scholarly journals Mechanistic Modeling of Biochemical Systems Without A Priori Parameter Values Using the Design Space Toolbox v.3.0

2020 ◽  
Author(s):  
Miguel Á. Valderrama-Gómez ◽  
Jason G. Lomnitz ◽  
Rick A. Fasani ◽  
Michael A. Savageau
Keyword(s):  
Design Space ◽  
A Priori ◽  
Design Principles ◽  
Mechanistic Modeling ◽  
Design And Optimization ◽  
Biological Design ◽  
Biological Phenomena ◽  
Biochemical Systems ◽  
Space Formalism ◽  
Parameter Values

SummaryMechanistic models of biochemical systems provide a rigorous kinetics-based description of various biological phenomena. They are indispensable to elucidate biological design principles and to devise and engineer systems with novel functionalities. To date, mathematical analysis and characterization of these models remain a challenging endeavor, the main difficulty being the lack of information for most system parameters. Here, we introduce the Design Space Toolbox v.3.0 (DST3), a software implementation of the Design Space formalism that enables mechanistic modeling of complex biological processes without requiring previous knowledge of the parameter values involved. This is achieved by making use of a phenotype-centric modeling approach, in which the system is first decomposed into a series of biochemical phenotypes. Parameter values realizing phenotypes of interest are predicted in a second step. DST3 represents the most generally applicable implementation of the Design Space formalism to date and offers unique advantages over earlier versions. By expanding the capabilities of the Design Space formalism and streamlining its distribution, DST3 represents a valuable tool for elucidating biological design principles and guiding the design and optimization of novel synthetic circuits.

scholarly journals Mechanistic Modeling of Biochemical Systems without A Priori Parameter Values Using the Design Space Toolbox v.3.0

iScience ◽  
2020 ◽  
Vol 23 (6) ◽  
pp. 101200 ◽  
Author(s):  
Miguel Á. Valderrama-Gómez ◽  
Jason G. Lomnitz ◽  
Rick A. Fasani ◽  
Michael A. Savageau
Keyword(s):  
Design Space ◽  
A Priori ◽  
Mechanistic Modeling ◽  
Biochemical Systems ◽  
Parameter Values

scholarly journals Maximization of information transmission influences selection of native phosphorelay architectures

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11558
Author(s):  
Rui Alves ◽  
Baldiri Salvadó ◽  
Ron Milo ◽  
Ester Vilaprinyo ◽  
Albert Sorribas
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Signal Transduction ◽  
Bacillus Subtilis ◽  
Mathematical Models ◽  
Information Transmission ◽  
Environmental Changes ◽  
Design Principles ◽  
Biological Design ◽  
Parameter Values ◽  
Selection Of

Phosphorelays are signal transduction circuits that sense environmental changes and adjust cellular metabolism. Five different circuit architectures account for 99% of all phosphorelay operons annotated in over 9,000 fully sequenced genomes. Here we asked what biological design principles, if any, could explain selection among those architectures in nature. We began by studying kinetically well characterized phosphorelays (Spo0 of Bacillus subtilis and Sln1 of Saccharomyces cerevisiae). We find that natural circuit architecture maximizes information transmission in both cases. We use mathematical models to compare information transmission among the architectures for a realistic range of concentration and parameter values. Mapping experimentally determined phosphorelay protein concentrations onto that range reveals that the native architecture maximizes information transmission in sixteen out of seventeen analyzed phosphorelays. These results suggest that maximization of information transmission is important in the selection of native phosphorelay architectures, parameter values and protein concentrations.

scholarly journals Phenotype-Centric Modeling for Rational Metabolic Engineering

2021 ◽  
Author(s):  
Miguel Angel Valderrama-Gomez ◽  
Michael A. Savageau
Keyword(s):  
Metabolic Engineering ◽  
Paradigm Shift ◽  
Design Space ◽  
Global Analysis ◽  
A Priori ◽  
Computationally Intensive ◽  
Modeling Strategy ◽  
Parameter Values ◽  
Intensive Parameter

Phenotype-centric modeling enables a paradigm shift in the analysis of kinetic models. It brings the focus to a network's biochemical phenotypes and their relationship with measurable traits (e.g., product yields, system dynamics, signal amplification factors, etc.) and away from computationally intensive parameter sampling and numerical simulation. Here, we explore applications of this new modeling strategy in the field of Rational Metabolic Engineering using the amorphadiene biosynthetic network as a case study. Our phenotype-centric approach not only identifies known beneficial intervention strategies for this network, but it also provides an understanding of the mechanistic context for the validity of these predictions. Additionally, we propose a set of hypothetical strains with the potential to outperform reported production strains and enhance the mechanistic understanding of the amorphadiene biosynthetic network. We believe that phenotype-centric modeling can advance the field of Rational Metabolic Engineering by enabling the development of next generation kinetics-based algorithms and methods that do not rely on a priori knowledge of kinetic parameters but allow a structured, global analysis of the design space of parameter values.

Evolutionary Multi-Agent Systems: An Adaptive and Dynamic Approach to Optimization

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Optimization Design ◽  
Design Space ◽  
A Priori ◽  
Single Agent ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a virtual team of specialized strategic software agents to cooperate and evolve to adaptively search an optimization design space. Our goal is to demonstrate and understand how such dynamically evolving teams may search more effectively than any single agent or a priori set strategy. We present a core framework and methodology that has potential applications in layout, scheduling, manufacturing, and other engineering design areas. The communal agent team organizational structure employed allows cooperation of agents through the products of their work and creates an ever changing set of individual solutions for the agents to work on. In addition, the organizational structure allows the framework to be adaptive to changes in the design space that may occur during the optimization process. An evolutionary approach is used, but evolution occurs at the strategic rather than the solution level, where the strategies of agents in the team are the decisions for when and how to choose and alter a solution, and the agents evolve over time. As an application of this approach in a static domain, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, with each agent employing a different solution strategy, must evolve to apply the solution strategies, which are most useful given the solution set at any point in the process. We discuss the extensions to our preliminary work that will make our framework useful to the design and optimization community.

Adaptive Geometry Representation of Turbine Vane Frames for Use in Optimization

2021 ◽  
Author(s):  
Sebastian F. Riebl ◽  
Christian Wakelam ◽  
Reinhard Niehuis
Keyword(s):  
Design Space ◽  
Cfd Simulation ◽  
A Priori ◽  
Three Dimensional ◽  
Optimization Method ◽  
Three Dimensions ◽  
Design Parameters ◽  
Adjustment Process ◽  
Turbine Vane ◽  
Integrated Concept

Abstract Turbine Vane Frames (TVF) are a way to realize more compact jet engine designs. Located between the high pressure turbine (HPT) and the low pressure turbine (LPT), they fulfill structural and aerodynamic tasks. When used as an integrated concept with splitters located between the structural load-bearing vanes, the TVF configuration contains more than one type of airfoil with sometimes pronouncedly different properties. This system of multidisciplinary demands and mixed blading poses an interesting opportunity for optimization. Within the scope of the present work, a full geometric parameterization of a TVF with splitters is presented. The parameterization is chosen as to minimize the number of parameters required to automatically and flexibly represent all blade types involved in a TVF row in all three dimensions. Typical blade design parameters are linked to the fourth order Bézier-curve controlled camber line-thickness parameterization. Based on conventional design rules, a procedure is presented, which sets the parameters within their permissible ranges according to the imposed constraints, using a proprietary developed code. The presented workflow relies on subsequent three dimensional geometry generation by transfer of the proposed parameter set to a commercially available CAD package. The interdependencies of parameters are discussed and their respective significance for the adjustment process is detailed. Furthermore, the capability of the chosen parameterization and adjustment process to rebuild an exemplary reference TVF geometry is demonstrated. The results are verified by comparing not only geometrical profile data, but also validated CFD simulation results between the rebuilt and original geometries. Measures taken to ensure the robustness of the method are highlighted and evaluated by exploring extremes in the permissible design space. Finally, the embedding of the proposed method within the framework of an automated, gradient free numerical optimization is discussed. Herein, implications of the proposed method on response surface modeling in combination with the optimization method are highlighted. The method promises to be an option for improvement of optimization efficiency in gradient free optimization of interdependent blade geometries, by a-priori excluding unsuitable blade combinations, yet keeping restrictions to the design space as limited as possible.

Enabling Decision-Based Design Using Solution Trajectory Correction and Backtracking Rules

2012 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos ◽  
Monica Majcher
Keyword(s):  
Optimal Design ◽  
Utility Function ◽  
Design Space ◽  
A Priori ◽  
Preference Order ◽  
Beam Design ◽  
Effective Decision ◽  
Suboptimal Design ◽  
Trajectory Correction ◽  
Decision Based Design

Optimization is needed for effective decision based design (DBD). However, a utility function assessed a priori in DBD does not usually capture the preferences of the decision maker over the entire design space. As a result, when the optimizer searches for the optimal design, it traverses (or ends up) in regions where the preference order among different solutions is different from the actual order. For a highly non-convex design space, this can lead to convergence to a grossly suboptimal design depending on the initial design. In this article, we propose two approaches to alleviate this issue. First, we map the trajectory of the solution as generated by the optimizer and generate ranking questions that are presented to the designer to verify the correctness of the utility function. We then propose backtracking rules if a local utility function is very different from the initially assessed function. We demonstrate our methodology using a mathematical example and a welded beam design problem.

Biological Design Principles of Complex Feedback Modules in the E. coli Ammonia Assimilation System

2011 ◽  
Vol 18 (1) ◽  
pp. 53-90 ◽  
Author(s):  
Koichi Masaki ◽  
Kazuhiro Maeda ◽  
Hiroyuki Kurata
Keyword(s):  
Feedback Control ◽  
Design Principles ◽  
Cellular Systems ◽  
Fine Tuning ◽  
Ammonia Assimilation ◽  
Control Module ◽  
E Coli ◽  
Biological Design ◽  
Modular Structures ◽  
Assimilation System

To synthesize natural or artificial life, it is critically important to understand the design principles of how biochemical networks generate particular cellular functions and evolve complex systems in comparison with engineering systems. Cellular systems maintain their robustness in the face of perturbations arising from environmental and genetic variations. In analogy to control engineering architectures, the complexity of modular structures within a cell can be attributed to the necessity of achieving robustness. To reveal such biological design, the E. coli ammonia assimilation system is analyzed, which consists of complex but highly structured modules: the glutamine synthetase (GS) activity feedback control module with bifunctional enzyme cascades for catalyzing reversible reactions, and the GS synthesis feedback control module with positive and negative feedback loops. We develop a full-scale dynamic model that unifies the two modules, and we analyze its robustness and fine tuning with respect to internal and external perturbations. The GS activity control is added to the GS synthesis module to improve its transient response to ammonia depletion, compensating the tradeoffs of each module, but its robustness to internal perturbations is lost. These findings suggest some design principles necessary for the synthesis of life.

On Adaptive Sampling for Single and Multi-Response Bayesian Surrogate Models

2006 ◽  
Author(s):  
David A. Romero ◽  
Cristina H. Amon ◽  
Susan Finger
Keyword(s):  
Experimental Design ◽  
Adaptive Sampling ◽  
Design Space Exploration ◽  
A Priori ◽  
Computer Experiments ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Sampling Techniques ◽  
Design And Optimization ◽  
Sampling Points

In order to reduce the time and resources devoted to design-space exploration during simulation-based design and optimization, the use of surrogate models, or metamodels, has been proposed in the literature. Key to the success of metamodeling efforts are the experimental design techniques used to generate the combinations of input variables at which the computer experiments are conducted. Several adaptive sampling techniques have been proposed to tailor the experimental designs to the specific application at hand, using the already-acquired data to guide further exploration of the input space, instead of using a fixed sampling scheme defined a priori. Though mixed results have been reported, it has been argued that adaptive sampling techniques can be more efficient, yielding better surrogate models with less sampling points. In this paper, we address the problem of adaptive sampling for single and multi-response metamodels, with a focus on Multi-stage Multi-response Bayesian Surrogate Models (MMBSM). We compare distance-optimal latin hypercube sampling, an entropy-based criterion and the maximum cross-validation variance criterion, originally proposed for one-dimensional output spaces and implemented in this paper for multi-dimensional output spaces. Our results indicate that, both for single and multi-response surrogate models, the entropy-based adaptive sampling approach leads to models that are more robust to the initial experimental design and at least as accurate (or better) when compared with other sampling techniques using the same number of sampling points.

Micro Autonomous Surface Vessels – Opportunities and Development

2015 ◽  
Author(s):  
Bradley Bishop ◽  
Paul Miller
Keyword(s):  
Cooperative Control ◽  
Design Space ◽  
Design Principles ◽  
Surface Vessels ◽  
Key Concepts

In this paper, we develop a set of design principles for micro ASV systems, defined as surface vessels less than eight feet long with a weight of less than 102 lbs, fully loaded. We outline the key properties of micro ASV systems, discuss potential missions for such devices, and address key concepts in vessel design and cooperative control of groups of micro ASV systems in a capability-based, mission-driven design space. Finally we outline key needs for the advancement of this type of vessel in operational environments.

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