6,775 research outputs found
Metabolic Futile Cycles and Their Functions: A Systems Analysis of Energy and Control
It has long been hypothesized that futile cycles in cellular metabolism are
involved in the regulation of biochemical pathways. Following the work of
Newsholme and Crabtree, we develop a quantitative theory for this idea based on
open-system thermodynamics and metabolic control analysis. It is shown that the
{\it stoichiometric sensitivity} of an intermediary metabolite concentration
with respect to changes in steady-state flux is governed by the effective
equilibrium constant of the intermediate formation, and the equilibrium can be
regulated by a futile cycle. The direction of the shift in the effective
equilibrium constant depends on the direction of operation of the futile cycle.
High stoichiometric sensitivity corresponds to ultrasensitivity of an
intermediate concentration to net flow through a pathway; low stoichiometric
sensitivity corresponds to super-robustness of concentration with respect to
changes in flux. Both cases potentially play important roles in metabolic
regulation. Futile cycles actively shift the effective equilibrium by expending
energy; the magnitude of changes in effective equilibria and sensitivities is a
function of the amount of energy used by a futile cycle. This proposed
mechanism for control by futile cycles works remarkably similarly to kinetic
proofreading in biosynthesis. The sensitivity of the system is also intimately
related to the rate of concentration fluctuations of intermediate metabolites.
The possibly different roles of the two major mechanisms for cellular
biochemical regulation, namely reversible chemical modifications via futile
cycles and shifting equilibrium by macromolecular binding, are discussed.Comment: 11 pages, 5 figure
The Berry phase and the pump flux in stochastic chemical kinetics
We study a classical two-state stochastic system in a sea of substrates and
products (absorbing states), which can be interpreted as a single
Michaelis-Menten catalyzing enzyme or as a channel on a cell surface. We
introduce a novel general method and use it to derive the expression for the
full counting statistics of transitions among the absorbing states. For the
evolution of the system under a periodic perturbation of the kinetic rates, the
latter contains a term with a purely geometrical (the Berry phase)
interpretation. This term gives rise to a pump current between the absorbing
states, which is due entirely to the stochastic nature of the system. We
calculate the first two cumulants of this current, and we argue that it is
observable experimentally
Interrogating metabolism as an electron flow system
Metabolism is generally considered as a neatly organised system of modular pathways, shaped by evolution under selection for optimal cellular growth. This view falls short of explaining and predicting a number of key observations about the structure and dynamics of metabolism. We highlight these limitations of a pathway-centric view on metabolism and summarise studies suggesting how these could be overcome by viewing metabolism as a thermodynamically and kinetically constrained, dynamical flow system. Such a systems-level, first-principles based view of metabolism can open up new avenues of metabolic engineering and cures for metabolic diseases and allow better insights to a myriad of physiological processes that are ultimately linked to metabolism. Towards further developing this view, we call for a closer interaction among physical and biological disciplines and an increased use of electrochemical and biophysical approaches to interrogate cellular metabolism together with the microenvironment in which it exists
Accurate implementation of leaping in space: The spatial partitioned-leaping algorithm
There is a great need for accurate and efficient computational approaches
that can account for both the discrete and stochastic nature of chemical
interactions as well as spatial inhomogeneities and diffusion. This is
particularly true in biology and nanoscale materials science, where the common
assumptions of deterministic dynamics and well-mixed reaction volumes often
break down. In this article, we present a spatial version of the
partitioned-leaping algorithm (PLA), a multiscale accelerated-stochastic
simulation approach built upon the tau-leaping framework of Gillespie. We pay
special attention to the details of the implementation, particularly as it
pertains to the time step calculation procedure. We point out conceptual errors
that have been made in this regard in prior implementations of spatial
tau-leaping and illustrate the manifestation of these errors through practical
examples. Finally, we discuss the fundamental difficulties associated with
incorporating efficient exact-stochastic techniques, such as the next-subvolume
method, into a spatial-leaping framework and suggest possible solutions.Comment: 15 pages, 9 figures, 2 table
Field-control, phase-transitions, and life's emergence
Instances of critical-like characteristics in living systems at each
organizational level as well as the spontaneous emergence of computation
(Langton), indicate the relevance of self-organized criticality (SOC). But
extrapolating complex bio-systems to life's origins, brings up a paradox: how
could simple organics--lacking the 'soft matter' response properties of today's
bio-molecules--have dissipated energy from primordial reactions in a controlled
manner for their 'ordering'? Nevertheless, a causal link of life's macroscopic
irreversible dynamics to the microscopic reversible laws of statistical
mechanics is indicated via the 'functional-takeover' of a soft magnetic
scaffold by organics (c.f. Cairns-Smith's 'crystal-scaffold'). A
field-controlled structure offers a mechanism for bootstrapping--bottom-up
assembly with top-down control: its super-paramagnetic components obey
reversible dynamics, but its dissipation of H-field energy for aggregation
breaks time-reversal symmetry. The responsive adjustments of the controlled
(host) mineral system to environmental changes would bring about mutual
coupling between random organic sets supported by it; here the generation of
long-range correlations within organic (guest) networks could include SOC-like
mechanisms. And, such cooperative adjustments enable the selection of the
functional configuration by altering the inorganic network's capacity to assist
a spontaneous process. A non-equilibrium dynamics could now drive the
kinetically-oriented system towards a series of phase-transitions with
appropriate organic replacements 'taking-over' its functions.Comment: 54 pages, pdf fil
Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models
<p>Abstract</p> <p>Background</p> <p>Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization.</p> <p>Results</p> <p>Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity.</p> <p>Conclusions</p> <p>Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.</p
Model composition through model reduction: a combined model of CD95 and NF-{\kappa}B signaling pathways
We propose a new approach to model composition, based on reducing several
models to the same level of complexity and subsequent combining them together.
Firstly, we suggest a set of model reduction tools that can be systematically
applied to a given model. Secondly, we suggest a notion of a minimal complexity
model. This model is the simplest one that can be obtained from the original
model using these tools and still able to approximate experimental data.
Thirdly, we propose a strategy for composing the reduced models together.
Connection with the detailed model is preserved, which can be advantageous in
some applications. A toolbox for model reduction and composition has been
implemented as part of the BioUML software and tested on the example of
integrating two previously published models of the CD95 (APO-1/Fas) signaling
pathways. We show that the reduced models lead to the same dynamical behavior
of observable species and the same predictions as in the precursor models. The
composite model is able to recapitulate several experimental datasets which
were used by the authors of the original models to calibrate them separately,
but also has new dynamical properties.Comment: Udated version of the paper published in BMC Systems Biology, with
corrected description of the method
Multiscale Hy3S: Hybrid stochastic simulation for supercomputers
BACKGROUND: Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of "small" systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs. RESULTS: Building on our previous work on developing novel hybrid stochastic algorithms, we have created the Hy3S software package to enable scientists and engineers to both study and design extremely large well-mixed biological systems with many thousands of reactions and chemical species. We have added adaptive stochastic numerical integrators to permit the robust simulation of dynamically stiff biological systems. In addition, Hy3S has many useful features, including embarrassingly parallelized simulations with MPI; special discrete events, such as transcriptional and translation elongation and cell division; mid-simulation perturbations in both the number of molecules of species and reaction kinetic parameters; combinatorial variation of both initial conditions and kinetic parameters to enable sensitivity analysis; use of NetCDF optimized binary format to quickly read and write large datasets; and a simple graphical user interface, written in Matlab, to help users create biological systems and analyze data. We demonstrate the accuracy and efficiency of Hy3S with examples, including a large-scale system benchmark and a complex bistable biochemical network with positive feedback. The software itself is open-sourced under the GPL license and is modular, allowing users to modify it for their own purposes. CONCLUSION: Hy3S is a powerful suite of simulation programs for simulating the stochastic dynamics of networks of biochemical reactions. Its first public version enables computational biologists to more efficiently investigate the dynamics of realistic biological systems
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