scholarly journals Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Claudio Angione
Keyword(s):  
Systems Biology ◽  
Precision Medicine ◽  
Model Building ◽  
Research Field ◽  
Point Of View ◽  
Patient Specific ◽  
Metabolic Modelling ◽  
Metabolic Systems ◽  
A Cell ◽  
Metabolic Models

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient’s disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, andin silicoclinical trials.

Learning from metabolic networks: current trends and future directions for precision medicine.

2020 ◽  
Vol 28 ◽  
Author(s):  
Ilaria Granata ◽  
Mario Manzo ◽  
Ari Kusumastuti ◽  
Mario R Guarracino
Keyword(s):  
Systems Biology ◽  
Precision Medicine ◽  
Metabolic Networks ◽  
Information Modeling ◽  
Model Systems ◽  
Specific Information ◽  
Important Indicator ◽  
Predictive Values ◽  
Metabolic Systems ◽  
Metabolic Models

Purpose: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype-phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition-specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning. Method: This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies which exploited metabolic networks for the study of several pathological conditions, not only those directly related to the metabolism. Conclusion: We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).

scholarly journals Fast recontruction of compact context-specific network models

2018 ◽  
Author(s):  
Maria Pires Pacheco
Keyword(s):  
Precision Medicine ◽  
High Throughput ◽  
Model Building ◽  
Metabolic Diseases ◽  
Target Function ◽  
Patient Specific ◽  
Metabolic Reconstruction ◽  
Reconstruction Algorithms ◽  
High Throughput Data ◽  
Context Specific

Recent progress in high-throughput data acquisition has shifted the focus from data generation to the processing and understanding of now easily collected patient-specific information.Metabolic models, which have already proven to be very powerful for the integration and analysis of such data sets, might be successfully applied in precision medicine in the near future.Context-specific reconstructions extracted from generic genome-scale models like Reconstruction X (ReconX) (Duarte et al., 2007; Thiele et al., 2013) or Human Metabolic Reconstruction (HMR) (Agren et al., 2012; Mardinoglu et al., 2014a) thereby have the potential to become a diagnostic and treatment tool tailored to the analysis of specific groups of individuals. The use of computational algorithms as a tool for the routinely diagnosis and analysis of metabolic diseases requires a high level of predictive power, robustness and sensitivity. Although multiple context-specific reconstruction algorithms were published in the last ten years, only a fractionof them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding.The aim of this thesis was to create a family of robust and fast algorithms for the building of context-specific models that could be used for the integration of different types of omics data and which should be sensitive enough to be used in the framework of precision medicine.

Judicial geography as a legal science and academic discipline

2019 ◽  
Vol 5 (4) ◽  
pp. 122-137
Author(s):  
Almaz F. Abdulvaliev
Keyword(s):  
Criminal Law ◽  
Climatic Factors ◽  
Academic Discipline ◽  
Preliminary Investigation ◽  
Criminal Procedure ◽  
Mapping Method ◽  
Research Field ◽  
Point Of View ◽  
Theoretical Development ◽  
New Research

This article presents the conceptual foundations for the formation of a new research field “Judicial Geography”, including the prerequisites for its creation, academic, and theoretical development, both in Russia and abroad. The purpose of the study is to study the possibility of applying geographical methods and means in criminal law, criminal procedure, and in judicial activity in general via the academic direction “Judicial Geography”. The author describes in detail the main elements of judicial geography and its role and significance for such legal sciences, as criminal law, criminal procedure, criminalistics, and criminology among others. The employed research methods allow showing the main vectors of the development of judicial geography, taking into account the previous achievements of Russian and worldwide academics. The author indicates the role and place of judicial geography in the system of legal sciences. This study suggests a concept of using scientific geographical methods in the study of various legal phenomena of a criminal and criminal-procedural nature when considering the idea of building judicial bodies and judicial instances, taking into account geographical and climatic factors. In this regard, the author advises to introduce the special course “Judicial Geography”, which would allow law students to study the specifics of the activities of the judiciary and preliminary investigation authorities from a geographical point of view, as well as to use various geographical methods, including the mapping method, in educational and practical activities. The author concludes that forensic geography may become a new milestone for subsequent scientific research in geography and jurisprudence.

scholarly journals Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment

2020 ◽  
Vol 41 (S1) ◽  
pp. s521-s522
Author(s):  
Debarka Sengupta ◽  
Vaibhav Singh ◽  
Seema Singh ◽  
Dinesh Tewari ◽  
Mudit Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Model Building ◽  
Medical Center ◽  
Bacterial Species ◽  
Model Performance ◽  
The United States ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Comparative Performance

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.Disclosures: None

scholarly journals Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Michelle Przedborski ◽  
Munisha Smalley ◽  
Saravanan Thiyagarajan ◽  
Aaron Goldman ◽  
Mohammad Kohandel
Keyword(s):  
Neural Networks ◽  
Systems Biology ◽  
Clinical Data ◽  
Treatment Protocol ◽  
Response Prediction ◽  
Clinical Findings ◽  
Patient Specific ◽  
Patient Response ◽  
Triple Combination Therapy ◽  
Parameter Values

AbstractAnti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient’s immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate. Using this approach, we determine biomarkers of patient response and identify potential mechanisms of drug resistance. We develop systems biology informed neural networks (SBINN) to calculate patient-specific kinetic parameter values and to predict clinical outcome. We show how transfer learning can be leveraged with simulated clinical data to significantly improve the response prediction accuracy of the SBINN. Further, we identify novel drug combinations and optimize the treatment protocol for triple combination therapy consisting of IL-6 inhibition, recombinant IL-12, and anti-PD-1 immunotherapy in order to maximize patient response. We also find unexpected differences in protein expression levels between response phenotypes which complement recent clinical findings. Our approach has the potential to aid in the development of targeted experiments for patient drug screening as well as identify novel therapeutic targets.

scholarly journals Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shinjo Yada
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Early Phase ◽  
Patient Specific ◽  
Specific Gene ◽  
Cancer Type ◽  
Background Characteristics ◽  
Number Of Patients ◽  
Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

scholarly journals Immunotherapy as a Precision Medicine Tool for the Treatment of Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 173
Author(s):  
Maria Adamaki ◽  
Vassilios Zoumpourlis
Keyword(s):  
Prostate Cancer ◽  
Precision Medicine ◽  
Checkpoint Inhibitors ◽  
Invasive Disease ◽  
Patient Specific ◽  
Treatment Alternative ◽  
Substantial Evidence ◽  
Significant Survival ◽  
Localized Disease ◽  
Specific Antigens

Prostate cancer (PCa) is the most frequently diagnosed type of cancer among Caucasian males over the age of 60 and is characterized by remarkable heterogeneity and clinical behavior, ranging from decades of indolence to highly lethal disease. Despite the significant progress in PCa systemic therapy, therapeutic response is usually transient, and invasive disease is associated with high mortality rates. Immunotherapy has emerged as an efficacious and non-toxic treatment alternative that perfectly fits the rationale of precision medicine, as it aims to treat patients on the basis of patient-specific, immune-targeted molecular traits, so as to achieve the maximum clinical benefit. Antibodies acting as immune checkpoint inhibitors and vaccines entailing tumor-specific antigens seem to be the most promising immunotherapeutic strategies in offering a significant survival advantage. Even though patients with localized disease and favorable prognostic characteristics seem to be the ones that markedly benefit from such interventions, there is substantial evidence to suggest that the survival benefit may also be extended to patients with more advanced disease. The identification of biomarkers that can be immunologically targeted in patients with disease progression is potentially amenable in this process and in achieving significant advances in the decision for precision treatment of PCa.

scholarly journals The Metano Modeling Toolbox MMTB: An Intuitive, Web-Based Toolbox Introduced by Two Use Cases

Metabolites ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 113
Author(s):  
Julia Koblitz ◽  
Sabine Will ◽  
S. Riemer ◽  
Thomas Ulas ◽  
Meina Neumann-Schaal ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Point Of View ◽  
Use Cases ◽  
Mathematical Methods ◽  
State Behavior ◽  
Web Based ◽  
Research Fields ◽  
Balance Analysis ◽  
Metabolic Models ◽  
User Friendly

Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens.

scholarly journals Mitochondria and Pharmacologic Cardiac Conditioning—At the Heart of Ischemic Injury

2021 ◽  
Vol 22 (6) ◽  
pp. 3224
Author(s):  
Christopher Lotz ◽  
Johannes Herrmann ◽  
Quirin Notz ◽  
Patrick Meybohm ◽  
Franz Kehl
Keyword(s):  
Cell Death ◽  
Mitochondrial Permeability Transition ◽  
Permeability Transition ◽  
Research Field ◽  
Calcium Handling ◽  
Control Mechanisms ◽  
Intrinsic Resistance ◽  
Atp Production ◽  
Cardiac Conditioning ◽  
A Cell

Pharmacologic cardiac conditioning increases the intrinsic resistance against ischemia and reperfusion (I/R) injury. The cardiac conditioning response is mediated via complex signaling networks. These networks have been an intriguing research field for decades, largely advancing our knowledge on cardiac signaling beyond the conditioning response. The centerpieces of this system are the mitochondria, a dynamic organelle, almost acting as a cell within the cell. Mitochondria comprise a plethora of functions at the crossroads of cell death or survival. These include the maintenance of aerobic ATP production and redox signaling, closely entwined with mitochondrial calcium handling and mitochondrial permeability transition. Moreover, mitochondria host pathways of programmed cell death impact the inflammatory response and contain their own mechanisms of fusion and fission (division). These act as quality control mechanisms in cellular ageing, release of pro-apoptotic factors and mitophagy. Furthermore, recently identified mechanisms of mitochondrial regeneration can increase the capacity for oxidative phosphorylation, decrease oxidative stress and might help to beneficially impact myocardial remodeling, as well as invigorate the heart against subsequent ischemic insults. The current review highlights different pathways and unresolved questions surrounding mitochondria in myocardial I/R injury and pharmacological cardiac conditioning.

Artificial Planning Experience by Means of a Heuristic Cell-Space Model: Simulating International Migration in the Urban Process

1995 ◽  
Vol 27 (10) ◽  
pp. 1647-1665 ◽  
Author(s):  
J Portugali ◽  
I Benenson
Keyword(s):  
Urban Policy ◽  
Initial Conditions ◽  
Point Of View ◽  
Town Planning ◽  
Space Model ◽  
Self Organized ◽  
A Cell ◽  
Policy And Planning ◽  
The City ◽  
Theory And Model

We suggest considering the city as a complex, open, and thus self-organized system, and describing it by means of a cell-space model. A central property of self-organizing systems is that they are not controllable—not by individuals, nor by economic, political, and planning institutions. The city, in this respect, is complex and untamable. Inability to recognize and accept this property is one of the reasons for the difficulties and problems of modernist town planning. The theory and model we present are built to describe the urban process as a historical one in which, given identical initial conditions, each simulation run is unique and never fully repeats itself. From the point of view of urban policy and planning, our heuristic model can guide decisionmakers by answering the following question: ‘given the initial conditions of an inflow of new immigrants (that is, from the ex-USSR), what possible urban scenarios can result, and what are their global structural properties?’.

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