Special Issue "Multiscale and Hybrid Modeling of the Living Systems"
Deadline for manuscript submissions: closed (31 October 2016)
Prof. Dr. Olga Solovyova
This Special Issue is intended to present recent advances and open problems in the development and computer implementation of high-resolution multi-parameter models, describing various levels of living systems organization. The relevant details of the experimental and mathematical techniques used for the quantitative characterization of the systems elements at different levels will be discussed.
Living systems are characterized by an enormous complexity of their structures, regulation, and dynamics. The mainstream approach to their analysis puts a strong emphasis on the acquisition of quantitative data and comprehensive measurements of a plethora of biological parameters. Nowadays it has become evident that, in order to gain a predictive understanding of the normal and pathological functioning of living systems, there is an urgent need of an efficient methodology for an information-rich, systems-based multiscale and hybrid modeling. The models which are developed to integrate a broad range of physical, chemical, biological phenomena with a large variation in their temporal and spatial scales represent a major challenge for numerical analysis and computational treatment. Advances in dynamic and global scale measurements of the system components at the gene-, cellular-, organ-, and whole organism-levels, and high resolution imaging technologies, should help to overcome the limitations of purely reductionist modeling approaches of the era preceding the development of systems biology. Our aim is to provide a comprehensive overview of the existing computational modeling approaches allowing one to include different scales into global models of living systems and enabling the identification of key targets to treat various diseases.
Prof. Dr. Gennady Bocharov
Prof. Dr. Olga Solovyova
Prof. Dr. Vitaly Volpert
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Living Systems
- Integrative modeling
- Multiscale models
- Network models
- Hybrid models
- Systems Biology and Medicine
- Computational methods
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: The protective role of enhanced dendritic cells trafficking and antigen presentation in a computational hybrid model of tuberculosis infection
Author: Simeone Marino and Denise Kirschner
Abstract: Tuberculosis (TB) is a world wide health problem with approximately 2 billion people infected with Mycobacterium tuberculosis (Mtb, the causative bacterium of TB). The pathologic hallmark of Mtb infection in humans and non-human primates (NHP) is the formation of spherical structures, primarily in the lungs, called granulomas. Infection occurs after inhalation of the bacteria into the lungs, where resident antigen presenting cells (APCs), take them up and initiate the immune response to Mtb infection. Dendritic cells (DCs), professional APCs, traffic from the site of infection (lung) to lung-draining lymph nodes (LNs) where they prime T cells to recognize Mtb. These T cells, circulating through the blood system, eventually migrate back to the lung to perform their immune effector functions. We recently developed a hybrid agent-based model (ABM, labeled GranSim) describing in silico cellular (i.e., macrophages and T cells), bacterial (Mtb) and molecular signaling proteins dynamics during Mtb infection in 3 physiological compartments: lung (where granulomas form), draining lymph node (LN, site of generation of immunity) and blood (an easily measurable compartment). We capture single granuloma formation and function using a spatio-temporal model (i.e., ABM), while LN and blood compartments represent temporal dynamics of the whole body in response to infection. In this current study, to have a better representation of APC trafficking from the lung to the lymphatics, and to better capture antigen presentation in the draining LN, we include dendritic cells (DCs) into GranSim. The addition of DCs allowed us to investigate in greater detail mechanisms of recruitment, trafficking and antigen presentation between physiological compartments, as well as to identify potential DC immuno-protective roles during Mtb infection and granuloma formation.
Title: Reducing the computational footprint for parameter estimation of spatio-temporal patterning models with scatter search
Author: Anton Crombach et al.
Abstract: Network inference is one of the hard problems in biology today. This is especially the case for developmental biology, where a multicellular context creates computational challenges. As a result, network inference often requires high-performance computing facilities to perform rigorous fitting of parameters. Such facilities come at a cost of specific know-how, application forms and reports to justify computing hours, waiting times in execution queues, and occasionally simply at a cost of money. Single workstations in the office avoid all these issues, and ongoing computational advances both in hardware and search techniques warrant the re-asssessment of their usefulness in the context of network inference challenges.
Here we use the reverse engineering of the gap gene system in the fruit fly Drosophila melanogaster (and scuttle fly Megaselia abdita) as a benchmark to compare recent advances in search techniques. We compare our default methodology, simulated annealing, to a relatively novel method named scatter search. We find that scatter search on a single workstation is capable of generating comparable results to simulated annealing on a HPC facility.
We conclude that a rigorous tackling of medium-sized network inference problems in developmental biology is becoming feasible with "modest" current-day computing power available on the desktop.