@article{cockx_foster_clegg_alden_arya_stekel_smets_kreft_2024, title={Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0}, url={https://doi.org/10.1371/journal.pcbi.1011303}, DOI={10.1371/journal.pcbi.1011303}, abstractNote={Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice.}, journal={PLOS Computational Biology}, author={Cockx, Bastiaan J. R. and Foster, Tim and Clegg, Robert J. and Alden, Kieran and Arya, Sankalp and Stekel, Dov J. and Smets, Barth F. and Kreft, Jan-Ulrich}, editor={Grilli, JacopoEditor}, year={2024}, month={Feb} }
 @article{todman_arya_baker_stekel_2023, title={A model of antibiotic resistance genes accumulation through lifetime exposure from food intake and antibiotic treatment}, volume={18}, ISSN={["1932-6203"]}, url={https://doi.org/10.1371/journal.pone.0289941}, DOI={10.1371/journal.pone.0289941}, abstractNote={Antimicrobial resistant bacterial infections represent one of the most serious contemporary global healthcare crises. Acquisition and spread of resistant infections can occur through community, hospitals, food, water or endogenous bacteria. Global efforts to reduce resistance have typically focussed on antibiotic use, hygiene and sanitation and drug discovery. However, resistance in endogenous infections, e.g. many urinary tract infections, can result from life-long acquisition and persistence of resistance genes in commensal microbial flora of individual patients, which is not normally considered. Here, using individual based Monte Carlo models calibrated using antibiotic use data and human gut resistomes, we show that the long-term increase in resistance in human gut microbiomes can be substantially lowered by reducing exposure to resistance genes found food and water, alongside reduced medical antibiotic use. Reduced dietary exposure is especially important during patient antibiotic treatment because of increased selection for resistance gene retention; inappropriate use of antibiotics can be directly harmful to the patient being treated for the same reason. We conclude that a holistic approach to antimicrobial resistance that additionally incorporates food production and dietary considerations will be more effective in reducing resistant infections than a purely medical-based approach.}, number={8}, journal={PLOS ONE}, author={Todman, Henry and Arya, Sankalp and Baker, Michelle and Stekel, Dov Joseph}, editor={Santos, RicardoEditor}, year={2023}, month={Aug} }
 @article{cockx_foster_clegg_alden_arya_stekel_smets_kreft_2023, title={Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0}, url={https://doi.org/10.1101/2023.06.27.546816}, DOI={10.1101/2023.06.27.546816}, abstractNote={Abstract Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the i ndividual-based Dyn amics o f Mi crobial C ommunities S imulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the “biofilms promote altruism” study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice. Author summary Microbes are fascinating in their own right and play a tremendously important role in ecosystems. They often form complex, self-organized communities with spatial heterogeneity that is changing over time. Such complexity is challenging to understand and manage without the help of mathematical models. Individual-based models are one type of mathematical model that is particularly suited if differences between individual microbes, local interactions and adaptive behavior are important. We have developed a completely overhauled version of iDynoMiCS, a software that allows users to develop, run and analyze a wide range of individual-based models without having to program the software themselves. There are several capability enhancements and numerous small improvements, for example the ability to model different shapes of cells combined with physically realistic mechanical interactions between neighboring cells. We showcase this by simulating the competition between filaments, long chains of cells, with single cells and find that filaments outcompete single cells as they can spread quickly to new territory with higher levels of resources. Users now have a wider choice of platforms so we provide guidance on which platform might be most suitable for a given purpose.}, journal={bioRxiv}, author={Cockx, Bastiaan J R and Foster, Tim and Clegg, Robert J and Alden, Kieran and Arya, Sankalp and Stekel, Dov J and Smets, Barth F and Kreft, Jan-Ulrich}, year={2023}, month={Jun} }
 @article{sidhu_ajmera_arya_lynch_2023, title={RootSlice – a novel functional-structural model for root anatomical phenotypes}, volume={6}, url={https://doi.org/10.1111/pce.14552}, DOI={10.1101/2022.06.29.498145}, abstractNote={ABSTRACT Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes. Summary statement Root anatomy remains an underutilized target for crop breeding. RootSlice , a multicellular functional-structural model of root anatomy, simulates the costs and benefits of diverse root anatomical phenotypes to estimate their utility for plant fitness in unfavorable soil environments.}, journal={bioRxiv}, publisher={Cold Spring Harbor Laboratory}, author={Sidhu, Jagdeep Singh and Ajmera, Ishan and Arya, Sankalp and Lynch, Jonathan P.}, year={2023}, month={Jan} }
 @article{sidhu_ajmera_arya_lynch_2023, title={RootSlice—A novel functional-structural model for root anatomical phenotypes}, volume={46}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85147584707&partnerID=MN8TOARS}, DOI={10.1111/pce.14552}, abstractNote={Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes.}, number={5}, journal={Plant Cell and Environment}, author={Sidhu, J.S. and Ajmera, I. and Arya, S. and Lynch, J.P.}, year={2023}, pages={1671–1690} }
 @article{arya_williams_reina_knapp_kreft_hobman_stekel_2021, title={Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids}, volume={275}, url={https://doi.org/10.1016/j.envpol.2021.116602}, DOI={10.1016/j.envpol.2021.116602}, abstractNote={Many antibiotic resistance genes co-occur with resistance genes for transition metals, such as copper, zinc, or mercury. In some environments, a positive correlation between high metal concentration and high abundance of antibiotic resistance genes has been observed, suggesting co-selection due to metal presence. Of particular concern is the use of copper and zinc in animal husbandry, leading to potential co-selection for antibiotic resistance in animal gut microbiomes, slurry, manure, or amended soils. For antibiotics, predicted no effect concentrations have been derived from laboratory measured minimum inhibitory concentrations and some minimal selective concentrations have been investigated in environmental settings. However, minimal co-selection concentrations for metals are difficult to identify. Here, we use mathematical modelling to provide a general mechanistic framework to predict minimal co-selective concentrations for metals, given knowledge of their toxicity at different concentrations. We apply the method to copper (Cu), zinc (Zn), mercury (Hg), lead (Pb) and silver (Ag), predicting their minimum co-selective concentrations in mg/L (Cu: 5.5, Zn: 1.6, Hg: 0.0156, Pb: 21.5, Ag: 0.152). To exemplify use of these thresholds, we consider metal concentrations from slurry and slurry-amended soil from a UK dairy farm that uses copper and zinc as additives for feed and antimicrobial footbath: the slurry is predicted to be co-selective, but not the slurry-amended soil. This modelling framework could be used as the basis for defining standards to mitigate risks of antimicrobial resistance applicable to a wide range of environments, including manure, slurry and other waste streams.}, journal={Environmental Pollution}, publisher={Elsevier BV}, author={Arya, Sankalp and Williams, Alexander and Reina, Saul Vazquez and Knapp, Charles W. and Kreft, Jan-Ulrich and Hobman, Jon L. and Stekel, Dov J.}, year={2021}, month={Apr}, pages={116602} }
 @article{arya_todman_baker_hooton_millard_kreft_hobman_stekel_2020, title={A generalised model for generalised transduction: the importance of co-evolution and stochasticity in phage mediated antimicrobial resistance transfer}, volume={96}, url={http://dx.doi.org/10.1093/femsec/fiaa100}, DOI={10.1093/femsec/fiaa100}, abstractNote={ABSTRACT Antimicrobial resistance is a major global challenge. Of particular concern are mobilizable elements that can transfer resistance genes between bacteria, leading to pathogens with new combinations of resistance. To date, mathematical models have largely focussed on transfer of resistance by plasmids, with fewer studies on transfer by bacteriophages. We aim to understand how best to model transfer of resistance by transduction by lytic phages. We show that models of lytic bacteriophage infection with empirically derived realistic phage parameters lead to low numbers of bacteria, which, in low population or localised environments, lead to extinction of bacteria and phage. Models that include antagonistic co-evolution of phage and bacteria produce more realistic results. Furthermore, because of these low numbers, stochastic dynamics are shown to be important, especially to spread of resistance. When resistance is introduced, resistance can sometimes be fixed, and at other times die out, with the probability of each outcome sensitive to bacterial and phage parameters. Specifically, that outcome most strongly depends on the baseline death rate of bacteria, with phage-mediated spread favoured in benign environments with low mortality over more hostile environments. We conclude that larger-scale models should consider spatial compartmentalisation and heterogeneous microenviroments, while encompassing stochasticity and co-evolution.}, number={7}, journal={FEMS Microbiology Ecology}, publisher={Oxford University Press (OUP)}, author={Arya, Sankalp and Todman, Henry and Baker, Michelle and Hooton, Steven and Millard, Andrew and Kreft, Jan-Ulrich and Hobman, Jon L and Stekel, Dov J}, year={2020}, month={Jul} }
 @article{arya_williams_reina_knapp_kreft_hobman_stekel_2020, title={Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85098903401&partnerID=MN8TOARS}, DOI={10.1101/2020.09.14.295766}, abstractNote={Abstract Many antibiotic resistance genes co-occur with resistance genes for transition metals, such as copper, zinc, or mercury. In some environments, a positive correlation between high metal concentration and high abundance of antibiotic resistance genes has been observed, suggesting co-selection due to metal presence. Of particular concern is the use of copper and zinc in animal husbandry, leading to potential co-selection for antibiotic resistance in animal gut microbiomes, slurry, manure, or amended soils. For antibiotics, predicted no effect concentrations have been derived from laboratory measured minimum inhibitory concentrations and some minimal selective concentrations have been investigated in environmental settings. However, minimal co-selection concentrations for metals are difficult to identify. Here, we use mathematical modelling to provide a general mechanistic framework to predict minimal co-selective concentrations for metals, given knowledge of their toxicity at different concentrations. We apply the method to copper (Cu), Zinc (Zn), mercury (Hg), lead (Pb) and silver (Ag), predicting their minimum co-selective concentrations in mg/L (Cu: 5.5, Zn: 1.6, Hg: 0.0156, Pb: 21.5, Ag: 0.152). To exemplify use of these thresholds, we consider metal concentrations from slurry and slurry-amended soil from a UK dairy farm that uses copper and zinc as additives for feed and antimicrobial footbath: the slurry is predicted to be co-selective, but not the slurry-amended soil. This modelling framework could be used as the basis for defining standards to mitigate risks of antimicrobial resistance applicable to a wide range of environments, including manure, slurry and other waste streams.}, journal={bioRxiv}, author={Arya, S. and Williams, A. and Reina, S.V. and Knapp, C.W. and Kreft, J.-U. and Hobman, J.L. and Stekel, D.J.}, year={2020} }
 @inbook{arya_dubey_sen_sharma_pathania_2019, title={Computational Prediction of sRNA in Acinetobacter baumannii}, volume={1946}, url={http://dx.doi.org/10.1007/978-1-4939-9118-1_27}, DOI={10.1007/978-1-4939-9118-1_27}, abstractNote={Small RNAs in bacteria are noncoding RNAs that act as posttranscriptional regulators of gene expression. Over time, they have gained importance as fine-tuners of expression of genes involved in critical biological processes like metabolism, fitness, virulence, and antibiotic resistance. The availability of various high-throughput strategies enable the detection of these molecules but are technically challenging and time-intensive. Thus, to fulfil the need of a simple computational algorithm pipeline to predict these sRNAs in bacterial species, we detail a user-friendly ensemble method with specific application in Acinetobacter spp. The developed algorithms primarily look for intergenic regions in the genome of related Acinetobacter spp., thermodynamic stability, and conservation of RNA secondary structures to generate a model input for the sRNAPredict3 tool which utilizes all this information to generate a list of putative sRNA. We confirmed the accuracy of the method by comparing its output with the RNA-seq data and found the method to be faster and more accurate for Acinetobacter baumannii ATCC 17978. Thus, this method improves the identification of sRNA in Acinetobacter and other bacterial species.}, booktitle={Methods in Molecular Biology}, publisher={Springer New York}, author={Arya, Sankalp and Dubey, Vineet and Sen, Deepak and Sharma, Atin and Pathania, Ranjana}, year={2019}, pages={307–320} }
 @inbook{pal_asiani_arya_rensing_stekel_larsson_hobman_2017, title={Metal Resistance and Its Association With Antibiotic Resistance}, volume={70}, url={http://dx.doi.org/10.1016/bs.ampbs.2017.02.001}, DOI={10.1016/bs.ampbs.2017.02.001}, abstractNote={Antibiotic resistance is recognised as a major global threat to public health by the World Health Organization. Currently, several hundred thousand deaths yearly can be attributed to infections with antibiotic-resistant bacteria. The major driver for the development of antibiotic resistance is considered to be the use, misuse and overuse of antibiotics in humans and animals. Nonantibiotic compounds, such as antibacterial biocides and metals, may also contribute to the promotion of antibiotic resistance through co-selection. This may occur when resistance genes to both antibiotics and metals/biocides are co-located together in the same cell (co-resistance), or a single resistance mechanism (e.g. an efflux pump) confers resistance to both antibiotics and biocides/metals (cross-resistance), leading to co-selection of bacterial strains, or mobile genetic elements that they carry. Here, we review antimicrobial metal resistance in the context of the antibiotic resistance problem, discuss co-selection, and highlight critical knowledge gaps in our understanding.}, booktitle={Microbiology of Metal Ions}, publisher={Elsevier}, author={Pal, Chandan and Asiani, Karishma and Arya, Sankalp and Rensing, Christopher and Stekel, Dov J. and Larsson, D.G. Joakim and Hobman, Jon L.}, year={2017}, pages={261–313} }
 @article{sharma_arya_patil_sharma_jain_navani_pathania_2014, title={Identification of Novel Regulatory Small RNAs in Acinetobacter baumannii}, volume={9}, url={http://dx.doi.org/10.1371/journal.pone.0093833}, DOI={10.1371/journal.pone.0093833}, abstractNote={Small RNA (sRNA) molecules are non-coding RNAs that have been implicated in regulation of various cellular processes in living systems, allowing them to adapt to changing environmental conditions. Till date, sRNAs have not been reported in Acinetobacter baumannii (A. baumannii), which has emerged as a significant multiple drug resistant nosocomial pathogen. In the present study, a combination of bioinformatic and experimental approach was used for identification of novel sRNAs. A total of 31 putative sRNAs were predicted by a combination of two algorithms, sRNAPredict and QRNA. Initially 10 sRNAs were chosen on the basis of lower E- value and three sRNAs (designated as AbsR11, 25 and 28) showed positive signal on Northern blot. These sRNAs are novel in nature as they do not have homologous sequences in other bacterial species. Expression of the three sRNAs was examined in various phases of bacterial growth. Further, the effect of various stress conditions on sRNA gene expression was determined. A detailed investigation revealed differential expression profile of AbsR25 in presence of varying amounts of ethidium bromide (EtBr), suggesting that its expression is influenced by environmental or internal signals such as stress response. A decrease in expression of AbsR25 and concomitant increase in the expression of bioinformatically predicted targets in presence of high EtBr was reverberated by the decrease in target gene expression when AbsR25 was overexpressed. This hints at the negative regulation of target genes by AbsR25. Interestingly, the putative targets include transporter genes and the degree of variation in expression of one of them (A1S_1331) suggests that AbsR25 is involved in regulation of a transporter. This study provides a perspective for future studies of sRNAs and their possible involvement in regulation of antibiotic resistance in bacteria specifically in cryptic A. baumannii.}, number={4}, journal={PLoS ONE}, publisher={Public Library of Science (PLoS)}, author={Sharma, Rajnikant and Arya, Sankalp and Patil, Supriya Deepak and Sharma, Atin and Jain, Pradeep Kumar and Navani, Naveen Kumar and Pathania, Ranjana}, editor={Pratap, JiteshEditor}, year={2014}, month={Apr}, pages={e93833} }