Expanding the Current Boundaries of Nature-based Modeling and Computing: Chem-Inspiration for Meta-Heuristics

High-throughput methods in science have created a trend to generate massive amount of data that challenge our ability to mine and search through massive information spaces. Thus more efficient and effective solutions for data analysis and optimization are required continuously. The best solutions for many problems-solving approaches in science could have many sources of inspiration coming from diverse natural phenomena. In this context, most Artificial Intelligence (AI) approaches benefit from emulation natural processes for their information processing strategy. Among the AI protocols, meta-heuristic algorithms for learning model and optimization have exploited a number of biological phenomena leading to highly effective search and learning engines. Examples of these processes are the ant colony organization, brain function and genetics among others. The evolution has turned all these biological events in highly efficient procedures, whose basics principles have then provided an excellent ground of new computational algorithms The aim of this report is pave the way to a new class of nature-based meta-heuristic methods which shall be based on diverse chemical and biomolecular systems. We present five examples from different subjects of Chemistry like Organic Chemistry, Chemical Physics and Biomolecules; and introduce how computational models could be inferred from them. Besides, we develop one of these models, in detail, which is based on protein evolution and folding principles. We consider that the wealth of systems and processes related to Chemistry, as those described in the present communication, might boost the development of relevant meta-heuristic and classification algorithms in upcoming years.

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On the modeling of the interaction between tumor growth and the immune system using some new fractional and fractional-fractal operators

Advances in Difference Equations ◽

10.1186/s13662-020-03040-x ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Behzad Ghanbari

Keyword(s):

Immune System ◽

Infectious Diseases ◽

Tumor Growth ◽

Computational Models ◽

Recent Literature ◽

Approximate Solutions ◽

Biological Phenomena ◽

Uniqueness Of The Solution ◽

Biological Concepts ◽

New Research

Abstract Humans are always exposed to the threat of infectious diseases. It has been proven that there is a direct link between the strength or weakness of the immune system and the spread of infectious diseases such as tuberculosis, hepatitis, AIDS, and Covid-19 as soon as the immune system has no the power to fight infections and infectious diseases. Moreover, it has been proven that mathematical modeling is a great tool to accurately describe complex biological phenomena. In the recent literature, we can easily find that these effective tools provide important contributions to our understanding and analysis of such problems such as tumor growth. This is indeed one of the main reasons for the need to study computational models of how the immune system interacts with other factors involved. To this end, in this paper, we present some new approximate solutions to a computational formulation that models the interaction between tumor growth and the immune system with several fractional and fractal operators. The operators used in this model are the Liouville–Caputo, Caputo–Fabrizio, and Atangana–Baleanu–Caputo in both fractional and fractal-fractional senses. The existence and uniqueness of the solution in each of these cases is also verified. To complete our analysis, we include numerous numerical simulations to show the behavior of tumors. These diagrams help us explain mathematical results and better describe related biological concepts. In many cases the approximate results obtained have a chaotic structure, which justifies the complexity of unpredictable and uncontrollable behavior of cancerous tumors. As a result, the newly implemented operators certainly open new research windows in further computational models arising in the modeling of different diseases. It is confirmed that similar problems in the field can be also be modeled by the approaches employed in this paper.

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ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

Molecules ◽

10.3390/molecules24101973 ◽

2019 ◽

Vol 24 (10) ◽

pp. 1973 ◽

Cited By ~ 32

Author(s):

Nalini Schaduangrat ◽

Chanin Nantasenamat ◽

Virapong Prachayasittikul ◽

Watshara Shoombuatong

Keyword(s):

Computational Models ◽

Helical Structure ◽

Support Vector ◽

Disulfide Bridges ◽

Cell Penetration ◽

Anticancer Activities ◽

New Class ◽

Anticancer Peptides ◽

Small Molecule Drugs

Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.

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Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis

Applied Sciences ◽

10.3390/app112210575 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10575

Author(s):

Antonio Agresta ◽

Marco Baioletti ◽

Chiara Biscarini ◽

Fabio Caraffini ◽

Alfredo Milani ◽

...

Keyword(s):

Cross Sections ◽

Computational Models ◽

River Flow ◽

Heuristic Algorithms ◽

Flow Analysis ◽

Differential Evolution Algorithm ◽

Optimal Estimation ◽

Evolution Strategy ◽

Computational Time ◽

Roughness Coefficient

Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manning’s n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘n’. First, an objective function is designed for measuring the quality of ‘candidate’ Manning’s coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers ‘Paglia’ and ‘Aniene’. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models.

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Scalable population-level modelling of biological cells incorporating mechanics and kinetics in continuous time

Royal Society Open Science ◽

10.1098/rsos.180379 ◽

2018 ◽

Vol 5 (8) ◽

pp. 180379 ◽

Cited By ~ 7

Author(s):

Stefan Engblom ◽

Daniel B. Wilson ◽

Ruth E. Baker

Keyword(s):

Continuous Time ◽

Computational Models ◽

Coarse Graining ◽

Population Level ◽

Biomechanical Properties ◽

Detailed Knowledge ◽

Computational Framework ◽

Rate Laws ◽

Biological Phenomena ◽

The Individual

The processes taking place inside the living cell are now understood to the point where predictive computational models can be used to gain detailed understanding of important biological phenomena. A key challenge is to extrapolate this detailed knowledge of the individual cell to be able to explain at the population level how cells interact and respond with each other and their environment. In particular, the goal is to understand how organisms develop, maintain and repair functional tissues and organs. In this paper, we propose a novel computational framework for modelling populations of interacting cells. Our framework incorporates mechanistic, constitutive descriptions of biomechanical properties of the cell population, and uses a coarse-graining approach to derive individual rate laws that enable propagation of the population through time. Thanks to its multiscale nature, the resulting simulation algorithm is extremely scalable and highly efficient. As highlighted in our computational examples, the framework is also very flexible and may straightforwardly be coupled with continuous-time descriptions of biochemical signalling within, and between, individual cells.

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A new class of heuristic algorithms for weighted perfect matching

Journal of the ACM ◽

10.1145/48014.48015 ◽

1988 ◽

Vol 35 (4) ◽

pp. 769-776 ◽

Cited By ~ 7

Author(s):

M. D. Grigoriadis ◽

B. Kalantari

Keyword(s):

Perfect Matching ◽

Heuristic Algorithms ◽

New Class

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Darwin, Charles Robert (1809–82)

Routledge Encyclopedia of Philosophy ◽

10.4324/9780415249126-q022-2 ◽

2018 ◽

Author(s):

Peter J. Bowler

Keyword(s):

Social Theory ◽

Moral Values ◽

Social Darwinism ◽

Radical Mechanism ◽

Theory Of Evolution ◽

Natural Processes ◽

Major Threat ◽

Biological Phenomena ◽

Living Things ◽

The Universe

Darwin’s On the Origin of Species (1859) popularized the theory that all living things have evolved by natural processes from preexisting forms. This displaced the traditional belief that species were designed by a wise and benevolent God. Darwin showed how many biological phenomena could be explained on the assumption that related species are descended from a common ancestor. Furthermore, he proposed a radical mechanism to explain how the transformations came about, namely, natural selection. This harsh and apparently purposeless mechanism was seen as a major threat to the claim that the universe has a transcendent goal. Because Darwin openly extended his evolutionism to include the human race, it was necessary to re-examine the foundations of psychology, ethics and social theory. Moral values might be merely the rationalization of instinctive behaviour patterns. Since the process which produced these patterns was driven by struggle, it could be argued that society must inevitably reflect the harshness of nature (‘social Darwinism’). Darwin’s book has been seen as the trigger for a ’scientific revolution’. It took many decades for both science and Western culture to assimilate the more radical aspects of Darwin’s theory. But since the mid-twentieth century Darwin’s selection mechanism has become the basis for a highly successful theory of evolution, the human consequences of which are still being debated.

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Intracranial and Abdominal Aortic Aneurysms: Similarities, Differences, and Need for a New Class of Computational Models

Annual Review of Biomedical Engineering ◽

10.1146/annurev.bioeng.10.061807.160439 ◽

2008 ◽

Vol 10 (1) ◽

pp. 221-246 ◽

Cited By ~ 215

Author(s):

J.D. Humphrey ◽

C.A. Taylor

Keyword(s):

Computational Models ◽

Abdominal Aortic Aneurysms ◽

Aortic Aneurysms ◽

New Class ◽

Abdominal Aortic

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Mechanistic Science: A New Approach to Comprehensive Psychopathology Research That Relates Psychological and Biological Phenomena

Clinical Psychological Science ◽

10.1177/2167702618810223 ◽

2019 ◽

Vol 7 (2) ◽

pp. 196-215 ◽

Cited By ~ 11

Author(s):

Joel G. Thomas ◽

Paul B. Sharp

Keyword(s):

Recent Work ◽

Present Article ◽

Computational Models ◽

Reward Processing ◽

Personality Pathology ◽

New Approach ◽

Psychological Constructs ◽

Psychological Phenomena ◽

Biological Phenomena ◽

Mechanistic Approach

Efforts to understand the causes of psychopathology have remained stifled in part because current practices do not clearly describe how psychological constructs differ from biological phenomena and how to integrate them in unified explanations. The present article extends recent work in philosophy of science by proposing a framework called mechanistic science as a promising way forward. This approach maintains that integrating psychological and biological phenomena involves demonstrating how psychological functions are implemented in biological structures. Successful early attempts to advance mechanistic explanations of psychological phenomena are reviewed, and lessons are derived to show how the framework can be applied to a range of clinical psychological phenomena, including gene by environment findings, computational models of reward processing in schizophrenia, and self-related processes in personality pathology. Pursuing a mechanistic approach can ultimately facilitate more productive and successful collaborations across a range of disciplines.

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AGENT-BASED MICRO-SIMULATIONS IN IMMUNOLOGY AND FINANCE

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2003vol1.1978 ◽

2006 ◽

Vol 1 ◽

pp. 319

Author(s):

F. Castiglione

Keyword(s):

Message Passing ◽

Degrees Of Freedom ◽

Computational Models ◽

Physical Models ◽

Discrete State ◽

Agent Based ◽

Computer Tools ◽

New Class ◽

Distributed Memory Machines ◽

Tools And Techniques

In the search for computational models that help to understand the dynamics of Complex Systems, one can take a great advantage from the impressive acceleration of computer tools and techniques. In fact the very structure of computation on digital computers has inspired the introduction of new class of models (algorithms), where interaction among degrees of freedom are expressed by logical rules acting over a discrete state space – something much closer to "biological language" than to standard (floating point) physical models. Starting from the definitions of spin systems, with little changes we reach a definition a new model that is well suited to describe different simulation systems. Such class of models is can be considered a subclass of the Agent-Based systems in vogue nowadays. Moreover, we shortly describe two microscopic simulators of this type, which are being used to study microscopic phenomena in two completely different fields of application, namely immunology and finance. As a final remark, given the lattice representation of space, such computational-modeling paradigm is well suited for efficient and "relatively simple" parallelization. Indeed, both models have been implemented to run on parallel computers adopting the Message Passing paradigm for Distributed Memory machines.

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