Fundamenta Informaticae - Volume 118, issue 4

Authors: Gambin, Anna | Marciniak-Czochra, Anna

Article Type: Other

DOI: 10.3233/FI-2012-717

Citation: Fundamenta Informaticae, vol. 118, no. 4, pp. i-ii, 2012

Authors: Bertolusso, Roberto | Kimmel, Marek

Article Type: Research Article

Abstract: Recently, Haseltine et al. published a mathematical model of dynamics of viral infection that has consisted of reaction-diffusion type differential equations for wild-type and infected cells, virions and interferon. The model serves as a mathematical description for two-dimensional viral infection spread experiments. We built a model which is different from Haseltine's and is an extension of another model by Getto et al. We investigated its deterministic and stochastic versions, using modeling software sbioPN created by Bertolusso. We found that in the range of parameters, which may be called “critical”, the stochastic model seems to display complex effects qualitatively different from …its deterministic counterpart. Also, the rates of infection in the stochastic model are generally slower than in the deterministic model, an effect, which can be traced to Jensen inequality known best in probability calculus. Although a direct experimental confirmation of these effects is still missing, they seem sufficiently interesting to deserve discussion. Show more

DOI: 10.3233/FI-2012-718

Citation: Fundamenta Informaticae, vol. 118, no. 4, pp. 327-343, 2012

Authors: Vakulenko, Sergei | Radulescu, Ovidiu

Article Type: Research Article

Abstract: We investigate the possibility of programming arbitrarily complex space-time patterns, and transitions between such patterns, by gene networks. We consider networks with two types of nodes. The v-nodes, called centers, are hyperconnected and interact one to another via u-nodes, called satellites. This centralized architecture realizes a bow-tie scheme and possesses interesting properties. Namely, this organization creates feedback loops that are capable to generate any prescribed patterning dynamics, chaotic or periodic, or stabilize a number of prescribed equilibrium states. We show that activation or silencing of a node can sharply switch the network dynamics, even if the activated or silenced node …is weakly connected. Centralized networks can keep their flexibility, and still be protected against environmental noises. Finding an optimized network that is both robust and flexible is a computationally hard problem in general, but it becomes feasible when the number of satellites is large. In theoretical biology, this class of models can be used to implement the Driesch-Wolpert program, allowing to go from morphogen gradients to multicellular organisms. Show more

Keywords: Gene networks, Pattern formation, Positional information, Driesch-Wolpert theory, Bifurcations, Viability, Computability

DOI: 10.3233/FI-2012-719

Citation: Fundamenta Informaticae, vol. 118, no. 4, pp. 345-369, 2012

Authors: Archuleta, Michelle N. | McDermott, Jason E. | Edwards, Jeremy S. | Resat, Haluk

Article Type: Research Article

Abstract: The spatio-temporal landscape of the plasma membrane regulates activation and signal transduction of membrane bound receptors by restricting their two-dimensional mobility and by inducing receptor clustering. This regulation also extends to complex formation between receptors and adaptor proteins, which are the intermediate signaling molecules involved in cellular signaling that relay the received cues from cell surface to cytoplasm and eventually to the nucleus. Although their investigation poses challenging technical difficulties, there is a crucial need to understand the impact of the receptor diffusivity, clustering, and spatial heterogeneity, and of receptor-adaptor protein complex formation on the cellular signal transduction patterns. Building …upon our earlier studies, we have developed an adaptive coarse-grained Monte Carlo method that can be used to investigate the role of diffusion, clustering and membrane corralling on receptor association and receptor-adaptor protein complex formation dynamics in three dimensions. The new Monte Carlo lattice based approach allowed us to introduce spatial resolution on the 2-D plasma membrane and to model the cytoplasm in three-dimensions. Being a multi-resolution approach, our new method makes it possible to represent various parts of the cellular system at different levels of detail and enabled us to utilize the locally homogeneous assumption when justified (e.g., cytoplasmic region away from the cell membrane) and avoid its use when high spatial resolution is needed (e.g., cell membrane and cytoplasmic region near the membrane) while keeping the required computational complexity manageable. Our results have shown that diffusion has a significant impact on receptor-receptor dimerization and receptor-adaptor protein complex formation kinetics. We have observed an adaptor protein hopping mechanism where the receptor binding proteins may hop between receptors to form short-lived transient complexes. This increased residence time of the adaptor proteins near cell membrane and their ability to frequently change signaling partners may explain the increase in signaling efficiency when receptors are clustered. We also hypothesize that the adaptor protein hopping mechanism can cause concurrent or sequential activation of multiple signaling pathways, thus leading to crosstalk between diverse biological functions. Show more

DOI: 10.3233/FI-2012-720

Citation: Fundamenta Informaticae, vol. 118, no. 4, pp. 371-384, 2012

Authors: Blazewicz, Jacek | Kasprzak, Marta

Article Type: Research Article

Abstract: The progress of research in the area of computational biology, visible in last decades, brought, among others, a new insight into the complexity issues. The latter, previously studied mainly on the ground of computer science or operational research, gained by a confrontation with problems from the new area. In the paper, several complexity issues inspired by computational biology are presented.

Keywords: computational complexity, computational biology, graph theory, nondeterministic Turing machines, DNA computing

DOI: 10.3233/FI-2012-721

Citation: Fundamenta Informaticae, vol. 118, no. 4, pp. 385-401, 2012

Authors: Setty, Yaki | Cohen, Irun R. | Harel, David

Article Type: Research Article

Abstract: In his pioneering 1952 paper, “The chemical basis of morphogenesis”, Alan Turing introduced, perhaps for the first time, a model of the morphogenesis of embryo development. Central to his theory is the concept of cells with chemical entities that interact with morphogens to drive embryonic development through changes in what he termed ‘the state of the system’. Turing's concepts have inspired many mathematical and computational models proposed since then. Here we discuss the way Turing's ideas inspired our approach to the state-based modeling of morphogenesis, which results in a fully executable program for the interactions between chemical entities and morphogens. …As a representative example we describe our modeling of pancreatic organogenesis, a complex developmental process that develops from a flat sheet of cells into a 3D cauliflower-like shape. We show how we constructed the model and tested the relations between morphogens and cells, and illustrate the analysis of the model against experimental data. Finally, we discuss a variant of the original Turing-Test for a machine's ability to demonstrate intelligence as a future means to validate computerized biological models, like the one presented here. Show more

DOI: 10.3233/FI-2012-722

Citation: Fundamenta Informaticae, vol. 118, no. 4, pp. 403-417, 2012

Authors: Batmanov, Kirill | Kuttler, Céline | Lhoussaine, Cédric | Saka, Yasushi

Article Type: Research Article

Abstract: For decades, scientists have sought to elucidate self-organized patterning during development of higher organisms. It has been shown that cell interaction plays a key role in this process. One example is the community effect, an interaction among undifferentiated cells. The community effect allows cell population to forge a common identity, that is, coordinated and sustained tissue-specific gene expression. The community effect was originally observed in muscle differentiation in Xenopus embryos, and is now thought to be a widespread phenomenon. From a modelling point of view, the community effect is the existence of a threshold size of cell populations, above which …the probability of tissue-specific gene expression for a sustained period increases significantly. Below this threshold size, the cell population fails to maintain tissue-specific gene expression after the initial induction. In this work, we examine the dynamics of a community effect in space and investigate its roles in two other processes of self-organized patterning by diffusible factors: Turing's reaction-diffusion system and embryonic induction by morphogens. Our major results are the following. First, we show that, starting from a one-dimensional space model with the simplest possible feedback loop, a community effect spreads in an unlimited manner in space. Second, this unrestricted expansion of a community effect can be avoided by additional negative feedback. In Turing's reaction-diffusion system with a built-in community effect, if induction is localized, sustained activation also remains localized. Third, when a simple cross-repression gene circuitry is combined with a community effect loop, the system self-organizes. A gene expression pattern with a well-demarcated boundary appears in response to a transient morphogen gradient. Surprisingly, even when the morphogen distribution eventually becomes uniform, the system can maintain the pattern. The regulatory network thus confers memory of morphogen dynamics. Show more

DOI: 10.3233/FI-2012-723

Citation: Fundamenta Informaticae, vol. 118, no. 4, pp. 419-461, 2012

Article Type: Other

Citation: Fundamenta Informaticae, vol. 118, no. 4, pp. 463-464, 2012