arXiv subjects

Herbert M. Sauro

Publications and source records attributed to Herbert M. Sauro.

33 records · Page 2Linked to original sources

arXiv2019

PyBioNetFit and the Biological Property Specification Language

In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit. PyBioNetFit is designed for parameterization, and also supports uncertainty quantification, checking models against known system properties, and solving design problems. PyBioNetFit introduces the Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data for parameterization model checking, and design. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms (differential evolution, particle swarm optimization, scatter search) that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving 31 example problems, including the challenging problem of parameterizing a model of cell cycle control in yeast. We benchmark PyBioNetFit's parallelization efficiency on computer clusters, using up to 288 cores. Finally, we demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of therapeutic interventions in autophagy signaling.

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arXiv2015

libRoadRunner: A High Performance SBML Simulation and Analysis Library

This paper presents libRoadRunner, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models \ expressed using Systems Biology Markup Language (SBML). SBML is the most widely used standard for representing dynamic networks, especially biochemical networks. libRoadRunner supports solution of both large models and multiple replicas of a single model on desktop, mobile and cluster computers. libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings andnteractively through its Python interface. The Python Application Programming Interface (API) is similar to the APIs of Matlab and SciPy, making it fast and easy to learn, even for new users. libRoadRunner uses a custom Just-In-Time (JIT) compiler built on the widely-used LLVM JIT compiler framework to compile SBML-specified models directly into very fast native machine code for a variety of processors, making it appropriate for solving very large models or multiple replicas of smaller models. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and nonlinear algebraic equations) and several of its extensions. It offers multiple deterministic and stochastic integrators, as well as tools for steady-state, stability analyses and flux balance analysis. We regularly update libRoadRunner binary distributions for Mac OS X, Linux and Windows and license them under Apache License Version 2.0. http://www.libroadrunner.org provides online documentation, full build instructions, binaries and a git source repository.

q-bio.SC↗

arXiv2014

One file to share them all: Using the COMBINE Archive and the OMEX format to share all information about a modeling project

Background: With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result. Results: We describe the Open Modeling EXchange format (OMEX). Together with the use of other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive, a single file that supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, listing the content of the archive, an optional metadata file adding information about the archive and its content, and the files describing the model. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. Several tools that support the COMBINE Archive are available, either as independent libraries or embedded in modeling software. Conclusions: The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails. We anticipate that the COMBINE Archive will become a significant help for modellers, as the domain moves to larger, more complex experiments such as multi-scale models of organs, digital organisms, and bioengineering.

cs.DL↗

arXiv2013

Nonlinear Biochemical Signal Processing via Noise Propagation

Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networks. Here we develop an intuitive, yet fully quantitative method for analyzing how noise affects cellular phenotypes based on identifying a system's nonlinearities and noise propagations. We observe that such noise can simultaneously enhance sensitivities in one behavioral region while reducing sensitivities in another. Employing this novel phenomenon we designed three biochemical signal processing modules: (a) A gene regulatory network that acts as a concentration detector with both enhanced amplitude and sensitivity. (b) A non-cooperative positive feedback system, with a graded dose-response in the deterministic case, that serves as a bistable switch due to noise-induced bimodality. (c) A noise-induced linear amplifier for gene regulation that requires no feedback. The methods developed in the present work allow one to understand and engineer nonlinear biochemical signal processors based on fluctuation-induced phenotypes.

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arXiv2010

Fan-out in Gene Regulatory Networks

In synthetic biology, gene regulatory circuits are often constructed by combining smaller circuit components. Connections between components are achieved by transcription factors acting on promoters. If the individual components behave as true modules and certain module interface conditions are satisfied, the function of the composite circuits can in principle be predicted. In this paper, we investigate one of the interface conditions: fan-out. We quantify the fan-out, a concept widely used in electric engineering, to indicate the maximum number of the downstream inputs that an upstream output transcription factor can regulate. We show that the fan-out is closely related to retroactivity studied by Del Vecchio, et al. We propose an efficient operational method for measuring the fan-out that can be applied to various types of module interfaces. We also show that the fan-out can be enhanced by self-inhibitory regulation on the output. We discuss the potential role of the inhibitory regulations found in gene regulatory networks and protein signal pathways. The proposed estimation method for fanout not only provides an experimentally efficient way for quantifying the level of modularity in gene regulatory circuits but also helps characterize and design module interfaces, enabling the modular construction of gene circuits.

q-bio.MN↗

arXiv2010

C Library for Simulated Evolution of Biological Networks

Simulated evolution of biological networks can be used to generate functional networks as well as investigate hypotheses regarding natural evolution. A handful of studies have shown how simulated evolution can be used for studying the functional space spanned by biochemical networks, studying natural evolution, or designing new synthetic networks. If there was a method for easily performing such studies, it can allow the community to further experiment with simulated evolution and explore all of its uses. As a result, we have developed a library written in the C language that performs all the basic functions needed to carry out simulated evolution of biological networks. The library comes with a generic genetic algorithm as well as genetic algorithms for specifically evolving genetic networks, protein networks, or mass-action networks. The library also comes with functions for simulating these networks. A user needs to specify a desired function. A GUI is provided for users to become oriented with all the options available in the library. The library is free and open source under the BSD lisence and can be obtained at evolvenetworks.sourceforge.net. It can be built on all major platforms. The code can be most conveniently compiled using cross-platform make (CMake).

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arXiv2009

Measuring the Degree of Modularity from Gene Expression Noise in Gene Regulatory Circuits

Gene regulatory circuits show significant stochastic fluctuations in their circuit signals due to the low copy number of transcription factors. When a gene circuit component is connected to an existing circuit, the dynamic properties of the existing circuit can be affected by the connected component. In this paper, we investigate modularity in the dynamics of the gene circuit based on stochastic fluctuations in the circuit signals. We show that the noise in the output signal of the existing circuit can be affected significantly when the output is connected to the input of another circuit component. More specifically, the output signal noise can show significantly longer correlations when the two components are connected. This equivalently means that the noise power spectral density becomes narrower. We define the relative change in the correlation time or the spectrum bandwidth by stochastic retroactivity, which is shown to be directly related to the retroactivity defined in the deterministic framework by del Vecchio et al. This provides an insight on how to measure retroactivity, by investigating stochastic fluctuations in gene expression levels, more specifically, by obtaining an autocorrelation function of the fluctuations. We also provide an interesting aspect of the frequency response of the circuit. We show that depending on the magnitude of operating frequencies, different kinds of signals need to be preferably chosen for circuit description in a modular fashion: at low enough frequency, expression level of transcription factor that are not bound to their specific promoter region needs to be chosen, and at high enough frequency, that of the total transcription factor, both bound and unbound, does.

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arXiv2009

Stochastic Control Analysis for Biochemical Reaction Systems

In this paper, we investigate how stochastic reaction processes are affected by external perturbations. We describe an extension of the deterministic metabolic control analysis (MCA) to the stochastic regime. We introduce stochastic sensitivities for mean and covariance values of reactant concentrations and reaction fluxes and show that there exist MCA-like summation theorems among these sensitivities. The summation theorems for flux variances are shown to depend on the size of the measurement time window ($ε$), within which reaction events are counted for measuring a single flux. The degree of the $ε$-dependency can become significant for processes involving multi-time-scale dynamics and is estimated by introducing a new measure of time scale separation. This $ε$-dependency is shown to be closely related to the power-law scaling observed in flux fluctuations in various complex networks. We propose a systematic way to control fluctuations of reactant concentrations while minimizing changes in mean concentration levels. Such orthogonal control is obtained by introducing a control vector indicating the strength and direction of parameter perturbations leading to a sensitive control. We also propose a possible implication in the control of flux fluctuation: The control distribution for flux fluctuations changes with the measurement time window size, $ε$. When a control engineer applies a specific control operation on a reaction system, the system can respond contrary to what is expected, depending on the time window size $ε$.

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arXiv2009

An Optimization Algorithm for Finding Parameters for Bistability

Motivation: Many biochemical pathways are known, but the numerous parameters required to correctly explore the dynamics of the pathways are not known. For this reason, algorithms that can make inferences by looking at the topology of a network are desirable. In this work, we are particular interested in the question of whether a given pathway can potentially harbor multiple stable steady states. In other words, the challenge is to find the set of parameters such that the dynamical system defined by a set of ordinary differential equations will contain multiple stable steady states. Being able to find parameters that cause a network to be bistable may also be benefitial for engineering synthetic bistable systems where the engineer needs to know a working set of parameters. Result: We have developed an algorithm that optimizes the parameters of a dynamical system so that the system will contain at least one saddle or unstable point. The algorithm then looks at trajectories around this saddle or unstable point to see whether the different trajectories converge to different stable points. The algorithm returns the parameters that causes the system to exhibit multiple stable points. Since this is an optimization algorithm, it is not quaranteed to find a solution. Repeated runs are often required to find a solution for systems where only a narrow set of parameters exhibit bistability. Availability: The C code for the algorithm is available at http://tinkercell.googlecode.com

q-bio.MN↗

arXiv2009

TinkerCell: Modular CAD Tool for Synthetic Biology

Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. An application named TinkerCell has been created in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various C and Python programs that are hosted by TinkerCell via an extensive C and Python API. TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. Because TinkerCell associates parameters and equations in a model with their respective part, parts can be loaded from databases along with their parameters and rate equations. The modular network design can be used to exchange modules as well as test the concept of modularity in biological systems. The flexible modeling framework along with the C and Python API allows TinkerCell to serve as a host to numerous third-party algorithms. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at www.tinkercell.com.

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arXiv2009

Athena: Modular CAM/CAD Software for Synthetic Biology

Synthetic biology is the engineering of cellular networks. It combines principles of engineering and the knowledge of biological networks to program the behavior of cells. Computational modeling techniques in conjunction with molecular biology techniques have been successful in constructing biological devices such as switches, oscillators, and gates. The ambition of synthetic biology is to construct complex systems from such fundamental devices, much in the same way electronic circuits are built from basic parts. As this ambition becomes a reality, engineering concepts such as interchangeable parts and encapsulation will find their way into biology. We realize that there is a need for computational tools that would support such engineering concepts in biology. As a solution, we have developed the software Athena that allows biological models to be constructed as modules. Modules can be connected to one another without altering the modules themselves. In addition, Athena houses various tools useful for designing synthetic networks including tools to perform simulations, automatically derive transcription rate expressions, and view and edit synthetic DNA sequences. New tools can be incorporated into Athena without modifying existing program via a plugin interface, IronPython scripts, Systems Biology Workbench interfacing and the R statistical language. The program is currently for Windows operating systems, and the source code for Athena is made freely available through CodePlex, www.codeplex.com/athena.

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arXiv2009

Enumeration and Online Library of Mass-Action Reaction Networks

The aim of this work is to make available to the community a large collection of mass-action reaction networks of a given size for further research. The set is limited to what can be computed on a modern multi-core desktop in reasonable time (< 20 days). We have currently generated over 47 million unique reaction networks. All currently generated sets of networks are available and as new sets are completed they will also be made available. Also provided are programs for translating them into different formats, along with documentation and examples. Source code and binaries for all the programs are included. These can be downloaded from (http://www.sys-bio.org/networkenumeration). This library of networks will allow for thorough studies of the reaction network space. Additionally, these methods serve as an example for future work on enumerating other types of biological networks, such as genetic regulatory networks and mass-action networks that include regulation.

q-bio.MN↗

arXiv2008

Sensitivity Regulation based on Noise Propagation in Stochastic Reaction Networks

In this work we focus on how noise propagates in biochemical reaction networks and affects sensitivities of the system. We discover that the stochastic fluctuations can enhance sensitivities in one region of the value of control parameters by reducing sensitivities in another region. Based on this compensation principle, we designed a concentration detector in which enhanced amplification is achieved by an incoherent feedforward reaction network.

q-bio.MN↗

arXiv2005

A p53 Oscillator Model of DNA Break Repair Control

The transcription factor p53 is an important regulator of cell fate. Mutations in p53 gene are associated with many cancers. In response to signals such as DNA damage, p53 controls the transcription of a series of genes that cause cell cycle arrest during which DNA damage is repaired, or triggers programmed cell death that eliminates possibly cancerous cells wherein DNA damage might have remained unrepaired. Previous experiments showed oscillations in p53 level in response to DNA damage, but the mechanism of oscillation remained unclear. Here we examine a model where the concentrations of p53 isoforms are regulated by Mdm22, Arf, Siah, and beta-catenin. The extent of DNA damage is signalled through the switch-like activity of a DNA damage sensor, the DNA-dependent protein kinase Atm. This switch is responsible for initiating and terminating oscillations in p53 concentration. The strength of the DNA damage signal modulates the number of oscillatory waves of p53 and Mdm22 but not the frequency or amplitude of oscillations{a result that recapitulates experimental findings. A critical fnding was that the phosphorylated form of Nbs11, a member of the DNA break repair complex Mre11-Rad50-Nbs11 (MRN), must augment the activity of Atm kinase. While there is in vitro support for this assumption, this activity appears essential for p53 dynamics. The model provides several predictions concerning, among others, degradation of the phosphorylated form of p53, the rate of DNA repair as a function of DNA damage, the sensitivity of p53 oscillation to transcription rates of SIAH, beta-CATENIN and ARF, and the hysteretic behavior of active Atm kinase levels with respect to the DNA damage signal

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arXiv2002

Sensitivity Analysis of Stoichiometric Networks: An Extension of Metabolic Control Analysis to Non-equilibrium Trajectories

A sensitivity analysis of general stoichiometric networks is considered. The results are presented as a generalization of Metabolic Control Analysis, which has been concerned primarily with system sensitivities at steady state. An expression for time-varying sensitivity coefficients is given, and the Summation and Connectivity Theorems are generalized. The results are compared to previous treatments. The analysis is accompanied by a discussion of the computation of the sensitivity coefficients and an application to a model of phototransduction.

physics.bio-ph↗