Computational and experimental analysis of wound healing processes

Wound healing in epidermis is a complex physiological process in which new cells are created to repair the damaged tissue. The timing of cell division and growth mechanisms in wound healing are influenced by biological, mechanical and medical factors. In this work we aim to provide a numerical model based on the observations realised in in-vitro experiments for the understanding of wound healing. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Hyperbolastic modeling of wound healing

A new mathematical model for wound healing is introduced and applied to three sets of experimental data. The model is easy to implement but can accommodate a wide range of factors affecting the wound healing process. The data sets represent the areas of trace elements, diabetic wounds, growth factors, and nutrition within the field of wound healing. The model produces an explicit function accurately representing the time course of healing wounds from a given data set. Such a function is used to study variations in the healing velocity among different types of wounds and at different stages in the healing process. A new multivariable model of wound healing capable of analyzing the effects of several variables on accelerating the wound healing process is also introduced. Such a model can help to formulate appropriate strategies to treat wounds. It also would enable us to evaluate the efficacy of different treatment modalities during the inflammatory, proliferative, and tissue remodeling phases.     

Macroscopic models for fibroproliferative disorders: A review

In this paper we review some mathematical modelling of organ reparative processes (wound healing) for both the physiological and pathological case. The natural process of healing consists in a series of overlapping phases involving cells, chemicals, extracellular matrix (ECM) and the environment surrounding the wound site. Sometimes the healing process fails and the reparative mechanism produces pathological conditions which are commonly termed fibrosis or fibroproliferative disorders. Biological insight into the pathogenesis, progression and possible regression of fibrosis is lacking and many issues are still open. Mathematical modelling can surely play its part in this field and this paper is aimed at showing what has been done so far and what has still to be done to achieve a unified framework for studying these kinds of problems. Due to the high complexity of this phenomenon, multi-scale modelling is certainly the appropriate approach that should be used for studying these kinds of problems. Unfortunately most of the mathematical literature on this topic consists of macroscopic continuous models which fail to investigate processes occurring at smaller length scales (cellular, sub-cellular). We present a review of some of the mathematical literature, showing the widely used approaches, focusing on the interpretation of results and indicating possible developments in the study of these highly complex systems.     

Models of shrinking clusters with applications to epidermal wound healing

Models of growing clusters, such as the Eden model and Diffusion Limited Aggregation (DLA), have been widely used to describe a variety of natural growth processes. In this paper, we develop models of shrinking clusters which we use to model epidermal wound healing. We present two approaches to modeling shrinking clusters. In the first approach, which is motivated by the Eden model, every point on the cluster periphery has equal chance of being healed. Noisy and noisefree versions of this model are investigated. In the second approach, DLA is employed in a unique way so that random walkers launched from infinity eventually reach the cluster and contribute to its reduction. Simulation results are presented which illustrate the evolution of the wound healing process for various wound shapes.     

Healing times for circular wounds on plane and spherical bone surfaces

A mathematical model is developed for the rate of healing of a circular wound in a spherical “skull”. The motivation for this model is based on experimental studies of the “critical size defect” (CSD) in animal models; this has been defined as the smallest intraosseous wound that does not heal by bone formation during the lifetime of the animal [1]. For practical purposes, this timescale can usually be taken as one year. In [2], the definition was further extended to a defect which has less than ten percent bony regeneration during the lifetime of the animal. CSDs can “heal” by fibrous connective tissue formation, but since this is not bone, it does not have the properties (strength, etc.) that a completely healed defect would. Earlier models of bone wound healing [3,4] have focused on the existence (or not) of a CSD based on a steady-state analysis, so the time development of the wound was not addressed. In this paper, the time development of a circular cylindrical wound is discussed from a general point of view. An integro-differential equation is derived for the radial contraction rate of the wound in terms of the wound radius and parameters related to the postulated healing mechanisms. This equation includes the effect of the curvature of the (spherical) skull, since it is clear from the experimental evidence that the size of the CSD increases monotonically with the size of the calvaria. Certain special cases for a planar wound are highlighted to illustrate the qualitative features of the model, in particular, the dependence of the wound healing time on the initial wound size and the thickness of the healing rim.     

Local meshless method for PDEs arising from models of wound healing

A meshless collocation procedure is proposed for one- and two-dimensional partial differential equations arising from modeling of wound healing processes (Sherratt and Murray, 1991). Main motivation of this choice is its straightforward application in higher dimensions for both regular and irregular domains on various nodal points distributions. In the case of numerical solution of convection-dominated wound healing PDE models, a stencil based upwind stabilization technique is coupled with the local meshless method to counter instabilities of the computed solution. To assess efficacy, efficiency and accuracy of the proposed method on regular and irregular domains, numerical approximations of different wound healing models are obtained and validated against the exact solution and medically tested healing time duration.     

Numerical techniques for simulating a fractional mathematical model of epidermal wound healing

A number of mathematical models investigating certain aspects of the complicated process of wound healing are reported in the literature in recent years. However, effective numerical methods and supporting error analysis for the fractional equations which describe the process of wound healing are still limited. In this paper, we consider the numerical simulation of a fractional mathematical model of epidermal wound healing (FMM-EWH), which is based on the coupled advection-diffusion equations for cell and chemical concentration in a polar coordinate system. The space fractional derivatives are defined in the Left and Right Riemann-Liouville sense. Fractional orders in the advection and diffusion terms belong to the intervals (0,1) or (1,2], respectively. Some numerical techniques will be used. Firstly, the coupled advection-diffusion equations are decoupled to a single space fractional advection-diffusion equation in a polar coordinate system. Secondly, we propose a new implicit difference method for simulating this equation by using the equivalent of Riemann-Liouville and Grünwald-Letnikov fractional derivative definitions. Thirdly, its stability and convergence are discussed, respectively. Finally, some numerical results are given to demonstrate the theoretical analysis.     

Algorithm engineering for optimal alignment of protein structure distance matrices

Protein structural alignment is an important problem in computational biology. In this paper, we present first successes on provably optimal pairwise alignment of protein inter-residue distance matrices, using the popular dali scoring function. We introduce the structural alignment problem formally, which enables us to express a variety of scoring functions used in previous work as special cases in a unified framework. Further, we propose the first mathematical model for computing optimal structural alignments based on dense inter-residue distance matrices. We therefore reformulate the problem as a special graph problem and give a tight integer linear programming model. We then present algorithm engineering techniques to handle the huge integer linear programs of real-life distance matrix alignment problems. Applying these techniques, we can compute provably optimal dali alignments for the very first time.     

A perturbation analysis of a mechanical model for stable spatial patterning in embryology

Summary We investigate a mechanical cell-traction mechanism that generates stationary spatial patterns. A linear analysis highlights the model's potential for these heterogeneous solutions. We use multiple-scale perturbation techniques to study the evolution of these solutions and compare our solutions with numerical simulations of the model system. We discuss some potential biological applications among which are the formation of ridge patterns, dermatoglyphs, and wound healing.     

Fast solving of weighted pairing least-squares systems

This paper presents a generalization of the “weighted least-squares” (WLS), named “weighted pairing least-squares” (WPLS), which uses a rectangular weight matrix and is suitable for data alignment problems. Two fast solving methods, suitable for solving full rank systems as well as rank deficient systems, are studied. Computational experiments clearly show that the best method, in terms of speed, accuracy, and numerical stability, is based on a special {1, 2, 3}-inverse, whose computation reduces to a very simple generalization of the usual “Cholesky factorization-backward substitution” method for solving linear systems.     

Analysis of an alignment algorithm for nonlinear dimensionality reduction

The goal of dimensionality reduction or manifold learning for a given set of high-dimensional data points, is to find a low-dimensional parametrization for them. Usually it is easy to carry out this parametrization process within a small region to produce a collection of local coordinate systems. Alignment is the process to stitch those local systems together to produce a global coordinate system and is done through the computation of a partial eigendecomposition of a so-called alignment matrix. In this paper, we present an analysis of the alignment process, giving conditions under which the null space of the alignment matrix recovers the global coordinate system up to an affine transformation. We also propose a post-processing step that can determine the global coordinate system up to a rigid motion. This in turn shows that Local Tangent Space Alignment method (LTSA) can recover a locally isometric embedding up to a rigid motion. AMS subject classification (2000)  65F15, 62H30, 15A18     

A very fast algorithm for matrix factorization

We present a very fast algorithm for general matrix factorization of a data matrix for use in the statistical analysis of high-dimensional data via latent factors. Such data are prevalent across many application areas and generate an ever-increasing demand for methods of dimension reduction in order to undertake the statistical analysis of interest. Our algorithm uses a gradient-based approach which can be used with an arbitrary loss function provided the latter is differentiable. The speed and effectiveness of our algorithm for dimension reduction is demonstrated in the context of supervised classification of some real high-dimensional data sets from the bioinformatics literature.     

Traveling waves in a coarse-grained model of volume-filling cell invasion: Simulations and comparisons

Many reaction–diffusion models produce traveling wave solutions that can be interpreted as waves of invasion in biological scenarios such as wound healing or tumor growth. These partial differential equation models have since been adapted to describe the interactions between cells and extracellular matrix (ECM), using a variety of different underlying assumptions. In this work, we derive a system of reaction–diffusion equations, with cross-species density-dependent diffusion, by coarse-graining an agent-based, volume-filling model of cell invasion into ECM. We study the resulting traveling wave solutions both numerically and analytically across various parameter regimes. Subsequently, we perform a systematic comparison between the behaviors observed in this model and those predicted by simpler models in the literature that do not take into account volume-filling effects in the same way. Our study justifies the use of some of these simpler, more analytically tractable models in reproducing the qualitative properties of the solutions in some parameter regimes, but it also reveals some interesting properties arising from the introduction of cell and ECM volume-filling effects, where standard model simplifications might not be appropriate.     

On the Possibility of Replacing the Backward Transformation in the Modified Simplex Method by a Forward Transformation

It is shown that every backward transformation of the modifiedsimplex method can be replaced by a forward transformation ifboth the original matrix and its transposed form are storedand transformed by two separate sets of inverse matrix factors.Replacing a backward transformation by a forward transformationmay improve both numerical stability and speed.     

Failure of adhesive bonds between proteoglycans causes viscoelasticity of soft tissues

Soft tissues can be considered as a composite material where a matrix (ground substance) is reinforced by collagen fibers. These fibers consist of fibrils, which are connected by proteoglycan (PG) bridges. The time-dependent properties of soft tissues appear to be mainly caused by proteoglycans [3]. This contribution presents a modeling approach where damage in the PG bridges arises due to the failure of the covalent bonds between two proteoglycans. The breakage of covalent bonds is reversible over time and incorporated using a healing formulation. A high PG density supports interfibrillar sliding and hence leads to a lower fibril stretch [8]. Accordingly, the damage propagation in PG bridges leads to a higher stretch in the fibrils and therefore to a stiffer material response. The strain energy of the fibrils is based on the response of single tropocollagen molecules and takes both, an entropic and an energetic regime into account [5]. Finally, the model is compared against experimental data available in the literature. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Adaptive error control during gradient search for an elliptic optimization problem

In this article we describe a cost effective adaptive procedure for optimization of a quantity of interest of a solution of an elliptic problem with respect to parameters in the data, using a gradient search approach. The numerical error in both the quantity of interest and the computed gradient may affect the progression of the search algorithm, while the errors generally change at each step during the search algorithm. We address this by using an accurate a posteriori estimate for the error in a quantity of interest that indicates the effect of error on the computed gradient and so provides a measure for how to refine the discretization as the search proceeds. Specifically, we devise an adaptive algorithm to refine and unrefine the finite element mesh at each step in the search algorithm. We give basic examples and apply this technique to a model of a healing wound.     

The mathematical model and optimistic algorithm of two protein structures alignment

Protein structure alignment is one of the most important computational problems in molecular biology. From the viewpoint of computational complexity, a pairwise structure alignment is a NP-hard problem. In this paper, based on the discrepancy of two proteins, we define the structure alignment as a mixed integer-programming (MIP) problem with the simpler form and prove the existence of optimal solution. The optimal alignment is achieved by incorporating improved complete information set method used to modify the score matrix into iterative double dynamic programming algorithm. Convergence of algorithm is proved. A number of benchmark examples are tested. The results show that our model and approach are general and improve computational efficiency as well as quality of the structure alignment.     

Parallel factorizations of matrix polynomials over an arbitrary field

We study the problem of the decomposition of a matrix polynomial over an arbitrary field into a product of factors of lower degrees with preassigned characteristic polynomials. We find necessary conditions for the existence of the required factorization, which are also sufficient for certain classes of matrix polynomials. The proposed method makes it possible to solve the problem completely for matrix polynomials with one nonconstant invariant factor. Translated fromMatematichni Metody i Fiziko-Mekhanichni Polya, Vol. 38, 1995.     

Balancing sparse Hamiltonian eigenproblems

Balancing a matrix by a simple and accurate similarity transformation can improve the speed and accuracy of numerical methods for computing eigenvalues. We describe balancing strategies for a large and sparse Hamiltonian matrix H.It is first shown how to permute H to irreducible form while retaining its structure. This form can be used to decompose the Hamiltonian eigenproblem into smaller-sized problems. Next, we discuss the computation of a symplectic scaling matrix D so that the norm of D⁻¹HD is reduced. The considered scaling algorithm is solely based on matrix-vector products and thus particularly suitable if the elements of H are not explicitly given. The merits of balancing for eigenvalue computations are illustrated by several practically relevant examples.