COMPUTER AND THEORETICAL BIOPHYSICS IN THE ERA OF BIG DATA AND ARTIFICIAL INTELLIGENCE

КОМПЬЮТЕРНАЯ И ТЕОРЕТИЧЕСКАЯ БИОФИЗИКА В ЭПОХУ БОЛЬШИХ ДАННЫХ И ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

Амангельдиева Бинара Азаматовна

студент, Казахский Национальный медицинский университет имени С. Д. Асфендиярова,

Казахстан, г. Алматы

Шакетова Алина Нурлыбеккызы

студент, Казахский Национальный медицинский университет имени С. Д. Асфендиярова,

Казахстан, г. Алматы

Абдрасилова Венера Оналбаевна

научный руководитель, магистр естественных наук, преподаватель кафедры нормальной физиологии с курсом биофизики, Казахский Национальный медицинский университет имени С. Д. Асфендиярова,

Казахстан, г. Алматы

ABSTRACT

The article examines the current state and prospects for the development of computer and theoretical biophysics as an interdisciplinary field that increasingly relies on artificial intelligence (AI) and big data analytics (Big Data).[1,3] Key methods, emerging problems and areas of further research are analyzed [2]. Special attention is paid to how AI algorithms and machine learning methods integrate with classical biophysical models, what new opportunities open up due to huge amounts of biological data, and what challenges researchers face (interpretability, data quality, standardization, FAIR principles [3,4]. Examples of applications are given, ranging from predicting protein structure to modeling cellular physiology [1.5]. For practicing biophysicists and computational researchers, the article provides an overview and guidance on new tools and methodologies [2].

АННОТАЦИЯ

В статье рассматривается современное состояние и перспективы развития компьютерной и теоретической биофизики как междисциплинарной области, которая всё больше опирается на методы искусственного интеллекта (AI) и аналитику больших данных (Big Data) [1,3]. Анализируются ключевые методы, возникающие проблемы и направления дальнейших исследований [2]. Особое внимание уделяется тому, как AI-алгоритмы и методы машинного обучения интегрируются с классическими биофизическими моделями, какие новые возможности открываются благодаря огромным объёмам биологических данных, и какие вызовы стоят перед исследователями (интерпретируемость, качество данных, стандартизация, FAIR-принципы) [3,4]. Приводятся примеры применения — от предсказания структуры белков до моделирования клеточной физиологии [1,5]. Для практикующих биофизиков и вычислительных исследователей статья предлагает обзор и ориентир по новым инструментам и методологиям [2].

Keywords: biophysics, computational biophysics, theoretical biophysics, artificial intelligence, machine learning, big data, modeling, multiscale modeling.

Ключевые слова: биофизика, вычислительная биофизика, теоретическая биофизика, искусственный интеллект, машинное обучение, большие данные, моделирование, мультимасштабное моделирование.

1. Introduction

Biophysics as a field of scientific knowledge was initially formed on the basis of experimental observations and classical physical models that made it possible to explain the fundamental mechanisms of biological systems [2]. For a long time, it was the laboratory experiment that was the key source of data. However, today the discipline is undergoing a significant transformation [3].

The rapid growth of biological data arrays from genomic and proteomic profiles to macromolecule structures, high-performance microscopy, and various omix technologies coincided with the progressive development of computing platforms and machine learning methods [1, 3, 5]. This combination has led to the formation of a new research paradigm.

In modern conditions, computer and theoretical biophysics are becoming increasingly important, where the central role belongs to modeling, numerical simulations and analytics based on artificial intelligence algorithms [1]. These approaches make it possible not only to process huge amounts of data, but also to identify patterns that are difficult to detect using traditional experimental methods [3].

This article is aimed at reviewing these trends, analyzing their status and prospects.

Fundamentals and methodologies.

2.1. Theoretical biophysics and multiscale modeling.

Modern theoretical biophysics covers a fairly wide range of different models. These models range from atomic interactions and molecular dynamics to the behavior of individual cells, tissues, and entire biological systems. At the lower levels, methods of statistical mechanics and molecular dynamics are used. They allow you to track the movement of atoms in biopolymers. These methods also help to assess energy landscapes. On a higher scale, the mechanical properties of cells and tissue interactions are studied. In addition, electrophysiological processes and regulatory circuits are considered.

The key tool here is a multiscale approach. It combines chemical, mechanical and electrical processes in a single model. This integrated view allows us to trace how microscopic events at the molecular level shape the behavior of a cell [2]. As a result, it helps to understand her reaction to external influences.

2.2. Big Data in biophysics.

The rapid development of data acquisition technologies is happening quite quickly. These technologies include high-performance microscopy, single-cell omix platforms, and large-scale numerical simulations. This development leads to the formation of information arrays. These arrays can no longer be analyzed using traditional methods. Such data requires strict standards of structuring and storage [3]. In addition, new principles of their interpretation are needed.

FAIR principles are becoming increasingly important in the scientific community. They stand for findable, accessible, interoperable, and reusable. These principles form the basis for the correct organization of biophysical data [4]. They ensure compatibility between different research groups. This is especially important for long-term storage of arrays of simulations, images, and experimental results.

2.3. Artificial intelligence and machine learning.

Artificial intelligence algorithms are becoming an integral part of biophysicist's tools [1,3]. Machine learning works effectively in cases where classical analytical models are insufficient. Or they require excessive computing costs. Deep neural networks are already being used to predict the structure of proteins. They are also used to analyze the trajectories of biomolecules and classify microscopic images. In addition, they help to recognize complex patterns in biological data.

The hybrid approach is of particular interest. It combines machine learning methods with physically based models [1]. In such systems, the mechanistic part ensures interpretability and scientific correctness. And machine learning gives you the ability to adapt quickly. It finds hidden connections and improves the accuracy of predictions. The emergence of such models opens up opportunities for more reliable analysis. It also helps in the design of biological processes.

3. Application areas

3.1. Prediction of protein structure and interactions

One of the central issues of biophysics remains the problem of protein folding and prediction of its three-dimensional structure. Recent advances in artificial intelligence such as AlphaFold class systems have demonstrated a qualitative leap in the accuracy of structural predictions [5].

In addition, machine learning methods are actively used to model ligand-protein interactions, evaluate affinity, identify potential binding sites, and predict conformational dynamics. What previously required large-scale computational experiments or lengthy laboratory studies can now be performed faster and often with comparable accuracy.

3.2. Cellular physiology and network models

Approaches combining biophysical principles and computational algorithms are actively developing at the cell and tissue levels. This includes modeling intracellular signaling, the propagation of calcium waves, the electrical activity of membranes, and the interaction of cell populations.

With the advent of large single-cell and spatial data such as RNA-seq at the level of individual cells or spatial transcriptomics, new analytical methods have become necessary [2]. This is where computational biophysics and AI make it possible not only to systematize information, but also to identify new functional patterns in cellular networks.

3.3. Microscopy, image analysis and particle tracking

Modern microscopic technologies generate huge amounts of images, video sequences, and multidimensional data. Manual analysis of such materials becomes almost impossible.

Deep learning algorithms successfully cope with the tasks of segmentation of structures, automatic particle tracking [3], reconstruction of three-dimensional and four-dimensional objects, as well as extraction of quantitative dynamics parameters. This allows researchers to focus on interpreting the results rather than on time-consuming image processing.

3.4. Biomedical applications and security issues

In biomedical biophysics, AI methods and big data are already having a significant impact on the development of diagnostics and therapy. Models of biophysical processes are used to predict the effectiveness of treatment, assess the dose distribution during radiation therapy, and model the physiological reactions of the body.

The integration of biophysical parameters with clinical data opens the way to personalized medicine [4], where therapeutic strategies can be adapted to the individual characteristics of the patient. The analysis of potential risks associated with new exposure methods is also becoming an important area, which makes AI-supported simulations an important tool in the field of biosafety.

4. Advantages and challenges

4.1. Advantages

The use of computational methods and AI in biophysics provides a number of significant advantages.

Firstly, the processing speed of complex and voluminous datasets is dramatically increasing, which is especially important in molecular dynamics and microscopy.

Secondly, modern algorithms are able to detect hidden dependencies and patterns that are difficult to formalize using traditional analytical tools.

In addition, due to the flexibility of computational approaches, the very range of tasks under study is expanding from the behavior of atoms to modeling entire physiological systems [2, 3].

Special attention should be paid to the development of hybrid methods that combine physically based models with data obtained from experiments or ML analytics. This approach makes modeling more accurate and interpretable.

4.2. Challenges

Despite significant achievements, modern biophysics still faces a number of unresolved problems.

One of the fundamental difficulties remains the issue of interpreting machine learning models [1]. In many cases, algorithms act as opaque systems, which makes it difficult to verify whether their predictions are consistent with the physical principles and real mechanisms of the processes under study.

Additional difficulties are related to the very nature of biophysical data: they are often characterized by high variability, the presence of noise and experimental artifacts [3, 4]. This requires the use of more advanced methods of data preparation, filtering, normalization, and rigorous validation of results.

The issue of standardization is equally important: the lack of uniform data storage formats and metadata descriptions hinders reproducibility and the exchange of results between research groups [4].

There is also a fundamental problem of integrating physical meaning into the ML model, how to make the algorithm take into account conservation laws, constraints of geometry and dynamics of the system.

Additional difficulties are associated with ethical aspects, especially when it comes to medical data, as well as the increasing computational costs required for training large models and conducting long-term simulations [4].

5. Prospects and research directions

5.1. Development of integrated and hybrid models

In the coming years, combining physical principles with data obtained by machine learning methods will be of particular importance [1]. Such integrated models make it possible to combine explainability and a rigorous physical basis with the high predictive power of statistical algorithms. One of the actively developing directions is an approach in which biological processes are described as differentiable computational schemes, which opens the way to more accurate optimization and adaptive models.

5.2. Expanding the role of intelligent agent systems and LLM

The emergence of new generation language models contributes to the formation of the concept of "digital scientific assistants" [6]. These systems can not only process data, but also act as independent analytical agents: participate in the formulation of hypotheses, model generation, and interpretation of biophysical processes. Such architectures create the prerequisites for the emergence of interactive platforms where researchers collaborate with AI on an equal footing as a cognitive partner.

5.3. Moving towards system-level models

Scaling of biophysical models from the molecular level to cellular, tissue, and organismic systems is becoming one of the key vectors of development [2]. This requires new theoretical formalisms that can simultaneously take into account spatial interactions, temporal dynamics, and complex regulatory contours. The creation of such models will allow for a deeper understanding of the mechanism of functioning of biological systems as integral structures.

5.4. Standardization, openness and reproducibility of research

The increase in data volumes forces the scientific community to pay special attention to issues of reproducibility. The principles of FAIR - accessibility, compatibility and reuse of data are becoming the basis for a new format of scientific communication [4]. The development of unified standards for storage, metadata, and the creation of public repositories are important conditions for the sustainable development of data biophysics.

5.5. Interdisciplinary training and formation of new competencies

Modern biophysics is a field that requires a wide range of knowledge, from fundamental physics and mathematical modeling to biology, bioinformatics and big data analysis. Therefore, educational programs aimed at training new generation specialists are of particular importance. The formation of interdisciplinary research teams will be a key factor in the development of innovative approaches [2].

6. Conclusion.

Modern computer and theoretical biophysics is now entering a period of very rapid growth. This growth is largely dependent on what artificial intelligence and methods of working with large amounts of data offer. Thanks to these new methods, scientists can study biological systems much more deeply and on a larger scale. Just ten years ago, such depth seemed simply impossible. At the same time, rapid progress brings with it many problems. These include methodological barriers and difficulties in interpreting models. There are also ethical issues and strict requirements for the reliability of the source data.

The success of this field in the future will be determined by how well it will be possible to combine physical laws with computational approaches and correctly collected biological data [1, 4]. Transparency of algorithms is becoming especially important these days. Standardization of research methods is also coming to the fore. And do not forget about the development of cooperation between different sciences.

Biophysics now has many prospects ahead of it. From a thorough analysis of molecular mechanisms to the creation of models that explain the work of entire systems in the body. This also applies to medical applications. The main thing is that the developed tools show not only accuracy. They should provide a scientific justification. Ensure that experiments can be repeated. And gain the trust of the entire scientific world.

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