Age- and size-related changes in hind limb muscles in two baboon species (Papio anubis and P. papio).

Body size has an impact on all biological functions and analyzing how body size impacts functional traits such as locomotion is critical. Body size does not only vary across species but also during ontogeny. Indeed, juvenile animals are often at a competitive disadvantage due to their smaller absolute size. Consequently, understanding size- and age-related changes in the locomotor system is critical for our understanding of adult phenotypes. Here, we address this question by exploring growth of the hind limb muscles in two species of closely related baboons that differ in their ecology, the olive baboon, Papio Anubis, the Guinea baboon, and Papio papio. To do so, we dissected 40 P. anubis and 10 P. papio and measured the mass and physiological cross-sectional area (PCSA) of the hind limb muscles. Our results showed no sexual differences in size- or age-related growth patterns, but did show differences between species. Whereas the scaling of muscle mass and PCSA was largely isometric in P. anubis, allometric scaling was more common in P. papio. Despite these differences between species, the knee extensors and external rotators at the knee scaled with positive allometry in both species highlighting their important role during adult locomotion. Although life-history data for P. papio are scarce, we suggest that differences between species may be associated with differences in adult body size and age of locomotor independence between species.

A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models.

Constraint-based metabolic models have been used for decades to predict the phenotype of microorganisms in different environments. However, quantitative predictions are limited unless labor-intensive measurements of media uptake fluxes are performed. We show how hybrid neural-mechanistic models can serve as an architecture for machine learning providing a way to improve phenotype predictions. We illustrate our hybrid models with growth rate predictions of Escherichia coli and Pseudomonas putida grown in different media and with phenotype predictions of gene knocked-out Escherichia coli mutants. Our neural-mechanistic models systematically outperform constraint-based models and require training set sizes orders of magnitude smaller than classical machine learning methods. Our hybrid approach opens a doorway to enhancing constraint-based modeling: instead of constraining mechanistic models with additional experimental measurements, our hybrid models grasp the power of machine learning while fulfilling mechanistic constrains, thus saving time and resources in typical systems biology or biological engineering projects.

Cell-Free Biosensors and AI Integration.

Cell-free biosensors hold a great potential as alternatives for traditional analytical chemistry methods providing low-cost low-resource measurement of specific chemicals. However, their large-scale use is limited by the complexity of their development.In this chapter, we present a standard methodology based on computer-aided design (CAD ) tools that enables fast development of new cell-free biosensors based on target molecule information transduction and reporting through metabolic and genetic layers, respectively. Such systems can then be repurposed to represent complex computational problems, allowing defined multiplex sensing of various inputs and integration of artificial intelligence in synthetic biological systems.

A versatile active learning workflow for optimization of genetic and metabolic networks.

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO2-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering.

Among the main learning methods reviewed in this study and used in synthetic biology and metabolic engineering are supervised learning, reinforcement and active learning, and in vitro or in vivo learning. In the context of biosynthesis, supervised machine learning is being exploited to predict biological sequence activities, predict structures and engineer sequences, and optimize culture conditions. Active and reinforcement learning methods use training sets acquired through an iterative process generally involving experimental measurements. They are applied to design, engineer, and optimize metabolic pathways and bioprocesses. The nascent but promising developments with in vitro and in vivo learning comprise molecular circuits performing simple tasks such as pattern recognition and classification.