scholarly journals Mathematical and Computational Modeling in Complex Biological Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zhiwei Ji ◽  
Ke Yan ◽  
Wenyang Li ◽  
Haigen Hu ◽  
Xiaoliang Zhu
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Cancer Progression ◽  
High Throughput ◽  
Computational Models ◽  
Molecular Mechanisms ◽  
Biological Process ◽  
Biological Systems ◽  
Complex Diseases ◽  
Modeling Approaches

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

scholarly journals Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Systems Biology ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Cell Proliferation ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Key Players ◽  
Protein Protein Interaction Network

<p>L-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins- SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.</p>

scholarly journals Key challenges facing data-driven multicellular systems biology

GigaScience ◽  
2019 ◽  
Vol 8 (10) ◽  
Author(s):  
Paul Macklin
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Large Scale ◽  
Computational Models ◽  
Short Review ◽  
Data Driven ◽  
Current State

Abstract Increasingly sophisticated experiments, coupled with large-scale computational models, have the potential to systematically test biological hypotheses to drive our understanding of multicellular systems. In this short review, we explore key challenges that must be overcome to achieve robust, repeatable data-driven multicellular systems biology. If these challenges can be solved, we can grow beyond the current state of isolated tools and datasets to a community-driven ecosystem of interoperable data, software utilities, and computational modeling platforms. Progress is within our grasp, but it will take community (and financial) commitment.

scholarly journals Insights from Systems Biology in Physiological Studies: Learning from Context

2017 ◽  
Vol 42 (3) ◽  
pp. 939-951
Author(s):  
Pedro Henrique Imenez Silva ◽  
Diogo Melo ◽  
Pedro Omori Ribeiro  de Mendonça
Keyword(s):  
Systems Biology ◽  
High Throughput ◽  
Biological System ◽  
Biological Systems ◽  
Experimental Studies ◽  
Holistic View ◽  
Physiological Studies ◽  
High Throughput Experiments

Systems biology presents an integrated view of biological systems, focusing on the relations between elements, whether functional or evolutionary, and providing a rich framework for the comprehension of life. At the same time, many low-throughput experimental studies are performed without influence from this integrated view, whilst high-throughput experiments use low-throughput results in their validation and interpretation. We propose an inversion in this logic, and ask which benefits could be obtained from a holistic view coming from high-throughput studies―and systems biology in particular―in interpreting and designing low-throughput experiments. By exploring some key examples from the renal and adrenal physiology, we try to show that network and modularity theory, along with observed patterns of association between elements in a biological system, can have profound effects on our ability to draw meaningful conclusions from experiments.

scholarly journals Working Together: Integrating Computational Modeling Approaches to Investigate Complex Phenomena

2020 ◽  
Author(s):  
Tom Bielik ◽  
Ehud Fonio ◽  
Ofer Feinerman ◽  
Ravit Golan Duncan ◽  
Sharona T. Levy
Keyword(s):  
Complex Systems ◽  
Computational Modeling ◽  
Conceptual Knowledge ◽  
Computational Models ◽  
Science Classroom ◽  
Modeling Approaches ◽  
8Th Grade ◽  
Working Together ◽  
Complex Phenomena ◽  
Dynamics Models

Abstract Complex systems are made up of many entities, whose interactions emerge into distinct collective patterns. Computational modeling platforms can provide a powerful means to investigate emergent phenomena in complex systems. Some research has been carried out in recent years about promoting students’ modeling practices, specifically using technologically advanced tools and approaches that allow students to create, manipulate, and test computational models. However, not much research had been carried out on the integration of several modeling approaches when investigating complex phenomena. In this paper, we describe the design principles used to develop a middle school unit about ants’ collective behavior that integrates three modeling approaches: conceptual drawn models, agent-based models, and system dynamics models. We provide results from an initial implementation of an 8th grade curricular unit, indicating that students engaged with several aspects of the modeling practice. Students’ conceptual knowledge about ant pheromone communication increased following learning the unit. We also found gains in students’ metamodeling knowledge about models as tools for investigating phenomena. We discuss the affordances and challenges of engaging students with several modeling approaches in science classroom.

scholarly journals Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Systems Biology ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Cell Proliferation ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Key Players ◽  
Protein Protein Interaction Network

L-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins-SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.

scholarly journals Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Systems Biology ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Cell Proliferation ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Key Players ◽  
Protein Protein Interaction Network

<p>L-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins- SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.</p>

scholarly journals Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Systems Biology ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Cell Proliferation ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Key Players ◽  
Protein Protein Interaction Network

AbstractL-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins-SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.

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