Deciphering the regulation of metabolism with dynamic optimization: an overview of recent advances

2017 ◽  
Vol 45 (4) ◽  
pp. 1035-1043 ◽  
Author(s):  
Jan Ewald ◽  
Martin Bartl ◽  
Christoph Kaleta
Keyword(s):  
Dynamic Optimization ◽  
Metabolic Pathways ◽  
Regulatory Networks ◽  
Optimization Problems ◽  
Treatment Strategies ◽  
Whole Cell ◽  
Recent Advances ◽  
Regulatory Strategies ◽  
Regulation Of Metabolism ◽  
Optimality Principles

Understanding optimality principles shaping the evolution of regulatory networks controlling metabolism is crucial for deriving a holistic picture of how metabolism is integrated into key cellular processes such as growth, adaptation and pathogenicity. While in the past the focus of research in pathway regulation was mainly based on stationary states, more recently dynamic optimization has proved to be an ideal tool to decipher regulatory strategies for metabolic pathways in response to environmental cues. In this short review, we summarize recent advances in the elucidation of optimal regulatory strategies and identification of optimal control points in metabolic pathways. We discuss biological implications of the discovered optimality principles on genome organization and provide examples how the derived knowledge can be used to identify new treatment strategies against pathogens. Furthermore, we briefly discuss the variety of approaches for solving dynamic optimization problems and emphasize whole-cell resource allocation models as an important emerging area of research that will allow us to study the regulation of metabolism on the whole-cell level.

scholarly journals Using optimal control to understand complex metabolic pathways

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Nikolaos Tsiantis ◽  
Julio R. Banga
Keyword(s):  
Optimal Control ◽  
Metabolic Pathways ◽  
Regulatory Networks ◽  
Complex Dynamics ◽  
Cost Benefit ◽  
Optimality Criteria ◽  
Enzyme Activation ◽  
Computational Framework ◽  
Trade Offs ◽  
Optimality Principles

Abstract Background Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria. Results Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments. Conclusions We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.

Radio-Susceptibility of Nasopharyngeal Carcinoma: Focus on Epstein- Barr Virus, MicroRNAs, Long Non-Coding RNAs and Circular RNAs

2020 ◽  
Vol 13 (3) ◽  
pp. 192-205 ◽  
Author(s):  
Fanghong Lei ◽  
Tongda Lei ◽  
Yun Huang ◽  
Mingxiu Yang ◽  
Mingchu Liao ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Regulatory Networks ◽  
Epstein Barr Virus ◽  
Molecular Mechanisms ◽  
Acquired Resistance ◽  
Treatment Strategies ◽  
Circular Rnas ◽  
Barr Virus ◽  
Non Coding Rnas ◽  
Epstein Barr

Nasopharyngeal carcinoma (NPC) is a type of head and neck cancer. As a neoplastic disorder, NPC is a highly malignant squamous cell carcinoma that is derived from the nasopharyngeal epithelium. NPC is radiosensitive; radiotherapy or radiotherapy combining with chemotherapy are the main treatment strategies. However, both modalities are usually accompanied by complications and acquired resistance to radiotherapy is a significant impediment to effective NPC therapy. Therefore, there is an urgent need to discover effective radio-sensitization and radio-resistance biomarkers for NPC. Recent studies have shown that Epstein-Barr virus (EBV)-encoded products, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), which share several common signaling pathways, can function in radio-related NPC cells or tissues. Understanding these interconnected regulatory networks will reveal the details of NPC radiation sensitivity and resistance. In this review, we discuss and summarize the specific molecular mechanisms of NPC radio-sensitization and radio-resistance, focusing on EBV-encoded products, miRNAs, lncRNAs and circRNAs. This will provide a foundation for the discovery of more accurate, effective and specific markers related to NPC radiotherapy. EBVencoded products, miRNAs, lncRNAs and circRNAs have emerged as crucial molecules mediating the radio-susceptibility of NPC. This understanding will improve the clinical application of markers and inform the development of novel therapeutics for NPC.

scholarly journals Metabolic Interplay between the Immune System and Melanoma Cells: Therapeutic Implications

Biomedicines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 607
Author(s):  
Alice Indini ◽  
Francesco Grossi ◽  
Mario Mandalà ◽  
Daniela Taverna ◽  
Valentina Audrito
Keyword(s):  
Metabolic Pathways ◽  
Cancer Metabolism ◽  
Acquired Resistance ◽  
Treatment Strategies ◽  
Predictive Biomarkers ◽  
Treatment Resistance ◽  
Metastatic Potential ◽  
Biological Behavior ◽  
Therapeutic Implications ◽  
Systemic Treatments

Malignant melanoma represents the most fatal skin cancer due to its aggressive biological behavior and high metastatic potential. Treatment strategies for advanced disease have dramatically changed over the last years due to the introduction of BRAF/MEK inhibitors and immunotherapy. However, many patients either display primary (i.e., innate) or eventually develop secondary (i.e., acquired) resistance to systemic treatments. Treatment resistance depends on multiple mechanisms driven by a set of rewiring processes, which involve cancer metabolism, epigenetic, gene expression, and interactions within the tumor microenvironment. Prognostic and predictive biomarkers are needed to guide patients’ selection and treatment decisions. Indeed, there are no recognized clinical or biological characteristics that identify which patients will benefit more from available treatments, but several biomarkers have been studied with promising preliminary results. In this review, we will summarize novel tumor metabolic pathways and tumor-host metabolic crosstalk mechanisms leading to melanoma progression and drug resistance, with an overview on their translational potential as novel therapeutic targets.

An algorithm comparison for dynamic optimization problems

2012 ◽  
Vol 12 (10) ◽  
pp. 3176-3192 ◽  
Author(s):  
Ignacio G. del Amo ◽  
David A. Pelta ◽  
Juan R. González ◽  
Antonio D. Masegosa
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Dynamic Optimization Problems ◽  
Algorithm Comparison

scholarly journals Pharmacology and Adverse Events of Emergency-Use Authorized Medication in Moderate to Severe COVID-19

2021 ◽  
Vol 14 (10) ◽  
pp. 955
Author(s):  
Jen-Yu Hsu ◽  
Yan-Chiao Mao ◽  
Po-Yu Liu ◽  
Kuo-Lung Lai
Keyword(s):  
Adverse Events ◽  
Metabolic Pathways ◽  
Case Reports ◽  
Secondary Infection ◽  
Treatment Strategies ◽  
Serious Adverse Events ◽  
Small Molecule Drugs ◽  
Post Market ◽  
Inflammatory Processes ◽  
Effective Drugs

Some effective drugs have been approved or issued an Emergency Use Authorization for the treatment of COVID-19 in hospitalized patients, but post-market surveillance is warranted to monitor adverse events. We reviewed clinical trials and case reports in patients with moderate-to-severe COVID-19 infection who received remdesivir, baricitinib, tocilizumab, or sarilumab. The drug-specific pharmacokinetics, toxicity, and drug interactions are summarized in this study. Remdesivir and baricitinib are small-molecule drugs that are mainly metabolized by the kidneys, while tocilizumab and sarilumab are monoclonal antibody drugs with metabolic pathways that are currently not fully understood. The most common adverse events of these drugs are alterations in liver function, but serious adverse events have rarely been attributed to them. Only a few studies have reported that remdesivir might be cardiotoxic and that baricitinib might cause thromboembolism. Biological agents such as baricitinib, tocilizumab, and sarilumab could inhibit the pathway of inflammatory processes, leading to immune dysregulation, so the risk of secondary infection should be assessed before prescribing. Further recognition of the pathogenic mechanism and risk factors of adverse events is essential for optimizing treatment strategies.

Sign in / Sign up

Export Citation Format

Share Document