Experimental Biological Protocols with Formal Semantics

Both experimental and computational biology is becoming increasingly automated. Laboratory experiments are now performed automatically on high-throughput machinery, while computational models are synthesized or inferred automatically from data. However, integration between automated tasks in the process of biological discovery is still lacking, largely due to incompatible or missing formal representations. While theories are expressed formally as computational models, existing languages for encoding and automating experimental protocols often lack formal semantics. This makes it challenging to extract novel understanding by identifying when theory and experimental evidence disagree due to errors in the models or the protocols used to validate them. To address this, we formalize the syntax of a core protocol language, which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols. We present both a deterministic and a stochastic semantics to this language, both defined in terms of hybrid processes. In particular, the stochastic semantics captures uncertainties in equipment tolerances, making it a suitable tool for both experimental and computational biologists. We illustrate how the proposed protocol language can be used for automated verification and synthesis of laboratory experiments on case studies from the fields of chemistry and molecular programming

A Hierarchy of Scheduler Classes for Stochastic Automata

Stochastic automata are a formal compositional model for concurrent stochastic timed systems, with general distributions and non-deterministic choices. Measures of interest are defined over schedulers that resolve the nondeterminism. In this paper we investigate the power of various theoretically and practically motivated classes of schedulers, considering the classic complete-information view and a restriction to non-prophetic schedulers. We prove a hierarchy of scheduler classes w.r.t. unbounded probabilistic reachability. We find that, unlike Markovian formalisms, stochastic automata distinguish most classes even in this basic setting. Verification and strategy synthesis methods thus face a tradeoff between powerful and efficient classes. Using lightweight scheduler sampling, we explore this tradeoff and demonstrate the concept of a useful approximative verification technique for stochastic automata

Plant growth drives soil nitrogen cycling and N-related microbial activity through changing root traits

Relationships between plants and nitrogen-related microbes may vary with plant growth. We investigated these dynamic relationships over three months by analyzing plant functional traits (PFT), arbuscular mycorrhizal fungal (AMF) colonization, potential N mineralization (PNM), potential nitrification (PNA) and denitrification activities (PDA) in Dactylis glomerata cultures. D. glomerata recruited AMF during early growth, and thereafter maintained a constant root colonization intensity. This may have permitted high enough plant nutrient acquisition over the three months as to offset reduced soil inorganic N. PFT changed with plant age and declining soil fertility, resulting in higher allocation to root biomass and higher root C:N ratio. Additional to root AMF presence, PR' changes may have favored denitrification over mineralization through changes in soil properties, particularly increasing the quality of the labile carbon soil fraction. Other PFT changes, such as N uptake, modified the plants' ability to compete with bacterial groups involved in N cycling. (C) 2020 Elsevier Ltd and British Mycological Society. All rights reserved.Peer reviewe

Compared to conventional, ecological intensive management promotes beneficial proteolytic soil microbial communities for agro-ecosystem functioning under climate change-induced rain regimes

Projected climate change and rainfall variability will affect soil microbial communities, biogeochemical cycling and agriculture. Nitrogen (N) is the most limiting nutrient in agroecosystems and its cycling and availability is highly dependent on microbial driven processes. In agroecosystems, hydrolysis of organic nitrogen (N) is an important step in controlling soil N availability. We analyzed the effect of management (ecological intensive vs. conventional intensive) on N-cycling processes and involved microbial communities under climate change-induced rain regimes. Terrestrial model ecosystems originating from agroecosystems across Europe were subjected to four different rain regimes for 263 days. Using structural equation modelling we identified direct impacts of rain regimes on N-cycling processes, whereas N-related microbial communities were more resistant. In addition to rain regimes, management indirectly affected N-cycling processes via modifications of N-related microbial community composition. Ecological intensive management promoted a beneficial N-related microbial community composition involved in N-cycling processes under climate change-induced rain regimes. Exploratory analyses identified phosphorus-associated litter properties as possible drivers for the observed management effects on N-related microbial community composition. This work provides novel insights into mechanisms controlling agro-ecosystem functioning under climate change

Au-Ag template stripped pattern for scanning probe investigations of DNA arrays produced by Dip Pen Nanolithography

We report on DNA arrays produced by Dip Pen Nanolithography (DPN) on a novel Au-Ag micro patterned template stripped surface. DNA arrays have been investigated by atomic force microscopy (AFM) and scanning tunnelling microscopy (STM) showing that the patterned template stripped substrate enables easy retrieval of the DPN-functionalized zone with a standard optical microscope permitting a multi-instrument and multi-technique local detection and analysis. Moreover the smooth surface of the Au squares (abput 5-10 angstrom roughness) allows to be sensitive to the hybridization of the oligonucleotide array with label-free target DNA. Our Au-Ag substrates, combining the retrieving capabilities of the patterned surface with the smoothness of the template stripped technique, are candidates for the investigation of DPN nanostructures and for the development of label free detection methods for DNA nanoarrays based on the use of scanning probes.Comment: Langmuir (accepted

Management versus site effects on the abundance of nitrifiers and denitrifiers in European mountain grasslands

It is well established that the abundances of nitrogen (N) transforming microbes are strongly influenced by land-use intensity in lowland grasslands. However, their responses to management change in less productive and less fertilized mountain grasslands are largely unknown. We studied eight mountain grasslands, positioned along gradients of management intensity in Austria, the UK, and France, which differed in their historical management trajectories. We measured the abundance of ammonia-oxidizing bacteria (AOB) and archaea (AOA) as well as nitrite-reducing bacteria using specific marker genes. We found that management affected the abundance of these microbial groups along each transect, though the specific responses differed between sites, due to different management histories and resulting variations in environmental parameters. In Austria, cessation of management caused an increase in nirK and nirS gene abundances. In the UK, intensification of grassland management led to 10-fold increases in the abundances of AOA and AOB and doubling of nirK gene abundance. In France, ploughing of previously mown grassland caused a 20-fold increase in AOA abundance. Across sites the abundance of AOB was most strongly related to soil NO3−-N availability, and AOA were favored by higher soil pH. Among the nitrite reducers, nirS abundance correlated most strongly with N parameters, such as soil NO3−-N, microbial N, leachate NH4+-N, while the abundance of nirK-denitrifiers was affected by soil total N, organic matter (SOM) and water content. We conclude that alteration of soil environmental conditions is the dominant mechanism by which land management practices influence the abundance of each group of ammonia oxidizers and nitrite reducers

Distributed Parametric and Statistical Model Checking

Statistical Model Checking (SMC) is a trade-off between testing and formal verification. The core idea of the approach is to conduct some simulations of the system and verify if they satisfy some given property. In this paper we show that SMC is easily parallelizable on a master/slaves architecture by introducing a series of algorithms that scale almost linearly with respect to the number of slave computers. Our approach has been implemented in the UPPAAL SMC toolset and applied on non-trivial case studies.Comment: In Proceedings PDMC 2011, arXiv:1111.006