Quantitative Biology

Recent efforts have focused on providing a systematic analysis of syntrophic microbial growth yields. These biokinetic parameters are key to developing an accurate mathematical description of the anaerobic digestion process. The agreement between experimentally determined growth yields and those obtained from bioenergetic estimations is therefore of great interest. Considering five important syntrophic groups, including acetoclastic and hydrogenotrophic methanogens, as well as propionate, butyrate and lactate oxidizers, previous findings suggest that measured and estimated growth yields were consistent only for acetoclastic methanogens. A re-analysis revealed that data are also consistent for lactate oxidizers and hydrogenotrophic methanogens, whereas the limited data available for propionate and butyrate oxidizers are unsupportive of firm conclusions. These results highlight pertinent challenges in the analysis of microbial syntrophy and encourage more accurate measurements of syntrophic microbial growth yields in the future.

Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM). However, clinical implementation is limited by lack of parameters standardization. We aimed to compare nine machine learning classifiers, with different optimization parameters, to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients, based on radiomic features from conventional and advanced MR. 156 adult patients with pathologic diagnosis of GBM were included. Three tumoral regions were analyzed: contrast-enhancing tumor, necrosis and non-enhancing tumor, selected by manual segmentation. Radiomic features were extracted with a custom version of Pyradiomics, and selected through Boruta algorithm. A Grid Search algorithm was applied when computing 4 times K-fold cross validation (K=10) to get the highest mean and lowest spread of accuracy. Once optimal parameters were identified, model performances were assessed in terms of Area Under The Curve-Receiver Operating Characteristics (AUC-ROC). Metaheuristic and ensemble classifiers showed the best performance across tasks. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,6%). Best performing features shed light on possible correlations between MR and tumor histology.

Infrared thermography (IRT) is a valuable diagnostic tool in equine veterinary medicine however, little is known about its application in donkeys. The aim was to find patterns in thermal images of donkeys and horses, and determine if these patterns share similarities. The study was carried out on 18 donkeys and 16 horses. All equids underwent thermal imaging with an infrared camera and measuring the skin thickness and hair coat length. On the class maps of each thermal image, 15 regions of interest (ROIs) were annotated and then combined into 10 groups of ROIs (GORs). The existence of statistically significant differences between surface temperatures in GORs was tested both `globally' for all animals of a given species and `locally' for each animal. Two special cases of animals that differ from the rest were also discussed. Our results indicated that the majority of thermal patterns are similar for both species however, average surface temperatures in horses are higher than in donkeys. It may be related to differences in the skin and hair coat. We concluded, the patterns of both species are associated with GORs, rather than an individual ROI, with higher uniformity of donkeys patterns.

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are also emerging as a valuable avenue for predicting, understanding and designing microbial communities. COMETS (Computation Of Microbial Ecosystems in Time and Space) was initially developed as an extension of dynamic flux balance analysis, which incorporates cellular and molecular diffusion, enabling simulations of multiple microbial species in spatially structured environments. Here we describe how to best use and apply the most recent version of this platform, COMETS 2, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, as well as several new biological simulation modules, including evolutionary dynamics and extracellular enzyme activity. COMETS 2 provides user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, and comprehensive documentation and tutorials, facilitating the use of COMETS for researchers at all levels of expertise with metabolic simulations. This protocol provides a detailed guideline for installing, testing and applying COMETS 2 to different scenarios, with broad applicability to microbial communities across biomes and scales.

We advocate here the use of computational logic for systems biology, as a \emph{unified and safe} framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including either induction, or a sequent calculus presentation enjoying cut-elimination and focusing), and should come with certified proof tools. Beyond providing a reliable framework, this allows the correct encodings of our biological systems. % For systems biology in general and biomedicine in particular, we have so far, for the modeling part, three candidate logics: all based on linear logic. The studied properties and their proofs are formalized in a very expressive (non linear) inductive logic: the Calculus of Inductive Constructions (CIC). The examples we have considered so far are relatively simple ones; however, all coming with formal semi-automatic proofs in the Coq system, which implements CIC. In neuroscience, we are directly using CIC and Coq, to model neurons and some simple neuronal circuits and prove some of their dynamic properties. % In biomedicine, the study of multi omic pathway interactions, together with clinical and electronic health record data should help in drug discovery and disease diagnosis. Future work includes using more automatic provers. This should enable us to specify and study more realistic examples, and in the long term to provide a system for disease diagnosis and therapy prognosis.

The existing computational models used to estimate motion sickness are incapable of describing the fact that the predictability of motion patterns affects motion sickness. Therefore, the present study proposes a computational model to describe the effect of the predictability of dynamics or the pattern of motion stimuli on motion sickness. In the proposed model, a submodel, in which a recursive Gaussian process regression is used to represent human features of online learning and future prediction of motion dynamics, is combined with a conventional model of motion sickness based on an observer theory. A simulation experiment was conducted in which the proposed model predicted motion sickness caused by a 900 s horizontal movement. The movement was composed of a 9 m repetitive back-and-forth movement pattern with a pause. Regarding the motion condition, the direction and timing of the motion were varied as follows: a) Predictable motion (M_P): the direction of the motion and duration of the pause were set to 8 s; b) Motion with unpredicted direction (M_dU): the pause duration was fixed as in (P), but the motion direction was randomly determined; c) Motion with unpredicted timing (M_tU): the motion direction was fixed as in (M_P), but the pause duration was randomly selected from 4 to 12 s. The results obtained using the proposed model demonstrated that the predicted motion sickness incidence for (M_P) was smaller than those for (M_dU) and (M_tU). This tendency agrees with the sickness patterns observed in a previous experimental study in which the human participants were subject to motion conditions similar to those used in our simulations. Moreover, no significant differences were found in the predicted motion sickness incidences at different conditions when the conventional model was used.

Drawing and visualisation of molecular structures are some of the most common tasks carried out in structural glycobiology, typically using various software. In this perspective article, we outline developments in the computational tools for the sketching, visualisation and modelling of glycans. The article also provides details on the standard representation of glycans, and glycoconjugates, which helps the communication of structure details within the scientific community. We highlight the comparative analysis of the available tools which could help researchers to perform various tasks related to structure representation and model building of glycans. These tools can be useful for glycobiologists or any researcher looking for a ready to use, simple program for the sketching or building of glycans.

Computer-aided design for synthetic biology promises to accelerate the rational and robust engineering of biological systems; it requires both detailed and quantitative mathematical and experimental models of the processes to (re)design, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. Computer-aided design strategies require quantitative representations of cells, able to capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems, and we discuss several challenges for the realization of our vision. The possibility to describe and build in silico whole-cells offers an opportunity to develop increasingly automatized, precise and accessible computer-aided design tools and strategies throughout novel interdisciplinary collaborations.

The removal of organic micropollutants (OMPs) has been investigated in constructed wetlands (CWs) operated as bioelectrochemical systems (BES). The operation of CWs as BES (CW-BES), either in the form of microbial fuel cells (MFC) or microbial electrolysis cells (MEC), has only been investigated in recent years. The presented experiment used CW meso-scale systems applying a realistic horizontal flow regime and continuous feeding of real urban wastewater spiked with four OMPs (pharmaceuticals), namely carbamazepine (CBZ), diclofenac (DCF), ibuprofen (IBU) and naproxen (NPX). The study evaluated the removal efficiency of conventional CW systems (CW-control) as well as CW systems operated as closed-circuit MFCs (CW-MFCs) and MECs (CW-MECs). Although a few positive trends were identified for the CW-BES compared to the CW-control (higher average CBZ, DCF and NPX removal by 10-17% in CW-MEC and 5% in CW-MFC), these proved to be not statistically significantly different. Mesoscale experiments with real wastewater could thus not confirm earlier positive effects of CW-BES found under strictly controlled laboratory conditions with synthetic wastewaters.

The National Institutes of Health's (NIH) Human Biomolecular Atlas Program (HuBMAP) aims to create a comprehensive high-resolution atlas of all the cells in the healthy human body. Multiple laboratories across the United States are collecting tissue specimens from different organs of donors who vary in sex, age, and body size. Integrating and harmonizing the data derived from these samples and 'mapping' them into a common three-dimensional (3D) space is a major challenge. The key to making this possible is a 'Common Coordinate Framework' (CCF), which provides a semantically annotated, 3D reference system for the entire body. The CCF enables contributors to HuBMAP to 'register' specimens and datasets within a common spatial reference system, and it supports a standardized way to query and 'explore' data in a spatially and semantically explicit manner. [...] This paper describes the construction and usage of a CCF for the human body and its reference implementation in HuBMAP. The CCF consists of (1) a CCF Clinical Ontology, which provides metadata about the specimen and donor (the 'who'); (2) a CCF Semantic Ontology, which describes 'what' part of the body a sample came from and details anatomical structures, cell types, and biomarkers (ASCT+B); and (3) a CCF Spatial Ontology, which indicates 'where' a tissue sample is located in a 3D coordinate system. An initial version of all three CCF ontologies has been implemented for the first HuBMAP Portal release. It was successfully used by Tissue Mapping Centers to semantically annotate and spatially register 48 kidney and spleen tissue blocks. The blocks can be queried and explored in their clinical, semantic, and spatial context via the CCF user interface in the HuBMAP Portal.