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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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2
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Nanda P, Budak M, Michael CT, Krupinsky K, Kirschner DE. Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566861. [PMID: 38014103 PMCID: PMC10680629 DOI: 10.1101/2023.11.13.566861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.
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3
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Stillman NR, Mayor R. Generative models of morphogenesis in developmental biology. Semin Cell Dev Biol 2023; 147:83-90. [PMID: 36754751 PMCID: PMC10615838 DOI: 10.1016/j.semcdb.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023]
Abstract
Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods.
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Affiliation(s)
- Namid R Stillman
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK.
| | - Roberto Mayor
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK; Center for Integrative Biology, Faculty of Sciences, Universidad Mayor; Santiago, Chile Santiago, Chile..
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4
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Gonçalves IG, García-Aznar JM. Hybrid computational models of multicellular tumour growth considering glucose metabolism. Comput Struct Biotechnol J 2023; 21:1262-1271. [PMID: 36814723 PMCID: PMC9939553 DOI: 10.1016/j.csbj.2023.01.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
Cancer cells metabolize glucose through metabolic pathways that differ from those used by healthy and differentiated cells. In particular, tumours have been shown to consume more glucose than their healthy counterparts and to use anaerobic metabolic pathways, even under aerobic conditions. Nevertheless, scientists have still not been able to explain why cancer cells evolved to present an altered metabolism and what evolutionary advantage this might provide them. Experimental and computational models have been increasingly used in recent years to understand some of these biological questions. Multicellular tumour spheroids are effective experimental models as they replicate the initial stages of avascular solid tumour growth. Furthermore, these experiments generate data which can be used to calibrate and validate computational studies that aim to simulate tumour growth. Hybrid models are of particular relevance in this field of research because they model cells as individual agents while also incorporating continuum representations of the substances present in the surrounding microenvironment that may participate in intracellular metabolic networks as concentration or density distributions. Henceforth, in this review, we explore the potential of computational modelling to reveal the role of metabolic reprogramming in tumour growth.
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Key Words
- ABM, agent-based model
- ATP, adenosine triphosphate
- CA, cellular automata
- CPM, cellular Potts model
- ECM, extracellular matrix
- FBA, Flux Balance Analysis
- FDG-PET, [18F]-fluorodeoxyglucose-positron emission tomography
- MCTS, multicellular tumour spheroids
- ODEs, ordinary differential equations
- PDEs, partial differential equations
- SBML, Systems Biology Markup Language
- Warburg effect
- agent-based models
- glucose metabolism
- hybrid modelling
- multicellular simulations
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Affiliation(s)
- Inês G Gonçalves
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Aragon, Spain
| | - José Manuel García-Aznar
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Aragon, Spain
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5
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Millar-Wilson A, Ward Ó, Duffy E, Hardiman G. Multiscale modeling in the framework of biological systems and its potential for spaceflight biology studies. iScience 2022; 25:105421. [DOI: 10.1016/j.isci.2022.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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6
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Navas-Zuloaga MG, Pavlic TP, Smith BH. Alternative model systems for cognitive variation: eusocial-insect colonies. Trends Cogn Sci 2022; 26:836-848. [PMID: 35864031 DOI: 10.1016/j.tics.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 11/20/2022]
Abstract
Understanding the origins and maintenance of cognitive variation in animal populations is central to the study of the evolution of cognition. However, the brain is itself a complex, hierarchical network of heterogeneous components, from diverse cell types to diverse neuropils, each of which may be of limited use to study in isolation or prohibitively challenging to manipulate in situ. Consequently, highly tractable alternative model systems may be valuable tools. Eusocial-insect colonies display emergent cognitive-like properties from relatively simple social interactions between diverse subunits that can be observed and manipulated while operating collectively. Here, we review the individual-scale mechanisms that cause group-level variation in how colonies solve problems analogous to cognitive challenges faced by brains, like decision-making, attention, and search.
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Affiliation(s)
| | - Theodore P Pavlic
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA; School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85287, USA; School of Sustainability, Arizona State University, Tempe, AZ 85287, USA; School of Complex Adaptive Systems, Arizona State University, Tempe, AZ 85287, USA
| | - Brian H Smith
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
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Vicencio-Jimenez S, Villalobos M, Maldonado PE, Vergara RC. The Energy Homeostasis Principle: A Naturalistic Approach to Explain the Emergence of Behavior. Front Syst Neurosci 2022; 15:782781. [PMID: 35069133 PMCID: PMC8770284 DOI: 10.3389/fnsys.2021.782781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
It is still elusive to explain the emergence of behavior and understanding based on its neural mechanisms. One renowned proposal is the Free Energy Principle (FEP), which uses an information-theoretic framework derived from thermodynamic considerations to describe how behavior and understanding emerge. FEP starts from a whole-organism approach, based on mental states and phenomena, mapping them into the neuronal substrate. An alternative approach, the Energy Homeostasis Principle (EHP), initiates a similar explanatory effort but starts from single-neuron phenomena and builds up to whole-organism behavior and understanding. In this work, we further develop the EHP as a distinct but complementary vision to FEP and try to explain how behavior and understanding would emerge from the local requirements of the neurons. Based on EHP and a strict naturalist approach that sees living beings as physical and deterministic systems, we explain scenarios where learning would emerge without the need for volition or goals. Given these starting points, we state several considerations of how we see the nervous system, particularly the role of the function, purpose, and conception of goal-oriented behavior. We problematize these conceptions, giving an alternative teleology-free framework in which behavior and, ultimately, understanding would still emerge. We reinterpret neural processing by explaining basic learning scenarios up to simple anticipatory behavior. Finally, we end the article with an evolutionary perspective of how this non-goal-oriented behavior appeared. We acknowledge that our proposal, in its current form, is still far from explaining the emergence of understanding. Nonetheless, we set the ground for an alternative neuron-based framework to ultimately explain understanding.
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Affiliation(s)
- Sergio Vicencio-Jimenez
- The Center for Hearing and Balance, Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mario Villalobos
- Escuela de Psicología y Filosofía, Universidad de Tarapacá, Arica, Chile
| | - Pedro E. Maldonado
- Laboratorio de Neurosistemas, Departamento de Neurociencia & BNI, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Rodrigo C. Vergara
- Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de las Ciencias de la Educación, Ñuñoa, Chile
- *Correspondence: Rodrigo C. Vergara
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8
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Sabri E, Brosseau C. Thin-layer approximation for the multi-physics and multiscale simulation of cell membrane electrodeformation. Bioelectrochemistry 2022; 145:108055. [DOI: 10.1016/j.bioelechem.2022.108055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 01/04/2022] [Accepted: 01/06/2022] [Indexed: 11/15/2022]
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9
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Prokop A. Towards the First Principles in Biology and Cancer: New Vistas in Computational Systems Biology of Cancer. Life (Basel) 2021; 12:21. [PMID: 35054414 PMCID: PMC8778485 DOI: 10.3390/life12010021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/06/2021] [Accepted: 12/15/2021] [Indexed: 01/02/2023] Open
Abstract
These days many leading scientists argue for a new paradigm for cancer research and propose a complex systems-view of cancer supported by empirical evidence. As an example, Thea Newman (2021) has applied "the lessons learned from physical systems to a critique of reductionism in medical research, with an emphasis on cancer". It is the understanding of this author that the mesoscale constructs that combine the bottom-up as well as top-down approaches, are very close to the concept of emergence. The mesoscale constructs can be said to be those effective components through which the system allows itself to be understood. A short list of basic concepts related to life/biology fundamentals are first introduced to demonstrate a lack of emphasis on these matters in literature. It is imperative that physical and chemical approaches are introduced and incorporated in biology to make it more conceptually sound, quantitative, and based on the first principles. Non-equilibrium thermodynamics is the only tool currently available for making progress in this direction. A brief outline of systems biology, the discovery of emergent properties, and metabolic modeling are introduced in the second part. Then, different cancer initiation concepts are reviewed, followed by application of non-equilibrium thermodynamics in the metabolic and genomic analysis of initiation and development of cancer, stressing the endogenous network hypothesis (ENH). Finally, extension of the ENH is suggested to include a cancer niche (exogenous network hypothesis). It is expected that this will lead to a unifying systems-biology approach for a future combination of the analytical and synthetic arms of two major hypotheses of cancer models (SMT and TOFT).
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Affiliation(s)
- Aleš Prokop
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235-1826, USA
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10
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Aghamiri SS, Amin R, Helikar T. Recent applications of quantitative systems pharmacology and machine learning models across diseases. J Pharmacokinet Pharmacodyn 2021; 49:19-37. [PMID: 34671863 PMCID: PMC8528185 DOI: 10.1007/s10928-021-09790-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/07/2021] [Indexed: 12/29/2022]
Abstract
Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.
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Affiliation(s)
- Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
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11
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Multiscale modeling and integration of a combined cycle power plant and a two-tank thermal energy storage system with gPROMS and SimCentral. KOREAN J CHEM ENG 2021. [DOI: 10.1007/s11814-021-0789-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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Schölzel C, Blesius V, Ernst G, Dominik A. Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective. NPJ Syst Biol Appl 2021; 7:27. [PMID: 34083542 PMCID: PMC8175692 DOI: 10.1038/s41540-021-00182-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/19/2021] [Indexed: 02/06/2023] Open
Abstract
Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.
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Affiliation(s)
- Christopher Schölzel
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany.
| | - Valeria Blesius
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
| | - Gernot Ernst
- Vestre Viken Hospital Trust, Kongsberg, Norway
- University of Oslo, Oslo, Norway
| | - Andreas Dominik
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
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13
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Purugganan MD, Jackson SA. Advancing crop genomics from lab to field. Nat Genet 2021; 53:595-601. [PMID: 33958781 DOI: 10.1038/s41588-021-00866-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 03/22/2021] [Indexed: 01/23/2023]
Abstract
Crop genomics remains a key element in ensuring scientific progress to secure global food security. It has been two decades since the sequence of the first plant genome, that of Arabidopsis thaliana, was released, and soon after that the draft sequencing of the rice genome was completed. Since then, the genomes of more than 100 crops have been sequenced, plant genome research has expanded across multiple fronts and the next few years promise to bring further advances spurred by the advent of new technologies and approaches. We are likely to see continued innovations in crop genome sequencing, genetic mapping and the acquisition of multiple levels of biological data. There will be exciting opportunities to integrate genome-scale information across multiple scales of biological organization, leading to advances in our mechanistic understanding of crop biological processes, which will, in turn, provide greater impetus for translation of laboratory results to the field.
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Affiliation(s)
- Michael D Purugganan
- Center for Genomics and Systems Biology, New York University, New York, NY, USA. .,Center for Genomics and Systems Biology, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
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14
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Gjindali A, Herrmann HA, Schwartz JM, Johnson GN, Calzadilla PI. A Holistic Approach to Study Photosynthetic Acclimation Responses of Plants to Fluctuating Light. FRONTIERS IN PLANT SCIENCE 2021; 12:668512. [PMID: 33936157 PMCID: PMC8079764 DOI: 10.3389/fpls.2021.668512] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 03/23/2021] [Indexed: 05/10/2023]
Abstract
Plants in natural environments receive light through sunflecks, the duration and distribution of these being highly variable across the day. Consequently, plants need to adjust their photosynthetic processes to avoid photoinhibition and maximize yield. Changes in the composition of the photosynthetic apparatus in response to sustained changes in the environment are referred to as photosynthetic acclimation, a process that involves changes in protein content and composition. Considering this definition, acclimation differs from regulation, which involves processes that alter the activity of individual proteins over short-time periods, without changing the abundance of those proteins. The interconnection and overlapping of the short- and long-term photosynthetic responses, which can occur simultaneously or/and sequentially over time, make the study of long-term acclimation to fluctuating light in plants challenging. In this review we identify short-term responses of plants to fluctuating light that could act as sensors and signals for acclimation responses, with the aim of understanding how plants integrate environmental fluctuations over time and tailor their responses accordingly. Mathematical modeling has the potential to integrate physiological processes over different timescales and to help disentangle short-term regulatory responses from long-term acclimation responses. We review existing mathematical modeling techniques for studying photosynthetic responses to fluctuating light and propose new methods for addressing the topic from a holistic point of view.
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Affiliation(s)
- Armida Gjindali
- Department of Earth and Environmental Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, United Kingdom
| | - Helena A. Herrmann
- Department of Earth and Environmental Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, United Kingdom
- Division of Evolution & Genomic Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Jean-Marc Schwartz
- Division of Evolution & Genomic Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Giles N. Johnson
- Department of Earth and Environmental Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, United Kingdom
| | - Pablo I. Calzadilla
- Department of Earth and Environmental Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, United Kingdom
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Li C, Qin J, Kuroyanagi K, Lu L, Nagasaki M, Satoru M. High-speed parameter search of dynamic biological pathways from time-course transcriptomic profiles using high-level Petri net. Biosystems 2021; 201:104332. [PMID: 33359226 DOI: 10.1016/j.biosystems.2020.104332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 10/16/2020] [Accepted: 12/16/2020] [Indexed: 11/28/2022]
Abstract
Dynamic simulation promises a deeper understanding of complex molecular mechanisms of biological pathways. How to determine the reaction kinetic parameters which govern the simulation results is still an open question in the field of systems biology. (1) Background: To execute simulation experiments, it is an essential first step to search effective values of model parameters. The complexity of biological systems and the experimental measurement technology severely limit the acquirement of accurate kinetic parameters. Previously proposed genomic data assimilation (GDA) approach enables users to handle parameter estimation using time-course information. However, it highly depends on successive time points and costs massive computational resource; (2) Methods: To address this problem, we present a new high-speed parameter search method for estimating the kinetic parameters of quantitative biological pathways using time-course transcriptomic profiles. The key idea of our method is to interactively prune the search space by introducing Probabilistic Linear-time Temporal Logic (PLTL) based model checking into GDA. (3) Results and conclusion: We demonstrated the effectiveness of our method by comparing with GDA on Mus musculus transcription circuits modelled by hybrid functional Petri net with extension. As a result, our method works faster and more accurate than GDA for both time-course datasets with dense and sparse observed values.
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Affiliation(s)
- Chen Li
- Department of Human Genetics, And Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
| | - Jiale Qin
- Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Keisuke Kuroyanagi
- Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan
| | - Lu Lu
- Department of Human Genetics, And Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Masao Nagasaki
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Shogoinkawahara-cho, Sakyo-ku, Kyoto-City, Kyoto, Japan.
| | - Miyano Satoru
- Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
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16
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The potential for complex computational models of aging. Mech Ageing Dev 2020; 193:111403. [PMID: 33220267 DOI: 10.1016/j.mad.2020.111403] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/24/2020] [Accepted: 11/11/2020] [Indexed: 12/15/2022]
Abstract
The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales - from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality. To do so, they must be systems-level models that incorporate interactions between measurable aspects of age-associated changes. To incorporate individual variability in the aging process, models must be stochastic. To be useful they should also be predictive, and so must be fit or parameterized by data from large populations of aging individuals. In this perspective, we outline where we have been, where we are, and where we hope to go with such computational models of aging. Our focus is on data-driven systems-level models, and on their great potential in aging research.
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Boudon F, Persello S, Jestin A, Briand AS, Grechi I, Fernique P, Guédon Y, Léchaudel M, Lauri PÉ, Normand F. V-Mango: a functional-structural model of mango tree growth, development and fruit production. ANNALS OF BOTANY 2020; 126:745-763. [PMID: 32391865 PMCID: PMC7489065 DOI: 10.1093/aob/mcaa089] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 05/06/2020] [Indexed: 05/28/2023]
Abstract
BACKGROUND AND AIMS Mango (Mangifera indica L.) is the fifth most widely produced fruit in the world. Its cultivation, mainly in tropical and sub-tropical regions, raises a number of issues such as the irregular fruit production across years, phenological asynchronisms that lead to long periods of pest and disease susceptibility, and the heterogeneity of fruit quality and maturity at harvest. To address these issues, we developed an integrative functional-structural plant model that synthesizes knowledge about the vegetative and reproductive development of the mango tree and opens up the possible simulation of cultivation practices. METHODS We designed a model of architectural development in order to precisely characterize the intricate developmental processes of the mango tree. The appearance of botanical entities was decomposed into elementary stochastic events describing occurrence, intensity and timing of development. These events were determined by structural (position and fate of botanical entities) and temporal (appearance dates) factors. Daily growth and development of growth units and inflorescences were modelled using empirical distributions and thermal time. Fruit growth was determined using an ecophysiological model that simulated carbon- and water-related processes at the fruiting branch scale. KEY RESULTS The model simulates the dynamics of the population of growth units, inflorescences and fruits at the tree scale during a growing cycle. Modelling the effects of structural and temporal factors makes it possible to simulate satisfactorily the complex interplays between vegetative and reproductive development. The model allowed the characterization of the susceptibility of mango tree to pests and the investigatation of the influence of tree architecture on fruit growth. CONCLUSIONS This integrative functional-structural model simulates mango tree vegetative and reproductive development over successive growing cycles, allowing a precise characterization of tree phenology and fruit growth and production. The next step is to integrate the effects of cultivation practices, such as pruning, into the model.
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Affiliation(s)
- Frédéric Boudon
- CIRAD, UMR AGAP, 34098 Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Séverine Persello
- CIRAD, UMR AGAP, 34098 Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CIRAD, UPR HortSys, 97455 Saint-Pierre, La Réunion,France
- HortSys, Univ Montpellier, CIRAD, Montpellier, France
| | - Alexandra Jestin
- CIRAD, UMR AGAP, 34098 Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CIRAD, UPR HortSys, 97455 Saint-Pierre, La Réunion,France
- HortSys, Univ Montpellier, CIRAD, Montpellier, France
| | - Anne-Sarah Briand
- CIRAD, UMR AGAP, 34098 Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CIRAD, UPR HortSys, 97455 Saint-Pierre, La Réunion,France
- HortSys, Univ Montpellier, CIRAD, Montpellier, France
| | - Isabelle Grechi
- CIRAD, UPR HortSys, 97455 Saint-Pierre, La Réunion,France
- HortSys, Univ Montpellier, CIRAD, Montpellier, France
| | - Pierre Fernique
- CIRAD, UMR AGAP, 34098 Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Yann Guédon
- CIRAD, UMR AGAP, 34098 Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Mathieu Léchaudel
- CIRAD, UMR QualiSud, 97130 Capesterre-Belle-Eau, Guadeloupe, France
- Qualisud, Univ Montpellier, Avignon Université, CIRAD, Institut Agro, Université de La Réunion, Montpellier, France
| | - Pierre-Éric Lauri
- UMR ABSys, INRAE, CIRAD, CIHEAM-IAMM, Institut Agro, Univ Montpellier, Montpellier, France
| | - Frédéric Normand
- CIRAD, UPR HortSys, 97455 Saint-Pierre, La Réunion,France
- HortSys, Univ Montpellier, CIRAD, Montpellier, France
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18
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González J, Pinzón A, Angarita-Rodríguez A, Aristizabal AF, Barreto GE, Martín-Jiménez C. Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches. Front Neuroinform 2020; 14:35. [PMID: 32848690 PMCID: PMC7426703 DOI: 10.3389/fninf.2020.00035] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022] Open
Abstract
The growing importance of astrocytes in the field of neuroscience has led to a greater number of computational models devoted to the study of astrocytic functions and their metabolic interactions with neurons. The modeling of these interactions demands a combined understanding of brain physiology and the development of computational frameworks based on genomic-scale reconstructions, system biology, and dynamic models. These computational approaches have helped to highlight the neuroprotective mechanisms triggered by astrocytes and other glial cells, both under normal conditions and during neurodegenerative processes. In the present review, we evaluate some of the most relevant models of astrocyte metabolism, including genome-scale reconstructions and astrocyte-neuron interactions developed in the last few years. Additionally, we discuss novel strategies from the multi-omics perspective and computational models of other glial cell types that will increase our knowledge in brain metabolism and its association with neurodegenerative diseases.
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Affiliation(s)
- Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, Colombia
| | - Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia.,Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, Colombia
| | - Andrés Felipe Aristizabal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - George E Barreto
- Department of Biological Sciences, University of Limerick, Limerick, Ireland.,Health Research Institute, University of Limerick, Limerick, Ireland
| | - Cynthia Martín-Jiménez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
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19
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Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
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20
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Tang HHF, Sly PD, Holt PG, Holt KE, Inouye M. Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges. Eur Respir J 2020; 55:13993003.00844-2019. [PMID: 31619470 DOI: 10.1183/13993003.00844-2019] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 09/12/2019] [Indexed: 12/15/2022]
Abstract
Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.
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Affiliation(s)
- Howard H F Tang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia .,Cambridge Baker Systems Genomics Initiative, Dept of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,School of BioSciences, The University of Melbourne, Parkville, Australia
| | - Peter D Sly
- Queensland Children's Medical Research Institute, The University of Queensland, Brisbane, Australia.,Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - Patrick G Holt
- Queensland Children's Medical Research Institute, The University of Queensland, Brisbane, Australia.,Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - Kathryn E Holt
- Dept of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia.,London School of Hygiene and Tropical Medicine, London, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia.,Cambridge Baker Systems Genomics Initiative, Dept of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,School of BioSciences, The University of Melbourne, Parkville, Australia.,The Alan Turing Institute, London, UK
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21
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Sherman WF, Grosberg A. Exploring cardiac form and function: A length-scale computational biology approach. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2019; 12:e1470. [PMID: 31793215 DOI: 10.1002/wsbm.1470] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/08/2019] [Accepted: 11/06/2019] [Indexed: 01/14/2023]
Abstract
The ability to adequately pump blood throughout the body is the result of tightly regulated feedback mechanisms that exist across many spatial scales in the heart. Diseases which impede the function at any one of the spatial scales can cause detrimental cardiac remodeling and eventual heart failure. An overarching goal of cardiac research is to use engineered heart tissue in vitro to study the physiology of diseased heart tissue, develop cell replacement therapies, and explore drug testing applications. A commonality within the field is to manipulate the flow of mechanical signals across the various spatial scales to direct self-organization and build functional tissue. Doing so requires an understanding of how chemical, electrical, and mechanical cues can be used to alter the cellular microenvironment. We discuss how mathematical models have been used in conjunction with experimental techniques to explore various structure-function relations that exist across numerous spatial scales. We highlight how a systems biology approach can be employed to recapitulate in vivo characteristics in vitro at the tissue, cell, and subcellular scales. Specific focus is placed on the interplay between experimental and theoretical approaches. Various modeling methods are showcased to demonstrate the breadth and power afforded to the systems biology approach. An overview of modeling methodologies exemplifies how the strengths of different scientific disciplines can be used to supplement and/or inspire new avenues of experimental exploration. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Cellular Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.
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Affiliation(s)
- William F Sherman
- Center for Complex Biological Systems, University of California Irvine, Irvine, California.,Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California Irvine, Irvine, California
| | - Anna Grosberg
- Center for Complex Biological Systems, University of California Irvine, Irvine, California.,Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California Irvine, Irvine, California.,Department of Biomedical Engineering, University of California Irvine, Irvine, California.,Department of Chemical and Biomolecular Engineering, University of California Irvine, Irvine, California.,NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, California
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22
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Bai YN, Huang N, Sun L, Wang L. Reliability-based topology design for large-scale networks. ISA TRANSACTIONS 2019; 94:144-150. [PMID: 31109724 DOI: 10.1016/j.isatra.2019.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 03/12/2019] [Accepted: 04/09/2019] [Indexed: 06/09/2023]
Abstract
Network topology has a critical effect on its reliability. Due to the complexity of networks, how to design a more reliable network topology is one of the fascinating and significant challenges. In this paper, inspired by the multi-scale model of biological systems which range from gene to individual cells, and up to the individual organism, we provide a novel method to design a reliable topology of large-scale networks. By means of fractal theory and its application in complex networks, a network topology can be described as consisting of the superposition of basic units, called fractal-cells, which are made up of several components and their connections. Based on this fractal-cell structure, we find that a fractal-cell network has structural similarity with its primitive structure, and then develop a method to build a reliable topology which is generated from a primitive ring structure. Numerical simulation compared with the existing methods and performing on real networks show that our proposed method is effective.
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Affiliation(s)
- Ya-Nan Bai
- School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, PR China
| | - Ning Huang
- School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, PR China; Key Laboratory of Science & Technology on Reliability & Environmental Engineering, Beihang University, Beijing, 100191, PR China.
| | - Lina Sun
- School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, PR China
| | - Lei Wang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, PR China
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23
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Renardy M, Hult C, Evans S, Linderman JJ, Kirschner DE. Global sensitivity analysis of biological multi-scale models. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2019; 11:109-116. [PMID: 32864523 DOI: 10.1016/j.cobme.2019.09.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Mathematical models of biological systems need to both reflect and manage the inherent complexities of biological phenomena. Through their versatility and ability to capture behavior at multiple scales, multi-scale models offer a valuable approach. Due to the typically nonlinear and stochastic nature of multi-scale models as well as unknown parameter values, various types of uncertainty are present; thus, effective assessment and quantification of such uncertainty through sensitivity analysis is important. In this review, we discuss global sensitivity analysis in the context of multi-scale and multi-compartment models and highlight its value in model development and analysis. We present an overview of sensitivity analysis methods, approaches for extending such methods to a multi-scale setting, and examples of how sensitivity analysis can inform model reduction. Through schematics and references to past work, we aim to emphasize the advantages and usefulness of such techniques.
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Affiliation(s)
- Marissa Renardy
- University of Michigan Medical School, Department of Microbiology and Immunology
| | - Caitlin Hult
- University of Michigan Medical School, Department of Microbiology and Immunology
- University of Michigan, Department of Chemical Engineering
| | - Stephanie Evans
- University of Michigan Medical School, Department of Microbiology and Immunology
| | | | - Denise E Kirschner
- University of Michigan Medical School, Department of Microbiology and Immunology
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24
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Harris LA, Beik S, Ozawa PMM, Jimenez L, Weaver AM. Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution. CURRENT OPINION IN SYSTEMS BIOLOGY 2019; 17:24-34. [PMID: 32642602 PMCID: PMC7343346 DOI: 10.1016/j.coisb.2019.09.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Cancer is a complex, dynamic disease that despite recent advances remains mostly incurable. Inter- and intratumoral heterogeneity are generally considered major drivers of therapy resistance, metastasis, and treatment failure. Recent advances in high-throughput experimentation have produced a wealth of data on tumor heterogeneity and researchers are increasingly turning to mathematical modeling to aid in the interpretation of these complex datasets. In this mini-review, we discuss three important classes of approaches for modeling cellular dynamics within heterogeneous tumors: agent-based models, population dynamics, and multiscale models. An important new focus, for which we provide an example, is the role of intratumoral cell-cell interactions.
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Affiliation(s)
- Leonard A. Harris
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Samantha Beik
- Cancer Biology Graduate Program, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Patricia M. M. Ozawa
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Lizandra Jimenez
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alissa M. Weaver
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
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25
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Buckley PR, Alden K, Coccia M, Chalon A, Collignon C, Temmerman ST, Didierlaurent AM, van der Most R, Timmis J, Andersen CA, Coles MC. Application of Modeling Approaches to Explore Vaccine Adjuvant Mode-of-Action. Front Immunol 2019; 10:2150. [PMID: 31572370 PMCID: PMC6751289 DOI: 10.3389/fimmu.2019.02150] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 08/27/2019] [Indexed: 01/12/2023] Open
Abstract
Novel adjuvant technologies have a key role in the development of next-generation vaccines, due to their capacity to modulate the duration, strength and quality of the immune response. The AS01 adjuvant is used in the malaria vaccine RTS,S/AS01 and in the licensed herpes-zoster vaccine (Shingrix) where the vaccine has proven its ability to generate protective responses with both robust humoral and T-cell responses. For many years, animal models have provided insights into adjuvant mode-of-action (MoA), generally through investigating individual genes or proteins. Furthermore, modeling and simulation techniques can be utilized to integrate a variety of different data types; ranging from serum biomarkers to large scale “omics” datasets. In this perspective we present a framework to create a holistic integration of pre-clinical datasets and immunological literature in order to develop an evidence-based hypothesis of AS01 adjuvant MoA, creating a unified view of multiple experiments. Furthermore, we highlight how holistic systems-knowledge can serve as a basis for the construction of models and simulations supporting exploration of key questions surrounding adjuvant MoA. Using the Systems-Biology-Graphical-Notation, a tool for graphical representation of biological processes, we have captured high-level cellular behaviors and interactions, and cytokine dynamics during the early immune response, which are substantiated by a series of diagrams detailing cellular dynamics. Through explicitly describing AS01 MoA we have built a consensus of understanding across multiple experiments, and so we present a framework to integrate modeling approaches into exploring adjuvant MoA, in order to guide experimental design, interpret results and inform rational design of vaccines.
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Affiliation(s)
- Paul R Buckley
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom.,Department of Electronic Engineering, University of York, York, United Kingdom
| | - Kieran Alden
- Department of Electronic Engineering, University of York, York, United Kingdom
| | | | | | | | | | | | | | - Jon Timmis
- Department of Electronic Engineering, University of York, York, United Kingdom.,Faculty of Technology, University of Sunderland, Sunderland, United Kingdom
| | | | - Mark C Coles
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
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26
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Novkovic M, Onder L, Cheng HW, Bocharov G, Ludewig B. Integrative Computational Modeling of the Lymph Node Stromal Cell Landscape. Front Immunol 2018; 9:2428. [PMID: 30405623 PMCID: PMC6206207 DOI: 10.3389/fimmu.2018.02428] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 10/02/2018] [Indexed: 11/13/2022] Open
Abstract
Adaptive immune responses develop in secondary lymphoid organs such as lymph nodes (LNs) in a well-coordinated series of interactions between migrating immune cells and resident stromal cells. Although many processes that occur in LNs are well understood from an immunological point of view, our understanding of the fundamental organization and mechanisms that drive these processes is still incomplete. The aim of systems biology approaches is to unravel the complexity of biological systems and describe emergent properties that arise from interactions between individual constituents of the system. The immune system is greater than the sum of its parts, as is the case with any sufficiently complex system. Here, we review recent work and developments of computational LN models with focus on the structure and organization of the stromal cells. We explore various mathematical studies of intranodal T cell motility and migration, their interactions with the LN-resident stromal cells, and computational models of functional chemokine gradient fields and lymph flow dynamics. Lastly, we discuss briefly the importance of hybrid and multi-scale modeling approaches in immunology and the technical challenges involved.
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Affiliation(s)
- Mario Novkovic
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Lucas Onder
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Hung-Wei Cheng
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Gennady Bocharov
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia
| | - Burkhard Ludewig
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
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27
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Yang L, Yurkovich JT, King ZA, Palsson BO. Modeling the multi-scale mechanisms of macromolecular resource allocation. Curr Opin Microbiol 2018; 45:8-15. [PMID: 29367175 PMCID: PMC6419967 DOI: 10.1016/j.mib.2018.01.002] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 12/16/2022]
Abstract
As microbes face changing environments, they dynamically allocate macromolecular resources to produce a particular phenotypic state. Broad 'omics' data sets have revealed several interesting phenomena regarding how the proteome is allocated under differing conditions, but the functional consequences of these states and how they are achieved remain open questions. Various types of multi-scale mathematical models have been used to elucidate the genetic basis for systems-level adaptations. In this review, we outline several different strategies by which microbes accomplish resource allocation and detail how mathematical models have aided in our understanding of these processes. Ultimately, such modeling efforts have helped elucidate the principles of proteome allocation and hold promise for further discovery.
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Affiliation(s)
- Laurence Yang
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA.
| | - James T Yurkovich
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Zachary A King
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard O Palsson
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
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28
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Michalaki LI, Goussis DA. Asymptotic analysis of a TMDD model: when a reaction contributes to the destruction of its product. J Math Biol 2018; 77:821-855. [DOI: 10.1007/s00285-018-1234-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 01/28/2018] [Indexed: 02/04/2023]
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29
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Melnikova NB, Svitenkov AI, Hose DR, Hoekstra AG. A cell-based mechanical model of coronary artery tunica media. J R Soc Interface 2018; 14:rsif.2017.0028. [PMID: 28679664 DOI: 10.1098/rsif.2017.0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 06/05/2017] [Indexed: 12/23/2022] Open
Abstract
A three-dimensional cell-based mechanical model of coronary artery tunica media is proposed. The model is composed of spherical cells forming a hexagonal close-packed lattice. Tissue anisotropy is taken into account by varying interaction forces with the direction of intercellular connection. Several cell-centre interaction potentials for repulsion and attraction are considered, including the Hertz contact model and its neo-Hookean extension, the Johnson-Kendall-Roberts model of adhesive contact, and a wormlike chain model. The model is validated against data from in vitro uni-axial tension tests performed on dissected strips of tunica media. The wormlike chain potential in combination with the neo-Hookean Hertz contact model produces stress-stretch curves which represent the experimental data very well.
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Affiliation(s)
- N B Melnikova
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russia .,Peter the Great State Polytechnic University, Saint Petersburg, Russia
| | - A I Svitenkov
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russia
| | - D R Hose
- University of Sheffield, Sheffield, UK
| | - A G Hoekstra
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russia.,University of Amsterdam, Amsterdam, The Netherlands
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30
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Abstract
BACKGROUND The pathophysiologic processes underlying the regulation of glucose homeostasis are considerably complex at both cellular and systemic level. A comprehensive and structured specification for the several layers of abstraction of glucose metabolism is often elusive, an issue currently solvable with the hierarchical description provided by multi-level models. In this study we propose a multi-level closed-loop model of whole-body glucose homeostasis, coupled with the molecular specifications of the insulin signaling cascade in adipocytes, under the experimental conditions of normal glucose regulation and type 2 diabetes. METHODOLOGY/PRINCIPAL FINDINGS The ordinary differential equations of the model, describing the dynamics of glucose and key regulatory hormones and their reciprocal interactions among gut, liver, muscle and adipose tissue, were designed for being embedded in a modular, hierarchical structure. The closed-loop model structure allowed self-sustained simulations to represent an ideal in silico subject that adjusts its own metabolism to the fasting and feeding states, depending on the hormonal context and invariant to circadian fluctuations. The cellular level of the model provided a seamless dynamic description of the molecular mechanisms downstream the insulin receptor in the adipocytes by accounting for variations in the surrounding metabolic context. CONCLUSIONS/SIGNIFICANCE The combination of a multi-level and closed-loop modeling approach provided a fair dynamic description of the core determinants of glucose homeostasis at both cellular and systemic scales. This model architecture is intrinsically open to incorporate supplementary layers of specifications describing further individual components influencing glucose metabolism.
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31
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Getz WM, Marshall CR, Carlson CJ, Giuggioli L, Ryan SJ, Romañach SS, Boettiger C, Chamberlain SD, Larsen L, D'Odorico P, O'Sullivan D. Making ecological models adequate. Ecol Lett 2017; 21:153-166. [PMID: 29280332 DOI: 10.1111/ele.12893] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 11/07/2017] [Accepted: 11/12/2017] [Indexed: 12/22/2022]
Abstract
Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.
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Affiliation(s)
- Wayne M Getz
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA.,Schools of Mathematical Sciences and Life Sciences, University of KwaZulu, Natal, South Africa
| | - Charles R Marshall
- Museum of Paleontology and Department Integrative Biology, University of California, Berkeley, CA, 94720, USA
| | - Colin J Carlson
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - Luca Giuggioli
- Bristol Centre for Complexity Sciences, Department of Engineering Mathematics, and School of Biological Sciences, University of Bristol, Bristol, UK
| | - Sadie J Ryan
- Department of Geography, and Emerging Pathogens Institute, University of Florida, Gainesville, FL, 32601, USA.,Schools of Mathematical Sciences and Life Sciences, University of KwaZulu, Natal, South Africa
| | - Stephanie S Romañach
- Wetland and Aquatic Research Center, U.S. Geological Survey, Fort Lauderdale, FL, 33314, USA
| | - Carl Boettiger
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - Samuel D Chamberlain
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - Laurel Larsen
- Department of Geography, University of California, Berkeley, CA, 94720, USA
| | - Paolo D'Odorico
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - David O'Sullivan
- Department of Geography, University of California, Berkeley, CA, 94720, USA
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32
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Pathak RK, Baunthiyal M, Pandey N, Pandey D, Kumar A. Modeling of the jasmonate signaling pathway in Arabidopsis thaliana with respect to pathophysiology of Alternaria blight in Brassica. Sci Rep 2017; 7:16790. [PMID: 29196636 PMCID: PMC5711873 DOI: 10.1038/s41598-017-16884-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 11/08/2017] [Indexed: 01/01/2023] Open
Abstract
The productivity of Oilseed Brassica, one of the economically important crops of India, is seriously affected by the disease, Alternaria blight. The disease is mainly caused by two major necrotrophic fungi, Alternaria brassicae and Alternaria brassicicola which are responsible for significant yield losses. Till date, no resistant source is available against Alternaria blight, hence plant breeding methods can not be used to develop disease resistant varieties. Jasmonate mediated signalling pathway, which is known to play crucial role during defense response against necrotrophs, could be strengthened in Brassica plants to combat the disease. Since scanty information is available in Brassica-Alternaria pathosystems at molecular level therefore, in the present study efforts have been made to model jasmonic acid pathway in Arabidopsis thaliana to simulate the dynamic behaviour of molecular species in the model. Besides, the developed model was also analyzed topologically for investigation of the hubs node. COI1 is identified as one of the promising candidate genes in response to Alternaria and other linked components of plant defense mechanisms against the pathogens. The findings from present study are therefore informative for understanding the molecular basis of pathophysiology and rational management of Alternaria blight for securing food and nutritional security.
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Affiliation(s)
- Rajesh Kumar Pathak
- Department of Biotechnology, Govind Ballabh Pant Institute of Engineering & Technology, Pauri Garhwal, 246194, Uttarakhand, India
| | - Mamta Baunthiyal
- Department of Biotechnology, Govind Ballabh Pant Institute of Engineering & Technology, Pauri Garhwal, 246194, Uttarakhand, India.
| | - Neetesh Pandey
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute (IASRI), Pusa, 110012, New Delhi, India
| | - Dinesh Pandey
- Department of Molecular Biology & Genetic Engineering, College of Basic Sciences & Humanities, G. B. Pant University of Agriculture & Technology, Pantnagar, 263145, India
| | - Anil Kumar
- Department of Molecular Biology & Genetic Engineering, College of Basic Sciences & Humanities, G. B. Pant University of Agriculture & Technology, Pantnagar, 263145, India.
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Mueller AJ, Peffers MJ, Proctor CJ, Clegg PD. Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategies. J Orthop Res 2017; 35:1573-1588. [PMID: 28318047 PMCID: PMC5574007 DOI: 10.1002/jor.23563] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 03/06/2017] [Indexed: 02/04/2023]
Abstract
Systems orientated research offers the possibility of identifying novel therapeutic targets and relevant diagnostic markers for complex diseases such as osteoarthritis. This review demonstrates that the osteoarthritis research community has been slow to incorporate systems orientated approaches into research studies, although a number of key studies reveal novel insights into the regulatory mechanisms that contribute both to joint tissue homeostasis and its dysfunction. The review introduces both top-down and bottom-up approaches employed in the study of osteoarthritis. A holistic and multiscale approach, where clinical measurements may predict dysregulation and progression of joint degeneration, should be a key objective in future research. The review concludes with suggestions for further research and emerging trends not least of which is the coupled development of diagnostic tests and therapeutics as part of a concerted effort by the osteoarthritis research community to meet clinical needs. © 2017 The Authors. Journal of Orthopaedic Research Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 35:1573-1588, 2017.
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Affiliation(s)
- Alan J. Mueller
- Faculty of Health and Life SciencesDepartment of Musculoskeletal BiologyInstitute of Ageing and Chronic DiseaseUniversity of LiverpoolWilliam Henry Duncan Building, 6 West Derby StreetLiverpoolL7 8TXUnited Kingdom
| | - Mandy J. Peffers
- Faculty of Health and Life SciencesDepartment of Musculoskeletal BiologyInstitute of Ageing and Chronic DiseaseUniversity of LiverpoolWilliam Henry Duncan Building, 6 West Derby StreetLiverpoolL7 8TXUnited Kingdom,The MRC‐Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA)LiverpoolUnited Kingdom
| | - Carole J. Proctor
- The MRC‐Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA)LiverpoolUnited Kingdom,Institute of Cellular MedicineNewcastle UniversityFramlington PlaceNewcastle upon TyneNE2 4HHUnited Kingdom
| | - Peter D. Clegg
- Faculty of Health and Life SciencesDepartment of Musculoskeletal BiologyInstitute of Ageing and Chronic DiseaseUniversity of LiverpoolWilliam Henry Duncan Building, 6 West Derby StreetLiverpoolL7 8TXUnited Kingdom,The MRC‐Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA)LiverpoolUnited Kingdom
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34
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Systems modelling ageing: from single senescent cells to simple multi-cellular models. Essays Biochem 2017; 61:369-377. [PMID: 28698310 PMCID: PMC5869859 DOI: 10.1042/ebc20160087] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 05/25/2017] [Accepted: 05/25/2017] [Indexed: 01/10/2023]
Abstract
Systems modelling has been successfully used to investigate several key molecular mechanisms of ageing. Modelling frameworks to allow integration of models and methods to enhance confidence in models are now well established. In this article, we discuss these issues and work through the process of building an integrated model for cellular senescence as a single cell and in a simple tissue context.
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35
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Schleicher J, Conrad T, Gustafsson M, Cedersund G, Guthke R, Linde J. Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions. Brief Funct Genomics 2017; 16:57-69. [PMID: 26857943 PMCID: PMC5439285 DOI: 10.1093/bfgp/elv064] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Recent and rapidly evolving progress on high-throughput measurement techniques and computational performance has led to the emergence of new disciplines, such as systems medicine and translational systems biology. At the core of these disciplines lies the desire to produce multiscale models: mathematical models that integrate multiple scales of biological organization, ranging from molecular, cellular and tissue models to organ, whole-organism and population scale models. Using such models, hypotheses can systematically be tested. In this review, we present state-of-the-art multiscale modelling of bacterial and fungal infections, considering both the pathogen and host as well as their interaction. Multiscale modelling of the interactions of bacteria, especially Mycobacterium tuberculosis, with the human host is quite advanced. In contrast, models for fungal infections are still in their infancy, in particular regarding infections with the most important human pathogenic fungi, Candida albicans and Aspergillus fumigatus. We reflect on the current availability of computational approaches for multiscale modelling of host-pathogen interactions and point out current challenges. Finally, we provide an outlook for future requirements of multiscale modelling.
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Affiliation(s)
| | | | | | | | | | - Jörg Linde
- Corresponding author: Jörg Linde, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Jena, Germany. Tel.: +49-3641-532-1290; E-mail:
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Parallelization and High-Performance Computing Enables Automated Statistical Inference of Multi-scale Models. Cell Syst 2017; 4:194-206.e9. [PMID: 28089542 DOI: 10.1016/j.cels.2016.12.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 09/14/2016] [Accepted: 11/30/2016] [Indexed: 01/18/2023]
Abstract
Mechanistic understanding of multi-scale biological processes, such as cell proliferation in a changing biological tissue, is readily facilitated by computational models. While tools exist to construct and simulate multi-scale models, the statistical inference of the unknown model parameters remains an open problem. Here, we present and benchmark a parallel approximate Bayesian computation sequential Monte Carlo (pABC SMC) algorithm, tailored for high-performance computing clusters. pABC SMC is fully automated and returns reliable parameter estimates and confidence intervals. By running the pABC SMC algorithm for ∼106 hr, we parameterize multi-scale models that accurately describe quantitative growth curves and histological data obtained in vivo from individual tumor spheroid growth in media droplets. The models capture the hybrid deterministic-stochastic behaviors of 105-106 of cells growing in a 3D dynamically changing nutrient environment. The pABC SMC algorithm reliably converges to a consistent set of parameters. Our study demonstrates a proof of principle for robust, data-driven modeling of multi-scale biological systems and the feasibility of multi-scale model parameterization through statistical inference.
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Abstract
This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computational biology i.e., to modelling and simulating biological systems by means of computational models, methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the field of multicellular systems biology is explored, focussing on the challenging scenarios of developmental biology. After motivating why agent-based abstractions are critical in representing multicellular systems behaviour, MABS is discussed as the source of the most natural and appropriate mechanism for analysing the self-organising behaviour of systems of cells. As a case study, an application of MABS to the development of Drosophila Melanogaster is finally presented, which exploits the ALCHEMIST platform for agent-based simulation.
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38
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Pérez-Rodríguez G, Pérez-Pérez M, Fdez-Riverola F, Lourenço A. High performance computing for three-dimensional agent-based molecular models. J Mol Graph Model 2016; 68:68-77. [DOI: 10.1016/j.jmgm.2016.06.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 05/26/2016] [Accepted: 06/07/2016] [Indexed: 12/28/2022]
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39
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Asymptotic Analysis of a Target-Mediated Drug Disposition Model: Algorithmic and Traditional Approaches. Bull Math Biol 2016; 78:1121-61. [DOI: 10.1007/s11538-016-0176-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 05/12/2016] [Indexed: 12/11/2022]
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40
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Pârvu O, Gilbert D. A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking. PLoS One 2016; 11:e0154847. [PMID: 27187178 PMCID: PMC4871515 DOI: 10.1371/journal.pone.0154847] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Accepted: 04/20/2016] [Indexed: 12/15/2022] Open
Abstract
Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems.
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Affiliation(s)
- Ovidiu Pârvu
- Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University London, London, United Kingdom
| | - David Gilbert
- Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University London, London, United Kingdom
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41
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Yu JS, Bagheri N. Multi-class and multi-scale models of complex biological phenomena. Curr Opin Biotechnol 2016; 39:167-173. [PMID: 27115496 DOI: 10.1016/j.copbio.2016.04.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Revised: 03/28/2016] [Accepted: 04/01/2016] [Indexed: 02/06/2023]
Abstract
Computational modeling has significantly impacted our ability to analyze vast (and exponentially increasing) quantities of experimental data for a variety of applications, such as drug discovery and disease forecasting. Single-scale, single-class models persist as the most common group of models, but biological complexity often demands more sophisticated approaches. This review surveys modeling approaches that are multi-class (incorporating multiple model types) and/or multi-scale (accounting for multiple spatial or temporal scales) and describes how these models, and combinations thereof, should be used within the context of the problem statement. We end by highlighting agent-based models as an intuitive, modular, and flexible framework within which multi-scale and multi-class models can be implemented.
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Affiliation(s)
- Jessica S Yu
- Chemical & Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Neda Bagheri
- Chemical & Biological Engineering, Northwestern University, Evanston, IL, United States.
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42
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Heinemann T, Raue A. Model calibration and uncertainty analysis in signaling networks. Curr Opin Biotechnol 2016; 39:143-149. [PMID: 27085224 DOI: 10.1016/j.copbio.2016.04.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 03/27/2016] [Accepted: 04/01/2016] [Indexed: 10/22/2022]
Abstract
For a long time the biggest challenges in modeling cellular signal transduction networks has been the inference of crucial pathway components and the qualitative description of their interactions. As a result of the emergence of powerful high-throughput experiments, it is now possible to measure data of high temporal and spatial resolution and to analyze signaling dynamics quantitatively. In addition, this increase of high-quality data is the basis for a better understanding of model limitations and their influence on the predictive power of models. We review established approaches in signal transduction network modeling with a focus on ordinary differential equation models as well as related developments in model calibration. As central aspects of the calibration process we discuss possibilities of model adaptation based on data-driven parameter optimization and the concomitant objective of reducing model uncertainties.
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Affiliation(s)
- Tim Heinemann
- Merrimack, One Kendall Sq., Suite B7201, Cambridge, MA 02139, USA
| | - Andreas Raue
- Merrimack, One Kendall Sq., Suite B7201, Cambridge, MA 02139, USA.
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43
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Toroghi MK, Cluett WR, Mahadevan R. A Multi-Scale Model of the Whole Human Body based on Dynamic Parsimonious Flux Balance Analysis. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.ifacol.2016.07.319] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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44
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Mizeranschi A, Groen D, Borgdorff J, Hoekstra AG, Chopard B, Dubitzky W. Anatomy and Physiology of Multiscale Modeling and Simulation in Systems Medicine. Methods Mol Biol 2016; 1386:375-404. [PMID: 26677192 DOI: 10.1007/978-1-4939-3283-2_17] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Systems medicine is the application of systems biology concepts, methods, and tools to medical research and practice. It aims to integrate data and knowledge from different disciplines into biomedical models and simulations for the understanding, prevention, cure, and management of complex diseases. Complex diseases arise from the interactions among disease-influencing factors across multiple levels of biological organization from the environment to molecules. To tackle the enormous challenges posed by complex diseases, we need a modeling and simulation framework capable of capturing and integrating information originating from multiple spatiotemporal and organizational scales. Multiscale modeling and simulation in systems medicine is an emerging methodology and discipline that has already demonstrated its potential in becoming this framework. The aim of this chapter is to present some of the main concepts, requirements, and challenges of multiscale modeling and simulation in systems medicine.
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Affiliation(s)
- Alexandru Mizeranschi
- Biomedical Sciences Research Institute, University of Ulster, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK
| | - Derek Groen
- Chemistry Department, Centre for Computational Science, University College London, 20 Gordon Street, WC1H 0AJ, London, UK
| | - Joris Borgdorff
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Alfons G Hoekstra
- Computational Science Lab, Institute for Informatics, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH, Amsterdam, The Netherlands
- Advanced Computing Lab, ITMO University, 197101, 49 Kronverkskiy av., St. Petersburg, Russia
| | - Bastien Chopard
- Computer Science Department, University of Geneva, 7 route de Drize, 1227, Carouge, Switzerland
| | - Werner Dubitzky
- Biomedical Sciences Research Institute, University of Ulster, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK.
- School of Biomedical Sciences, University of Ulster, Coleraine campus, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK.
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45
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Cosgrove J, Butler J, Alden K, Read M, Kumar V, Cucurull-Sanchez L, Timmis J, Coles M. Agent-Based Modeling in Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:615-29. [PMID: 26783498 PMCID: PMC4716580 DOI: 10.1002/psp4.12018] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Revised: 06/29/2015] [Accepted: 07/31/2015] [Indexed: 02/06/2023]
Abstract
Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent‐based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM‐specific strengths have yielded success in the area of preclinical mechanistic modeling.
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Affiliation(s)
- J Cosgrove
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK; Department of ElectronicsUniversity of YorkYorkUK
| | - J Butler
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK; Department of ElectronicsUniversity of YorkYorkUK
| | - K Alden
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK
| | - M Read
- Charles Perkins Centre University of Sydney Sydney Australia
| | - V Kumar
- University of California School of Medicine LA Jolla California USA
| | | | - J Timmis
- York Computational Immunology LabUniversity of YorkYorkUK; Department of ElectronicsUniversity of YorkYorkUK; SimOmicsYorkUK
| | - M Coles
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK; SimOmicsYorkUK
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46
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Knap J, Spear CE, Borodin O, Leiter KW. Advancing a distributed multi-scale computing framework for large-scale high-throughput discovery in materials science. NANOTECHNOLOGY 2015; 26:434004. [PMID: 26443333 DOI: 10.1088/0957-4484/26/43/434004] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We describe the development of a large-scale high-throughput application for discovery in materials science. Our point of departure is a computational framework for distributed multi-scale computation. We augment the original framework with a specialized module whose role is to route evaluation requests needed by the high-throughput application to a collection of available computational resources. We evaluate the feasibility and performance of the resulting high-throughput computational framework by carrying out a high-throughput study of battery solvents. Our results indicate that distributed multi-scale computing, by virtue of its adaptive nature, is particularly well-suited for building high-throughput applications.
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Affiliation(s)
- J Knap
- Simulation Sciences Branch, RDRL-CIH-C, US Army Research Laboratory, Aberdeen Proving Ground, MD, 21005-5066, USA
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47
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Integrative Utilization of Microenvironments, Biomaterials and Computational Techniques for Advanced Tissue Engineering. J Biotechnol 2015; 212:71-89. [DOI: 10.1016/j.jbiotec.2015.08.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Revised: 08/02/2015] [Accepted: 08/11/2015] [Indexed: 01/13/2023]
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48
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Jones PJM, Sim A, Taylor HB, Bugeon L, Dallman MJ, Pereira B, Stumpf MPH, Liepe J. Inference of random walk models to describe leukocyte migration. Phys Biol 2015; 12:066001. [DOI: 10.1088/1478-3975/12/6/066001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Abstract
BACKGROUND Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. MOTIVATION This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. RESULTS For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline.
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Affiliation(s)
- Giulio Caravagna
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, I-20126 Milan, Italy
| | - Luca De Sano
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, I-20126 Milan, Italy
| | - Marco Antoniotti
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, I-20126 Milan, Italy
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50
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Agent-based spatiotemporal simulation of biomolecular systems within the open source MASON framework. BIOMED RESEARCH INTERNATIONAL 2015; 2015:769471. [PMID: 25874228 PMCID: PMC4385633 DOI: 10.1155/2015/769471] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Accepted: 10/30/2014] [Indexed: 11/18/2022]
Abstract
Agent-based modelling is being used to represent biological systems with increasing frequency and success. This paper presents the implementation of a new tool for biomolecular reaction modelling in the open source Multiagent Simulator of Neighborhoods framework. The rationale behind this new tool is the necessity to describe interactions at the molecular level to be able to grasp emergent and meaningful biological behaviour. We are particularly interested in characterising and quantifying the various effects that facilitate biocatalysis. Enzymes may display high specificity for their substrates and this information is crucial to the engineering and optimisation of bioprocesses. Simulation results demonstrate that molecule distributions, reaction rate parameters, and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of realistic cell environments. While higher percentage of collisions with occurrence of reaction increases the affinity of the enzyme to the substrate, a faster reaction (i.e., turnover number) leads to a smaller number of time steps. Slower diffusion rates and molecular crowding (physical hurdles) decrease the collision rate of reactants, hence reducing the reaction rate, as expected. Also, the random distribution of molecules affects the results significantly.
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