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Trivellin C, Rugbjerg P, Olsson L. Performance and robustness analysis reveals phenotypic trade-offs in yeast. Life Sci Alliance 2024; 7:e202302215. [PMID: 37903627 PMCID: PMC10618107 DOI: 10.26508/lsa.202302215] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/20/2023] [Accepted: 10/20/2023] [Indexed: 11/01/2023] Open
Abstract
To design strains that can function efficiently in complex industrial settings, it is crucial to consider their robustness, that is, the stability of their performance when faced with perturbations. In the present study, we cultivated 24 Saccharomyces cerevisiae strains under conditions that simulated perturbations encountered during lignocellulosic bioethanol production, and assessed the performance and robustness of multiple phenotypes simultaneously. The observed negative correlations confirmed a trade-off between performance and robustness of ethanol yield, biomass yield, and cell dry weight. Conversely, the specific growth rate performance positively correlated with the robustness, presumably because of evolutionary selection for robust, fast-growing cells. The Ethanol Red strain exhibited both high performance and robustness, making it a good candidate for bioproduction in the tested perturbation space. Our results experimentally map the robustness-performance trade-offs, previously demonstrated mainly by single-phenotype and computational studies.
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Affiliation(s)
- Cecilia Trivellin
- https://ror.org/040wg7k59 Department of Life Sciences, Division of Industrial Biotechnology, Chalmers University of Technology, Gothenburg, Sweden
| | - Peter Rugbjerg
- https://ror.org/040wg7k59 Department of Life Sciences, Division of Industrial Biotechnology, Chalmers University of Technology, Gothenburg, Sweden
- Enduro Genetics ApS, Copenhagen, Denmark
| | - Lisbeth Olsson
- https://ror.org/040wg7k59 Department of Life Sciences, Division of Industrial Biotechnology, Chalmers University of Technology, Gothenburg, Sweden
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2
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Evolutionary dynamics, evolutionary forces, and robustness: A nonequilibrium statistical mechanics perspective. Proc Natl Acad Sci U S A 2022; 119:e2112083119. [PMID: 35312370 PMCID: PMC9060472 DOI: 10.1073/pnas.2112083119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Evolution through natural selection is an overwhelmingly complex process, and it is not surprising that theoretical approaches are strongly simplifying it. For instance, population genetics considers mainly dynamics of gene allele frequencies. Here, we develop a complementary approach to evolutionary dynamics based on three elements—organism reproduction, variations, and selection—that are essential for any evolutionary theory. By considering such general dynamics as a stochastic thermodynamic process, we clarify the nature and action of the evolutionary forces. We show that some of the forces cannot be described solely in terms of fitness landscapes. We also find that one force contribution can make organism reproduction insensitive (robust) to variations. Any realistic evolutionary theory has to consider 1) the dynamics of organisms that reproduce and possess heritable traits, 2) the appearance of stochastic variations in these traits, and 3) the selection of those organisms that better survive and reproduce. These elements shape the “evolutionary forces” that characterize the evolutionary dynamics. Here, we introduce a general model of reproduction–variation–selection dynamics. By treating these dynamics as a nonequilibrium thermodynamic process, we make precise the notion of the forces that characterize evolution. One of these forces, in particular, can be associated with the robustness of reproduction to variations. Some of the detailed predictions of our model can be tested by quantitative laboratory experiments, similar to those performed in the past on evolving populations of proteins or viruses.
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Araujo RP, Liotta LA. The topological requirements for robust perfect adaptation in networks of any size. Nat Commun 2018; 9:1757. [PMID: 29717141 PMCID: PMC5931626 DOI: 10.1038/s41467-018-04151-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 04/03/2018] [Indexed: 12/13/2022] Open
Abstract
Robustness, and the ability to function and thrive amid changing and unfavorable environments, is a fundamental requirement for living systems. Until now it has been an open question how large and complex biological networks can exhibit robust behaviors, such as perfect adaptation to a variable stimulus, since complexity is generally associated with fragility. Here we report that all networks that exhibit robust perfect adaptation (RPA) to a persistent change in stimulus are decomposable into well-defined modules, of which there exist two distinct classes. These two modular classes represent a topological basis for all RPA-capable networks, and generate the full set of topological realizations of the internal model principle for RPA in complex, self-organizing, evolvable bionetworks. This unexpected result supports the notion that evolutionary processes are empowered by simple and scalable modular design principles that promote robust performance no matter how large or complex the underlying networks become. Robust perfect adaptation (RPA), the ability of a system to return to its pre-stimulus state in the presence of a new signal, enables organisms to respond to further changes in stimuli. Here, the authors identify the modular structure of the full set of network topologies that can confer RPA on complex networks.
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Affiliation(s)
- Robyn P Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, 4000, Australia. .,Institute of Health and Biomedical Innovation (IHBI), 60 Musk Avenue, Kelvin Grove, Brisbane, QLD, 4059, Australia.
| | - Lance A Liotta
- Center for Applied Proteomics and Molecular Medicine, George Mason University, 10920 George Mason Circle, Manassas, Virginia, 20110, USA
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He F, Murabito E, Westerhoff HV. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering. J R Soc Interface 2016; 13:rsif.2015.1046. [PMID: 27075000 DOI: 10.1098/rsif.2015.1046] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2015] [Accepted: 03/21/2016] [Indexed: 12/25/2022] Open
Abstract
Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.
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Affiliation(s)
- Fei He
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK
| | - Ettore Murabito
- The Manchester Centre for Integrative Systems Biology, Manchester Institute for Biotechnology, School for Chemical Engineering and Analytical Science, University of Manchester, Manchester M1 7DN, UK
| | - Hans V Westerhoff
- The Manchester Centre for Integrative Systems Biology, Manchester Institute for Biotechnology, School for Chemical Engineering and Analytical Science, University of Manchester, Manchester M1 7DN, UK Department of Synthetic Systems Biology, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands Department of Molecular Cell Physiology, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
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Matsuoka Y, Jahan N, Kurata H. S-system-based analysis of the robust properties common to many biochemical network models. Bioprocess Biosyst Eng 2016; 39:735-46. [PMID: 26861555 DOI: 10.1007/s00449-016-1554-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 01/22/2016] [Indexed: 11/30/2022]
Abstract
Robustness is a key feature to characterize the adaptation of organisms to changes in their internal and external environments. A broad range of kinetic or dynamic models of biochemical systems have been developed. Robustness analyses are attractive for exploring some common properties of many biochemical models. To reveal such features, we transform different types of mathematical equations into a standard or intelligible formula and use the multiple parameter sensitivity (MPS) to identify some factors critically responsible for the total robustness to many perturbations. The MPS would be determined by the top quarter of the highly sensitive parameters rather than the single parameter with the maximum sensitivity. The MPS did not show any correlation to the network size. The MPS is closely related to the standard deviation of the sensitivity profile. A decrease in the standard deviation enhanced the total robustness, which shows the hallmark of distributed robustness that many factors (pathways) involve the total robustness.
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Affiliation(s)
- Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Nusrat Jahan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan. .,Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
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Michael E, Singh BK. Heterogeneous dynamics, robustness/fragility trade-offs, and the eradication of the macroparasitic disease, lymphatic filariasis. BMC Med 2016; 14:14. [PMID: 26822124 PMCID: PMC4731922 DOI: 10.1186/s12916-016-0557-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Accepted: 01/13/2016] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The current WHO-led initiative to eradicate the macroparasitic disease, lymphatic filariasis (LF), based on single-dose annual mass drug administration (MDA) represents one of the largest health programs devised to reduce the burden of tropical diseases. However, despite the advances made in instituting large-scale MDA programs in affected countries, a challenge to meeting the goal of global eradication is the heterogeneous transmission of LF across endemic regions, and the impact that such complexity may have on the effort required to interrupt transmission in all socioecological settings. METHODS Here, we apply a Bayesian computer simulation procedure to fit transmission models of LF to field data assembled from 18 sites across the major LF endemic regions of Africa, Asia and Papua New Guinea, reflecting different ecological and vector characteristics, to investigate the impacts and implications of transmission heterogeneity and complexity on filarial infection dynamics, system robustness and control. RESULTS We find firstly that LF elimination thresholds varied significantly between the 18 study communities owing to site variations in transmission and initial ecological parameters. We highlight how this variation in thresholds lead to the need for applying variable durations of interventions across endemic communities for achieving LF elimination; however, a major new result is the finding that filarial population responses to interventions ultimately reflect outcomes of interplays between dynamics and the biological architectures and processes that generate robustness/fragility trade-offs in parasite transmission. Intervention simulations carried out in this study further show how understanding these factors is also key to the design of options that would effectively eliminate LF from all settings. In this regard, we find how including vector control into MDA programs may not only offer a countermeasure that will reliably increase system fragility globally across all settings and hence provide a control option robust to differential locality-specific transmission dynamics, but by simultaneously reducing transmission regime variability also permit more reliable macroscopic predictions of intervention effects. CONCLUSIONS Our results imply that a new approach, combining adaptive modelling of parasite transmission with the use of biological robustness as a design principle, is required if we are to both enhance understanding of complex parasitic infections and delineate options to facilitate their elimination effectively.
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Affiliation(s)
- Edwin Michael
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
| | - Brajendra K Singh
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
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Kell DB, Kenny LC. A Dormant Microbial Component in the Development of Preeclampsia. Front Med (Lausanne) 2016; 3:60. [PMID: 27965958 PMCID: PMC5126693 DOI: 10.3389/fmed.2016.00060] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 11/04/2016] [Indexed: 12/12/2022] Open
Abstract
Preeclampsia (PE) is a complex, multisystem disorder that remains a leading cause of morbidity and mortality in pregnancy. Four main classes of dysregulation accompany PE and are widely considered to contribute to its severity. These are abnormal trophoblast invasion of the placenta, anti-angiogenic responses, oxidative stress, and inflammation. What is lacking, however, is an explanation of how these themselves are caused. We here develop the unifying idea, and the considerable evidence for it, that the originating cause of PE (and of the four classes of dysregulation) is, in fact, microbial infection, that most such microbes are dormant and hence resist detection by conventional (replication-dependent) microbiology, and that by occasional resuscitation and growth it is they that are responsible for all the observable sequelae, including the continuing, chronic inflammation. In particular, bacterial products such as lipopolysaccharide (LPS), also known as endotoxin, are well known as highly inflammagenic and stimulate an innate (and possibly trained) immune response that exacerbates the inflammation further. The known need of microbes for free iron can explain the iron dysregulation that accompanies PE. We describe the main routes of infection (gut, oral, and urinary tract infection) and the regularly observed presence of microbes in placental and other tissues in PE. Every known proteomic biomarker of "preeclampsia" that we assessed has, in fact, also been shown to be raised in response to infection. An infectious component to PE fulfills the Bradford Hill criteria for ascribing a disease to an environmental cause and suggests a number of treatments, some of which have, in fact, been shown to be successful. PE was classically referred to as endotoxemia or toxemia of pregnancy, and it is ironic that it seems that LPS and other microbial endotoxins really are involved. Overall, the recognition of an infectious component in the etiology of PE mirrors that for ulcers and other diseases that were previously considered to lack one.
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Affiliation(s)
- Douglas B. Kell
- School of Chemistry, The University of Manchester, Manchester, UK
- The Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals, The University of Manchester, Manchester, UK
- *Correspondence: Douglas B. Kell,
| | - Louise C. Kenny
- The Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Cork, Ireland
- Department of Obstetrics and Gynecology, University College Cork, Cork, Ireland
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Westerhoff HV, Nakayama S, Mondeel TDGA, Barberis M. Systems Pharmacology: An opinion on how to turn the impossible into grand challenges. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 15:23-31. [PMID: 26464087 DOI: 10.1016/j.ddtec.2015.06.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Revised: 06/23/2015] [Accepted: 06/25/2015] [Indexed: 11/20/2022]
Abstract
A pharmacology that hits single disease-causing molecules with a single drug passively distributing to the target tissue, was almost ready. Such a pharmacology is not (going to be) effective however: a great many diseases are systems biology diseases; complex networks of some hundred thousand types of molecule, determine the functions that constitute human health, through nonlinear interactions. Malfunctions are caused by a variety of molecular failures at the same time; rarely the same variety in different individuals; in complex constellations of OR and AND logics. Few molecules cause disease single-handedly and few drugs will cure the disease all by themselves when dosed for a limited amount of time. We here discuss the implications that this discovery of the network nature of disease should have for pharmacology. We suggest ways in which pharmacokinetics, pharmacodynamics, but also systems biology and genomics may have to change so as better to deal with systems-biology diseases.
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Affiliation(s)
- Hans V Westerhoff
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands; Molecular Cell Physiology, Amsterdam Institute for Molecules, Medicines and Systems, VU University Amsterdam, The Netherlands; Manchester Centre for Integrative Systems Biology, The University of Manchester, UK.
| | - Shintaro Nakayama
- Molecular Cell Physiology, Amsterdam Institute for Molecules, Medicines and Systems, VU University Amsterdam, The Netherlands
| | - Thierry D G A Mondeel
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands
| | - Matteo Barberis
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands
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9
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De Vos D, Vissenberg K, Broeckhove J, Beemster GTS. Putting theory to the test: which regulatory mechanisms can drive realistic growth of a root? PLoS Comput Biol 2014; 10:e1003910. [PMID: 25358093 PMCID: PMC4214622 DOI: 10.1371/journal.pcbi.1003910] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Accepted: 09/15/2014] [Indexed: 01/20/2023] Open
Abstract
In recent years there has been a strong development of computational approaches to mechanistically understand organ growth regulation in plants. In this study, simulation methods were used to explore which regulatory mechanisms can lead to realistic output at the cell and whole organ scale and which other possibilities must be discarded as they result in cellular patterns and kinematic characteristics that are not consistent with experimental observations for the Arabidopsis thaliana primary root. To aid in this analysis, a ‘Uniform Longitudinal Strain Rule’ (ULSR) was formulated as a necessary condition for stable, unidirectional, symplastic growth. Our simulations indicate that symplastic structures are robust to differences in longitudinal strain rates along the growth axis only if these differences are small and short-lived. Whereas simple cell-autonomous regulatory rules based on counters and timers can produce stable growth, it was found that steady developmental zones and smooth transitions in cell lengths are not feasible. By introducing spatial cues into growth regulation, those inadequacies could be avoided and experimental data could be faithfully reproduced. Nevertheless, a root growth model based on previous polar auxin-transport mechanisms violates the proposed ULSR due to the presence of lateral gradients. Models with layer-specific regulation or layer-driven growth offer potential solutions. Alternatively, a model representing the known cross-talk between auxin, as the cell proliferation promoting factor, and cytokinin, as the cell differentiation promoting factor, predicts the effect of hormone-perturbations on meristem size. By down-regulating PIN-mediated transport through the transcription factor SHY2, cytokinin effectively flattens the lateral auxin gradient, at the basal boundary of the division zone, (thereby imposing the ULSR) to signal the exit of proliferation and start of elongation. This model exploration underlines the value of generating virtual root growth kinematics to dissect and understand the mechanisms controlling this biological system. The growth of a plant root is driven by cell division and cell expansion occurring in spatially distinct developmental zones. Although these zones are in principle stable, depending on the conditions, their size and properties can be modulated. This has been meticulously described by kinematic studies, which have led to the proposal of mechanisms underpinning those observations. At the same time, much knowledge of the identities and interactions of molecules involved in these mechanisms has accumulated, in particular from the model species Arabidopsis thaliana. Here we attempt to resolve the longstanding question whether observed growth patterns can be explained by autonomous decision-making at the level of individual cells or if the aid of some external signal is required. We then ask, building on the accumulated molecular information, which minimal models can provide for stable growth while keeping sufficient flexibility to regulate growth. Therefore, we constructed computational models for different growth mechanisms operating in a virtual two-dimensional Arabidopsis root and compared their behaviour with biological experiments. The simulations provide strong indications that spatial signals are required for realistic and flexible root growth, likely orchestrated by the plant hormones auxin and cytokinin.
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Affiliation(s)
- Dirk De Vos
- Molecular Plant Physiology and Biotechnology, Department of Biology, University of Antwerp, Antwerp, Belgium
| | - Kris Vissenberg
- Plant Growth and Development, Department of Biology, University of Antwerp, Antwerp, Belgium
| | - Jan Broeckhove
- Computational Modelling and Programming, Department of Mathematics and Informatics, University of Antwerp, Antwerp, Belgium
| | - Gerrit T. S. Beemster
- Molecular Plant Physiology and Biotechnology, Department of Biology, University of Antwerp, Antwerp, Belgium
- * E-mail:
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10
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Geppert T, Koeppen H. Biological Networks and Drug Discovery-Where Do We Stand? Drug Dev Res 2014; 75:271-82. [DOI: 10.1002/ddr.21207] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Tim Geppert
- Lead Identification and Optimization Support; Boehringer Ingelheim Pharma GmbH & Co. KG; Biberach/Riss 88397 Germany
| | - Herbert Koeppen
- Lead Identification and Optimization Support; Boehringer Ingelheim Pharma GmbH & Co. KG; Biberach/Riss 88397 Germany
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11
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Westerhoff HV, Brooks AN, Simeonidis E, García-Contreras R, He F, Boogerd FC, Jackson VJ, Goncharuk V, Kolodkin A. Macromolecular networks and intelligence in microorganisms. Front Microbiol 2014; 5:379. [PMID: 25101076 PMCID: PMC4106424 DOI: 10.3389/fmicb.2014.00379] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2014] [Accepted: 07/05/2014] [Indexed: 11/13/2022] Open
Abstract
Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity - particularly activity of the human brain - with a phenomenon we call "intelligence." Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as "human" and "brain" out of the defining features of "intelligence," all forms of life - from microbes to humans - exhibit some or all characteristics consistent with "intelligence." We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.
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Affiliation(s)
- Hans V. Westerhoff
- Department of Molecular Cell Physiology, Vrije Universiteit AmsterdamAmsterdam, Netherlands
- Manchester Centre for Integrative Systems Biology, The University of ManchesterManchester, UK
- Synthetic Systems Biology, University of AmsterdamAmsterdam, Netherlands
| | - Aaron N. Brooks
- Institute for Systems BiologySeattle, WA, USA
- Molecular and Cellular Biology Program, University of WashingtonSeattle, WA, USA
| | - Evangelos Simeonidis
- Institute for Systems BiologySeattle, WA, USA
- Luxembourg Centre for Systems Biomedicine, University of LuxembourgEsch-sur-Alzette, Luxembourg
| | | | - Fei He
- Department of Automatic Control and Systems Engineering, The University of SheffieldSheffield, UK
| | - Fred C. Boogerd
- Department of Molecular Cell Physiology, Vrije Universiteit AmsterdamAmsterdam, Netherlands
| | | | - Valeri Goncharuk
- Netherlands Institute for NeuroscienceAmsterdam, Netherlands
- Russian Cardiology Research CenterMoscow, Russia
- Department of Medicine, Center for Alzheimer and Neurodegenerative Research, University of AlbertaEdmonton, AB, Canada
| | - Alexey Kolodkin
- Institute for Systems BiologySeattle, WA, USA
- Luxembourg Centre for Systems Biomedicine, University of LuxembourgEsch-sur-Alzette, Luxembourg
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12
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Kell DB, Goodacre R. Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov Today 2014; 19:171-82. [PMID: 23892182 PMCID: PMC3989035 DOI: 10.1016/j.drudis.2013.07.014] [Citation(s) in RCA: 118] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Revised: 07/03/2013] [Accepted: 07/16/2013] [Indexed: 02/06/2023]
Abstract
Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.
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Affiliation(s)
- Douglas B Kell
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
| | - Royston Goodacre
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
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13
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Kurata H, Maeda K, Matsuoka Y. Dynamic Modeling of Metabolic and Gene Regulatory Systems toward Developing Virtual Microbes. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2014. [DOI: 10.1252/jcej.13we152] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
- Biomedical Informatics R&D Center, Kyushu Institute of Technology
| | - Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
| | - Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
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14
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He F, Fromion V, Westerhoff HV. (Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis. BMC SYSTEMS BIOLOGY 2013; 7:131. [PMID: 24261908 PMCID: PMC4222491 DOI: 10.1186/1752-0509-7-131] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Accepted: 11/12/2013] [Indexed: 12/16/2022]
Abstract
Background Metabolic control analysis (MCA) and supply–demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply–demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. Results This study integrates control engineering and classical MCA augmented with supply–demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the ‘integral control’ (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of ‘integral control’ should rarely be expected to lead to the ‘perfect adaptation’: although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. Conclusions A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the ‘perfect’ regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering.
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Affiliation(s)
| | | | - Hans V Westerhoff
- The Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK.
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