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Ghosh S, Baltussen MG, Ivanov NM, Haije R, Jakštaitė M, Zhou T, Huck WTS. Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems. Chem Rev 2024; 124:2553-2582. [PMID: 38476077 PMCID: PMC10941194 DOI: 10.1021/acs.chemrev.3c00681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/13/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024]
Abstract
The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence and sustenance of life. Constructing such networks in vitro enables stepwise build up in complexity and introduces the opportunity to control enzymatic activity using physicochemical stimuli. Rational design and modulation of network motifs enable the engineering of artificial systems with emergent functionalities. Such functional systems are useful for a variety of reasons such as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules for synthetic cells, and even enabling molecular computation. In this review, we offer insights into the chemical characteristics of ERNs while also delving into their potential applications and associated challenges.
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Affiliation(s)
- Souvik Ghosh
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Mathieu G. Baltussen
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Nikita M. Ivanov
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Rianne Haije
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Miglė Jakštaitė
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Tao Zhou
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Wilhelm T. S. Huck
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
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Ilker E, Hinczewski M. Bioenergetic costs and the evolution of noise regulation by microRNAs. Proc Natl Acad Sci U S A 2024; 121:e2308796121. [PMID: 38386708 PMCID: PMC10907262 DOI: 10.1073/pnas.2308796121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 01/14/2024] [Indexed: 02/24/2024] Open
Abstract
Noise control, together with other regulatory functions facilitated by microRNAs (miRNAs), is believed to have played important roles in the evolution of multicellular eukaryotic organisms. miRNAs can dampen protein fluctuations via enhanced degradation of messenger RNA (mRNA), but this requires compensation by increased mRNA transcription to maintain the same expression levels. The overall mechanism is metabolically expensive, leading to questions about how it might have evolved in the first place. We develop a stochastic model of miRNA noise regulation, coupled with a detailed analysis of the associated metabolic costs. Additionally, we calculate binding free energies for a range of miRNA seeds, the short sequences which govern target recognition. We argue that natural selection may have fine-tuned the Michaelis-Menten constant [Formula: see text] describing miRNA-mRNA affinity and show supporting evidence from analysis of experimental data. [Formula: see text] is constrained by seed length, and optimal noise control (minimum protein variance at a given energy cost) is achievable for seeds of 6 to 7 nucleotides in length, the most commonly observed types. Moreover, at optimality, the degree of noise reduction approaches the theoretical bound set by the Wiener-Kolmogorov linear filter. The results illustrate how selective pressure toward energy efficiency has potentially shaped a crucial regulatory pathway in eukaryotes.
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Affiliation(s)
- Efe Ilker
- Max Planck Institute for the Physics of Complex Systems, Dresden01187, Germany
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, OH44106
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3
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Weisenberger C, Hathcock D, Hinczewski M. Cellular Signaling beyond the Wiener-Kolmogorov Limit. J Phys Chem B 2021; 125:12698-12711. [PMID: 34756045 DOI: 10.1021/acs.jpcb.1c07894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Accurate propagation of signals through stochastic biochemical networks involves significant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress fluctuations in biomolecular populations. Wiener-Kolmogorov (WK) optimal noise filter theory, originally developed for engineering problems, has recently emerged as a valuable tool to estimate the maximum performance achievable in such biological systems for a given metabolic cost. However, WK theory has one assumption that potentially limits its applicability: it relies on a linear, continuum description of the reaction dynamics. Despite this, up to now no explicit test of the theory in nonlinear signaling systems with discrete molecular populations has ever seen performance beyond the WK bound. Here we report the first direct evidence of the bound being broken. To accomplish this, we develop a theoretical framework for multilevel signaling cascades, including the possibility of feedback interactions between input and output. In the absence of feedback, we introduce an analytical approach that allows us to calculate exact moments of the stationary distribution for a nonlinear system. With feedback, we rely on numerical solutions of the system's master equation. The results show WK violations in two common network motifs: a two-level signaling cascade and a negative feedback loop. However, the magnitude of the violation is biologically negligible, particularly in the parameter regime where signaling is most effective. The results demonstrate that while WK theory does not provide strict bounds, its predictions for performance limits are excellent approximations, even for nonlinear systems.
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Affiliation(s)
- Casey Weisenberger
- Department of Physics, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - David Hathcock
- Department of Physics, Cornell University, Ithaca, New York 14853, United States
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, Ohio 44106, United States
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Staal J, Driege Y, Haegman M, Kreike M, Iliaki S, Vanneste D, Lork M, Afonina IS, Braun H, Beyaert R. Defining the combinatorial space of PKC::CARD‐CC signal transduction nodes. FEBS J 2020; 288:1630-1647. [DOI: 10.1111/febs.15522] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 07/12/2020] [Accepted: 07/30/2020] [Indexed: 12/14/2022]
Affiliation(s)
- Jens Staal
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
| | - Yasmine Driege
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
| | - Mira Haegman
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
| | - Marja Kreike
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
| | - Styliani Iliaki
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
| | - Domien Vanneste
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
| | - Marie Lork
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
| | - Inna S. Afonina
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
| | - Harald Braun
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
| | - Rudi Beyaert
- Department of Biomedical Molecular Biology Ghent University Ghent Belgium
- Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation VIB Ghent Belgium
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Ilker E, Hinczewski M. Modeling the Growth of Organisms Validates a General Relation between Metabolic Costs and Natural Selection. PHYSICAL REVIEW LETTERS 2019; 122:238101. [PMID: 31298905 DOI: 10.1103/physrevlett.122.238101] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 11/11/2018] [Indexed: 06/10/2023]
Abstract
Metabolism and evolution are closely connected: if a mutation incurs extra energetic costs for an organism, there is a baseline selective disadvantage that may or may not be compensated for by other adaptive effects. A long-standing, but to date unproven, hypothesis is that this disadvantage is equal to the fractional cost relative to the total resting metabolic expenditure. We validate this result from physical principles through a general growth model and show it holds to excellent approximation for experimental parameters drawn from a wide range of species.
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Affiliation(s)
- Efe Ilker
- Physico-Chimie Curie UMR 168, Institut Curie, PSL Research University, 26 rue d'Ulm, 75248 Paris Cedex 05, France
- Department of Physics, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, Ohio 44106, USA
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Biswas A, Banik SK. Interplay of synergy and redundancy in diamond motif. CHAOS (WOODBURY, N.Y.) 2018; 28:103102. [PMID: 30384656 DOI: 10.1063/1.5044606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 09/13/2018] [Indexed: 06/08/2023]
Abstract
The formalism of partial information decomposition provides a number of independent components which altogether constitute the total information provided by the source variable(s) about the target variable(s). These non-overlapping terms are recognized as unique information, synergistic information, and redundant information. The metric of net synergy conceived as the difference between synergistic and redundant information is capable of detecting effective synergy, effective redundancy, and information independence among stochastic variables. The net synergy can be quantified using appropriate combinations of different Shannon mutual information terms. The utilization of the net synergy in network motifs with the nodes representing different biochemical species, involved in information sharing, uncovers rich store for exciting results. In the current study, we use this formalism to obtain a comprehensive understanding of the relative information processing mechanism in a diamond motif and two of its sub-motifs, namely, bifurcation and integration motif embedded within the diamond motif. The emerging patterns of effective synergy and effective redundancy and their contribution toward ensuring high fidelity information transmission are duly compared in the sub-motifs. Investigation on the metric of net synergy in independent bifurcation and integration motifs are also executed. In all of these computations, the crucial roles played by various systemic time scales, activation coefficients, and signal integration mechanisms at the output of the network topologies are especially emphasized. Following this plan of action, we become confident that the origin of effective synergy and effective redundancy can be architecturally justified by decomposing a diamond motif into bifurcation and integration motif. According to our conjecture, the presence of a common source of fluctuations creates effective redundancy. Our calculations reveal that effective redundancy empowers signal fidelity. Moreover, to achieve this, input signaling species avoids strong interaction with downstream intermediates. This strategy is capable of making the diamond motif noise-tolerant. Apart from the topological features, our study also puts forward the active contribution of additive and multiplicative signal integration mechanisms to nurture effective redundancy and effective synergy.
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Affiliation(s)
- Ayan Biswas
- Department of Chemistry, Bose Institute, 93/1 A P C Road, Kolkata 700 009, India
| | - Suman K Banik
- Department of Chemistry, Bose Institute, 93/1 A P C Road, Kolkata 700 009, India
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Samanta HS, Hinczewski M, Thirumalai D. Optimal information transfer in enzymatic networks: A field theoretic formulation. Phys Rev E 2017; 96:012406. [PMID: 29347079 DOI: 10.1103/physreve.96.012406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2017] [Indexed: 06/07/2023]
Abstract
Signaling in enzymatic networks is typically triggered by environmental fluctuations, resulting in a series of stochastic chemical reactions, leading to corruption of the signal by noise. For example, information flow is initiated by binding of extracellular ligands to receptors, which is transmitted through a cascade involving kinase-phosphatase stochastic chemical reactions. For a class of such networks, we develop a general field-theoretic approach to calculate the error in signal transmission as a function of an appropriate control variable. Application of the theory to a simple push-pull network, a module in the kinase-phosphatase cascade, recovers the exact results for error in signal transmission previously obtained using umbral calculus [Hinczewski and Thirumalai, Phys. Rev. X 4, 041017 (2014)2160-330810.1103/PhysRevX.4.041017]. We illustrate the generality of the theory by studying the minimal errors in noise reduction in a reaction cascade with two connected push-pull modules. Such a cascade behaves as an effective three-species network with a pseudointermediate. In this case, optimal information transfer, resulting in the smallest square of the error between the input and output, occurs with a time delay, which is given by the inverse of the decay rate of the pseudointermediate. Surprisingly, in these examples the minimum error computed using simulations that take nonlinearities and discrete nature of molecules into account coincides with the predictions of a linear theory. In contrast, there are substantial deviations between simulations and predictions of the linear theory in error in signal propagation in an enzymatic push-pull network for a certain range of parameters. Inclusion of second-order perturbative corrections shows that differences between simulations and theoretical predictions are minimized. Our study establishes that a field theoretic formulation of stochastic biological signaling offers a systematic way to understand error propagation in networks of arbitrary complexity.
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Affiliation(s)
- Himadri S Samanta
- Department of Chemistry, The University of Texas at Austin, Texas 78712, USA
| | | | - D Thirumalai
- Department of Chemistry, The University of Texas at Austin, Texas 78712, USA
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