246 research outputs found
Shape, Size, and Robustness: Feasible Regions in the Parameter Space of Biochemical Networks
The concept of robustness of regulatory networks has received much attention in the last decade. One measure of robustness has been associated with the volume of the feasible region, namely, the region in the parameter space in which the system is functional. In this paper, we show that, in addition to volume, the geometry of this region has important consequences for the robustness and the fragility of a network. We develop an approximation within which we could algebraically specify the feasible region. We analyze the segment polarity gene network to illustrate our approach. The study of random walks in the parameter space and how they exit the feasible region provide us with a rich perspective on the different modes of failure of this network model. In particular, we found that, between two alternative ways of activating Wingless, one is more robust than the other. Our method provides a more complete measure of robustness to parameter variation. As a general modeling strategy, our approach is an interesting alternative to Boolean representation of biochemical networks
A general computational method for robustness analysis with applications to synthetic gene networks
Motivation: Robustness is the capacity of a system to maintain a function in the face of perturbations. It is essential for the correct functioning of natural and engineered biological systems. Robustness is generally defined in an ad hoc, problem-dependent manner, thus hampering the fruitful development of a theory of biological robustness, recently advocated by Kitano
Model evaluation for glycolytic oscillations in yeast biotransformations of xenobiotics
Anaerobic glycolysis in yeast perturbed by the reduction of xenobiotic
ketones is studied numerically in two models which possess the same topology
but different levels of complexity. By comparing both models' predictions for
concentrations and fluxes as well as steady or oscillatory temporal behavior we
answer the question what phenomena require what kind of minimum model
abstraction. While mean concentrations and fluxes are predicted in agreement by
both models we observe different domains of oscillatory behavior in parameter
space. Generic properties of the glycolytic response to ketones are discussed
Incorporating expression data in metabolic modeling: a case study of lactate dehydrogenase
Integrating biological information from different sources to understand
cellular processes is an important problem in systems biology. We use data from
mRNA expression arrays and chemical kinetics to formulate a metabolic model
relevant to K562 erythroleukemia cells. MAP kinase pathway activation alters
the expression of metabolic enzymes in K562 cells. Our array data show changes
in expression of lactate dehydrogenase (LDH) isoforms after treatment with
phorbol 12-myristate 13-acetate (PMA), which activates MAP kinase signaling. We
model the change in lactate production which occurs when the MAP kinase pathway
is activated, using a non-equilibrium, chemical-kinetic model of homolactic
fermentation. In particular, we examine the role of LDH isoforms, which
catalyze the conversion of pyruvate to lactate. Changes in the isoform ratio
are not the primary determinant of the production of lactate. Rather, the total
concentration of LDH controls the lactate concentration.Comment: In press, Journal of Theoretical Biology. 27 pages, 9 figure
Identification of a Topological Characteristic Responsible for the Biological Robustness of Regulatory Networks
Attribution of biological robustness to the specific structural properties of a regulatory network is an important yet unsolved problem in systems biology. It is widely believed that the topological characteristics of a biological control network largely determine its dynamic behavior, yet the actual mechanism is still poorly understood. Here, we define a novel structural feature of biological networks, termed ‘regulation entropy’, to quantitatively assess the influence of network topology on the robustness of the systems. Using the cell-cycle control networks of the budding yeast (Saccharomyces cerevisiae) and the fission yeast (Schizosaccharomyces pombe) as examples, we first demonstrate the correlation of this quantity with the dynamic stability of biological control networks, and then we establish a significant association between this quantity and the structural stability of the networks. And we further substantiate the generality of this approach with a broad spectrum of biological and random networks. We conclude that the regulation entropy is an effective order parameter in evaluating the robustness of biological control networks. Our work suggests a novel connection between the topological feature and the dynamic property of biological regulatory networks
Amplified biochemical oscillations in cellular systems
We describe a mechanism for pronounced biochemical oscillations, relevant to
microscopic systems, such as the intracellular environment. This mechanism
operates for reaction schemes which, when modeled using deterministic rate
equations, fail to exhibit oscillations for any values of rate constants. The
mechanism relies on amplification of the underlying stochasticity of reaction
kinetics within a narrow window of frequencies. This amplification allows
fluctuations to beat the central limit theorem, having a dominant effect even
though the number of molecules in the system is relatively large. The mechanism
is quantitatively studied within simple models of self-regulatory gene
expression, and glycolytic oscillations.Comment: 35 pages, 6 figure
Specialization Can Drive the Evolution of Modularity
Organismal development and many cell biological processes are organized in a modular fashion, where regulatory molecules form groups with many interactions within a group and few interactions between groups. Thus, the activity of elements within a module depends little on elements outside of it. Modularity facilitates the production of heritable variation and of evolutionary innovations. There is no consensus on how modularity might evolve, especially for modules in development. We show that modularity can increase in gene regulatory networks as a byproduct of specialization in gene activity. Such specialization occurs after gene regulatory networks are selected to produce new gene activity patterns that appear in a specific body structure or under a specific environmental condition. Modules that arise after specialization in gene activity comprise genes that show concerted changes in gene activities. This and other observations suggest that modularity evolves because it decreases interference between different groups of genes. Our work can explain the appearance and maintenance of modularity through a mechanism that is not contingent on environmental change. We also show how modularity can facilitate co-option, the utilization of existing gene activity to build new gene activity patterns, a frequent feature of evolutionary innovations
Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions
During development, signaling networks control the formation of multicellular patterns. To what extent quantitative fluctuations in these complex networks may affect multicellular phenotype remains unclear. Here, we describe a computational approach to predict and analyze the phenotypic diversity that is accessible to a developmental signaling network. Applying this framework to vulval development in C. elegans, we demonstrate that quantitative changes in the regulatory network can render ~500 multicellular phenotypes. This phenotypic capacity is an order-of-magnitude below the theoretical upper limit for this system but yet is large enough to demonstrate that the system is not restricted to a select few outcomes. Using metrics to gauge the robustness of these phenotypes to parameter perturbations, we identify a select subset of novel phenotypes that are the most promising for experimental validation. In addition, our model calculations provide a layout of these phenotypes in network parameter space. Analyzing this landscape of multicellular phenotypes yielded two significant insights. First, we show that experimentally well-established mutant phenotypes may be rendered using non-canonical network perturbations. Second, we show that the predicted multicellular patterns include not only those observed in C. elegans, but also those occurring exclusively in other species of the Caenorhabditis genus. This result demonstrates that quantitative diversification of a common regulatory network is indeed demonstrably sufficient to generate the phenotypic differences observed across three major species within the Caenorhabditis genus. Using our computational framework, we systematically identify the quantitative changes that may have occurred in the regulatory network during the evolution of these species. Our model predictions show that significant phenotypic diversity may be sampled through quantitative variations in the regulatory network without overhauling the core network architecture. Furthermore, by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species, we systematically trace the quantitative regulatory changes that may have occurred during the evolution of the Caenorhabditis genus
Ghost Factors of Laboratory Carbonate Chemistry Are Haunting Our Experiments
For many historical and contemporary experimental studies in marine biology, seawater carbonate chemistry remains a ghost factor, an uncontrolled, unmeasured, and
often dynamic variable affecting experimental organisms or
the treatments to which investigators subject them. We highlight how environmental variability, such as seasonal upwelling and biological respiration, drive variation in seawater carbonate chemistry that can influence laboratory experiments in
unintended ways and introduce a signal consistent with ocean
acidification. As the impacts of carbonate chemistry on biochemical pathways that underlie growth, development, reproduction, and behavior become better understood, the hidden
effects of this previously overlooked variable need to be acknowledged. Here we bring this emerging challenge to the attention of the wider community of experimental biologists who
rely on access to organisms and water from marine and estuarine laboratories and who may benefit from explicit considerations of a growing literature on the pervasive effects of aquatic
carbonate chemistry changes.AWEG and JBS were supported by Oregon Sea Grant
(OSG; R/ECO-37-Galloway1820) from the National Oceanic
and Atmospheric Administration’s National Sea Grant College Program, from the U.S. Department of Commerce, and
by appropriations made by the Oregon State Legislature. GvD
was supported by grants from the National Science Foundation (NSF; MCB-1614606) and National Institutes of Health
(GM052932). RMY was supported by the NSF Graduate Research Fellowship (1309047). FC was supported by OSG (R/
ECO-32-Chan). KJK was supported by the David and Lucille
Packard Foundation and the NSF (OCE-1752600). The statements, findings, conclusions, and recommendations are those
of the authors and do not necessarily reflect the views of these
funders. We appreciate the thoughtful and constructive comments from two anonymous peer reviewersYe
Surprisingly Simple Mechanical Behavior of a Complex Embryonic Tissue
Background: Previous studies suggest that mechanical feedback could coordinate morphogenetic events in embryos. Furthermore, embryonic tissues have complex structure and composition and undergo large deformations during morphogenesis. Hence we expect highly non-linear and loading-rate dependent tissue mechanical properties in embryos. Methodology/Principal Findings: We used micro-aspiration to test whether a simple linear viscoelastic model was sufficient to describe the mechanical behavior of gastrula stage Xenopus laevis embryonic tissue in vivo. We tested whether these embryonic tissues change their mechanical properties in response to mechanical stimuli but found no evidence of changes in the viscoelastic properties of the tissue in response to stress or stress application rate. We used this model to test hypotheses about the pattern of force generation during electrically induced tissue contractions. The dependence of contractions on suction pressure was most consistent with apical tension, and was inconsistent with isotropic contraction. Finally, stiffer clutches generated stronger contractions, suggesting that force generation and stiffness may be coupled in the embryo. Conclusions/Significance: The mechanical behavior of a complex, active embryonic tissue can be surprisingly well described by a simple linear viscoelastic model with power law creep compliance, even at high deformations. We found no evidence of mechanical feedback in this system. Together these results show that very simple mechanical models can be useful in describing embryo mechanics. © 2010 von Dassow et al
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