scholarly journals A practical guide to mechanistic systems modeling in biology using a logic-based approach

2020 ◽  
Author(s):  
Anna Niarakis ◽  
Tomáš Helikar
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
Regulatory System ◽  
Mechanistic Modeling ◽  
Lac Operon ◽  
Modeling Framework ◽  
Validation Criteria ◽  
Hands On ◽  
Modeling Software ◽  
Logical Modeling

Abstract Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process—the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.

scholarly journals Changes in students’ mental models from computational modeling of gene regulatory networks

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Joseph T. Dauer ◽  
Heather E. Bergan-Roller ◽  
Gretchen P. King ◽  
McKenzie Kjose ◽  
Nicholas J. Galt ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Computational Modeling ◽  
Mental Models ◽  
Mental Model ◽  
Regulatory Networks ◽  
Computational Models ◽  
Conceptual Models ◽  
Regulatory System ◽  
Gene Regulatory ◽  
Context Specific

Abstract Background Computational modeling is an increasingly common practice for disciplinary experts and therefore necessitates integration into science curricula. Computational models afford an opportunity for students to investigate the dynamics of biological systems, but there is significant gap in our knowledge of how these activities impact student knowledge of the structures, relationships, and dynamics of the system. We investigated how a computational modeling activity affected introductory biology students’ mental models of a prokaryotic gene regulatory system (lac operon) by analyzing conceptual models created before and after the activity. Results Students’ pre-lesson conceptual models consisted of provided, system-general structures (e.g., activator, repressor) connected with predominantly incorrect relationships, representing an incomplete mental model of gene regulation. Students’ post-lesson conceptual models included more context-specific structures (e.g., cAMP, lac repressor) and increased in total number of structures and relationships. Student conceptual models also included higher quality relationships among structures, indicating they learned about these context-specific structures through integration with their expanding mental model rather than in isolation. Conclusions Student mental models meshed structures in a manner indicative of knowledge accretion while they were productively re-constructing their understanding of gene regulation. Conceptual models can inform instructors about how students are relating system structures and whether students are developing more sophisticated models of system-general and system-specific dynamics.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Hae Deok Jung ◽  
Yoo Jin Sung ◽  
Hyun Uk Kim
Keyword(s):  
Machine Learning ◽  
Computational Modeling ◽  
Cancer Treatment ◽  
Effective Treatment ◽  
Cancer Cells ◽  
Computational Models ◽  
Omics Data ◽  
Drug Resistant ◽  
Learning Models ◽  
Machine Learning Models

Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells.

scholarly journals A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6852
Author(s):  
Grant Buster ◽  
Paul Siratovich ◽  
Nicole Taverna ◽  
Michael Rossol ◽  
Jon Weers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
High Reliability ◽  
Geothermal Systems ◽  
Low Carbon ◽  
Modeling Framework ◽  
Operational Optimization ◽  
Modeling Software ◽  
Geothermal Power ◽  
Nominal Performance

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.

Computing, Philosophy and Reality

2010 ◽  
pp. 238-252
Author(s):  
Joseph Brenner
Keyword(s):  
Quantum Computing ◽  
Cellular Automata ◽  
Computational Modeling ◽  
Computational Models ◽  
Mathematical Structure ◽  
Quantum Superposition ◽  
Information Theoretic ◽  
Process View ◽  
New Interpretation ◽  
The Universe

The conjunction of the disciplines of computing and philosophy implies that discussion of computational models and approaches should include explicit statements of their underlying worldview, given the fact that reality includes both computational and non-computational domains. As outlined at ECAP08, both domains of reality can be characterized by the different logics applicable to them. A new “Logic in Reality” (LIR) was proposed as best describing the dynamics of real, non-computable processes. The LIR process view of the real macroscopic world is compared here with recent computational and information-theoretic models. Proposals that the universe can be described as a mathematical structure equivalent to a computer or by simple cellular automata are deflated. A new interpretation of quantum superposition as supporting a concept of paraconsistent parallelism in quantum computing and an appropriate ontological commitment for computational modeling are discussed.

scholarly journals A mechanistic modeling framework for gas-phase adsorption kinetics and fixed-bed transport

AIChE Journal ◽  
2017 ◽  
Vol 63 (11) ◽  
pp. 5029-5043 ◽  
Author(s):  
Austin P. Ladshaw ◽  
Sotira Yiacoumi ◽  
Ronghong Lin ◽  
Yue Nan ◽  
Lawrence L. Tavlarides ◽  
...  
Keyword(s):  
Gas Phase ◽  
Adsorption Kinetics ◽  
Fixed Bed ◽  
Mechanistic Modeling ◽  
Modeling Framework ◽  
Gas Phase Adsorption

Computational Models in Developmental Psychology

2013 ◽  
pp. 476-504
Author(s):  
Thomas R. Shultz
Keyword(s):  
Neural Networks ◽  
Computational Modeling ◽  
Computational Models ◽  
Developmental Psychology ◽  
Probability Distributions ◽  
Network Models ◽  
Developmental Theory ◽  
Negative Evidence ◽  
Developmental Robotics ◽  
Neural Network Models

Computational modeling implements developmental theory in a precise manner, allowing generation, explanation, integration, and prediction. Several modeling techniques are applied to development: symbolic rules, neural networks, dynamic systems, Bayesian processing of probability distributions, developmental robotics, and mathematical analysis. The relative strengths and weaknesses of each approach are identified and examples of each technique are described. Ways in which computational modeling contributes to developmental issues are documented. A probabilistic model of the vocabulary spurt shows that various psychological explanations for it are unnecessary. Constructive neural networks clarify the distinction between learning and development and show how it is possible to escape Fodor’s paradox. Connectionist modeling reveals different versions of innateness and how learning and evolution might interact. Agent-based models analyze the basic principles of evolution in a testable, experimental fashion that generates complete evolutionary records. Challenges posed by stimulus poverty and lack of negative examples are explored in neural-network models that learn morphology or syntax probabilistically from indirect negative evidence.

scholarly journals A Review of Cell-Based Computational Modeling in Cancer Biology

2019 ◽  
pp. 1-13 ◽  
Author(s):  
John Metzcar ◽  
Yafei Wang ◽  
Randy Heiland ◽  
Paul Macklin
Keyword(s):  
Stem Cells ◽  
Computational Modeling ◽  
Single Cell ◽  
Stromal Cells ◽  
Cancer Biology ◽  
Large Scale ◽  
Computational Models ◽  
Three Dimensional ◽  
Discrete Models ◽  
Simulation Studies

Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer’s ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.

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