Hybrid modelling of biological systems: current progress and future prospects

Liu, Fei; Heiner, Monika; Gilbert, David

doi:10.1093/bib/bbac081

Cited by 14 publications

(14 citation statements)

References 95 publications

(123 reference statements)

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“…Hybrid models are such a framework, by assembling modules modelled using different formalisms. In Liu et al (2022), five complementary combinations of modelling formalisms are used for biological systems: hybrid discrete-continuous methods, often used for intracellular modelling as above; hybrid stochasticdeterministic methods, introducing random delays; hybrid flux balance analysis methods, integrating metabolic, regulatory and signalling networks; hybrid logicquantitative methods, combining quantitative methods with Boolean networks or fuzzy logic; and spatial hybrid methods, combining spatial stochastic and deterministic models to bridge three scales: time, abundance and space (Widmer & Stelling, 2018).…”

Section: Extending the Scope Of Community Modelling With Hybrid Modelsmentioning

confidence: 99%

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Cerk,

Ugalde‐Salas,

Nedjad

et al. 2024

Microbial Biotechnology

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Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome‐scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta‐)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third‐generation sequencing, and we discuss the opportunities of long‐read sequencing, strain‐level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

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Section: Extending the Scope Of Community Modelling With Hybrid Modelsmentioning

confidence: 99%

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Cerk,

Ugalde‐Salas,

Nedjad

et al. 2024

Microbial Biotechnology

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“…The Lennon et al ( 2017 ) study was useful for understanding the contributions of microorganisms to the overlooked sink of greenhouse gases that may be important for local and regional climate‐change modelling. The field of systems biology further exemplifies the value of hybrid theory‐based + practical approaches (Liu et al, 2022 ; Westerhoff, 2011 ), and the study of halophile ecophysiology study by one of us, M.M.Y., and his colleagues (La Cono et al, 2020 ); see below.…”

Section: When Theory Is the Most‐appropriate Route To Scientific Noveltymentioning

confidence: 99%

Scientific novelty beyond the experiment

Hallsworth

Udaondo

Pedrós-Alió

et al. 2023

Microbial Biotechnology

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Practical experiments drive important scientific discoveries in biology, but theory-based research studies also contribute novel-sometimes paradigmchanging-findings. Here, we appraise the roles of theory-based approaches focusing on the experiment-dominated wet-biology research areas of microbial growth and survival, cell physiology, host-pathogen interactions, and competitive or symbiotic interactions. Additional examples relate to analyses of genome-sequence data, climate change and planetary health, habitability, and astrobiology. We assess the importance of thought at each step of the research process; the roles of natural philosophy, and inconsistencies in logic and language, as drivers of scientific progress; the value of thought experiments; the use and limitations of artificial intelligence technologies, including their potential for interdisciplinary and transdisciplinary research; and other instances when theory is the most-direct and most-scientifically robust route to scientific novelty including the development of techniques for practical experimentation or fieldwork. We highlight the intrinsic need for human engagement in scientific innovation, an issue pertinent to the ongoing controversy over papers authored using/authored by artificial intelligence (such as the large language model/chatbot ChatGPT). Other issues discussed are the way in which aspects of language can bias thinking towards the spatial rather than the temporal (and how this biased thinking can lead to skewed scientific terminology); receptivity to research that is non-mainstream; and the importance of theory-based science in education and epistemology. Whereas we briefly highlight classic works (those by Oakes Ames, Francis

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“…In addition, there is no specific guideline on the choice of ML algorithm, or the proportion of greyness to be inscribed in model development. The next generation of hybrid models has to be flexible enough to support different modeling formalisms and available powerful tools with user interfaces (Liu et al, 2022b). The development of software tools with the option to integrate various sources of knowledge to formulate the best hybrid model for a given application is warranted (Sansana et al, 2021).…”

Section: Hybrid Model Applications Practical Challenges and Future Re...mentioning

confidence: 99%

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges

Mahanty

2023

Biotech & Bioengineering

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Hybrid modeling, with an appropriate blend of the mechanistic and data‐driven framework, is increasingly being adopted in bioprocess modeling, model‐based experimental design (digital‐twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data‐driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an “optimal” model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data‐driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid‐model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

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Hybrid modelling of biological systems: current progress and future prospects

Cited by 14 publications

References 95 publications

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Scientific novelty beyond the experiment

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges

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