28,074 research outputs found
Special Algorithm for Stability Analysis of Multistable Biological Regulatory Systems
We consider the problem of counting (stable) equilibriums of an important
family of algebraic differential equations modeling multistable biological
regulatory systems. The problem can be solved, in principle, using real
quantifier elimination algorithms, in particular real root classification
algorithms. However, it is well known that they can handle only very small
cases due to the enormous computing time requirements. In this paper, we
present a special algorithm which is much more efficient than the general
methods. Its efficiency comes from the exploitation of certain interesting
structures of the family of differential equations.Comment: 24 pages, 5 algorithms, 10 figure
Bifurcation Analysis of Reaction Diffusion Systems on Arbitrary Surfaces
In this paper we present computational techniques to investigate the
solutions of two-component, nonlinear reaction-diffusion (RD) systems on
arbitrary surfaces. We build on standard techniques for linear and nonlinear
analysis of RD systems, and extend them to operate on large-scale meshes for
arbitrary surfaces. In particular, we use spectral techniques for a linear
stability analysis to characterize and directly compose patterns emerging from
homogeneities. We develop an implementation using surface finite element
methods and a numerical eigenanalysis of the Laplace-Beltrami operator on
surface meshes. In addition, we describe a technique to explore solutions of
the nonlinear RD equations using numerical continuation. Here, we present a
multiresolution approach that allows us to trace solution branches of the
nonlinear equations efficiently even for large-scale meshes. Finally, we
demonstrate the working of our framework for two RD systems with applications
in biological pattern formation: a Brusselator model that has been used to
model pattern development on growing plant tips, and a chemotactic model for
the formation of skin pigmentation patterns. While these models have been used
previously on simple geometries, our framework allows us to study the impact of
arbitrary geometries on emerging patterns.Comment: This paper was submitted at the Journal of Mathematical Biology,
Springer on 07th July 2015, in its current form (barring image references on
the last page and cosmetic changes owning to rebuild for arXiv). The complete
body of work presented here was included and defended as a part of my PhD
thesis in Nov 2015 at the University of Ber
Categorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society
Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.\ud
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KEYWORDS: Emergence of Life and Human Consciousness;\ud
Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments. \ud
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Reduction of dimension for nonlinear dynamical systems
We consider reduction of dimension for nonlinear dynamical systems. We
demonstrate that in some cases, one can reduce a nonlinear system of equations
into a single equation for one of the state variables, and this can be useful
for computing the solution when using a variety of analytical approaches. In
the case where this reduction is possible, we employ differential elimination
to obtain the reduced system. While analytical, the approach is algorithmic,
and is implemented in symbolic software such as {\sc MAPLE} or {\sc SageMath}.
In other cases, the reduction cannot be performed strictly in terms of
differential operators, and one obtains integro-differential operators, which
may still be useful. In either case, one can use the reduced equation to both
approximate solutions for the state variables and perform chaos diagnostics
more efficiently than could be done for the original higher-dimensional system,
as well as to construct Lyapunov functions which help in the large-time study
of the state variables. A number of chaotic and hyperchaotic dynamical systems
are used as examples in order to motivate the approach.Comment: 16 pages, no figure
Approximation of dynamical systems using S-systems theory : application to biological systems
In this paper we propose a new symbolic-numeric algorithm to find positive
equilibria of a n-dimensional dynamical system. This algorithm implies a
symbolic manipulation of ODE in order to give a local approximation of
differential equations with power-law dynamics (S-systems). A numerical
calculus is then needed to converge towards an equilibrium, giving at the same
time a S-system approximating the initial system around this equilibrium. This
algorithm is applied to a real biological example in 14 dimensions which is a
subsystem of a metabolic pathway in Arabidopsis Thaliana
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