299 research outputs found
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
Robustness Analysis and Behavior Discrimination in Enzymatic Reaction Networks
Characterizing the behavior and robustness of enzymatic networks with numerous variables and unknown parameter values is a major challenge in biology, especially when some enzymes have counter-intuitive properties or switch-like behavior between activation and inhibition. In this paper, we propose new methodological and tool-supported contributions, based on the intuitive formalism of temporal logic, to express in a rigorous manner arbitrarily complex dynamical properties. Our multi-step analysis allows efficient sampling of the parameter space in order to define feasible regions in which the model exhibits imposed or experimentally observed behaviors. In a first step, an algorithmic methodology involving sensitivity analysis is conducted to determine bifurcation thresholds for a limited number of model parameters or initial conditions. In a second step, this boundary detection is supplemented by a global robustness analysis, based on quasi-Monte Carlo approach that takes into account all model parameters. We apply this method to a well-documented enzymatic reaction network describing collagen proteolysis by matrix metalloproteinase MMP2 and membrane type 1 metalloproteinase (MT1-MMP) in the presence of tissue inhibitor of metalloproteinase TIMP2. For this model, our method provides an extended analysis and quantification of network robustness toward paradoxical TIMP2 switching activity between activation or inhibition of MMP2 production. Further implication of our approach is illustrated by demonstrating and analyzing the possible existence of oscillatory behaviors when considering an extended open configuration of the enzymatic network. Notably, we construct bifurcation diagrams that specify key parameters values controlling the co-existence of stable steady and non-steady oscillatory proteolytic dynamics
On a model of online analog computation in the cell with absolute functional robustness: algebraic characterization, function compiler and error control
The Turing completeness of continuous Chemical Reaction Networks (CRNs)
states that any computable real function can be computed by a continuous CRN on
a finite set of molecular species, possibly restricted to elementary reactions,
i.e. with at most two reactants and mass action law kinetics. In this paper, we
introduce a more stringent notion of robust online analog computation, and
Absolute Functional Robustness (AFR), for the CRNs that stabilize the
concentration values of some output species to the result of one function of
the input species concentrations, in a perfectly robust manner with respect to
perturbations of both intermediate and output species. We prove that the set of
real functions stabilized by a CRN with mass action law kinetics is precisely
the set of real algebraic functions. Based on this result, we present a
compiler which takes as input any algebraic function (defined by one polynomial
and one point for selecting one branch of the algebraic curve defined by the
polynomial) and generates an abstract CRN to stabilize it. Furthermore, we
provide error bounds to estimate and control the error of an unperturbed
system, under the assumption that the environment inputs are driven by
k-Lipschitz functions.Comment: arXiv admin note: substantial text overlap with arXiv:2206.0962
Multicriteria global optimization for biocircuit design
One of the challenges in Synthetic Biology is to design circuits with
increasing levels of complexity. While circuits in Biology are complex and
subject to natural tradeoffs, most synthetic circuits are simple in terms of
the number of regulatory regions, and have been designed to meet a single
design criterion. In this contribution we introduce a multiobjective
formulation for the design of biocircuits. We set up the basis for an advanced
optimization tool for the modular and systematic design of biocircuits capable
of handling high levels of complexity and multiple design criteria. Our
methodology combines the efficiency of global Mixed Integer Nonlinear
Programming solvers with multiobjective optimization techniques. Through a
number of examples we show the capability of the method to generate non
intuitive designs with a desired functionality setting up a priori the desired
level of complexity. The presence of more than one competing objective provides
a realistic design setting where every design solution represents a trade-off
between different criteria. The tool can be useful to explore and identify
different design principles for synthetic gene circuits
Formalizing Theories Of Development: A Fugue On The Orderliness Of Change
This chapter looks at developmental biology as performance. Each animal inherits score (the DNA), mechanisms for interpreting of the score, and mechanisms for improvisation should the score be deficient. Developmental causation is found to be both upwards from the genome, downward from the environment, and laterally between cells. Developmental plasticity, organicism, phenotypic heterogeneity, symbiotic co-development, and cytoplasmic localization are each examples of causation from the environment downward. Stereocomplementary relationships are the key components of most developmental interactions. These interactions can be placed into a formal language of graph theory. Morphogenesis can be depicted in the general structure, where nouns cover tissues, molecules and networks and verbs describe processes such as moves, differentiates, grows and apoptoses. This manner of depicting development emphasizes the distributed nature of causality in morphogenesis and can be annotated with associated information or IDs (e.g. cell types, publications, gene-expression data) that link to external online resources that may be regularly updated. This graph approach portrays dynamic processes as the drivers of developmental momentum, and, since the same processes are used many times during development, they can be viewed as modules whose underlying networks are genomic subroutines
Stability and Control of Biomolecular Circuits through Structure
Due to omnipresent uncertainties and environmental disturbances, natural and engineered biological organisms face the challenging control problem of achieving robust performance using unreliable parts. The key to overcoming this challenge rests in identifying structures of biomolecular circuits that are largely invariant despite uncertainties, and building feedback control through such structures. In this work, we develop the tool of log derivatives to capture structures in how the production and degradation rates of molecules depend on concentrations of reactants. We show that log derivatives could establish stability of fixed points based on structure, despite large variations in rates and functional forms of models. Furthermore, we demonstrate how control objectives, such as robust perfect adaptation (i.e. step disturbance rejection), could be implemented through the structures captured. Due to the method's simplicity, structural properties for analysis and design of biomolecular circuits can often be determined by a glance at the equations
Network, degeneracy and bow tie. Integrating paradigms and architectures to grasp the complexity of the immune system
Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information
Modelling molecular networks: relationships between different formalisms and levels of details
This document is the deliverable 1.3 of French ANR CALAMAR. It presents a study of different formalisms used for modelling and analyzing large molecular regulation networks, their formal links, in terms of mutual encodings and of abstractions, and the corresponding levels of detail captured
French Roadmap for complex Systems 2008-2009
This second issue of the French Complex Systems Roadmap is the outcome of the
Entretiens de Cargese 2008, an interdisciplinary brainstorming session
organized over one week in 2008, jointly by RNSC, ISC-PIF and IXXI. It
capitalizes on the first roadmap and gathers contributions of more than 70
scientists from major French institutions. The aim of this roadmap is to foster
the coordination of the complex systems community on focused topics and
questions, as well as to present contributions and challenges in the complex
systems sciences and complexity science to the public, political and industrial
spheres
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