SBML Level 3 Package Proposal: Flux

This document describes an easy to implement package for storing information related 
to flux balance analysis of SBML Level 3 models (the FBA package). In addition, 
we provide an example of how this package may be implemented and used as a SBML
Level 2 annotation

SBML Level 3 Package: Flux Balance Constraints ('fbc')

Constraint based modeling is a widely accepted methodology used to analyze and study biological networks on both a small and whole organism (genome) scale. Typically these models are underdetermined and constraint based methods (e.g. linear, quadratic optimization) are used to optimize specific model properties. This is assumed to occur under a defined set of constraints (e.g. stoichiometric, metabolic) and bounds (e.g. thermodynamic, experimental and environmental) on the values that the solution fluxes can obtain. Perhaps the most well known (and widely used) analysis method is Flux Balance Analysis (FBA; Orth et al., 2010) which is performed on Genome Scale Reconstructions (GSR’s; Oberhardt et al., 2009). Using FBA a target flux is optimized (e.g. maximizing a flux to biomass or minimizing ATP production) while other fluxes can be bounded to simulate a selected growth environment or specific metabolic state. As constraint based models are generally underdetermined, i.e. few or none of the kinetic rate equations and related parameters are known, it is crucial that a model definition includes the ability to define optimization parameters such as objective functions, flux bounds and constraints. Currently this is not possible in the Systems Biology Markup Language (SBML) Level 2 or Level 3 core specification (Hucka et al., 2011, 2003). The question of how to encode constraint based (also referred to as steady state or FBA) models in SBML is not new. However, advances in the methods used to construct genome scale constraint based models and the wider adoption of constraint based modeling in biotechnological/medical applications have led to a rapid increase in both the number of models being constructed and the tools used to analyze them. Faced with such growth, both in number and diversity, the need for a standardized data format for the definition, exchange and annotation of constraint based models has become critical. As the core model components (e.g. species, reactions, stoichiometry) can already be efficiently described in SBML (with its associated active community, software and tool support) the Flux Balance Constraints package aims to extend SBML Level 3 core by adding the elements necessary to encode current and future constraint based models

Progress report: SBML Level 3 package FBA

The SBML Level 3 "FBA" package is a proposal for an extension to the current Level 3 Core specification that allows for the description and annotation of constraint based models.

This allows one to e.g. store information related to flux balance analysis in SBML Level 3 models

SBML Level 3 Package: Flux Balance Constraints version 2

Constraint-based modeling is a well established modeling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size and complexity such steady-state flux models are, typically, analyzed using constraint-based optimization techniques, for example, flux balance analysis (FBA). The Flux balance constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modeling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. Version two expands on the original release by adding official support for encoding gene-protein associations and their associated elements. In addition to providing the elements necessary to unambiguously encode existing constraint-based models, the FBC Package provides an open platform facilitating the continued, cross-community development of an interoperable, constraint-based model encoding format

FAME, the Flux Analysis and Modeling Environment

<p>Abstract</p> <p>Background</p> <p>The creation and modification of genome-scale metabolic models is a task that requires specialized software tools. While these are available, subsequently running or visualizing a model often relies on disjoint code, which adds additional actions to the analysis routine and, in our experience, renders these applications suboptimal for routine use by (systems) biologists.</p> <p>Results</p> <p>The Flux Analysis and Modeling Environment (FAME) is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps. FAME is written in PHP and uses the Python-based PySCeS-CBM for its linear solving capabilities. It comes with a comprehensive manual and a quick-start tutorial, and can be accessed online at <url>http://f-a-m-e.org/</url>.</p> <p>Conclusions</p> <p>With FAME, we present the community with an open source, user-friendly, web-based "one stop shop" for stoichiometric modeling. We expect the application will be of substantial use to investigators and educators alike.</p

Simulation and database software for computational systems biology : PySCes and JWS Online

Thesis (PhD)--Stellenbosch University, 2005.ENGLISH ABSTRACT: Since their inception, biology and biochemistry have been spectacularly successful in characterising the living cell and its components. As the volume of information about cellular components continues to increase, we need to ask how we should use this information to understand the functioning of the living cell? Computational systems biology uses an integrative approach that combines theoretical exploration, computer modelling and experimental research to answer this question. Central to this approach is the development of computational models, new modelling strategies and computational tools. Against this background, this study aims to: (i) develop a new modelling package: PySCeS, (ii) use PySCeS to study discontinuous behaviour in a metabolic pathway in a way that was very difficult, if not impossible, with existing software, (iii) develop an interactive, web-based repository (JWS Online) of cellular system models. Three principles that, in our opinion, should form the basis of any new modelling software were laid down: accessibility (there should be as few barriers as possible to PySCeS use and distribution), flexibility (pySCeS should be extendable by the user, not only the developers) and usability (PySCeS should provide the tools we needed for our research). After evaluating various alternatives we decided to base PySCeS on the freely available programming language, Python, which, in combination with the large collection of science and engineering algorithms in the SciPy libraries, would give us a powerful modern, interactive development environment.AFRIKAANSE OPSOMMING: Sedert hul totstandkoming was biologie en, meer spesifiek, biochemie uiters suksesvol in die karakterisering van die lewende sel se komponente. Steeds groei die hoeveelheid informasie oor die molekulêre bestanddele van die sel daagliks; ons moet onself dus afvra hoe ons hierdie informasie kan integreer tot 'n verstaanbare beskrywing van die lewende sel se werking. Om dié vraag te beantwoord gebruik rekenaarmatige sisteembiologie 'n geïntegreerde benadering wat teorie, rekenaarmatige modellering en eksperimenteeIe navorsing kombineer. Sentraal tot die benadering is die ontwikkeling van nuwe modelle, strategieë vir modellering, en sagteware. Teen hierdie agtergrond is die hoofdoelstelling van hierdie projek: (i) die ontwikkeling van 'n nuwe modelleringspakket, PySCeS (ii) die benutting van PySCeS om diskontinue gedrag in n metaboliese sisteem te bestudeer (iets wat met die huidiglik beskikbare sagteware redelik moeilik is), (en iii) die ontwikkeling vann interaktiewe, internet-gebaseerde databasis van sellulêre sisteem modelle, JWS Online. Ons is van mening dat nuwe sagteware op drie belangrike beginsels gebaseer behoort te wees: toeganklikheid (die sagteware moet maklik bekombaar en bruikbaar wees), buigsaamheid (die gebruiker moet self PySCeS kan verander en ontwikkel) en bruikbaarheid (al die funksionalitiet wat ons vir ons navorsing nodig moet in PySCeS ingebou wees). Ons het verskeie opsies oorweeg en besluit om die vrylik verkrygbare programmeringstaal, Python, in samehang die groot kolleksie wetenskaplike algoritmes, SciPy, te gebruik. Hierdie kombinasie verskaf n kragtige, interaktiewe ontwikkelings- en gebruikersomgewing. PySCeS is ontwikkel om onder beide die Windows en Linux bedryfstelsels te werk en, meer spesifiek, om gebruik te maak van 'n 'command line interface'. Dit beteken dat PySCeS op enige interaktiewe rekenaar-terminaal Python ondersteun sal werk. Hierdie eienskap maak ook moontlik die gebruik van PySCeS as 'n modelleringskomponent in 'n groter sagteware pakket onder enige bedryfstelsel wat Python ondersteun. PySCeS is op 'n modulere ontwerp gebaseer, wat dit moontlik vir die eindgebruiker maak om die sagteware se bronkode verder te ontwikkel. As 'n toepassing is PySCeS gebruik om die oorsaak van histeretiese gedrag van 'n lineêre, eindproduk-geïnhibeerde metaboliese pad te ondersoek. Ons het hierdie interessante gedrag in 'n vorige studie ontdek, maar kon nie, met die sagteware wat op daardie tydstip tot ons beskikking was, hierdie studie voortsit nie. Met PySCeS se ingeboude vermoë om parameter kontinuering te doen, kon ons die oorsake van hierdie diskontinuë gedrag volledig karakteriseer. Verder het ons 'n nuwe metode ontwikkel om hierdie gedrag te visualiseer as 'n interaksie tussen die volledige sisteem se subkomponente. Tydens PySCeS se ontwikkeling het ons opgemerk dat dit baie moeilik was om metaboliese modelle wat in die literature gepubliseer is te herbou en te bestudeer. Hierdie situasie is grotendeels die gevolg van die feit dat nêrens 'n sentrale databasis vir metaboliese modelle bestaan nie (soos dit wel bestaan vir genomiese data of proteïen strukture). Die JWS Online databasis is spesifiek ontwikkel om hierdie leemte te vul. JWS Online maak dit vir die gebruiker moontlik om, via die internet en sonder die installasie van enige gespesialiseerde modellerings sagteware, gepubliseerde modelle te bestudeer en ook af te laai vir gebruik met ander modelleringspakkette soos bv. PySCeS. JWS Online het alreeds 'n onmisbare hulpbron vir sisteembiologiese navorsing en onderwys geword

Non-detection of Contamination by Stellar Activity in the Spitzer Transit Light Curves of TRAPPIST-1

We apply the transit light curve self-contamination technique of Morris et al. (2018) to search for the effect of stellar activity on the transits of the ultracool dwarf TRAPPIST-1 with 2018 Spitzer photometry. The self-contamination method fits the transit light curves of planets orbiting spotted stars, allowing the host star to be a source of contaminating positive or negative flux which influences the transit depths but not the ingress/egress durations. We find that none of the planets show statistically significant evidence for self-contamination by bright or dark regions of the stellar photosphere. However, we show that small-scale magnetic activity, analogous in size to the smallest sunspots, could still be lurking in the transit photometry undetected.Comment: Accepted for publication in ApJ

Hunt for Starspots in HARPS Spectra of G and K Stars

We present a method for detecting starspots on cool stars using the cross-correlation function (CCF) of high resolution molecular spectral templates applied to archival high-resolution spectra of G and K stars observed with HARPS/HARPS-N. We report non-detections of starspots on the Sun even when the Sun was spotted, the solar twin 18 Scorpii, and the very spotted Sun-like star HAT-P-11, suggesting that Sun-like starspot distributions will be invisible to the CCF technique, and should not produce molecular absorption signals which might be confused for signatures of exoplanet atmospheres. We detect strong TiO absorption in the T Tauri K-dwarfs LkCa 4 and AA Tau, consistent with significant coverage by cool regions. We show that despite the non-detections, the technique is sensitive to relatively small spot coverages on M dwarfs and large starspot areas on Sun-like stars.Comment: 12 pages, 8 figures, accepted to A

Nature-inspired flow-fields and water management for PEM fuel cells

Flow-field design is crucial to polymer electrolyte membrane fuel cell (PEMFC) performance, since non-uniform transport of species to and from the membrane electrode assembly (MEA) results in significant power losses. The long channels of conventional serpentine flow-fields cause large pressure drops between inlets and outlets, thus large parasitic energy losses and low fuel cell performance. Here, a lung-inspired approach is used to design flow-fields guided by the structure of a lung. The fractal geometry of the human lung has been shown to ensure uniform distribution of air from a single outlet (trachea) to multiple outlets (alveoli). Furthermore, the human lung transitions between two flow regimes: 14-16 upper generations of branches dominated by convection, and 7-9 lower generations of space-filling acini dominated by diffusion. The upper generations of branches are designed to slow down the gas flow to a rate compatible with the rate in the diffusional regime (Pé ~ 1), resulting in uniform distribution of entropy production in both regimes. By employing a three-dimensional (3D) fractal structure as flow-field inlet channel, we aim to yield similar benefits from replicating these characteristics of the human lung. The fractal pattern consists of repeating “H” shapes where daughter “H’s” are located at the four tips of the parent “H”. The fractal geometry obeys Murray’s law, much like the human lung, hereby leading to minimal mechanical energy losses. Furthermore, the three-dimensional branching structure provide uniform local conditions on the surface of the catalyst layer as only the outlets of the fractal inlet channel are exposed to the MEA. Numerical simulations were conducted to determine the number of generations required to achieve uniform reactant distribution and minimal entropy production. The results reveal that the ideal number of generations for minimum entropy production lies between N = 5 and 7. Guided by the simulation results, three flow-fields with N = 3, 4 and 5 (10 cm2 surface area) were 3D printed via direct metal laser sintering (DMLS), and experimentally validated against conventional serpentine flow-fields. The fractal flow-fields with N = 4 and 5 generations showed ~20% and ~30% increase in performance and maximum power density over serpentine flow-fields above 0.8 A cm-2 at 50% RH. At fully humidified conditions, though, the performance of fractal N = 5 flow-field significantly deteriorates due to flooding issues. Another defining characteristic of the fractal approach is scalability, which is an important feature in nature. Fractal flow-fields can bridge multiple length scales by adding further generations, while preserving the building units and microscopic function of the system. Larger, 3D printed fractal flow-fields (25 cm2 surface area) with N = 4 are compared to conventional serpentine flow-field based PEMFCs. Performance results show that fractal and serpentine flow-field based PEMFCs have similar polarization curves, which is attributed to the significantly higher pressure drop (~ 25 kPa) of large serpentine flow-fields compared to fractal flow-fields. However, such excessive pressure drop renders the use of a large scale serpentine flow-field prohibitive, thus favouring the fractal flow-field. A major shortcoming of using fractal flow-field is, though, susceptibility to flooding in the gas channels due to slow gas velocity. This problem has led to the development of a nature-inspired water management mechanism that draws inspiration from the ability of the Thorny Devil (Australian lizard) to passively transport liquid water across its skin using capillary pressure. We have recently integrated this strategy with the fractal N = 4 flow-fields and verified the viability of the strategy using neutron imaging at Helmholtz-Zentrum Berlin (HZB). Implementation of this water management strategy is expected to circumvent remaining problems of high-generation fractal flow-fields