537 research outputs found
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model
This is the author accepted manuscript. The final version is available from American Chemical Society via the DOI in this record.We extend a spatially explicit agent based model (ABM) developed previously to investigate entrainment and control of the emergent behavior of a population of synchronized oscillating cells in a microfluidic chamber. Unlike most of the work in models of control of cellular systems which focus on temporal changes, we model individual cells with spatial dependencies which may contribute to certain behavioral responses. We use the model to investigate the response of both open loop and closed loop strategies, such as proportional control (P-control), proportional-integral control (PI-control) and proportional-integral-derivative control (PID-control), to heterogeinities and growth in the cell population, variations of the control parameters and spatial effects such as diffusion in the spatially explicit setting of a microfluidic chamber setup. We show that, as expected from the theory of phase locking in dynamical systems, open loop control can only entrain the cell population in a subset of forcing periods, with a wide variety of dynamical behaviors obtained outside these regions of entrainment. Closed-loop control is shown instead to guarantee entrainment in a much wider region of control parameter space although presenting limitations when the population size increases over a certain threshold. In silico tracking experiments are also performed to validate the ability of classical control approaches to achieve other reference behaviors such as a desired constant output or a linearly varying one. All simulations are carried out in BSim, an advanced agent-based simulator of microbial population which is here extended ad hoc to include the effects of control strategies acting onto the population.The authors declare no competing interests. We thank Dr. Nigel J. Savery at the University of Bristol for useful discussions around the subject of GRNs and for his help in developing the original ABM model. We also wish to thank Dr Gianfranco Fiore at the University of Bristol and the anonymous reviewers for reading the revised manuscript carefully and providing insightful comments that led to a consistent revision of the original manuscript. P.M. was supported by EPSRC Grant EP/E501214/1 and K.T.-A. by EPSRC Grant EP/I018638/1. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol, http://www.bris.ac.uk/acrc/
Machine learning for advancing low-temperature plasma modeling and simulation
Machine learning has had an enormous impact in many scientific disciplines.
Also in the field of low-temperature plasma modeling and simulation it has
attracted significant interest within the past years. Whereas its application
should be carefully assessed in general, many aspects of plasma modeling and
simulation have benefited substantially from recent developments within the
field of machine learning and data-driven modeling. In this survey, we approach
two main objectives: (a) We review the state-of-the-art focusing on approaches
to low-temperature plasma modeling and simulation. By dividing our survey into
plasma physics, plasma chemistry, plasma-surface interactions, and plasma
process control, we aim to extensively discuss relevant examples from
literature. (b) We provide a perspective of potential advances to plasma
science and technology. We specifically elaborate on advances possibly enabled
by adaptation from other scientific disciplines. We argue that not only the
known unknowns, but also unknown unknowns may be discovered due to the inherent
propensity of data-driven methods to spotlight hidden patterns in data
Numerical analysis of a mechanotransduction dynamical model reveals homoclinic bifurcations of extracellular matrix mediated oscillations of the mesenchymal stem cell fate
We perform one and two-parameter numerical bifurcation analysis of a
mechanotransduction model approximating the dynamics of mesenchymal stem cell
differentiation into neurons, adipocytes, myocytes and osteoblasts. For our
analysis, we use as bifurcation parameters the stiffness of the extracellular
matrix and parameters linked with the positive feedback mechanisms that
up-regulate the production of the YAP/TAZ transcriptional regulators (TRs) and
the cell adhesion area. Our analysis reveals a rich nonlinear behaviour of the
cell differentiation including regimes of hysteresis and multistability, stable
oscillations of the effective adhesion area, the YAP/TAZ TRs and the
PPAR receptors associated with the adipogenic fate, as well as
homoclinic bifurcations that interrupt relatively high-amplitude oscillations
abruptly. The two-parameter bifurcation analysis of the Andronov-Hopf points
that give birth to the oscillating patterns predicts their existence for soft
extracellular substrates (), a regime that favours the neurogenic and
the adipogenic cell fate. Furthermore, in these regimes, the analysis reveals
the presence of homoclinic bifurcations that result in the sudden loss of the
stable oscillations of the cell-substrate adhesion towards weaker adhesion and
high expression levels of the gene encoding Tubulin beta-3 chain, thus
favouring the phase transition from the adipogenic to the neurogenic fate
Unraveling the intricacies of spatial organization of the ErbB receptors and downstream signaling pathways
Faced with the complexity of diseases such as cancer which has 1012 mutations, altering gene expression, and disrupting regulatory networks, there has been a paradigm shift in the biological sciences and what has emerged is a much more quantitative field of biology. Mathematical modeling can aid in biological discovery with the development of predictive models that provide future direction for experimentalist. In this work, I have contributed to the development of novel computational approaches which explore mechanisms of receptor aggregation and predict the effects of downstream signaling. The coupled spatial non-spatial simulation algorithm, CSNSA is a tool that I took part in developing, which implements a spatial kinetic Monte Carlo for capturing receptor interactions on the cell membrane with Gillespies stochastic simulation algorithm, SSA, for temporal cytosolic interactions. Using this framework we determine that receptor clustering significantly enhances downstream signaling. In the next study the goal was to understand mechanisms of clustering. Cytoskeletal interactions with mobile proteins are known to hinder diffusion. Using a Monte Carlo approach we simulate these interactions, determining at what cytoskeletal distribution and receptor concentration optimal clustering occurs and when it is inhibited. We investigate oligomerization induced trapping to determine mechanisms of clustering, and our results show that the cytoskeletal interactions lead to receptor clustering. After exploring the mechanisms of clustering we determine how receptor aggregation effects downstream signaling. We further proceed by implementing the adaptively coarse grained Monte Carlo, ACGMC to determine if \u27receptor-sharing\u27 occurs when receptors are clustered. In our proposed \u27receptor-sharing\u27 mechanism a cytosolic species binds with a receptor then disassociates and rebinds a neighboring receptor. We tested our hypothesis using a novel computational approach, the ACGMC, an algorithm which enables the spatial temporal evolution of the system in three dimensions by using a coarse graining approach. In this framework we are modeling EGFR reaction-diffusion events on the plasma membrane while capturing the spatial-temporal dynamics of proteins in the cytosol. From this framework we observe \u27receptor-sharing\u27 which may be an important mechanism in the regulation and overall efficiency of signal transduction. In summary, I have helped to develop predictive computational tools that take systems biology in a new direction.\u2
Graph Representation And Distributed Control Of Lumped And Distributed Parameter System Networks
University of Minnesota Ph.D. dissertation. 2019. Major: Chemical Engineering. Advisor: Prodromos Daoutidis. 1 computer file (PDF); 166 pages.Chemical plants are complex, integrated networks of individual process systems. The process system dynamics along with the interconnections among them make the task of controlling chemical plants challenging. Distributed control is a promising approach towards achieving plant-wide control of tightly integrated networks. The identification of sparsely interacting sub-networks in a given chemical network is key towards achieving superior performance from the distributed control structure. To this end, community detection algorithms have been adopted to determine the optimal decompositions for chemical networks by maximization of modularity. These algorithms are based on equation graph representations of the network. For lumped parameter system (LPS) networks, such representations are standard. Since chemical networks usually comprise lumped as well as distributed parameter systems (DPSs), this thesis aims at incorporating within the framework described above, the variables and topology of DPSs, to develop a unified framework to obtain optimal network decompositions (control structures) for distributed control. To this end, an equation graph representation for a generic DPS and a parameter which captures the strength of structural interactions among its variables analogous to relative degree in LPSs are proposed. A relationship is established between the length of the input-output path in the equation graph and the structural interaction parameter, which enables the incorporation of DPSs variables in the graph based community detection algorithms. Also, since in chemical networks, often the measurement of the entire state is not available and estimation of the unmeasured variables is a computationally expensive task, this thesis also addresses the problem of combined distributed state estimation and distributed control, using community detection for determining network decompositions for estimation as well as control
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