109:054404. [PMID: 38907460 DOI: 10.1103/physreve.109.054404] [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: 02/10/2022] [Accepted: 04/05/2024] [Indexed: 06/24/2024]
Abstract
By diversifying, cells in a clonal population can together overcome the limits of individuals. Diversity in single-cell growth rates allows the population to survive environmental stresses, such as antibiotics, and grow faster than the undiversified population. These functional cell-cell variations can arise stochastically, from noise in biochemical reactions, or deterministically, by asymmetrically distributing damaged components. While each of the mechanisms is well understood, the effect of the combined mechanisms is unclear. To evaluate the contribution of the deterministic component we developed a mathematical model by mapping the growing population to the Ising model. To analyze the combined effects of stochastic and deterministic contributions we introduced the analytical results of the Ising-mapping into an Euler-Lotka framework. Model results, confirmed by simulations and experimental data, show that deterministic cell-cell variations increase near-linearly with stress. As a consequence, we predict that the gain in population doubling time from cell-cell variations is primarily stochastic at low stress but may cross over to deterministic at higher stresses. Furthermore, we find that while the deterministic component minimizes population damage, stochastic variations antagonize this effect. Together our results may help identifying stress-tolerant pathogenic cells and thus inspire novel antibiotic strategies.
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Affiliation(s)
- Søren Vedel
- Niels Bohr International Academy, Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, DK-2100 Copenhagen, Denmark
- Center for Models of Life, Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, DK-2100 Copenhagen, Denmark
| | - Andrej Košmrlj
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, USA
- Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA
| | - Harry Nunns
- Center for Models of Life, Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, DK-2100 Copenhagen, Denmark
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 E. California Boulevard, Pasadena, California 91125, USA
| | - Ala Trusina
- Center for Models of Life, Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, DK-2100 Copenhagen, Denmark
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6
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Bermudez A, Latham ZD, Ma AJ, Bi D, Hu JK, Lin NYC. Regulation of Chromatin Modifications through Coordination of Nucleus Size and Epithelial Cell Morphology Heterogeneity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590164. [PMID: 38712099 PMCID: PMC11071433 DOI: 10.1101/2024.04.18.590164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Cell morphology heterogeneity within epithelial collectives is a pervasive phenomenon intertwined with tissue mechanical properties. Despite its widespread occurrence, the underlying mechanisms driving cell morphology heterogeneity and its consequential biological ramifications remain elusive. Here, we investigate the dynamic evolution of epithelial cell morphology and nucleus morphology during crowding, unveiling a consistent correlation between the two. Our investigation reveals a persistent log-normal probability distribution characterizing both cell and nucleus areas across diverse crowding stages and epithelial model systems. We showed that this morphological diversity arises from asymmetric partitioning during cell division and is perpetuated through actomyosin-mediated regulation of cell-nucleus size coordination. Moreover, we provide insights into the impact of nucleus morphology on chromatin dynamics, demonstrating that constraining nucleus area leads to downregulation of the euchromatic mark H3K9ac and upregulation of the heterochromatic mark H3K27me3 through modulation of histone demethylase UTX expression. These findings under-score the significance of cell morphology heterogeneity as a driver of chromatin state diversity, shaping functional variability within epithelial tissues.
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7
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Mitchell KJ. Variability in Neural Circuit Formation. Cold Spring Harb Perspect Biol 2024; 16:a041504. [PMID: 38253418 PMCID: PMC10910361 DOI: 10.1101/cshperspect.a041504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The study of neural development is usually concerned with the question of how nervous systems get put together. Variation in these processes is usually of interest as a means of revealing these normative mechanisms. However, variation itself can be an object of study and is of interest from multiple angles. First, the nature of variation in both the processes and the outcomes of neural development is relevant to our understanding of how these processes and outcomes are encoded in the genome. Second, variation in the wiring of the brain in humans may underlie variation in all kinds of psychological and behavioral traits, as well as neurodevelopmental disorders. And third, genetic variation that affects circuit development provides the raw material for evolutionary change. Here, I examine these different aspects of variation in circuit development and consider what they may tell us about these larger questions.
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Affiliation(s)
- Kevin J Mitchell
- Smurfit Institute of Genetics and Institute of Neuroscience, Trinity College Dublin, Dublin D02 PN40, Ireland
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8
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Andrews SS, Wiley HS, Sauro HM. Design patterns of biological cells. Bioessays 2024; 46:e2300188. [PMID: 38247191 PMCID: PMC10922931 DOI: 10.1002/bies.202300188] [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: 09/30/2023] [Revised: 12/03/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.
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Affiliation(s)
- Steven S. Andrews
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - H. Steven Wiley
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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9
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Bull JW. Life Is Uncertain: Inherent Variability Exhibited by Organisms, and at Higher Levels of Biological Organization. ASTROBIOLOGY 2024; 24:318-327. [PMID: 38350125 DOI: 10.1089/ast.2023.0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Organisms act stochastically. A not uncommon view in the ecological literature is that this is mainly due to the observer having insufficient information or a stochastic environment-and not partly because organisms themselves respond with inherent unpredictability. In this study, I compile the evidence that contradicts that view. Organisms generate uncertainty internally, which results in irreducible stochastic responses. I consider why: for instance, stochastic responses are associated with greater adaptability to changing environments and resource availability. Over longer timescales, biologically generated uncertainty influences behavior, evolution, and macroecological processes. Indeed, it could be stated that organisms are systems defined by the internal generation, magnification, and record-keeping of uncertainty as inputs to responses. Important practical implications arise if organisms can indeed be defined by an association with specific classes of inherent uncertainty: not least that isolating those signatures then provides a potential means for detecting life, for considering the forms that life could theoretically take, and for exploring the wider limits to how life might become distributed. These are all fundamental goals in astrobiology.
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Affiliation(s)
- Joseph W Bull
- Department of Biology, University of Oxford, Oxford, United Kingdom
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10
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Li W, Chang C, Kundu S, Long Q. Accounting for network noise in graph-guided Bayesian modeling of structured high-dimensional data. Biometrics 2024; 80:ujae012. [PMID: 38483282 PMCID: PMC10938547 DOI: 10.1093/biomtc/ujae012] [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: 11/18/2022] [Revised: 12/31/2023] [Accepted: 02/14/2024] [Indexed: 03/17/2024]
Abstract
There is a growing body of literature on knowledge-guided statistical learning methods for analysis of structured high-dimensional data (such as genomic and transcriptomic data) that can incorporate knowledge of underlying networks derived from functional genomics and functional proteomics. These methods have been shown to improve variable selection and prediction accuracy and yield more interpretable results. However, these methods typically use graphs extracted from existing databases or rely on subject matter expertise, which are known to be incomplete and may contain false edges. To address this gap, we propose a graph-guided Bayesian modeling framework to account for network noise in regression models involving structured high-dimensional predictors. Specifically, we use 2 sources of network information, including the noisy graph extracted from existing databases and the estimated graph from observed predictors in the dataset at hand, to inform the model for the true underlying network via a latent scale modeling framework. This model is coupled with the Bayesian regression model with structured high-dimensional predictors involving an adaptive structured shrinkage prior. We develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. We demonstrate the advantages of our method over existing methods in simulations, and through analyses of a genomics dataset and another proteomics dataset for Alzheimer's disease.
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Affiliation(s)
- Wenrui Li
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, PA 19104, United States
| | - Changgee Chang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Suprateek Kundu
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, PA 19104, United States
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11
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Wang X, Cichos F. Harnessing synthetic active particles for physical reservoir computing. Nat Commun 2024; 15:774. [PMID: 38287028 PMCID: PMC10825170 DOI: 10.1038/s41467-024-44856-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/08/2024] [Indexed: 01/31/2024] Open
Abstract
The processing of information is an indispensable property of living systems realized by networks of active processes with enormous complexity. They have inspired many variants of modern machine learning, one of them being reservoir computing, in which stimulating a network of nodes with fading memory enables computations and complex predictions. Reservoirs are implemented on computer hardware, but also on unconventional physical substrates such as mechanical oscillators, spins, or bacteria often summarized as physical reservoir computing. Here we demonstrate physical reservoir computing with a synthetic active microparticle system that self-organizes from an active and passive component into inherently noisy nonlinear dynamical units. The self-organization and dynamical response of the unit are the results of a delayed propulsion of the microswimmer to a passive target. A reservoir of such units with a self-coupling via the delayed response can perform predictive tasks despite the strong noise resulting from the Brownian motion of the microswimmers. To achieve efficient noise suppression, we introduce a special architecture that uses historical reservoir states for output. Our results pave the way for the study of information processing in synthetic self-organized active particle systems.
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Affiliation(s)
- Xiangzun Wang
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, 04105, Leipzig, Germany
| | - Frank Cichos
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany.
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12
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Bano R, Mears P, Golding I, Chemla YR. Flagellar dynamics reveal fluctuations and kinetic limit in the Escherichia coli chemotaxis network. Sci Rep 2023; 13:22891. [PMID: 38129516 PMCID: PMC10739816 DOI: 10.1038/s41598-023-49784-w] [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: 09/11/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
The Escherichia coli chemotaxis network, by which bacteria modulate their random run/tumble swimming pattern to navigate their environment, must cope with unavoidable number fluctuations ("noise") in its molecular constituents like other signaling networks. The probability of clockwise (CW) flagellar rotation, or CW bias, is a measure of the chemotaxis network's output, and its temporal fluctuations provide a proxy for network noise. Here we quantify fluctuations in the chemotaxis signaling network from the switching statistics of flagella, observed using time-resolved fluorescence microscopy of individual optically trapped E. coli cells. This approach allows noise to be quantified across the dynamic range of the network. Large CW bias fluctuations are revealed at steady state, which may play a critical role in driving flagellar switching and cell tumbling. When the network is stimulated chemically to higher activity, fluctuations dramatically decrease. A stochastic theoretical model, inspired by work on gene expression noise, points to CheY activation occurring in bursts, driving CW bias fluctuations. This model also shows that an intrinsic kinetic ceiling on network activity places an upper limit on activated CheY and CW bias, which when encountered suppresses network fluctuations. This limit may also prevent cells from tumbling unproductively in steep gradients.
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Affiliation(s)
- Roshni Bano
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Patrick Mears
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ido Golding
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yann R Chemla
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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13
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YOUSEF M, ALLMER J. Deep learning in bioinformatics. Turk J Biol 2023; 47:366-382. [PMID: 38681776 PMCID: PMC11045206 DOI: 10.55730/1300-0152.2671] [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: 09/20/2023] [Revised: 12/28/2023] [Accepted: 12/18/2023] [Indexed: 05/01/2024] Open
Abstract
Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis. We also discuss future directions and opportunities for deep learning in bioinformatics. We aim to provide an overview of deep learning so that bioinformaticians applying deep learning models can consider all critical technical and ethical aspects. Thus, our target audience is biomedical informatics researchers who use deep learning models for inference. This review will inspire more bioinformatics researchers to adopt deep-learning methods for their research questions while considering fairness, potential biases, explainability, and accountability.
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Affiliation(s)
- Malik YOUSEF
- Department of Information Systems, Zefat Academic College, Zefat,
Israel
| | - Jens ALLMER
- Medical Informatics and Bioinformatics, Institute for Measurement Engineering and Sensor Technology, Hochschule Ruhr West, University of Applied Sciences, Mülheim an der Ruhr,
Germany
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14
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Lewis JS, van Oijen AM, Spenkelink LM. Embracing Heterogeneity: Challenging the Paradigm of Replisomes as Deterministic Machines. Chem Rev 2023; 123:13419-13440. [PMID: 37971892 PMCID: PMC10790245 DOI: 10.1021/acs.chemrev.3c00436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 10/15/2023] [Accepted: 10/20/2023] [Indexed: 11/19/2023]
Abstract
The paradigm of cellular systems as deterministic machines has long guided our understanding of biology. Advancements in technology and methodology, however, have revealed a world of stochasticity, challenging the notion of determinism. Here, we explore the stochastic behavior of multi-protein complexes, using the DNA replication system (replisome) as a prime example. The faithful and timely copying of DNA depends on the simultaneous action of a large set of enzymes and scaffolding factors. This fundamental cellular process is underpinned by dynamic protein-nucleic acid assemblies that must transition between distinct conformations and compositional states. Traditionally viewed as a well-orchestrated molecular machine, recent experimental evidence has unveiled significant variability and heterogeneity in the replication process. In this review, we discuss recent advances in single-molecule approaches and single-particle cryo-EM, which have provided insights into the dynamic processes of DNA replication. We comment on the new challenges faced by structural biologists and biophysicists as they attempt to describe the dynamic cascade of events leading to replisome assembly, activation, and progression. The fundamental principles uncovered and yet to be discovered through the study of DNA replication will inform on similar operating principles for other multi-protein complexes.
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Affiliation(s)
- Jacob S. Lewis
- Macromolecular
Machines Laboratory, The Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Antoine M. van Oijen
- Molecular
Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales 2522, Australia
| | - Lisanne M. Spenkelink
- Molecular
Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales 2522, Australia
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15
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Zhu H, O’Shaughnessy B. Actomyosin pulsing rescues embryonic tissue folding from disruption by myosin fluctuations. RESEARCH SQUARE 2023:rs.3.rs-2948564. [PMID: 37886516 PMCID: PMC10602173 DOI: 10.21203/rs.3.rs-2948564/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
During early development, myosin II mechanically reshapes and folds embryo tissue. A muchstudied example is ventral furrow formation in Drosophila, marking the onset of gastrulation. Furrowing is driven by contraction of actomyosin networks on apical cell surfaces, but how the myosin patterning encodes tissue shape is unclear, and elastic models failed to reproduce essential features of experimental cell contraction profiles. The myosin patterning exhibits substantial cell-to-cell fluctuations with pulsatile time-dependence, a striking but unexplained feature of morphogenesis in many organisms. Here, using biophysical modeling we find viscous forces offer the principal resistance to actomyosin-driven apical constriction. In consequence, tissue shape is encoded in the direction-dependent curvature of the myosin patterning which orients an anterior-posterior furrow. Tissue contraction is highly sensitive to cell-to-cell myosin fluctuations, explaining furrowing failure in genetically perturbed embryos whose fluctuations are temporally persistent. In wild-type embryos this disastrous outcome is averted by pulsatile myosin time-dependence, which rescues furrowing by eliminating high frequencies in the fluctuation power spectrum. This low pass filter mechanism may underlie the usage of actomyosin pulsing in diverse morphogenetic processes across many organisms.
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Affiliation(s)
- Hongkang Zhu
- Department of Chemical Engineering, Columbia University, New York, NY 10027, USA
| | - Ben O’Shaughnessy
- Department of Chemical Engineering, Columbia University, New York, NY 10027, USA
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16
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Jia BZ, Qi Y, Wong-Campos JD, Megason SG, Cohen AE. A bioelectrical phase transition patterns the first vertebrate heartbeats. Nature 2023; 622:149-155. [PMID: 37758945 DOI: 10.1038/s41586-023-06561-z] [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: 12/23/2022] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
A regular heartbeat is essential to vertebrate life. In the mature heart, this function is driven by an anatomically localized pacemaker. By contrast, pacemaking capability is broadly distributed in the early embryonic heart1-3, raising the question of how tissue-scale activity is first established and then maintained during embryonic development. The initial transition of the heart from silent to beating has never been characterized at the timescale of individual electrical events, and the structure in space and time of the early heartbeats remains poorly understood. Using all-optical electrophysiology, we captured the very first heartbeat of a zebrafish and analysed the development of cardiac excitability and conduction around this singular event. The first few beats appeared suddenly, had irregular interbeat intervals, propagated coherently across the primordial heart and emanated from loci that varied between animals and over time. The bioelectrical dynamics were well described by a noisy saddle-node on invariant circle bifurcation with action potential upstroke driven by CaV1.2. Our work shows how gradual and largely asynchronous development of single-cell bioelectrical properties produces a stereotyped and robust tissue-scale transition from quiescence to coordinated beating.
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Affiliation(s)
- Bill Z Jia
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Systems, Synthetic and Quantitative Biology PhD Program, Harvard University, Cambridge, MA, USA
| | - Yitong Qi
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - J David Wong-Campos
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Sean G Megason
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
| | - Adam E Cohen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
- Department of Physics, Harvard University, Cambridge, MA, USA.
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17
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Lord JS, Bonsall MB. Mechanistic modelling of within-mosquito viral dynamics: Insights into infection and dissemination patterns. PLoS Comput Biol 2023; 19:e1011520. [PMID: 37812643 PMCID: PMC10586656 DOI: 10.1371/journal.pcbi.1011520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 10/19/2023] [Accepted: 09/15/2023] [Indexed: 10/11/2023] Open
Abstract
Vector or host competence can be defined as the ability of an individual to become infected and subsequently transmit a pathogen. Assays to measure competence play a key part in the assessment of the factors affecting mosquito-borne virus transmission and of potential pathogen-blocking control tools for these viruses. For mosquitoes, competence for arboviruses can be measured experimentally and results are usually analysed using standard statistical approaches. Here we develop a mechanistic approach to studying within-mosquito virus dynamics that occur during vector competence experiments. We begin by developing a deterministic model of virus replication in the mosquito midgut and subsequent escape and replication in the hemocoel. We then extend this to a stochastic model to capture the between-individual variation observed in vector competence experiments. We show that the dose-response of the probability of mosquito midgut infection and variation in the dissemination rate can be explained by stochastic processes generated from a small founding population of virions, caused by a relatively low rate of virion infection of susceptible cells. We also show that comparing treatments or species in competence experiments by fitting mechanistic models could provide further insight into potential differences. Generally, our work adds to the growing body of literature emphasizing the importance of intrinsic stochasticity in biological systems.
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Affiliation(s)
- Jennifer S. Lord
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
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18
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Bocci F, Jia D, Nie Q, Jolly MK, Onuchic J. Theoretical and computational tools to model multistable gene regulatory networks. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2023; 86:10.1088/1361-6633/acec88. [PMID: 37531952 PMCID: PMC10521208 DOI: 10.1088/1361-6633/acec88] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/02/2023] [Indexed: 08/04/2023]
Abstract
The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.
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Affiliation(s)
- Federico Bocci
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
- Department of Mathematics, University of California, Irvine, CA 92697, USA
| | - Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
| | - Qing Nie
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
- Department of Mathematics, University of California, Irvine, CA 92697, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - José Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
- Department of Physics and Astronomy, Rice University, Houston, TX 77005, USA
- Department of Chemistry, Rice University, Houston, TX 77005, USA
- Department of Biosciences, Rice University, Houston, TX 77005, USA
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19
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Sigawi T, Ilan Y. Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems. Biomimetics (Basel) 2023; 8:359. [PMID: 37622964 PMCID: PMC10452845 DOI: 10.3390/biomimetics8040359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.
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Affiliation(s)
| | - Yaron Ilan
- Department of Medicine, Hadassah Medical Center, Faculty of Medicine, Hebrew University, Jerusalem P.O. Box 12000, Israel;
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20
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Loman TE, Locke JCW. The σB alternative sigma factor circuit modulates noise to generate different types of pulsing dynamics. PLoS Comput Biol 2023; 19:e1011265. [PMID: 37540712 PMCID: PMC10431680 DOI: 10.1371/journal.pcbi.1011265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 08/16/2023] [Accepted: 06/12/2023] [Indexed: 08/06/2023] Open
Abstract
Single-cell approaches are revealing a high degree of heterogeneity, or noise, in gene expression in isogenic bacteria. How gene circuits modulate this noise in gene expression to generate robust output dynamics is unclear. Here we use the Bacillus subtilis alternative sigma factor σB as a model system for understanding the role of noise in generating circuit output dynamics. σB controls the general stress response in B. subtilis and is activated by a range of energy and environmental stresses. Recent single-cell studies have revealed that the circuit can generate two distinct outputs, stochastic pulsing and a single pulse response, but the conditions under which each response is generated are under debate. We implement a stochastic mathematical model of the σB circuit to investigate this and find that the system's core circuit can generate both response types. This is despite one response (stochastic pulsing) being stochastic in nature, and the other (single response pulse) being deterministic. We demonstrate that the main determinant for whichever response is generated is the degree with which the input pathway activates the core circuit, although the noise properties of the input pathway also biases the system towards one or the other type of output. Thus, our work shows how stochastic modelling can reveal the mechanisms behind non-intuitive gene circuit output dynamics.
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Affiliation(s)
- Torkel E. Loman
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - James C. W. Locke
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
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21
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Trinh DC, Martin M, Bald L, Maizel A, Trehin C, Hamant O. Increased gene expression variability hinders the formation of regional mechanical conflicts leading to reduced organ shape robustness. Proc Natl Acad Sci U S A 2023; 120:e2302441120. [PMID: 37459526 PMCID: PMC10372692 DOI: 10.1073/pnas.2302441120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 05/04/2023] [Indexed: 07/20/2023] Open
Abstract
To relate gene networks and organ shape, one needs to address two wicked problems: i) Gene expression is often variable locally, and shape is reproducible globally; ii) gene expression can have cascading effects on tissue mechanics, with possibly counterintuitive consequences for the final organ shape. Here, we address such wicked problems, taking advantage of simpler plant organ development where shape only emerges from cell division and elongation. We confirm that mutation in VERNALIZATION INDEPENDENCE 3 (VIP3), a subunit of the conserved polymerase-associated factor 1 complex (Paf1C), increases gene expression variability in Arabidopsis. Then, we focused on the Arabidopsis sepal, which exhibits a reproducible shape and stereotypical regional growth patterns. In vip3 sepals, we measured higher growth heterogeneity between adjacent cells. This even culminated in the presence of negatively growing cells in specific growth conditions. Interestingly, such increased local noise interfered with the stereotypical regional pattern of growth. We previously showed that regional differential growth at the wild-type sepal tip triggers a mechanical conflict, to which cells resist by reinforcing their walls, leading to growth arrest. In vip3, the disturbed regional growth pattern delayed organ growth arrest and increased final organ shape variability. Altogether, we propose that gene expression variability is managed by Paf1C to ensure organ robustness by building up mechanical conflicts at the regional scale, instead of the local scale.
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Affiliation(s)
- Duy-Chi Trinh
- Laboratoire de Reproduction et Développement des Plantes, Université de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS, 69364Lyon Cedex 07, France
- Department of Pharmacological, Medical and Agronomical Biotechnology, University of Science and Technology of Hanoi, Cau Giay District, Hanoi11300, Vietnam
| | - Marjolaine Martin
- Laboratoire de Reproduction et Développement des Plantes, Université de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS, 69364Lyon Cedex 07, France
| | - Lotte Bald
- Center for Organismal Studies, University of Heidelberg, 69120Heidelberg, Germany
| | - Alexis Maizel
- Center for Organismal Studies, University of Heidelberg, 69120Heidelberg, Germany
| | - Christophe Trehin
- Laboratoire de Reproduction et Développement des Plantes, Université de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS, 69364Lyon Cedex 07, France
| | - Olivier Hamant
- Laboratoire de Reproduction et Développement des Plantes, Université de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS, 69364Lyon Cedex 07, France
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22
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Proverbio D, Skupin A, Gonçalves J. Systematic analysis and optimization of early warning signals for critical transitions using distribution data. iScience 2023; 26:107156. [PMID: 37456849 PMCID: PMC10338236 DOI: 10.1016/j.isci.2023.107156] [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: 11/04/2022] [Revised: 04/21/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free early warning signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. Notably, there are still ongoing debates whether such signals can be successfully extracted from data in particular from biological experiments. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimized combination to trigger warnings as early as possible, eventually verified on experimental data from microbiological populations. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimized composite indicator to alert for impending critical transitions using distribution data.
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Affiliation(s)
- Daniele Proverbio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QL, UK
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- National Center for Microscopy and Imaging Research, University of California San Diego, Gilman Drive, La Jolla, CA 9500, USA
- Department of Physics and Material Science, University of Luxembourg, 162a Avenue de La Faiencerie, 1511 Luxembourg, Luxembourg
| | - Jorge Gonçalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
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23
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Robitaille M, Yang H, Wang L, Deng B, Kim NY. Deep neural network analysis models for complex random telegraph signals. Sci Rep 2023; 13:10403. [PMID: 37369708 DOI: 10.1038/s41598-023-37142-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. A reliable RTS analysis is a crucial prerequisite to identify underlying mechanisms related to device performance and sensitivity. When numerous levels are involved, complex patterns of multilevel RTSs occur and make their quantitative analysis exponentially difficult, hereby systematic approaches are often elusive. In this work, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of the early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we resort to three deep neural network architectures whose trained models can process raw temporal data directly. We furthermore demonstrate the model accuracy extensively with a large dataset of different RTS types in terms of additional background noise types and amplitude size. Our protocol offers structured schemes to extract the parameter values of complex RTSs as imperative information with which researchers can draw meaningful and relevant interpretations and inferences of given devices and systems.
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Affiliation(s)
- Marcel Robitaille
- Institute for Quantum Computing, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
| | - HeeBong Yang
- Institute for Quantum Computing, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
- Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
| | - Lu Wang
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
| | - Bowen Deng
- Institute for Quantum Computing, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
| | - Na Young Kim
- Institute for Quantum Computing, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
- Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
- Department of Chemistry, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
- Perimeter Institute, University of Waterloo, 31 Caroline St N, Waterloo, ON, N2L 2Y5, Canada.
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24
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Hartmann J, Mayor R. Self-organized collective cell behaviors as design principles for synthetic developmental biology. Semin Cell Dev Biol 2023; 141:63-73. [PMID: 35450765 DOI: 10.1016/j.semcdb.2022.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 04/12/2022] [Indexed: 10/18/2022]
Abstract
Over the past two decades, molecular cell biology has graduated from a mostly analytic science to one with substantial synthetic capability. This success is built on a deep understanding of the structure and function of biomolecules and molecular mechanisms. For synthetic biology to achieve similar success at the scale of tissues and organs, an equally deep understanding of the principles of development is required. Here, we review some of the central concepts and recent progress in tissue patterning, morphogenesis and collective cell migration and discuss their value for synthetic developmental biology, emphasizing in particular the power of (guided) self-organization and the role of theoretical advances in making developmental insights applicable in synthesis.
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Affiliation(s)
- Jonas Hartmann
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK.
| | - Roberto Mayor
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK.
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25
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Lacquaniti F, La Scaleia B, Zago M. Noise and vestibular perception of passive self-motion. Front Neurol 2023; 14:1159242. [PMID: 37181550 PMCID: PMC10169592 DOI: 10.3389/fneur.2023.1159242] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 03/29/2023] [Indexed: 05/16/2023] Open
Abstract
Noise defined as random disturbances is ubiquitous in both the external environment and the nervous system. Depending on the context, noise can degrade or improve information processing and performance. In all cases, it contributes to neural systems dynamics. We review some effects of various sources of noise on the neural processing of self-motion signals at different stages of the vestibular pathways and the resulting perceptual responses. Hair cells in the inner ear reduce the impact of noise by means of mechanical and neural filtering. Hair cells synapse on regular and irregular afferents. Variability of discharge (noise) is low in regular afferents and high in irregular units. The high variability of irregular units provides information about the envelope of naturalistic head motion stimuli. A subset of neurons in the vestibular nuclei and thalamus are optimally tuned to noisy motion stimuli that reproduce the statistics of naturalistic head movements. In the thalamus, variability of neural discharge increases with increasing motion amplitude but saturates at high amplitudes, accounting for behavioral violation of Weber's law. In general, the precision of individual vestibular neurons in encoding head motion is worse than the perceptual precision measured behaviorally. However, the global precision predicted by neural population codes matches the high behavioral precision. The latter is estimated by means of psychometric functions for detection or discrimination of whole-body displacements. Vestibular motion thresholds (inverse of precision) reflect the contribution of intrinsic and extrinsic noise to perception. Vestibular motion thresholds tend to deteriorate progressively after the age of 40 years, possibly due to oxidative stress resulting from high discharge rates and metabolic loads of vestibular afferents. In the elderly, vestibular thresholds correlate with postural stability: the higher the threshold, the greater is the postural imbalance and risk of falling. Experimental application of optimal levels of either galvanic noise or whole-body oscillations can ameliorate vestibular function with a mechanism reminiscent of stochastic resonance. Assessment of vestibular thresholds is diagnostic in several types of vestibulopathies, and vestibular stimulation might be useful in vestibular rehabilitation.
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Affiliation(s)
- Francesco Lacquaniti
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Systems Medicine, Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy
| | - Barbara La Scaleia
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Myrka Zago
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Civil Engineering and Computer Science Engineering, Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy
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26
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Bi S, Kargeti M, Colin R, Farke N, Link H, Sourjik V. Dynamic fluctuations in a bacterial metabolic network. Nat Commun 2023; 14:2173. [PMID: 37061520 PMCID: PMC10105761 DOI: 10.1038/s41467-023-37957-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 04/06/2023] [Indexed: 04/17/2023] Open
Abstract
The operation of the central metabolism is typically assumed to be deterministic, but dynamics and high connectivity of the metabolic network make it potentially prone to generating fluctuations. However, time-resolved measurements of metabolite levels in individual cells that are required to characterize such fluctuations remained a challenge, particularly in small bacterial cells. Here we use single-cell metabolite measurements based on Förster resonance energy transfer, combined with computer simulations, to explore the real-time dynamics of the metabolic network of Escherichia coli. We observe that steplike exposure of starved E. coli to glycolytic carbon sources elicits large periodic fluctuations in the intracellular concentration of pyruvate in individual cells. These fluctuations are consistent with predicted oscillatory dynamics of E. coli metabolic network, and they are primarily controlled by biochemical reactions around the pyruvate node. Our results further indicate that fluctuations in glycolysis propagate to other cellular processes, possibly leading to temporal heterogeneity of cellular states within a population.
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Affiliation(s)
- Shuangyu Bi
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Manika Kargeti
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany
| | - Remy Colin
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany
| | - Niklas Farke
- University of Tübingen, D-72076, Tübingen, Germany
| | - Hannes Link
- University of Tübingen, D-72076, Tübingen, Germany
| | - Victor Sourjik
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany.
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27
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Bonizzato M, Guay Hottin R, Côté SL, Massai E, Choinière L, Macar U, Laferrière S, Sirpal P, Quessy S, Lajoie G, Martinez M, Dancause N. Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys. Cell Rep Med 2023; 4:101008. [PMID: 37044093 PMCID: PMC10140617 DOI: 10.1016/j.xcrm.2023.101008] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 02/16/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023]
Abstract
Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged neurostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces. Here, we leveraged an algorithmic class, Gaussian-process (GP)-based Bayesian optimization (BO), to solve this problem. We show that GP-BO efficiently explores the neurostimulation space, outperforming other search strategies after testing only a fraction of the possible combinations. Through a series of real-time multi-dimensional neurostimulation experiments, we demonstrate optimization across diverse biological targets (brain, spinal cord), animal models (rats, non-human primates), in healthy subjects, and in neuroprosthetic intervention after injury, for both immediate and continual learning over multiple sessions. GP-BO can embed and improve "prior" expert/clinical knowledge to dramatically enhance its performance. These results advocate for broader establishment of learning agents as structural elements of neuroprosthetic design, enabling personalization and maximization of therapeutic effectiveness.
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Affiliation(s)
- Marco Bonizzato
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada; Department of Electrical Engineering and Institute of Biomedical Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada; CIUSSS du Nord-de-l'Île-de-Montréal, Montreal, QC H4J 1C5, Canada; Mila - Québec AI Institute, Montreal, QC H2S 3H1, Canada.
| | - Rose Guay Hottin
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada; Department of Electrical Engineering and Institute of Biomedical Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada; Mila - Québec AI Institute, Montreal, QC H2S 3H1, Canada
| | - Sandrine L Côté
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Elena Massai
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Léo Choinière
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada; Mila - Québec AI Institute, Montreal, QC H2S 3H1, Canada
| | - Uzay Macar
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada; Mila - Québec AI Institute, Montreal, QC H2S 3H1, Canada
| | - Samuel Laferrière
- Mila - Québec AI Institute, Montreal, QC H2S 3H1, Canada; Computer Science Department, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Parikshat Sirpal
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada; Mila - Québec AI Institute, Montreal, QC H2S 3H1, Canada
| | - Stephan Quessy
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Guillaume Lajoie
- Mila - Québec AI Institute, Montreal, QC H2S 3H1, Canada; Mathematics and Statistics Department, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Marina Martinez
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada; CIUSSS du Nord-de-l'Île-de-Montréal, Montreal, QC H4J 1C5, Canada
| | - Numa Dancause
- Department of Neurosciences and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada.
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Zhu H, Oâ Shaughnessy B. Actomyosin pulsing rescues embryonic tissue folding from disruption by myosin fluctuations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.533016. [PMID: 36993262 PMCID: PMC10055118 DOI: 10.1101/2023.03.16.533016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
During early development, myosin II mechanically reshapes and folds embryo tissue. A much-studied example is ventral furrow formation in Drosophila , marking the onset of gastrulation. Furrowing is driven by contraction of actomyosin networks on apical cell surfaces, but how the myosin patterning encodes tissue shape is unclear, and elastic models failed to reproduce essential features of experimental cell contraction profiles. The myosin patterning exhibits substantial cell-to-cell fluctuations with pulsatile time-dependence, a striking but unexplained feature of morphogenesis in many organisms. Here, using biophysical modeling we find viscous forces offer the principle resistance to actomyosin-driven apical constriction. In consequence, tissue shape is encoded in the direction-dependent curvature of the myosin patterning which orients an anterior-posterior furrow. Tissue contraction is highly sensitive to cell-to-cell myosin fluctuations, explaining furrowing failure in genetically perturbed embryos whose fluctuations are temporally persistent. In wild-type embryos, this catastrophic outcome is averted by pulsatile myosin time-dependence, a time-averaging effect that rescues furrowing. This low pass filter mechanism may underlie the usage of actomyosin pulsing in diverse morphogenetic processes across many organisms.
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Miroshnikova YA, Shahbazi MN, Negrete J, Chalut KJ, Smith A. Cell state transitions: catch them if you can. Development 2023; 150:dev201139. [PMID: 36930528 PMCID: PMC10655867 DOI: 10.1242/dev.201139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
The Company of Biologists' 2022 workshop on 'Cell State Transitions: Approaches, Experimental Systems and Models' brought together an international and interdisciplinary team of investigators spanning the fields of cell and developmental biology, stem cell biology, physics, mathematics and engineering to tackle the question of how cells precisely navigate between distinct identities and do so in a dynamic manner. This second edition of the workshop was organized after a successful virtual workshop on the same topic that took place in 2021.
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Affiliation(s)
- Yekaterina A. Miroshnikova
- Stem Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Marta N. Shahbazi
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
| | - Jose Negrete
- Institute of Bioengineering, School of Life Sciences and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Kevin J. Chalut
- Altos Labs, Cambridge Institute of Science, Cambridge CB2 0AW, UK
| | - Austin Smith
- Living Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK
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30
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Amiri B, Heyn JCJ, Schreiber C, Rädler JO, Falcke M. On multistability and constitutive relations of cell motion on fibronectin lanes. Biophys J 2023; 122:753-766. [PMID: 36739476 PMCID: PMC10027452 DOI: 10.1016/j.bpj.2023.02.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/12/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Cell motility on flat substrates exhibits coexisting steady and oscillatory morphodynamics, the biphasic adhesion-velocity relation, and the universal correlation between speed and persistence (UCSP) as simultaneous observations common to many cell types. Their universality and concurrency suggest a unifying mechanism causing all three of them. Stick-slip models for cells on one-dimensional lanes suggest multistability to arise from the nonlinear friction of retrograde flow. This study suggests a mechanical mechanism controlled by integrin signaling on the basis of a biophysical model and analysis of trajectories of MDA-MB-231 cells on fibronectin lanes, which additionally explains the constitutive relations. The experiments exhibit cells with steady or oscillatory morphodynamics and either spread or moving with spontaneous transitions between the dynamic regimes, spread and moving, and spontaneous direction reversals. Our biophysical model is based on the force balance at the protrusion edge, the noisy clutch of retrograde flow, and a response function of friction and membrane drag to integrin signaling. The theory reproduces the experimentally observed cell states, characteristics of oscillations, and state probabilities. Analysis of experiments with the biophysical model establishes a stick-slip oscillation mechanism, and explains multistability of cell states and the statistics of state transitions. It suggests protrusion competition to cause direction reversal events, the statistics of which explain the UCSP. The effect of integrin signaling on drag and friction explains the adhesion-velocity relation and cell behavior at fibronectin density steps. The dynamics of our mechanism are nonlinear flow mechanics driven by F-actin polymerization and shaped by the noisy clutch of retrograde flow friction, protrusion competition via membrane tension, and drag forces. Integrin signaling controls the parameters of the mechanical system.
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Affiliation(s)
- Behnam Amiri
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Johannes C J Heyn
- Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
| | - Christoph Schreiber
- Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
| | - Joachim O Rädler
- Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU), Munich, Germany.
| | - Martin Falcke
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany; Department of Physics, Humboldt University, Berlin, Germany.
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31
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Canova CT, Inguva PK, Braatz RD. Mechanistic modeling of viral particle production. Biotechnol Bioeng 2023; 120:629-641. [PMID: 36461898 DOI: 10.1002/bit.28296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/05/2022]
Abstract
Viral systems such as wild-type viruses, viral vectors, and virus-like particles are essential components of modern biotechnology and medicine. Despite their importance, the commercial-scale production of viral systems remains highly inefficient for multiple reasons. Computational strategies are a promising avenue for improving process development, optimization, and control, but require a mathematical description of the system. This article reviews mechanistic modeling strategies for the production of viral particles, both at the cellular and bioreactor scales. In many cases, techniques and models from adjacent fields such as epidemiology and wild-type viral infection kinetics can be adapted to construct a suitable process model. These process models can then be employed for various purposes such as in-silico testing of novel process operating strategies and/or advanced process control.
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Affiliation(s)
- Christopher T Canova
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Pavan K Inguva
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Richard D Braatz
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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32
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Ilan Y. Making use of noise in biological systems. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 178:83-90. [PMID: 36640927 DOI: 10.1016/j.pbiomolbio.2023.01.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/07/2022] [Accepted: 01/09/2023] [Indexed: 01/12/2023]
Abstract
Disorder and noise are inherent in biological systems. They are required to provide systems with the advantages required for proper functioning. Noise is a part of the flexibility and plasticity of biological systems. It provides systems with increased routes, improves information transfer, and assists in response triggers. This paper reviews recent studies on noise at the genome, cellular, and whole organ levels. We focus on the need to use noise in system engineering. We present some of the challenges faced in studying noise. Optimizing the efficiency of complex systems requires a degree of variability in their functions within certain limits. Constrained noise can be considered a method for improving system robustness by regulating noise levels in continuously dynamic settings. The digital pill-based artificial intelligence (AI)-based platform is the first to implement second-generation AI comprising variability-based signatures. This platform enhances the efficacy of the therapeutic regimens. Systems requiring variability and mechanisms regulating noise are mandatory for understanding biological functions.
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Affiliation(s)
- Yaron Ilan
- Hebrew University, Faculty of Medicine, Department of Medicine, Hadassah Medical Center, POB 1200, IL91120, Jerusalem, Israel.
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33
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Kuyyamudi C, Menon SN, Sinha S. Precision of morphogen-driven tissue patterning during development is enhanced through contact-mediated cellular interactions. Phys Rev E 2023; 107:024407. [PMID: 36932610 DOI: 10.1103/physreve.107.024407] [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: 11/16/2021] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Cells in developing embryos reliably differentiate to attain location-specific fates, despite fluctuations in morphogen concentrations that provide positional information and in molecular processes that interpret it. We show that local contact-mediated cell-cell interactions utilize inherent asymmetry in the response of patterning genes to the global morphogen signal yielding a bimodal response. This results in robust developmental outcomes with a consistent identity for the dominant gene at each cell, substantially reducing the uncertainty in the location of boundaries between distinct fates.
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Affiliation(s)
- Chandrashekar Kuyyamudi
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India
- Homi Bhabha National Institute, Anushaktinagar, Mumbai 400 094, India
| | - Shakti N Menon
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India
| | - Sitabhra Sinha
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India
- Homi Bhabha National Institute, Anushaktinagar, Mumbai 400 094, India
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34
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Biswas S, Clawson W, Levin M. Learning in Transcriptional Network Models: Computational Discovery of Pathway-Level Memory and Effective Interventions. Int J Mol Sci 2022; 24:ijms24010285. [PMID: 36613729 PMCID: PMC9820177 DOI: 10.3390/ijms24010285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/23/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Trainability, in any substrate, refers to the ability to change future behavior based on past experiences. An understanding of such capacity within biological cells and tissues would enable a particularly powerful set of methods for prediction and control of their behavior through specific patterns of stimuli. This top-down mode of control (as an alternative to bottom-up modification of hardware) has been extensively exploited by computer science and the behavioral sciences; in biology however, it is usually reserved for organism-level behavior in animals with brains, such as training animals towards a desired response. Exciting work in the field of basal cognition has begun to reveal degrees and forms of unconventional memory in non-neural tissues and even in subcellular biochemical dynamics. Here, we characterize biological gene regulatory circuit models and protein pathways and find them capable of several different kinds of memory. We extend prior results on learning in binary transcriptional networks to continuous models and identify specific interventions (regimes of stimulation, as opposed to network rewiring) that abolish undesirable network behavior such as drug pharmacoresistance and drug sensitization. We also explore the stability of created memories by assessing their long-term behavior and find that most memories do not decay over long time periods. Additionally, we find that the memory properties are quite robust to noise; surprisingly, in many cases noise actually increases memory potential. We examine various network properties associated with these behaviors and find that no one network property is indicative of memory. Random networks do not show similar memory behavior as models of biological processes, indicating that generic network dynamics are not solely responsible for trainability. Rational control of dynamic pathway function using stimuli derived from computational models opens the door to empirical studies of proto-cognitive capacities in unconventional embodiments and suggests numerous possible applications in biomedicine, where behavior shaping of pathway responses stand as a potential alternative to gene therapy.
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Affiliation(s)
- Surama Biswas
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA
- Department of Computer Science & Engineering and Information Technology, Meghnad Saha Institute of Technology, Kolkata 700150, India
| | - Wesley Clawson
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
- Correspondence: ; Tel.: +1-617-627-6161
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35
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Bistability and noise-induced transient behaviour of steady states in a cancer network with the regulation of microRNA. J Theor Biol 2022; 554:111262. [PMID: 36099939 DOI: 10.1016/j.jtbi.2022.111262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 07/28/2022] [Accepted: 08/24/2022] [Indexed: 01/14/2023]
Abstract
MicroRNAs (miRs) regulatory network models are highly non-linear due to the negative regulation of gene expression at the post-transcriptional level by miRs and can produce interesting dynamics of the system such as bistability for miR-transcriptional factors interactions. In this article, we focus on the miR-17-92 cluster and its interaction with transcriptional factors (proteins) E2F and Myc. Environmental fluctuations (noise) and randomness in the bio-chemical reactions can be very important and change the dynamical role of miR-17-92 in the regulatory network. We have formulated a stochastically forced miR-17-92 and E2F-Myc interaction model and study the phenomena of intrinsic and extrinsic noise which can induce random switching between steady states or the destruction of the bistability. Using a method that employs stochastic sensitivity functions we have constructed confidence ellipses to determine the configurational arrangements of equilibrium and spatial arrangements of random states near stable equilibria. Simulations are carried out to numerically show the flow of the solution trajectories under noise. Finally, we summarize the simulation results and the impact of noise on the dual non-linear role of miR-17-92 cluster to act as an oncogene or as a tumour suppressor gene.
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36
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Computationally efficient mechanism discovery for cell invasion with uncertainty quantification. PLoS Comput Biol 2022; 18:e1010599. [DOI: 10.1371/journal.pcbi.1010599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 11/30/2022] [Accepted: 09/23/2022] [Indexed: 11/17/2022] Open
Abstract
Parameter estimation for mathematical models of biological processes is often difficult and depends significantly on the quality and quantity of available data. We introduce an efficient framework using Gaussian processes to discover mechanisms underlying delay, migration, and proliferation in a cell invasion experiment. Gaussian processes are leveraged with bootstrapping to provide uncertainty quantification for the mechanisms that drive the invasion process. Our framework is efficient, parallelisable, and can be applied to other biological problems. We illustrate our methods using a canonical scratch assay experiment, demonstrating how simply we can explore different functional forms and develop and test hypotheses about underlying mechanisms, such as whether delay is present. All code and data to reproduce this work are available at https://github.com/DanielVandH/EquationLearning.jl.
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37
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Djeghdi K, Steiner U, Wilts BD. 3D Tomographic Analysis of the Order-Disorder Interplay in the Pachyrhynchus congestus mirabilis Weevil. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2202145. [PMID: 35852001 PMCID: PMC9475527 DOI: 10.1002/advs.202202145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/15/2022] [Indexed: 06/15/2023]
Abstract
The bright colors of Pachyrhynchus weevils originate from complex dielectric nanostructures within their elytral scales. In contrast to previous work exhibiting highly ordered single-network diamond-type photonic crystals, here, it is shown by combining optical microscopy and spectroscopy measurements with 3D focused ion beam (FIB) tomography that the blue scales of P. congestus mirabilis differ from that of an ordered diamond structure. Through the use of FIB tomography on elytral scales filled with platinum (Pt) by electron beam-assisted deposition, it is revealed that the red scales of this weevil possess a periodic diamond structure, while the network morphology of the blue scales exhibit diamond morphology only on the single scattering unit level with disorder on longer length scales. Full wave simulations performed on the reconstructed volumes indicate that this local order is sufficient to open a partial photonic bandgap even at low dielectric constant contrast between chitin and air in the absence of long-range or translational order. The observation of disordered and ordered photonic crystals within a single organism opens up interesting questions on the cellular origin of coloration and studies on bio-inspired replication of angle-independent colors.
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Affiliation(s)
- Kenza Djeghdi
- Adolphe Merkle InstituteUniversity of FribourgChemin des Verdiers 4Fribourg1700Switzerland
| | - Ullrich Steiner
- Adolphe Merkle InstituteUniversity of FribourgChemin des Verdiers 4Fribourg1700Switzerland
| | - Bodo D. Wilts
- Adolphe Merkle InstituteUniversity of FribourgChemin des Verdiers 4Fribourg1700Switzerland
- Chemistry and Physics of MaterialsUniversity of SalzburgJakob‐Haringer‐Straße 2aSalzburg5020Austria
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38
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Williams S, Jeanneret R, Tuval I, Polin M. Confinement-induced accumulation and de-mixing of microscopic active-passive mixtures. Nat Commun 2022; 13:4776. [PMID: 35970896 PMCID: PMC9378696 DOI: 10.1038/s41467-022-32520-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding the out-of-equilibrium properties of noisy microscale systems and the extent to which they can be modulated externally, is a crucial scientific and technological challenge. It holds the promise to unlock disruptive new technologies ranging from targeted delivery of chemicals within the body to directed assembly of new materials. Here we focus on how active matter can be harnessed to transport passive microscopic systems in a statistically predictable way. Using a minimal active-passive system of weakly Brownian particles and swimming microalgae, we show that spatial confinement leads to a complex non-monotonic steady-state distribution of colloids, with a pronounced peak at the boundary. The particles’ emergent active dynamics is well captured by a space-dependent Poisson process resulting from the space-dependent motion of the algae. Based on our findings, we then realise experimentally the de-mixing of the active-passive suspension, opening the way for manipulating colloidal objects via controlled activity fields. Understanding how order emerges in active matter may facilitate macroscopic control of microscopic objects. Here, Williams et al. show how to control the transport of passive microscopic particles in presence of motile algae in conjunction with boundary-induced accumulation of microswimmers.
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Affiliation(s)
- Stephen Williams
- Department of Physics, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Raphaël Jeanneret
- Laboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005, Paris, France
| | - Idan Tuval
- Departament de Física, Universitat de les Illes Balears, 07071, Palma de Mallorca, Spain.,Instituto Mediterráneo de Estudios Avanzados, IMEDEA, Miquel Marques 21, 07190, Esporles, Spain
| | - Marco Polin
- Department of Physics, University of Warwick, Coventry, CV4 7AL, United Kingdom. .,Departament de Física, Universitat de les Illes Balears, 07071, Palma de Mallorca, Spain. .,Instituto Mediterráneo de Estudios Avanzados, IMEDEA, Miquel Marques 21, 07190, Esporles, Spain.
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Batra A, Banerjee SC, Sharma R. Persistent Correlation in Cellular Noise Determines Longevity of Viral Infections. J Phys Chem Lett 2022; 13:7252-7260. [PMID: 35913772 DOI: 10.1021/acs.jpclett.2c01875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The slowly decaying viral dynamics, even after 2-3 weeks from diagnosis, is one of the characteristics of COVID-19 infection that is still unexplored in theoretical and experimental studies. This long-lived characteristic of viral infections in the framework of inherent variations or noise present at the cellular level is often overlooked. Therefore, in this work, we aim to understand the effect of these variations by proposing a stochastic non-Markovian model that not only captures the coupled dynamics between the immune cells and the virus but also enables the study of the effect of fluctuations. Numerical simulations of our model reveal that the long-range temporal correlations in fluctuations dictate the long-lived dynamics of a viral infection and, in turn, also affect the rates of immune response. Furthermore, predictions of our model system are in agreement with the experimental viral load data of COVID-19 patients from various countries.
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Affiliation(s)
- Abhilasha Batra
- Department of Chemistry, Indian Institute of Science Education and Research (IISER), Bhopal, Madhya Pradesh 462066, India
| | - Shoubhik Chandan Banerjee
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER), Bhopal, Madhya Pradesh 462066, India
| | - Rati Sharma
- Department of Chemistry, Indian Institute of Science Education and Research (IISER), Bhopal, Madhya Pradesh 462066, India
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40
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Abstract
At present, there is no simple, first principles-based, and general model for quantitatively describing the full range of observed biological temperature responses. Here we derive a general theory for temperature dependence in biology based on Eyring-Evans-Polanyi's theory for chemical reaction rates. Assuming only that the conformational entropy of molecules changes with temperature, we derive a theory for the temperature dependence of enzyme reaction rates which takes the form of an exponential function modified by a power law and that describes the characteristic asymmetric curved temperature response. Based on a few additional principles, our model can be used to predict the temperature response above the enzyme level, thus spanning quantum to classical scales. Our theory provides an analytical description for the shape of temperature response curves and demonstrates its generality by showing the convergence of all temperature dependence responses onto universal relationships-a universal data collapse-under appropriate normalization and by identifying a general optimal temperature, around 25 ∘C, characterizing all temperature response curves. The model provides a good fit to empirical data for a wide variety of biological rates, times, and steady-state quantities, from molecular to ecological scales and across multiple taxonomic groups (from viruses to mammals). This theory provides a simple framework to understand and predict the impact of temperature on biological quantities based on the first principles of thermodynamics, bridging quantum to classical scales.
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41
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Galbraith M, Bocci F, Onuchic JN. Stochastic fluctuations promote ordered pattern formation of cells in the Notch-Delta signaling pathway. PLoS Comput Biol 2022; 18:e1010306. [PMID: 35862460 PMCID: PMC9345490 DOI: 10.1371/journal.pcbi.1010306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 08/02/2022] [Accepted: 06/16/2022] [Indexed: 11/18/2022] Open
Abstract
The Notch-Delta signaling pathway mediates cell differentiation implicated in many regulatory processes including spatiotemporal patterning in tissues by promoting alternate cell fates between neighboring cells. At the multicellular level, this "lateral inhibition” principle leads to checkerboard patterns with alternation of Sender and Receiver cells. While it is well known that stochasticity modulates cell fate specification, little is known about how stochastic fluctuations at the cellular level propagate during multicell pattern formation. Here, we model stochastic fluctuations in the Notch-Delta pathway in the presence of two different noise types–shot and white–for a multicell system. Our results show that intermediate fluctuations reduce disorder and guide the multicell lattice toward checkerboard-like patterns. By further analyzing cell fate transition events, we demonstrate that intermediate noise amplitudes provide enough perturbation to facilitate “proofreading” of disordered patterns and cause cells to switch to the correct ordered state (Sender surrounded by Receivers, and vice versa). Conversely, high noise can override environmental signals coming from neighboring cells and lead to switching between ordered and disordered patterns. Therefore, in analogy with spin glass systems, intermediate noise levels allow the multicell Notch system to escape frustrated patterns and relax towards the lower energy checkerboard pattern while at large noise levels the system is unable to find this ordered base of attraction. The Notch pathway is involved in many biological processes and is known to form precise spatial patterns alternating Sender and Receiver cell states. Quantifying the implications of stochastic fluctuations provided insight that patterns formed in Notch-mediated pathways must follow a predetermined path towards checkerboard or exist in a noisy environment which promotes order through error correction. We model Notch pattern formation stochastically and analyze the spatiotemporal dynamics. Our results show multicellular systems equilibrate towards ordered systems, but mistakes in the initial lattice propagate causing the systems to relax into frustrated systems. Only through existing in a noisy environment are the systems able to relax into the checkerboard pattern. Analyzing the temporal dynamics confirms, in environments with intermediate noise, the “incorrect” cells (Sender in a Sender environment, and vice versa) can be flipped to the correct state (Sender in a Receiver environment, and vice versa). Comparing with the spin glass energy landscape, we suggest the multicellular model follows a rugged landscape to form patterns with stochastic fluctuations required to enforce order throughout the system.
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Affiliation(s)
- Madeline Galbraith
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Department of Physics and Astronomy, Rice University, Houston, Texas, United States of America
| | - Federico Bocci
- NSF-Simons Center for Multiscale Cell Fate research, University of California Irvine, California, United States of America
- * E-mail: (FB); (JNO)
| | - José N. Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Department of Physics and Astronomy, Rice University, Houston, Texas, United States of America
- Department of Chemistry, Rice University, Houston, Texas, United States of America
- Department of Biosciences, Rice University, Houston, Texas, United States of America
- * E-mail: (FB); (JNO)
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42
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Khan MF, Kalyan G, Chakrabarty S, Mursaleen M. Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine. Nutrients 2022; 14:nu14142794. [PMID: 35889751 PMCID: PMC9318145 DOI: 10.3390/nu14142794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 07/01/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022] Open
Abstract
The recent elevation of cases infected from novel COVID-19 has placed the human life in trepidation mode, especially for those suffering from comorbidities. Most of the studies in the last few months have undeniably raised concerns for hypertensive patients that face greater risk of fatality from COVID-19. Furthermore, one of the recent WHO reports has estimated a total of 1.13 billion people are at a risk of hypertension of which two-thirds live in low and middle income countries. The gradual escalation of the hypertension problem andthe sudden rise of COVID-19 cases have placed an increasingly higher number of human lives at risk in low and middle income countries. To lower the risk of hypertension, most physicians recommend drugs that have angiotensin-converting enzyme (ACE) inhibitors. However, prolonged use of such drugs is not recommended due to metabolic risks and the increase in the expression of ACE-II which could facilitate COVID-19 infection. In contrast, the intake of optimal macronutrients is one of the possible alternatives to naturally control hypertension. In the present study, a nontrivial feature selection and machine learning algorithm is adopted to intelligently predict the food-derived antihypertensive peptide. The proposed idea of the paper lies in reducing the computational power while retaining the performance of the support vector machine (SVM) by estimating the dominant pattern in the features space through feature filtering. The proposed feature filtering algorithm has reported a trade-off performance by reducing the chances of Type I error, which is desirable when recommending a dietary food to patients suffering from hypertension. The maximum achievable accuracy of the best performing SVM models through feature selection are 86.17% and 85.61%, respectively.
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Affiliation(s)
| | - Gazal Kalyan
- Department of Pathology, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND 58202, USA;
| | - Sohom Chakrabarty
- Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India;
| | - M. Mursaleen
- Department of Medical Research, China Medical University Hospital, China Medical University (Taiwan), Taichung 40402, Taiwan
- Correspondence:
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43
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Fluctuating asymmetry as an indicator of stress. Emerg Top Life Sci 2022; 6:295-301. [PMID: 35788314 DOI: 10.1042/etls20210274] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 01/23/2023]
Abstract
Fluctuating asymmetry as a special kind of asymmetry can be defined as deviations from a known predetermined ratio of the parts of morphological structure under study. As a special type of phenotypic variability fluctuating asymmetry is a manifestation of ontogenetic noise or developmental variability. This type of variability is ubiquitous and plays a significant role in the observed phenotypic diversity. The level of fluctuating asymmetry turns out to be an indicator of optimal developmental conditions and genetic coadaptation. It is also considered as a parameter of fitness. Thus, fluctuating asymmetry acts as a measure of developmental stability in developmental biology and as a measure of population condition in population biology.
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44
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Nicoletti G, Busiello DM. Mutual information in changing environments: Nonlinear interactions, out-of-equilibrium systems, and continuously varying diffusivities. Phys Rev E 2022; 106:014153. [PMID: 35974654 DOI: 10.1103/physreve.106.014153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
Biochemistry, ecology, and neuroscience are examples of prominent fields aiming at describing interacting systems that exhibit nontrivial couplings to complex, ever-changing environments. We have recently shown that linear interactions and a switching environment are encoded separately in the mutual information of the overall system. Here we first generalize these findings to a broad class of nonlinear interacting models. We find that a new term in the mutual information appears, quantifying the interplay between nonlinear interactions and environmental changes, and leading to either constructive or destructive information interference. Furthermore, we show that a higher mutual information emerges in out-of-equilibrium environments with respect to an equilibrium scenario. Finally, we generalize our framework to the case of continuously varying environments. We find that environmental changes can be mapped exactly into an effective spatially varying diffusion coefficient, shedding light on modeling of biophysical systems in inhomogeneous media.
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Affiliation(s)
- Giorgio Nicoletti
- Laboratory of Interdisciplinary Physics, Department of Physics and Astronomy "G. Galilei," University of Padova, Padova 35121, Italy
| | - Daniel Maria Busiello
- Institute of Physics, École Polytechnique Fédérale de Lausanne-EPFL, 1015 Lausanne, Switzerland
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45
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Lu J, Şimşek E, Silver A, You L. Advances and challenges in programming pattern formation using living cells. Curr Opin Chem Biol 2022; 68:102147. [DOI: 10.1016/j.cbpa.2022.102147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 11/29/2022]
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46
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Smirnov P, Smith I, Safikhani Z, Ba-Alawi W, Khodakarami F, Lin E, Yu Y, Martin S, Ortmann J, Aittokallio T, Hafner M, Haibe-Kains B. Evaluation of statistical approaches for association testing in noisy drug screening data. BMC Bioinformatics 2022; 23:188. [PMID: 35585485 PMCID: PMC9118710 DOI: 10.1186/s12859-022-04693-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 04/15/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. RESULTS To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. CONCLUSIONS We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.
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Affiliation(s)
- Petr Smirnov
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | - Ian Smith
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | - Zhaleh Safikhani
- Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | - Wail Ba-Alawi
- Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | | | - Eva Lin
- Department of Discovery Oncology, Genentech Inc., South San Francisco, USA
| | - Yihong Yu
- Department of Discovery Oncology, Genentech Inc., South San Francisco, USA
| | - Scott Martin
- Department of Discovery Oncology, Genentech Inc., South San Francisco, USA
| | - Janosch Ortmann
- Département d'analytique, opérations et technologies de l'information, École des sciences de la gestion, Université du Québec à Montréal, Montréal, Canada
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.,Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Marc Hafner
- Department of Oncology Bioinformatics, Genentech Inc., South San Francisco, USA
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, Canada. .,Princess Margaret Cancer Center, University Health Network, Toronto, Canada. .,Vector Institute, Toronto, Canada.
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47
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Cell Chirality Regulates Coherent Angular Motion on Small Circular Substrates. Biophys J 2022; 121:1931-1939. [DOI: 10.1016/j.bpj.2022.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/01/2022] [Accepted: 04/06/2022] [Indexed: 11/17/2022] Open
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48
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Martineau S, Saffold T, Chang TT, Ronellenfitsch H. Enhancing Synchronization by Optimal Correlated Noise. PHYSICAL REVIEW LETTERS 2022; 128:098301. [PMID: 35302804 DOI: 10.1103/physrevlett.128.098301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/11/2022] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
From the flashes of fireflies to Josephson junctions and power infrastructure, networks of coupled phase oscillators provide a powerful framework to describe synchronization phenomena in many natural and engineered systems. Most real-world networks are under the influence of noisy, random inputs, potentially inhibiting synchronization. While noise is unavoidable, here we show that there exist optimal noise patterns which minimize desynchronizing effects and even enhance order. Specifically, using analytical arguments we show that in the case of a two-oscillator model, there exists a sharp transition from a regime where the optimal synchrony-enhancing noise is perfectly anticorrelated, to one where the optimal noise is correlated. More generally, we then use numerical optimization methods to demonstrate that there exist anticorrelated noise patterns that optimally enhance synchronization in large complex oscillator networks. Our results may have implications in networks such as power grids and neuronal networks, which are subject to significant amounts of correlated input noise.
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Affiliation(s)
- Sherwood Martineau
- Physics Department, Williams College, 33 Lab Campus Drive, Williamstown, Massachusetts 01267, USA
| | - Tim Saffold
- Physics Department, Williams College, 33 Lab Campus Drive, Williamstown, Massachusetts 01267, USA
| | - Timothy T Chang
- Physics Department, Williams College, 33 Lab Campus Drive, Williamstown, Massachusetts 01267, USA
| | - Henrik Ronellenfitsch
- Physics Department, Williams College, 33 Lab Campus Drive, Williamstown, Massachusetts 01267, USA
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Biswas A. Pathway-resolved decomposition demonstrates correlation and noise dependencies of redundant information processing in recurrent feed-forward topologies. Phys Rev E 2022; 105:034406. [PMID: 35428055 DOI: 10.1103/physreve.105.034406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
In a biochemical assay that converts fan-in networks into feed-forward loops (FFLs), we show that the inter-regulator redundant information about the output gene product can be decomposed into finer components, mediated by the constituent pathways. Variance-based information within the linear noise regime facilitates quantifying these submodular redundancies. Contrary to the conventional wisdom on information decomposition, we report that information redundancy depends nontrivially on inter-regulator correlation. For the type-1 coherent (C1) and incoherent (I1) FFLs, the direct regulatory path-mediated redundancy is certainly correlation independent. However, components induced by the indirect regulatory path and interpathway interference are correlation dependent in (non)linear fashion. The trade-off between information redundancy and similarly decomposable extrinsic noise from input to output node has been demonstrated for the pathways and full motifs. Our analyses suggest that the interpathway cross redundancy positively and negatively influences the superposition of elementary redundancies in the C1- and I1-FFLs, respectively. Their corresponding total extrinsic noise is produced by the weighted sum and difference of the pathway-specific components. We find that the I1-FFL is able to manufacture more varied redundancy and extrinsic noise responses compared to the C1-FFL. Underlying the differing characteristics of the composite metrics across FFL variants, there exist uniformly behaving pathway-dependent elements. The decomposition framework has been meticulously explored in biologically rational parametric realizations through analytical estimates and stochastic simulations.
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Affiliation(s)
- Ayan Biswas
- Department of Chemistry, Bose Institute, 93/1 A P C Road, Kolkata 700009, India
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50
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Doering GN, Drawert B, Lee C, Pruitt JN, Petzold LR, Dalnoki-Veress K. Noise resistant synchronization and collective rhythm switching in a model of animal group locomotion. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211908. [PMID: 35291326 PMCID: PMC8905150 DOI: 10.1098/rsos.211908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 02/11/2022] [Indexed: 06/14/2023]
Abstract
Biology is suffused with rhythmic behaviour, and interacting biological oscillators often synchronize their rhythms with one another. Colonies of some ant species are able to synchronize their activity to fall into coherent bursts, but models of this phenomenon have neglected the potential effects of intrinsic noise and interspecific differences in individual-level behaviour. We investigated the individual and collective activity patterns of two Leptothorax ant species. We show that in one species (Leptothorax sp. W), ants converge onto rhythmic cycles of synchronized collective activity with a period of about 20 min. A second species (Leptothorax crassipilis) exhibits more complex collective dynamics, where dominant collective cycle periods range from 16 min to 2.8 h. Recordings that last 35 h reveal that, in both species, the same colony can exhibit multiple oscillation frequencies. We observe that workers of both species can be stimulated by nest-mates to become active after a refractory resting period, but the durations of refractory periods differ between the species and can be highly variable. We model the emergence of synchronized rhythms using an agent-based model informed by our empirical data. This simple model successfully generates synchronized group oscillations despite the addition of noise to ants' refractory periods. We also find that adding noise reduces the likelihood that the model will spontaneously switch between distinct collective cycle frequencies.
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Affiliation(s)
- Grant Navid Doering
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada L8S 4K1
| | - Brian Drawert
- National Environmental Modeling and Analysis Center, University of North Carolina at Asheville, Asheville, NC 28804, USA
| | - Carmen Lee
- Department of Physics and Astronomy, McMaster University, Hamilton, Ontario, Canada L8S 4K1
| | - Jonathan N. Pruitt
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada L8S 4K1
| | - Linda R. Petzold
- Department of Computer Science, University of California, Santa Barbara, CA 93106, USA
- Department of Mechanical Engineering, University of California, Santa Barbara, CA 93106, USA
| | - Kari Dalnoki-Veress
- Department of Physics and Astronomy, McMaster University, Hamilton, Ontario, Canada L8S 4K1
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