Inmicrobiome studies, we may wish to investigate the association between the overall composition of human microbiota, including hundreds of microbial taxa, and multiple host metabolites from aparticular metabolic pathway [3, 4].

We develop a novel data-driven framework as an alternative to dynamic flux balance analysis, bypassing the demand for deep domain knowledge and manual efforts to formulate the optimization problem. The proposed framework is end-toend, which trains a structured neural ODE process (SNODEP) model to estimate flux and balance samples using gene-expression time-series data. SNODEP is designed to circumvent the limitations of the standard neural ODE process model, including restricting the latent and decoder sampling distributions to be normal and lacking structure between context points for calculating the latent, thus more suitable for modeling the underlying dynamics of a metabolic system. Through comprehensive experiments (156 in total), we demonstrate that SNODEP not only predicts the unseen time points of real-world gene-expression data and the flux and balance estimates well but can even generalize to more challenging unseen knockout configurations and irregular data sampling scenarios, all essential for metabolic pathway analysis. We hope our work can serve as a catalyst for building more scalable and powerful models for genome-scale metabolic analysis. A distinctive characteristic of deep neural networks is their capability to implicitly learn complicated features and dynamics from data, significantly saving human effort in composing those handcrafted features and devising complex models. Therefore, there has been a growing interest in using them in a variety of scientific contexts, such as quantum chemistry (von Glehn et al., 2022), tokamak controller design (Degrave et al., 2022), climate sciences (Lam et al., 2022; Nguyen et al., 2023), molecule generation (Hoogeboom et al., 2022) and drug discovery (Askr et al., 2023), to name a few.

Abstract: Protein subcellular localization is an important factor in normal cellular processes and dis- ease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization an- notations from curated pathway databases. We also perform a case study where we con- struct biological pathways and predict localizations of human fibroblasts undergoing viral infection.

The most successful targets for precision medicine can be found by using algorithms created by University of Michigan researchers. These algorithms successfully identify the weakest targets in ovarian cancer cells--genes these cells depend on to live in the human body. Cancer cells delete DNA when they go to the dark side, so a team of doctors and engineers targeted the'backup plans' that run essential cell functions. Researchers at the University of Michigan and Indiana University have discovered a cancer weakness. They found that the way that tumor cells enable their uncontrolled growth is also a weakness that can be harnessed to treat cancer.

The diverse metabolic pathways are fundamental to all living organisms, as they harvest energy, synthesize biomass components, produce molecules to interact with the microenvironment, and neutralize toxins. While discovery of new metabolites and pathways continues, the prediction of pathways for new metabolites can be challenging. It can take vast amounts of time to elucidate pathways for new metabolites; thus, according to HMDB only 60% of metabolites get assigned to pathways. Here, we present an approach to identify pathways based on metabolite structure. We extracted 201 features from SMILES annotations, and identified new metabolites from PubMed abstracts and HMDB. After applying clustering algorithms to both groups of features, we quantified correlations between metabolites, and found the clusters accurately linked 92% of known metabolites to their respective pathways. Thus, this approach could be valuable for predicting metabolic pathways for new metabolites.

Associate Professor Christopher J. Vavricka, Graduate School of Science, Technology and Innovation, Kobe University, Assistant Professor Shunsuke Takahashi, Faculty of Science and Technology, Tokyo Electric University, Michihiro Araki, Deputy Director, AI Health and Pharmaceutical Research Center, Institute of Pharmaceutical Sciences, Health and Nutrition, Kobe University A research group led by Professor Masahisa Hasunuma of the Advanced Bioengineering Research Center has succeeded in producing microorganisms for plant-derived pharmaceutical raw materials by developing a machine learning prediction model capable of discovering unknown enzymes and linking it with metabolic engineering. In the future, it is expected to accelerate the bioproduction of various useful substances, functional materials, and general-purpose chemicals. The results of this research were published in the British scientific journal Nature Communications on March 16 .With the progress of synthetic biology in recent years, microbial fermentation production of plant-derived pharmaceutical raw materials is expected. When targeting BIA, which is widely used as a raw material for analgesics, the problem was that some of the enzymes that make up the metabolic pathway were unknown. To solve the problem of enzyme discovery, we developed by biotechology a machine learning prediction model and linked it to the DBTL workflow of design ( D esign) -construction ( B uild) -evaluation ( T est) -learning ( L earn).

In this work a machine learning approach for identifying the multi-omicsmetabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in termof their multi-omics follows from the analysis of these circuits . This is a consequenceof the alternation of the omic values of codon usage and protein abundance along thecircuits. In this work, the E.Coli's Glycolysis and its multi-omic circuit features areshown as an example. 1 Background In the bacterial metabolic pathways, it is possible to identify different small circuitsthat lead from an intermediate compound to another. Each bacterial pathway could be considered as a highly specific directed graph that presents more than one multi-omic circuit (MOC).

We introduce, test and discuss a method for classifying and clustering data modeled as directed graphs. The idea is to start diffusion processes from any subset of a data collection, generating corresponding distributions for reaching points in the network. These distributions take the form of high-dimensional numerical vectors and capture essential topological properties of the original dataset. We show how these diffusion vectors can be successfully applied for getting state-of-the-art accuracies in the problem of extracting pathways from metabolic networks. We also provide a guideline to illustrate how to use our method for classification problems, and discuss important details of its implementation. In particular, we present a simple dimensionality reduction technique that lowers the computational cost of classifying diffusion vectors, while leaving the predictive power of the classification process substantially unaltered. Although the method has very few parameters, the results we obtain show its flexibility and power. This should make it helpful in many other contexts.