Publications
16 results found
Lee J, Mannan AA, Miyano T, et al., 2024, In silico elucidation of key drivers of S. aureus-S. epidermidis-induced skin damage in atopic dermatitis lesions, JID Innovations, ISSN: 2667-0267
Staphylococcus aureus (SA) colonises and can damage skin in atopic dermatitis (AD) lesions, despite being commonly found with Staphylococcus epidermidis (SE), a commensal that can inhibit SA’s virulence and kill SA. Here, we developed an in silico model, termed a “virtual skin site”, describing the dynamic interplay between SA, SE, and the skin barrier in AD lesions to investigate the mechanisms driving skin damage by SA and SE. We generated 106 virtual skin sites by varying model parameters to represent different skin physiologies and bacterial properties. In silico analysis revealed that virtual skin sites with no skin damage in the model were characterised by parameters representing stronger SA and SE growth attenuation compared to those with skin damage. This inspired a treatment strategy combining SA-killing with an enhanced SA-SE growth attenuation, which in silico simulations found recovers many more damaged virtual skin sites to a non-damaged state, compared to SA-killing alone. This study demonstrates that in silico modelling can help elucidate key factors driving skin damage caused by SA-SE colonisation in AD lesions and help propose strategies to control it, which we envision will contribute to the design of promising treatments for clinical studies.
Lee J, Mannan A, Miyano T, et al., 2024, In silico simulations reveal strategy to treat S. aureus-S. epidermidis-driven skin damage in atopic dermatitis lesions, JID Innovations, ISSN: 2667-0267
Staphylococcus aureus (SA) colonises and can damage skin in atopic dermatitis (AD) lesions, despite being commonly found with Staphylococcus epidermidis (SE), a commensal that can inhibit SA’s virulence and kill SA. Here, we developed an in silico model, termed a “virtual skin site”, describing the dynamic interplay between SA, SE, and the skin barrier in AD lesions to investigate the mechanisms driving skin damage by SA and SE. We generated 106 virtual skin sites by varying model parameters to represent different skin physiologies and bacterial properties. In silico analysis revealed that virtual skin sites with no skin damage in the model were characterised by parameters representing stronger SA and SE growth attenuation compared to those with skin damage. This inspired a treatment strategy combining SA-killing with an enhanced SA-SE growth attenuation, which in silico simulations found recovers many more damaged virtual skin sites to a non-damaged state, compared to SA-killing alone. This study demonstrates that in silico modelling can help reveal strategies to treat skin damage caused by SA-SE colonisation in AD lesions, which we envision will contribute to the design of promising treatments for clinical studies.
Mannan AA, Lee J, Miyano T, et al., 2023, In silico prediction of the targeted killing of S. aureus in coinfections with S. epidermidis in atopic dermatitis lesions, 1st International Societies for Investigative Dermatology Meeting, Publisher: ELSEVIER SCIENCE INC, Pages: S173-S173, ISSN: 0022-202X
Legon L, Corre C, Bates DG, et al., 2022, gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs, BIOINFORMATICS, ISSN: 1367-4803
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- Citations: 2
Verma BK, Mannan AA, Zhang F, et al., 2022, Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering, ACS SYNTHETIC BIOLOGY, Vol: 11, Pages: 228-240, ISSN: 2161-5063
Lee J, Mannan AA, Miyano T, et al., 2022, OPTIMISING <i>STAPHYLOCOCCUS AUREUS-</i>TARGETED THERAPIES FOR ATOPIC DERMATITIS USING A MATHEMATICAL MODELLING APPROACH, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 34-34, ISSN: 0001-5555
Mannan AA, Bates DG, 2021, Designing an irreversible metabolic switch for scalable induction of microbial chemical production, NATURE COMMUNICATIONS, Vol: 12, ISSN: 2041-1723
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- Citations: 11
Hartline CJ, Mannan AA, Liu D, et al., 2020, Metabolite Sequestration Enables Rapid Recovery from Fatty Acid Depletion in <i>Escherichia coli</i>, MBIO, Vol: 11, ISSN: 2150-7511
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- Citations: 11
Otero-Muras I, Mannan AA, Banga JR, et al., 2019, Multiobjective optimization of gene circuits for metabolic engineering, 8th Conference on Foundations of Systems Biology in Engineering (FOBSE), Publisher: ELSEVIER, Pages: 13-16, ISSN: 2405-8963
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- Citations: 6
Liu D, Mannan AA, Han Y, et al., 2018, Dynamic metabolic control: towards precision engineering of metabolism, Journal of Industrial Microbiology and Biotechnology, Vol: 45, Pages: 535-543, ISSN: 1367-5435
Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.
Mannan AA, Liu D, Zhang F, et al., 2017, Fundamental design principles for transcription-factor-based metabolite biosensors, ACS Synthetic Biology, Vol: 6, Pages: 1851-1859, ISSN: 2161-5063
Metabolite biosensors are central to current efforts toward precision engineering of metabolism. Although most research has focused on building new biosensors, their tunability remains poorly understood and is fundamental for their broad applicability. Here we asked how genetic modifications shape the dose–response curve of biosensors based on metabolite-responsive transcription factors. Using the lac system in Escherichia coli as a model system, we built promoter libraries with variable operator sites that reveal interdependencies between biosensor dynamic range and response threshold. We developed a phenomenological theory to quantify such design constraints in biosensors with various architectures and tunable parameters. Our theory reveals a maximal achievable dynamic range and exposes tunable parameters for orthogonal control of dynamic range and response threshold. Our work sheds light on fundamental limits of synthetic biology designs and provides quantitative guidelines for biosensor design in applications such as dynamic pathway control, strain optimization, and real-time monitoring of metabolism.
Weisse AY, Mannan AA, Oyarzun DA, 2016, Signaling tug-of-war delivers the whole message, Cell Systems, Vol: 3, Pages: 414-46, ISSN: 2405-4720
How do cells transmit biochemical signals accurately? It turns out,pushing and pulling can go a long way.
Wu H, von Kamp A, Leoncikas V, et al., 2016, MUFINS: multi-formalism interaction network simulator., npj Systems Biology and Applications, Vol: 2, ISSN: 2056-7189
Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. We extend the constraint-based modeling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modeling of networks simultaneously describing gene regulation, signaling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome-Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi-Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signaling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through the analysis of 262 individual tumor transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualization, which facilitates use by researchers who are not experienced in coding and mathematical modeling environments.
Mannan AA, Toya Y, Shimizu K, et al., 2015, Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism, PLOS One, Vol: 10, ISSN: 1932-6203
An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-‘omics’ steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population.
Mendum TA, Newcombe J, Mannan AA, et al., 2011, Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera., Genome Biology, Vol: 12, ISSN: 1474-760X
BACKGROUND: Neisseria meningitidis is an important human commensal and pathogen that causes several thousand deaths each year, mostly in young children. How the pathogen replicates and causes disease in the host is largely unknown, particularly the role of metabolism in colonization and disease. Completed genome sequences are available for several strains but our understanding of how these data relate to phenotype remains limited. RESULTS: To investigate the metabolism of N. meningitidis we generated and then selected a representative Tn5 library on rich medium, a minimal defined medium and in human serum to identify genes essential for growth under these conditions. To relate these data to a systems-wide understanding of the pathogen's biology we constructed a genome-scale metabolic network: Nmb_iTM560. This model was able to distinguish essential and non-essential genes as predicted by the global mutagenesis. These essentiality data, the library and the Nmb_iTM560 model are powerful and widely applicable resources for the study of meningococcal metabolism and physiology. We demonstrate the utility of these resources by predicting and demonstrating metabolic requirements on minimal medium, such as a requirement for phosphoenolpyruvate carboxylase, and by describing the nutritional and biochemical status of N. meningitidis when grown in serum, including a requirement for both the synthesis and transport of amino acids. CONCLUSIONS: This study describes the application of a genome scale transposon library combined with an experimentally validated genome-scale metabolic network of N. meningitidis to identify essential genes and provide novel insight into the pathogen's metabolism both in vitro and during infection.
Kadir TA, Mannan AA, Kierzek AM, et al., 2010, Modeling and simulation of the main metabolism in Escherichia coli and its several single-gene knockout mutants with experimental verification., Microbial Cell Factories, Vol: 9, ISSN: 1475-2859
BACKGROUND: It is quite important to simulate the metabolic changes of a cell in response to the change in culture environment and/or specific gene knockouts particularly for the purpose of application in industry. If this could be done, the cell design can be made without conducting exhaustive experiments, and one can screen out the promising candidates, proceeded by experimental verification of a select few of particular interest. Although several models have so far been proposed, most of them focus on the specific metabolic pathways. It is preferred to model the whole of the main metabolic pathways in Escherichia coli, allowing for the estimation of energy generation and cell synthesis, based on intracellular fluxes and that may be used to characterize phenotypic growth. RESULTS: In the present study, we considered the simulation of the main metabolic pathways such as glycolysis, TCA cycle, pentose phosphate (PP) pathway, and the anapleorotic pathways using enzymatic reaction models of E. coli. Once intracellular fluxes were computed by this model, the specific ATP production rate, the specific CO₂ production rate, and the specific NADPH production rate could be estimated. The specific ATP production rate thus computed was used for the estimation of the specific growth rate. The CO₂ production rate could be used to estimate cell yield, and the specific NADPH production rate could be used to determine the flux of the oxidative PP pathway. The batch and continuous cultivations were simulated where the changing patterns of extracellular and intra-cellular metabolite concentrations were compared with experimental data. Moreover, the effects of the knockout of such pathways as Ppc, Pck and Pyk on the metabolism were simulated. It was shown to be difficult for the cell to grow in Ppc mutant due to low concentration of OAA, while Pck mutant does not necessarily show this phenomenon. The slower growth rate of the Ppc mutant was properly estimated by taking into account t
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