Metabolic Pathway Analysis in the Presence of Biological Constraints

Metabolic pathway analysis is a key method to study a metabolism in its steady state, and the concept of elementary fluxes (EFs) plays a major role in the analysis of a network in terms of non-decomposable pathways. The supports of the EFs contain in particular those of the elementary flux modes (EFMs), which are the support-minimal pathways, and EFs coincide with EFMs when the only flux constraints are given by the irreversibility of certain reactions. Practical use of both EFMs and EFs has been hampered by the combinatorial explosion of their number in large, genome-scale systems. The EFs give the possible pathways in a steady state but the real pathways are limited by biological constraints, such as thermodynamic or, more generally, kinetic constraints and regulatory constraints from the genetic network. We provide results on the mathematical structure and geometrical characterization of the solution space in the presence of such biological constraints (which is no longer a convex polyhedral cone or a convex polyhedron) and revisit the concept of EFMs and EFs in this framework. We show that most of the results depend only on very general properties of compatibility of constraints with vector signs: either sign-invariance, satisfied by regulatory constraints, or sign-monotonicity (a stronger property), satisfied by thermodynamic and kinetic constraints. We show in particular that the solution space for sign-monotone constraints is a union of particular faces of the original polyhedral cone or polyhedron and that EFs still coincide with EFMs and are just those of the original EFs that satisfy the constraint, and we show how to integrate their computation efficiently in the double description method, the most widely used method in the tools dedicated to EFs computation. We show that, for sign-invariant constraints, the situation is more complex: the solution space is a disjoint union of particular semi-open faces (i.e., without some of their own faces of lesser dimension) of the original polyhedral cone or polyhedron and, if EFs are still those of the original EFs that satisfy the constraint, their computation cannot be incrementally integrated into the double description method, and the result is not true for EFMs, that are in general strictly more numerous than those of the original EFMs that satisfy the constraint.

The long lives of primates and the ‘invariant rate of ageing’ hypothesis

Is it possible to slow the rate of ageing, or do biological constraints limit its plasticity? We test the ‘invariant rate of ageing’ hypothesis, which posits that the rate of ageing is relatively fixed within species, with a collection of 39 human and nonhuman primate datasets across seven genera. We first recapitulate, in nonhuman primates, the highly regular relationship between life expectancy and lifespan equality seen in humans. We next demonstrate that variation in the rate of ageing within genera is orders of magnitude smaller than variation in pre-adult and age-independent mortality. Finally, we demonstrate that changes in the rate of ageing, but not other mortality parameters, produce striking, species-atypical changes in mortality patterns. Our results support the invariant rate of ageing hypothesis, implying biological constraints on how much the human rate of ageing can be slowed.

Biological Constraints Can Improve Prediction in Precision Oncology

Machine learning (ML) algorithms are used to build predictive models or classifiers for specific disease outcomes using transcriptomic data. However, some of these models show deteriorating performance when tested on unseen data which undermines their clinical utility. In this study, we show the importance of directly embedding prior biological knowledge into the classifier decision rules to build simple and interpretable gene signatures. We tested this in two important classification examples: a) progression in non-muscle invasive bladder cancer; and b) response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC) using different ML algorithms. For each algorithm, we developed two sets of classifiers: agnostic, trained using either individual gene expression values or the corresponding pairwise ranks without biological consideration; and mechanistic, trained by restricting the search to a set of gene pairs capturing important biological relations. Both types were trained on the same training data and their performance was evaluated on unseen testing data using different methodologies and multiple evaluation metrics. Our analysis shows that mechanistic models outperform their agnostic counterparts when tested on independent data and show more consistency to their performance in the training with enhanced interpretability. These findings suggest that using biological constraints in the training process can yield more robust and interpretable gene signatures with high translational potential.

The new SARS-CoV-2 variant and reinfection in the resurgence of COVID-19 outbreaks in Manaus, Brazil

Manaus, a city of 2.2 million population, the capital of Amazonas state of Brazil was hit badly by two waves of COVID-19 with more than 10,000 severe acute respiratory syndrome deaths by the end of February 2021. It was estimated that the first wave infected over three quarters of the population in Manaus based on routine blood donor data, and the second wave was largely due to reinfection with a new variant named P1 strain. In this work, we revisit these claims, and discuss biological constraints. In particular, we model the two waves with a two-strain model without a significant proportion of reinfections.

Integration of APSIM and PROSAIL models to develop more precise radiometric estimation of crop traits using deep learning

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. This can be addressed by using radiative transfer models (RTMs) to generate training dataset representing ‘real-world’ data in situations with varying crop types and growth status as well as various observation configurations. However, this approach can lead to “ill-posed” problems related to assumptions in the sampling strategy and due to uncertainty in the model, resulting in unsatisfactory inversion results for retrieval of target variables. In order to address this problem, this research investigates a practical way to generate higher quality ‘synthetic’ training data by integrating a crop growth model (CGM, in this case APSIM) with an RTM (in this case PROSAIL). This allows control of uncertainties of the RTM by imposing biological constraints on distribution and co-distribution of related variables. Subsequently, the method was theoretically validated on two types of synthetic dataset generated by PROSAIL or the coupling of APSIM and PROSAIL through comparing estimation precision for leaf area index (LAI), leaf chlorophyll content (Cab), leaf dry matter (Cm) and leaf water content (Cw). Additionally, the capabilities of current deep learning techniques using high spectral resolution hyperspectral data were investigated. The main findings include: (1) Feedforward neural network (FFNN) provided with appropriate configuration is a promising technique to retrieve crop traits from input features consisting of 1 nm-wide hyperspectral bands across 400-2500 nm range and observation configuration (solar and viewing angles), leading to a precise joint estimation for LAI (RMSE=0.061 m2 m-2), Cab (RMSE=1.42 μg cm-2), Cm (RMSE=0.000176 g cm-2) and Cw (RMSE=0.000319 g cm-2); (2) For the aim of model simplification, a narrower range in 400-1100 nm without observation configuration in input of FFNN model provided less precise estimation for LAI (RMSE=0.087 m2 m-2), Cab (RMSE=1.92 μg cm-2), Cm (RMSE=0.000299 g cm-2) and Cw (RMSE=0.001271 g cm-2); (3) The introduction of biological constraints in training datasets improved FFNN model performance in both average precision and stability, resulting in a much accurate estimation for LAI (RMSE=0.006 m2 m-2), Cab (RMSE=0.45 μg cm-2), Cm (RMSE=0.000039 g cm-2) and Cw (RMSE=0.000072 g cm-2), and this improvement could be further increased by enriching sample diversity in training dataset.