This work presents an omics-driven modeling pipeline that integrates machine-learning tools to facilitate the dynamic modeling of multiscale biological systems. Random forests and permutation feature importance are proposed to mine omics datasets, guiding feature selection and dimensionality reduction for dynamic modeling. Continuous and differentiable machine-learning functions can be trained to link the reduced omics feature set to key components of the dynamic model, resulting in a hybrid model. As proof of concept, we apply this framework to a high-dimensional proteomics dataset of $\textit{Saccharomyces cerevisiae}$. After identifying key intracellular proteins that correlate with cell growth, targeted dynamic experiments are designed, and key model parameters are captured as functions of the selected proteins using Gaussian processes. This approach captures the dynamic behavior of yeast strains under varying proteome profiles while estimating the uncertainty in the hybrid model's predictions. The outlined modeling framework is adaptable to other scenarios, such as integrating additional layers of omics data for more advanced multiscale biological systems, or employing alternative machine-learning methods to handle larger datasets. Overall, this study outlines a strategy for leveraging omics data to inform multiscale dynamic modeling in systems biology and bioprocess engineering.

The regulatory process of Drosophila has been thoroughly studied for understanding a great variety of systems biology principles. While pattern-forming gene networks are further analysed in the transcription step, post-transcriptional events (e.g. translation, protein processing) play an important role in establishing protein expression patterns and levels. Since post-transcriptional regulation of gap genes in Drosophila depends on spatiotemporal interactions between mRNAs and gap proteins, proper physically-inspired stochastic models are required to study the existing link between both quantities. Previous research attempts have shown that the use of Gaussian processes (GPs) and differential equations leads to promising predictions when analysing regulatory networks. Here we aim at further investigating two types of physically-inspired GP models based on a reaction-diffusion equation where the main difference lies on whether the GP prior is placed. While one of them has been studied previously using gap protein data only, the other is novel and yields a simplistic approach requiring only the differentiation of kernel functions. In contrast to other stochastic frameworks, discretising the spatial space is not required here. Both GP models are tested under different conditions depending on the availability of gap gene mRNA expression data. Finally, their performances are assessed on a high-resolution dataset describing the blastoderm stage of the early embryo of Drosophila melanogaster.

Artificial intelligence originally aspired to replace doctors. Researchers imagined robots that could ask you questions, run the answers through an algorithm that would learn with experience and tell whether you had the flu or a cold. However, those promises largely failed, as artificial intelligent algorithms were too rudimentary to perform those functions. Particularly tricky was the variability between people, which caused basic machine learning algorithms to miss the patterns. Eventually though, a subset of AI called deep learning became sensitive enough to recognize speech from voice data.

Erik Birkeneder is an intellectual property attorney at Nixon Peabody focused on digital health and medical device companies. Artificial intelligence originally aspired to replace doctors. Researchers imagined robots that could ask you questions, run the answers through an algorithm that would learn with experience and tell whether you had the flu or a cold. However, those promises largely failed, as artificial intelligent algorithms were too rudimentary to perform those functions. Particularly tricky was the variability between people, which caused basic machine learning algorithms to miss the patterns.

Modelling the dynamics of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. While some of them are relatively easy to measure, such as mRNA decay rates and mRNA abundance levels, it is still very hard to measure the active concentration levels of the transcription factor proteins that drive the process and the sensitivity of target genes to these concentrations. In this paper we show how these quantities for a given transcription factor can be inferred from gene expression levels of a set of known target genes. We treat the protein concentration as a latent function with a Gaussian process prior, and include the sensitivities, mRNA decay rates and baseline expression levels as hyperparameters. We apply this procedure to a human leukemia dataset, focusing on the tumour repressor p53 and obtaining results in good accordance with recent biological studies.

Modelling the dynamics of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. While some of them are relatively easy to measure, such as mRNA decay rates and mRNA abundance levels, it is still very hard to measure the active concentration levels of the transcription factor proteins that drive the process and the sensitivity of target genes to these concentrations. In this paper we show how these quantities for a given transcription factor can be inferred from gene expression levels of a set of known target genes. We treat the protein concentration as a latent function with a Gaussian process prior, and include the sensitivities, mRNA decay rates and baseline expression levels as hyperparameters. We apply this procedure to a human leukemia dataset, focusing on the tumour repressor p53 and obtaining results in good accordance with recent biological studies.

Modelling the dynamics of transcriptional processes in the cell requires the knowledge of a number of key biological quantities.