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Ecological Applications : a Publication... Dec 2021Liebig's law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co-limited growth. However, its mechanistic...
Liebig's law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co-limited growth. However, its mechanistic foundation is rarely discussed, even though its validity has been questioned since its introduction in the 1820s. Here we first show that LLM is a crude approximation of the law of mass action, the state of art theory of biochemical reactions, and the LLM model is less accurate than two other approximations of the law of mass action: the synthesizing unit model and the additive model. We corroborate this conclusion using empirical data sets of algae and plants grown under two co-limiting substrates. Based on our analysis, we show that when growth is modeled directly as a function of substrate uptake, the LLM model improperly restricts the organism to be of fixed elemental stoichiometry, making it incapable of consistently resolving biological adaptation, ecological evolution, and community assembly. When growth is modeled as a function of the cellular nutrient quota, the LLM model may obtain good results at the risk of incorrect model parameters as compared to those inferred from the more accurate synthesizing unit model. However, biogeochemical models that implement these three formulations are needed to evaluate which formulation is acceptably accurate and their impacts on predicted long-term ecosystem dynamics. In particular, studies are needed that explore the extent to which parameter calibration can rescue model performance when the mechanistic representation of a biogeochemical process is known to be deficient.
Topics: Chlorophyta; Ecosystem; Models, Biological; Plant Development; Plants
PubMed: 34529311
DOI: 10.1002/eap.2458 -
Journal of Bacteriology Apr 2024
Topics: Soil Microbiology; Models, Biological
PubMed: 38529952
DOI: 10.1128/jb.00073-24 -
Briefings in Bioinformatics Jan 2022Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models...
Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.
Topics: Calibration; Models, Biological; Systems Biology
PubMed: 34619769
DOI: 10.1093/bib/bbab387 -
Trends in Ecology & Evolution Aug 2019How does novelty, a new, genetically based function, evolve? A compelling answer has been elusive because there are few model systems where both the genetic mechanisms... (Review)
Review
How does novelty, a new, genetically based function, evolve? A compelling answer has been elusive because there are few model systems where both the genetic mechanisms generating novel functions and the ecological conditions that govern their origin and spread can be studied in detail. This review article considers what we have learned about the evolution of novelty from microbial selection experiments. This work reveals that the genetic routes to novelty can be more highly variable than standard models have led us to believe and underscores the importance of considering both genetics and ecology in this process.
Topics: Biological Evolution; Ecology; Models, Biological; Selection, Genetic
PubMed: 31027838
DOI: 10.1016/j.tree.2019.03.008 -
Progress in Biophysics and Molecular... Oct 2017Computational models in biology and biomedical science are often constructed to aid people's understanding of phenomena or to inform decisions with socioeconomic... (Review)
Review
Computational models in biology and biomedical science are often constructed to aid people's understanding of phenomena or to inform decisions with socioeconomic consequences. Model credibility is the willingness of people to trust a model's predictions and is often difficult to establish for computational biology models. A 3 × 3 matrix has been proposed to allow such models to be categorised with respect to their testability and epistemic foundation in order to guide the selection of an appropriate process of validation to supply evidence to establish credibility. Three approaches to validation are identified that can be deployed depending on whether a model is deemed untestable, testable or lies somewhere in between. In the latter two cases, the validation process involves the quantification of uncertainty which is a key output. The issues arising due to the complexity and inherent variability of biological systems are discussed and the creation of 'digital twins' proposed as a means to alleviate the issues and provide a more robust, transparent and traceable route to model credibility and acceptance.
Topics: Humans; Models, Biological; Reproducibility of Results
PubMed: 27702656
DOI: 10.1016/j.pbiomolbio.2016.08.007 -
Disease Models & Mechanisms Oct 2015Model systems, including laboratory animals, microorganisms, and cell- and tissue-based systems, are central to the discovery and development of new and better drugs for...
Model systems, including laboratory animals, microorganisms, and cell- and tissue-based systems, are central to the discovery and development of new and better drugs for the treatment of human disease. In this issue, Disease Models & Mechanisms launches a Special Collection that illustrates the contribution of model systems to drug discovery and optimisation across multiple disease areas. This collection includes reviews, Editorials, interviews with leading scientists with a foot in both academia and industry, and original research articles reporting new and important insights into disease therapeutics. This Editorial provides a summary of the collection's current contents, highlighting the impact of multiple model systems in moving new discoveries from the laboratory bench to the patients' bedsides.
Topics: Animals; Disease Models, Animal; Drug Discovery; Genetic Engineering; Humans; Mice; Models, Biological; Translational Research, Biomedical; Zebrafish
PubMed: 26438689
DOI: 10.1242/dmm.023036 -
Briefings in Bioinformatics Jan 2015Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as... (Review)
Review
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex.
Topics: Computational Biology; Models, Biological
PubMed: 24227161
DOI: 10.1093/bib/bbt077 -
PLoS Computational Biology Jan 2016As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number... (Review)
Review
As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.
Topics: Computer Simulation; Models, Biological; Software; Systems Biology
PubMed: 26795950
DOI: 10.1371/journal.pcbi.1004591 -
Disease Models & Mechanisms Sep 2016Accumulating epidemiological evidence indicates a strong clinical association between obesity and an increased risk of cancer. The global pandemic of obesity indicates a... (Review)
Review
Accumulating epidemiological evidence indicates a strong clinical association between obesity and an increased risk of cancer. The global pandemic of obesity indicates a public health trend towards a substantial increase in cancer incidence and mortality. However, the mechanisms that link obesity to cancer remain incompletely understood. The fruit fly Drosophila melanogaster has been increasingly used to model an expanding spectrum of human diseases. Fly models provide a genetically simpler system that is ideal for use as a first step towards dissecting disease interactions. Recently, the combining of fly models of diet-induced obesity with models of cancer has provided a novel model system in which to study the biological mechanisms that underlie the connections between obesity and cancer. In this Review, I summarize recent advances, made using Drosophila, in our understanding of the interplay between diet, obesity, insulin resistance and cancer. I also discuss how the biological mechanisms and therapeutic targets that have been identified in fly studies could be utilized to develop preventative interventions and treatment strategies for obesity-associated cancers.
Topics: Animals; Diet, High-Fat; Disease Models, Animal; Drosophila melanogaster; Humans; Models, Biological; Neoplasms; Obesity
PubMed: 27604692
DOI: 10.1242/dmm.025320 -
Biomedical Journal 2015When compared to photon beams, particle beams have distinct spatial distributions on the energy depositions in both the macroscopic and microscopic volumes. In a... (Review)
Review
When compared to photon beams, particle beams have distinct spatial distributions on the energy depositions in both the macroscopic and microscopic volumes. In a macroscopic volume, the absorbed dose distribution shows a rapid increase near the particle range, that is, Bragg peak, as particle penetrates deep inside the tissue. In a microscopic volume, individual particle deposits its energy along the particle track by producing localized ionizations through the formation of clusters. These highly localized clusters can induce complex types of deoxyribonucleic acid (DNA) damage which are more difficult to repair and lead to higher relative biological effectiveness (RBE) as compared to photons. To describe the biological actions, biophysical models on a microscopic level have been developed. In this review, microdosimetric approaches are discussed for the determination of RBE at different depths in a patient under particle therapy. These approaches apply the microdosimetric lineal energy spectra obtained from measurements or calculations. Methods to determine these spectra will be focused on the tissue equivalent proportional counter and the Monte Carlo program. Combining the lineal energy spectrum and the biological model, RBE can be determined. Three biological models are presented. A simplified model applies the dose-mean lineal energy and the measured RBE (linear energy transfer) data. A more detailed model makes use of the full lineal energy spectrum and the biological weighting function spectrum. A comprehensive model calculates the spectrum-averaged yields of DNA damages caused by all primary and secondary particles of a particle beam. Results of these models are presented for proton beams.
Topics: Humans; Linear Energy Transfer; Models, Biological; Monte Carlo Method; Proton Therapy; Relative Biological Effectiveness
PubMed: 26459792
DOI: 10.4103/2319-4170.167072