Frontiers in systems biology最新文献

Introduction: Chemical reaction networks (CRNs) are powerful tools for describing the complex nature of cancer's onset, progression, and therapy. The main reason for their effectiveness is in the fact that these networks can be rather naturally encoded as a dynamical system whose asymptotic solution mimics the proteins' concentration profile at equilibrium. Methods and Results: This paper relies on a complex CRN previously designed for modeling colorectal cells in their G1-S transition phase and presents a mathematical method to investigate global and local effects triggered on the network by partial and complete mutations occurring mainly in its mitogen-activated protein kinase (MAPK) pathway. Further, this same approach allowed the in-silico modeling and dosage of a multi-target therapeutic intervention that utilizes MAPK as its molecular target. Discussion: Overall the results shown in this paper demonstrate how the proposed approach can be exploited as a tool for the in-silico comparison and evaluation of different targeted therapies. Future effort will be devoted to refine the model so to incorporate more biologically sound partial mutations and drug combinations.

The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other domains have a role in certain biomedical AI systems, primarily related to image processing, there is a critical gap in the generation of time series data for AI tasks where it is necessary to know how the system works. This is most pronounced in the ability to generate synthetic multi-dimensional molecular time series data (subsequently referred to as synthetic mediator trajectories or SMTs); this is the type of data that underpins research into biomarkers and mediator signatures for forecasting various diseases and is an essential component of the drug development pipeline. We argue the insufficiency of statistical and data-centric machine learning (ML) means of generating this type of synthetic data is due to a combination of factors: perpetual data sparsity due to the Curse of Dimensionality, the inapplicability of the Central Limit Theorem in terms of making assumptions about the statistical distributions of this type of data, and the inability to use ab initio simulations due to the state of perpetual epistemic incompleteness in cellular/molecular biology. Alternatively, we present a rationale for using complex multi-scale mechanism-based simulation models, constructed and operated on to account for perpetual epistemic incompleteness and the need to provide maximal expansiveness in concordance with the Maximal Entropy Principle. These procedures provide for the generation of SMT that minimizes the known shortcomings associated with neural network AI systems, namely overfitting and lack of generalizability. The generation of synthetic data that accounts for the identified factors of multi-dimensional time series data is an essential capability for the development of mediator-biomarker based AI forecasting systems, and therapeutic control development and optimization.

Introduction: A thorough understanding of the microbial ecology within the swine value chain is essential to develop new strategies to optimize the microbiological quality of pork products. To our knowledge, no study to date has followed the microbiota through the value chain from live farm animals to the cuts of meat obtained for market. The objective of this study is to evaluate how the microbiota of pigs and their environment influence the microbial composition of samples collected throughout the value chain, including the meat plant and meat cuts. Method and results: Results from 16S rDNA sequencing, short-chain fatty acid concentrations and metabolomic analysis of pig feces revealed that the microbiota from two farms with differing sanitary statuses were distinctive. The total aerobic mesophilic bacteria and Enterobacteriaceae counts from samples collected at the meat plant after the pre-operation cleaning and disinfection steps were at or around the detection limit and the pigs from the selected farms were the first to be slaughtered on each shipment days. The bacterial counts of individual samples collected at the meat plant did not vary significantly between the farms. Alpha diversity results indicate that as we move through the steps in the value chain, there is a clear reduction in the diversity of the microbiota. A beta diversity analysis revealed a more distinct microbiota at the farms compared to the meat plant which change and became more uniform as samples were taken towards the end of the value chain. The source tracker analysis showed that only 12.92% of the microbiota in shoulder samples originated from the farms and 81% of the bacteria detected on the dressed carcasses were of unknown origin. Discussion: Overall, the results suggest that with the current level of microbial control at farms, it is possible to obtain pork products with similar microbiological quality from different farms. However, broader studies are required to determine the impact of the sanitary status of the herd on the final products.

Prediction of a new molecule's exposure in plasma is a critical first step toward understanding its efficacy/toxicity profile and concluding whether it is a possible first-in-class, best-in-class candidate. For this prediction, traditional pharmacometrics use a variety of scaling methods that are heavily based on pre-clinical pharmacokinetic (PK) data. We here propose a novel framework based on which preclinical exposure prediction is performed by applying machine learning (ML) in tandem with mechanism-based modeling. In our proposed method, a relationship is initially established between molecular structure and physicochemical (PC)/PK properties using ML, and then the ML-driven PC/PK parameters are used as input to mechanistic models that ultimately predict the plasma exposure of new candidates. To understand the feasibility of our proposed framework, we evaluated a number of mechanistic models (1-compartment, physiologically based pharmacokinetic (PBPK)), PBPK distribution models (Berezhkovskiy, PK-Sim standard, Poulin and Theil, Rodgers and Rowland, and Schmidt), and PBPK parameterizations (using in vivo, or in vitro clearance). For most of the scenarios tested, our results demonstrate that PK profiles can be adequately predicted based on the proposed framework. Our analysis further indicates some limitations when liver microsomal intrinsic clearance (CLint) is used as the only clearance pathway and underscores the necessity of investigating the variability emanating from the different distribution models when providing PK predictions. The suggested approach aims at earlier exposure prediction in the drug development process so that critical decisions on molecule screening, chemistry design, or dose selection can be made as early as possible.

Mathematical modeling has made significant contributions to drug design, development, and optimization. Virtual clinical trials that integrate mathematical models to explore patient heterogeneity and its impact on a variety of therapeutic questions have recently risen in popularity. Here, we outline best practices for creating virtual patients from mathematical models to ultimately implement and execute a virtual clinical trial. In this practical guide, we discuss and provide examples of model design, parameter estimation, parameter sensitivity, model identifiability, and virtual patient cohort creation. Our goal is to help researchers adopt these approaches to further the use of virtual population-based analysis and virtual clinical trials.

Alzheimer's disease (AD), a neurodegenerative disorder, is characterized by its ability to cause memory loss and damage other cognitive functions. Aggregation of amyloid beta (Aβ) plaques and neurofibrillary tangles in the brain are responsible for the development of Alzheimer's disease (AD). While attempts targeting Aβ and tau proteins have been extensively conducted in the past decades, only two FDA-approved drugs (i.e., monoclonal antibodies) tackle the underlying biology of Alzheimer's disease. In this study, an integrated computational framework was developed to identify new drug targets for Alzheimer's disease and identify small molecules as potential therapeutical options. A systematic investigation of the gene networks firstly revealed that the Apolipoprotein E4 (ApoE4) gene plays a central role among genes associated with Alzheimer's disease. The ApoE4 protein was then chosen as the protein target based on its role in the main pathological hallmarks of AD, which has been shown to increase Aβ accumulation by directly binding to Aβ as well as interfering with Aβ clearance that is associated with other receptors. A library of roughly 1.5 million compounds was then virtually screened via a ligand-protein docking program to identify small-molecule compounds with potential binding capacity to the ApoE4 N-terminal domain. On the basis of compound properties, 312 compounds were selected, analyzed and clustered to further identify common structures and essential functional groups that play an important role in binding ApoE4. The in silico prediction suggested that compounds with four common structures of sulfon-amine-benzene, 1,2-benzisothiazol-3-amine 1,1-dioxide, N-phenylbenzamide, and furan-amino-benzene presented strong hydrogen bonds with residues E27, W34, R38, D53, D153, or Q156 in the N terminal of ApoE4. These structures might also form strong hydrophobic interactions with residues W26, E27, L28, L30, G31, L149, and A152. While the 312 compounds can serve as drug candidates for further experiment assays, the four common structures, along with the residues for hydrogen bond or hydrophobic interaction, pave the foundation to further optimize the compounds as better binders of ApoE4.

The recent surge in single-cell genomics, including the development of a wide range of experimental and computational approaches, has provided insights into the complex molecular networks of cells during development and in human diseases at unprecedented resolution. Single-cell transcriptome analysis has enabled high-resolution investigation of cellular heterogeneity in a wide range of cell populations ranging from early embryos to complex tissues-while posing the risk of only capturing a partial picture of the cells' complex molecular networks. Single-cell multiomics technologies aim to bridge this gap by providing a more holistic view of the cell by simultaneously measuring multiple molecular types from the same cell and providing a more complete view of the interactions and combined functions of multiple regulatory layers at cell-type resolution. In this review, we briefly summarized the recent advances in multimodal single-cell technologies and discussed the challenges and opportunities of the field.

Elucidating cell regulation remains a challenging task due to the complexity of metabolism and the difficulty of experimental measurements. Here we present a method for prediction of cell regulation to maximize cell growth rate while maintaining the solvent capacity of the cell. Prediction is formulated as an optimization problem using a thermodynamic framework that can leverage experimental data. We develop a formulation and variable initialization procedure that allows for computing solutions of the optimization with an interior point method. The approach is applied to photoheterotrophic growth of Rhodospirilium rubrum using ethanol as a carbon source, which has applications to biosynthesis of ethylene production. Growth is captured as the rate of synthesis of amino acids into proteins, and synthesis of nucleotide triphoshaptes into RNA and DNA. The method predicts regulation that produces a high rate of protein and RNA synthesis while DNA synthesis is reduced close to zero in agreement with production of DNA being turned off for much of the cell cycle.

The metabolic axis linking the gut microbiome and heart is increasingly being researched in the context of cardiovascular health. The gut microbiota-derived trimethylamine/trimethylamine N-oxide (TMA/TMAO) pathway is responsible along this axis for the bioconversion of dietary precursors into TMA/TMAO and has been implicated in the progression of heart failure and dysbiosis through a positive-feedback interaction. Systems biology approaches in the context of researching this interaction offer an additional dimension for deepening the understanding of metabolism along the gut-heart axis. For instance, genome-scale metabolic models allow to study the functional role of pathways of interest in the context of an entire cellular or even whole-body metabolic network. In this mini review, we provide an overview of the latest findings on the TMA/TMAO super pathway and summarize the current state of knowledge in a curated pathway map on the community platform WikiPathways. The pathway map can serve both as a starting point for continual curation by the community as well as a resource for systems biology modeling studies. This has many applications, including addressing remaining gaps in our understanding of the gut-heart axis. We discuss how the curated pathway can inform a further curation and implementation of the pathway in existing whole-body metabolic models, which will allow researchers to computationally simulate this pathway to further understand its role in cardiovascular metabolism.