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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 -
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 -
Mathematical Biosciences and... Oct 2023The chronological age used in demography describes the linear evolution of the life of a living being. The chronological age cannot give precise information about the...
The chronological age used in demography describes the linear evolution of the life of a living being. The chronological age cannot give precise information about the exact developmental stage or aging processes an organism has reached. On the contrary, the biological age (or epigenetic age) represents the true evolution of the tissues and organs of the living being. Biological age is not always linear and sometimes proceeds by discontinuous jumps. These jumps can be negative (we then speak of rejuvenation) or positive (in the event of premature aging), and they can be dependent on endogenous events such as pregnancy (negative jump) or stroke (positive jump) or exogenous ones such as surgical treatment (negative jump) or infectious disease (positive jump). The article proposes a mathematical model of the biological age by defining a valid model for the two types of jumps (positive and negative). The existence and uniqueness of the solution are solved, and its temporal dynamic is analyzed using a moments equation. We also provide some individual-based stochastic simulations.
Topics: Stochastic Processes; Models, Biological; Population Dynamics
PubMed: 38052618
DOI: 10.3934/mbe.2023870 -
Bulletin of Mathematical Biology Oct 2023Coordination of cell behaviour is key to a myriad of biological processes including tissue morphogenesis, wound healing, and tumour growth. As such, individual-based...
Coordination of cell behaviour is key to a myriad of biological processes including tissue morphogenesis, wound healing, and tumour growth. As such, individual-based computational models, which explicitly describe inter-cellular interactions, are commonly used to model collective cell dynamics. However, when using individual-based models, it is unclear how descriptions of cell boundaries affect overall population dynamics. In order to investigate this we define three cell boundary descriptions of varying complexities for each of three widely used off-lattice individual-based models: overlapping spheres, Voronoi tessellation, and vertex models. We apply our models to multiple biological scenarios to investigate how cell boundary description can influence tissue-scale behaviour. We find that the Voronoi tessellation model is most sensitive to changes in the cell boundary description with basic models being inappropriate in many cases. The timescale of tissue evolution when using an overlapping spheres model is coupled to the boundary description. The vertex model is demonstrated to be the most stable to changes in boundary description, though still exhibits timescale sensitivity. When using individual-based computational models one should carefully consider how cell boundaries are defined. To inform future work, we provide an exploration of common individual-based models and cell boundary descriptions in frequently studied biological scenarios and discuss their benefits and disadvantages.
Topics: Mathematical Concepts; Models, Biological; Software; Cell Communication; Morphogenesis
PubMed: 37805982
DOI: 10.1007/s11538-023-01214-8 -
The Journal of Physiology May 2016Mathematical models of cardiac electrophysiology are instrumental in determining mechanisms of cardiac arrhythmias. However, the foundation of a realistic multiscale... (Review)
Review
Mathematical models of cardiac electrophysiology are instrumental in determining mechanisms of cardiac arrhythmias. However, the foundation of a realistic multiscale heart model is only as strong as the underlying cell model. While there have been myriad advances in the improvement of cellular-level models, the identification of model parameters, such as ion channel conductances and rate constants, remains a challenging problem. The primary limitations to this process include: (1) such parameters are usually estimated from data recorded using standard electrophysiology voltage-clamp protocols that have not been developed with model building in mind, and (2) model parameters are typically tuned manually to subjectively match a desired output. Over the last decade, methods aimed at overcoming these disadvantages have emerged. These approaches include the use of optimization or fitting tools for parameter estimation and incorporating more extensive data for output matching. Here, we review recent advances in parameter estimation for cardiomyocyte models, focusing on the use of more complex electrophysiology protocols and global search heuristics. We also discuss future applications of such parameter identification, including development of cell-specific and patient-specific mathematical models to investigate arrhythmia mechanisms and predict therapy strategies.
Topics: Algorithms; Animals; Electrophysiological Phenomena; Humans; Models, Biological; Myocytes, Cardiac; Patient-Specific Modeling
PubMed: 26661516
DOI: 10.1113/JP270618 -
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 -
ELife Oct 2016A small transparent crustacean called has become a powerful model system for the study of limb and appendage regeneration.
A small transparent crustacean called has become a powerful model system for the study of limb and appendage regeneration.
Topics: Amphipoda; Animals; Extremities; Models, Biological; Regeneration
PubMed: 27776630
DOI: 10.7554/eLife.21583 -
Philosophical Transactions of the Royal... Sep 2020Biological processes, such as embryonic development, wound repair and cancer invasion, or bacterial swarming and fruiting body formation, involve collective motion of... (Review)
Review
Biological processes, such as embryonic development, wound repair and cancer invasion, or bacterial swarming and fruiting body formation, involve collective motion of cells as a coordinated group. Collective cell motion of eukaryotic cells often includes interactions that result in polar alignment of cell velocities, while bacterial patterns typically show features of apolar velocity alignment. For analysing the population-scale effects of these different alignment mechanisms, various on- and off-lattice agent-based models have been introduced. However, discriminating model-specific artefacts from general features of collective cell motion is challenging. In this work, we focus on equivalence criteria at the population level to compare on- and off-lattice models. In particular, we define prototypic off- and on-lattice models of polar and apolar alignment, and show how to obtain an on-lattice from an off-lattice model of velocity alignment. By characterizing the behaviour and dynamical description of collective migration models at the macroscopic level, we suggest the type of phase transitions and possible patterns in the approximative macroscopic partial differential equation descriptions as informative equivalence criteria between on- and off-lattice models. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.
Topics: Cell Movement; Models, Biological
PubMed: 32713300
DOI: 10.1098/rstb.2019.0378 -
PLoS Computational Biology May 2021Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and...
Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.
Topics: Arteries; Blood Circulation; Computational Biology; Computer Graphics; Computer Simulation; Humans; Models, Biological; Models, Cardiovascular; Semantics; Software
PubMed: 33983945
DOI: 10.1371/journal.pcbi.1008859 -
Theoretical Population Biology Apr 2022The pioneering work of Kermack and McKendrick (1927, 1932, 1933) is now most known for introducing the SIR model, which divides a population into discrete compartments...
The pioneering work of Kermack and McKendrick (1927, 1932, 1933) is now most known for introducing the SIR model, which divides a population into discrete compartments for susceptible, infected and removed individuals. The SIR model is the archetype of widely used compartmental models for epidemics. It is sometimes forgotten, that Kermack and McKendrick introduced the SIR model as a special case of a more general framework. This general framework distinguishes individuals not only by whether they are susceptible, infected or removed, but additionally tracks the time passed since they got infected. Such time-since-infection models can mechanistically link within-host dynamics to the population level. This allows the models to account for more details of the disease dynamics, such as delays of infectiousness and symptoms during the onset of an infection. Details like this can be vital for interpreting epidemiological data. The time-since-infection framework was originally formulated for a host population with a single pathogen. However, the interactions of multiple pathogens within hosts and within a population can be crucial for understanding the severity and spread of diseases. Current models for multiple pathogens mostly rely on compartmental models. While such models are relatively easy to set up, they do not have the same mechanistic underpinning as time-since-infection models. To approach this gap of connecting within-host dynamics of multiple pathogens to the population level, we here extend the time-since-infection framework of Kermack and McKendrick for two pathogens. We derive formulas for the basic reproduction numbers in the system. Those numbers determine whether a pathogen can invade a population, potentially depending on whether the other pathogen is present or not. We then demonstrate use of the framework by setting up a simple within-host model that we connect to the population model. The example illustrates the context-specific information required for this type of model, and shows how the system can be simulated numerically. We verify that the formulas for the basic reproduction numbers correctly specify the invasibility conditions.
Topics: Basic Reproduction Number; Epidemics; Epidemiological Models; Humans; Models, Biological
PubMed: 35051523
DOI: 10.1016/j.tpb.2022.01.001