Digital Biology Market Outlook

In 2023, the global market size for Digital Biology was estimated at $4.2 billion and is projected to reach $15.6 billion by 2032, growing at a CAGR of 15.4% over the forecast period. The primary growth factor driving this market is the increasing integration of digital tools and technologies in biological research and applications. As the field of biology continues to evolve, the adoption of digital solutions offers unprecedented capabilities in data analysis, simulation, and modeling.

One of the key growth factors for the Digital Biology market is the accelerating pace of technological advancements in bioinformatics and computational biology. The introduction of high-throughput sequencing technologies and advanced data analytics tools has revolutionized the way biological data is collected, processed, and interpreted. This technological progression enables more accurate and faster analysis, which is critical for the development of personalized medicine, advanced research, and innovative biotechnological products. Such advancements are likely to further fuel the demand for digital biology solutions in the coming years.

Another significant factor contributing to the growth of the Digital Biology market is the increasing investment in life sciences research and development. Governments, private organizations, and academic institutions worldwide are investing heavily in R&D activities to discover new drugs, understand complex biological systems, and develop sustainable agricultural practices. These investments are driving the need for sophisticated digital biology tools that can handle complex datasets, model biological processes, and provide insights that were previously unattainable. As funding and support for biological research continue to rise, the demand for digital biology solutions is expected to grow correspondingly.

Moreover, the growing emphasis on personalized medicine and healthcare is also a major driver of market growth. Personalized medicine aims to tailor medical treatment to the individual characteristics of each patient, which requires a deep understanding of genetic, environmental, and lifestyle factors. Digital biology tools provide the necessary computational power and analytical capabilities to process vast amounts of biological data, identify patterns, and predict outcomes. This capability is essential for the development of targeted therapies and precision medicine, making digital biology an indispensable tool in modern healthcare.

Biosimulation Technology is emerging as a transformative force within the digital biology landscape. By enabling the virtual testing and modeling of biological processes, biosimulation technology allows researchers to predict the behavior of biological systems under various conditions. This capability is particularly valuable in drug development, where biosimulation can reduce the time and cost associated with clinical trials by identifying promising drug candidates and optimizing their formulations before they reach the testing phase. Furthermore, biosimulation technology supports the advancement of personalized medicine by simulating how individual patients might respond to specific treatments, thus paving the way for more tailored and effective healthcare solutions.

Regionally, North America holds a significant share of the Digital Biology market, driven by the presence of a robust healthcare infrastructure, a high level of technological adoption, and substantial investment in research and development. The Asia Pacific region is expected to witness the highest growth rate, with a CAGR of 17.1%, due to increasing government initiatives, rising healthcare expenditure, and growing awareness about the benefits of digital biology. Europe also represents a substantial market share, attributed to the strong presence of pharmaceutical companies and research institutes in the region.

Component Analysis

The Digital Biology market is segmented into software, hardware, and services. The software segment holds the largest market share due to the increasing demand for bioinformatics software, data analysis tools, and simulation models. As biological data becomes increasingly complex, the need for sophisticated software solutions capable of handling large datasets and providing accurate results is paramount. These software solutions enable researchers to model biological processes, analyze genetic data, and simulate drug interactions, making them indispensable tools in

The size of the Digital Biology market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

The Digital Biology Market size valuation is expected to reach USD 15.2 billion in 2034 expanding at a CAGR of 8.8%. The Digital Biology Market report classifies market by key companies, drivers, demand, trend, and forecast insights.

Discover the explosive growth of the digital biology market, projected to reach $4.2 billion by 2033. This comprehensive analysis explores key drivers, trends, and restraints, profiling leading companies like DUNA Bioinformatics and Precigen. Learn about market segmentation, regional breakdowns, and the transformative impact of AI & ML on drug discovery and personalized medicine.

Beginning in late 1979, the Alabama Coastal Area Board (CAB) funded a series of baseline surveys on the coastal resources of Alabama, from which they could develop a monitoring program to observe any significant changes in the resources over time. Eight stations within Mobile Bay, Alabama were sampled monthly from April 1980 to April 1981. Data collected included samples for benthic fauna, pelagic fauna, sediment particle size, total organic carbon, foraminifera, zooplankton, phytoplankton, chlorophyll, turbidity, river flow, and hydrographic parameters. The subset of data presented here are for the benthic fauna, which were sampled by 0.1 m^2 Peterson grab. Fauna were enumerated and identified to the lowest taxon possible, and mainly included crustaceans, molluscs, polychaetes, and echinoderms. Data in readily accessible digital form are available from April 1980 to February 1981.

Algorithmic parameter mapping (PM) sonification is an innovative approach to scientific data representation using sound, with task-specific projects showing promise across a range of biological data for a range of purposes. This work develops a range of novel task-specific PM sonification algorithms for the representation of biological data. In Chapter 1, a sonification concert is presented, hosted in collaboration with other sonification researchers, showcasing works created using PM sonification of biological sequence data (nucleotides and amino acids) as a method of public engagement with molecular evolution. In Chapter 2, an approach to inspire action on biodiversity loss is presented, featuring a PM sonification method representing global biodiversity index data (Living Planet Index) by the soundwave-level deletion of data from familiar/recognisable audio. In Chapter 3, a proposed zoo installation engaging visitors with genomic research into the stewardship of endangered species through the example of the Mauritian pink pigeon (Nesoenas mayeri) in presented, featuring a PM sonification method of genomic analysis data representing the risk of deleterious mutations in potential offspring. In Chapter 4, an approach to digital biological education is presented, using a method integrating narrative scaffolding techniques, podcasting sound design, and the PM sonification of biological concepts. Overall, the work uses a range of innovations in sonification methods to create sound outputs of a high quality, borrowing techniques from music/radio production and sound design, including the editing of existing recordings, narrative scaffolding techniques, use of the MIDI protocol, and mixing and mastering via Digital Audio Workstations (DAWs). Design processes focus on the listening experience of end-users, centring the task-specific nature of PM sonification and emphasising intended use-cases and context at all stages in design. Varied ways of understanding sound and features of human psychoacoustics are used in the design of these approaches. To emphasise the shareability and longevity of the work, which is adaptable for future applications with both biological and non-biological data sets, the works utilises open-source software, publicly available code sharing repositories, and a modular implementation approach facilitating post hoc sound design. The evaluation of these methods is end-user-focussed, using a suite of techniques from social research methods to provide evidence of success and insight into the subjective experiences of listeners. These include positivist experiments to measure the success of users in task-completion, alongside asking experts and end-users to affirm or deny the success of the methods, and interpretivist investigations into listener experience through interviews, focus groups, and questionnaires, which are also compared to design intentions. Overall, the works shows that centring the purpose of the approach and the experience of listeners in data processing, mapping, sound design, and evaluation methods helps create successful PM sonification projects.

The Digital Biology market is booming, projected to reach $60 billion by 2033 with a CAGR of 18%. Discover key drivers, trends, and restraints shaping this rapidly evolving sector, including insights from leading companies like DUNA Bioinformatics and Precigen. Explore market size, segmentation, and regional analysis in this comprehensive market report.

Middle East digital biology market valued at $160M, driven by biotech investments and genomic data platforms, growing rapidly.

Computational Biology Market Outlook

According to our latest research, the global computational biology market size in 2024 stands at USD 7.9 billion, demonstrating robust growth driven by the increasing integration of digital technologies in life sciences. The market is projected to expand at a CAGR of 15.2% from 2025 to 2033, reaching an estimated USD 23.9 billion by the end of the forecast period. This impressive trajectory is primarily fueled by advancements in genomics, rising demand for personalized medicine, and the need for efficient drug discovery processes. The computational biology sector is witnessing significant investments from both private and public entities, further accelerating its development globally.

One of the primary growth drivers for the computational biology market is the surge in demand for faster and more cost-effective drug discovery and development. Traditional drug discovery is both time-consuming and expensive, often taking over a decade and costing billions of dollars per drug. Computational biology leverages sophisticated algorithms and high-performance computing to simulate biological processes, predict drug interactions, and optimize lead compounds, thereby dramatically reducing both time and cost. The COVID-19 pandemic further highlighted the importance of computational biology, as researchers worldwide relied on computational models to accelerate vaccine and therapeutic developments, setting a precedent for future pharmaceutical innovation.

Another significant growth factor is the increasing adoption of computational biology tools in genomics and precision medicine. With the advent of next-generation sequencing (NGS) and the falling costs of genomic data generation, there is an exponential increase in biological data. Computational biology provides the necessary infrastructure and analytical capabilities to interpret this vast data, enabling personalized treatment strategies tailored to individual genetic profiles. This trend is not only transforming patient care but also opening new avenues for research in rare and complex diseases, further expanding the market’s scope and relevance across healthcare and life sciences.

The expanding scope of computational biology applications beyond drug discovery is also propelling market growth. Applications such as cellular and biological simulation, disease modeling, and human body simulation are becoming increasingly critical in academic research, clinical trials, and even hospital settings. These applications are instrumental in understanding complex biological systems, predicting disease progression, and optimizing therapeutic interventions. Furthermore, collaborations between academic institutions, biotech companies, and healthcare providers are fostering innovation and driving the adoption of computational biology solutions across diverse end-user segments.

Regionally, North America continues to dominate the computational biology market, accounting for the largest share in 2024, followed closely by Europe. The Asia Pacific region, however, is emerging as the fastest-growing market, driven by significant investments in healthcare infrastructure, expanding biotechnology sectors, and supportive government initiatives. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a slower pace, as these regions gradually build their research capabilities and digital health ecosystems. Overall, the global computational biology market is on a strong upward trajectory, supported by technological advancements, growing research investments, and increasing awareness of the benefits of computational approaches in biology and medicine.

Offering Analysis

The computational biology market by offering is segmented into software, hardware, and services. Among these, software solutions represent the largest and most dynamic segment, as computational biology relies heavily on complex algorithms, simulation platforms, and data analytics tools for modeling biological systems and processes. The demand for advanced software is propelled by the need to handle and analyze vast datasets generated from genomics, proteomics, and other omics technologies. Leading software solutions are equipped with artificial intelligence (AI) and machine learning (ML) capabilities, enabling researchers to extract meaningful insights from complex biological data and streamline the drug discovery pipeline. As the complexity of biological questions increases, the sophistication and

An online open-access open-data journal, publishing ''big-data'' studies from the entire spectrum of life and biomedical sciences whose publication format links standard manuscript publication with its affiliated database, GigaDB, that hosts all associated data, provides data analysis tools, cloud-computing resources, and a DOI assignment to every dataset. GigaScience covers not just ''omic'' type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale sharable data. Supporting the open-data movement, they require that all supporting data and source code be publicly available in a suitable public repository and/or under a public domain CC0 license in the BGI GigaScience database. Using the BGI cloud as a test environment, they also consider open-source software tools / methods for the analysis or handling of large-scale data. When submitting a manuscript, please contact them if you have datasets or cloud applications you would like them to host. To maximize data usability submitters are encouraged to follow best practice for metadata reporting and are given the opportunity to submit in ISA-Tab format.

This repository contains the structures, features and compositions of Bio-hMOFs database.

1. Fragments and Composition: Contains the building block fragments used to generate the Bio-hMOFs.
2. Structures-CIFs: Contains the structures of Bio-hMOFs
3. Geometric and RACs: Contains the geomtric and RACs features of Bio-hMOFs
4. Adsorption Capacity: Contains the adsorption uptake of NO and CO adsorption simulated under 298K under 1 bar and 10 bar.
5. Mechanical Properties; Computed mechanical properties

The United Nations Convention on Biological Diversity (CBD) formally recognized the sovereign rights of nations over their biological diversity. Implicit within the treaty is the idea that mega-biodiverse countries will provide genetic resources and grant access to them and scientists in high-income countries will use these resources and share back benefits. However, little research has been conducted on how this framework is reflected in real-life scientific practice. Currently, parties to the CBD are debating whether digital sequence information (DSI) should be regulated under a new benefit-sharing framework. At this critical time point in the upcoming international negotiations, we test the fundamental hypothesis of provision and use by looking at the global patterns of access and use in scientific publications. Our data reject the provider-user relationship and suggest far more complex information flow for digital sequence information. Therefore, any new policy decisions on digital sequence information should be aware of the high level of use of DSI across low- and middle-income countries and seek to preserve open access to this crucial common good.

View Tomy Digital Biology Co Limited import export trade data, including shipment records, HS codes, top buyers, suppliers, trade values, and global market insights.

View Tomy Digital Biology Co Limited Japa import export trade data, including shipment records, HS codes, top buyers, suppliers, trade values, and global market insights.

Genetic data are being generated at unprecedented rates. Policies of many journals, institutions and funding bodies aim to ensure that these data are publicly archived so that published results are reproducible. Additionally, publicly archived data can be ‘repurposed’ to address new questions in the future. In 2011, along with other leading journals in ecology and evolution, Molecular Ecology implemented mandatory public data archiving (the Joint Data Archiving Policy). To evaluate the effect of this policy, we assessed the genetic, spatial and temporal data archived for 419 data sets from 289 articles in Molecular Ecology from 2009 to 2013. We then determined whether archived data could be used to reproduce analyses as presented in the manuscript. We found that the journal's mandatory archiving policy has had a substantial positive impact, increasing genetic data archiving from 49 (pre-2011) to 98% (2011–present). However, 31% of publicly archived genetic data sets could not be recreat...

The size of the Bioinformatics Market was valued at USD 20.72 USD Billion in 2023 and is projected to reach USD 64.45 USD Billion by 2032, with an expected CAGR of 17.6% during the forecast period. Recent developments include: October 2023 – Bionl, Inc., a pioneering company in biomedical and bioinformatics research, launched a no-code biomedical research platform that enables researchers, students, and professionals to investigate biomedicine using natural language queries., October 2023 – BioBam Bioinformatics launched OmicsBox 3.1 to empower researchers, scientists, and bioinformaticians in their pursuit of advanced omics data analysis and interpretation., April 2023 – Absci Corp. collaborated with Aster Insights (formerly named M2GEN) to expedite the development of new cancer medicines., December 2022 – Analytical Biosciences Limited partnered with Mission Bio to co-develop bioinformatics packages for translational and clinical research applications in hematological cancers., April 2022 – ATCC signed an agreement with QIAGEN to provide sequencing data from its collection of biological data. QIAGEN Digital Insights aims to establish a database from this information to develop and deliver high-value digital biology content for the biotechnology and pharmaceutical industries.. Key drivers for this market are: Increased Funding for Genomics Research to Surge Demand for Bioinformatic Solutions. Potential restraints include: Increased Funding for Genomics Research to Surge Demand for Bioinformatic Solutions. Notable trends are: Increased Funding for Genomics Research to Surge Demand for Bioinformatic Solutions.

Occurrence details of New Zealand marine fauna and flora from around the coastline and offshore. Information is assimilated from a variety of sources including unpublished data sets and digitised from journal articles. Where the source is from a published paper the source paper citation is listed at the record level.Data was then assimilated from digital and non digital sources (such as journal publications, reports, work sheets) into a central dataset.Marine species occurrence data collated from research events along the coast and in New Zealand waters.Biological data was sampled in-situ using a variety of equipment such as trawls, pots, grabs, dredges, and beach surveys.The scientific names have been mapped to the World Register of Marine Species (WoRMS), using the online taxon match tool.All sampling locations have been plotted on a map to perform a visual check. The most important check would be to see if all sampling locations are (1) in the marine and/or brackish environment and (2) within the described sampling area.Citation: SWPRON (2014). Marine biological observation data from coastal and offshore surveys around New Zealand. Southwestern Pacific OBIS, National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand, 5707 records. Online: https://nzobisipt.niwa.co.nz/resource?r=mbis_nz Released on April 17, 2015._Item Page Created: 2016-06-09 02:01 Item Page Last Modified: 2025-04-05 20:45Owner: NIWA_OpenDataMBIS_NZNo data edit dates availableFields: id,modified,language,bibliographicCitation,references_,institutionCode,collectionCode,ownerInstitutionCode,basisOfRecord,dynamicProperties,catalogNumber,occurrenceRemarks,individualCount,sex,lifeStage,occurrenceStatus,associatedTaxa,eventID,samplingProtocol,eventDate,startDayOfYear,year,month,day,fieldNumber,waterBody,country,stateProvince,county,locality,minimumDepthInMeters,maximumDepthInMeters,decimalLatitude,decimalLongitude,geodeticDatum,coordinateUncertaintyInMeters,footprintWKT,identifiedBy,typeStatus,scientificNameID,scientificName,kingdom,phylum,class,order_,family,genus,subgenus,specificEpithet,infraspecificEpithet,scientificNameAuthorship,FID

Synthetic Biology Design AI Market Outlook

According to our latest research, the global synthetic biology design AI market size reached USD 1.42 billion in 2024. The sector is experiencing robust expansion, with a recorded compound annual growth rate (CAGR) of 32.7% from 2025 to 2033. Based on this CAGR, the market is forecasted to reach USD 15.48 billion by 2033. This impressive growth is primarily driven by the increasing integration of artificial intelligence in synthetic biology processes, which is accelerating innovation in gene editing, metabolic engineering, and biological system design. As per our latest research, the market is witnessing significant investments from both public and private sectors, further propelling advancements in AI-driven synthetic biology solutions.

The primary growth factor fueling the synthetic biology design AI market is the rapid technological convergence between artificial intelligence and biotechnology. AI algorithms are revolutionizing the way scientists design, simulate, and optimize biological systems, enabling unprecedented precision in gene synthesis, protein engineering, and metabolic pathway construction. This integration is significantly reducing the time and cost associated with synthetic biology projects, making it feasible to tackle complex biological challenges such as developing novel therapeutics, sustainable biofuels, and biodegradable materials. The ability of AI to analyze massive datasets and predict biological outcomes is catalyzing a new era of digital biology, where iterative design cycles are becoming faster and more reliable, ultimately expanding the scope of synthetic biology applications across multiple industries.

Another critical driver is the increasing demand for sustainable and personalized solutions in healthcare, agriculture, and industrial biotechnology. In healthcare, AI-powered synthetic biology platforms are instrumental in designing custom gene therapies, vaccines, and biosensors, addressing the growing need for personalized medicine and rapid response to emerging diseases. In agriculture, these technologies are enabling the creation of genetically optimized crops with improved yield, resilience, and nutritional value, supporting global food security initiatives. Industrial biotechnology is also benefiting from AI-driven synthetic biology, with the development of efficient microbial strains for bio-manufacturing and environmental applications, such as waste degradation and carbon capture. The convergence of market needs and technological capabilities is fostering a fertile environment for the growth of the synthetic biology design AI market.

Strategic partnerships, increased funding, and supportive regulatory frameworks are further accelerating the adoption of synthetic biology design AI solutions. Governments and international organizations are recognizing the potential of AI-enhanced synthetic biology to address critical challenges in public health, environmental sustainability, and industrial competitiveness. This has led to the establishment of funding programs, innovation hubs, and regulatory sandboxes, encouraging startups and established firms alike to invest in research and development. The influx of venture capital and corporate investments is enabling companies to scale their AI-driven synthetic biology platforms, expand their product offerings, and enter new markets. As a result, the competitive landscape is evolving rapidly, with new entrants and incumbents vying for leadership in this transformative sector.

AI in Biotech is increasingly becoming a cornerstone in the advancement of synthetic biology. The integration of artificial intelligence within the biotech sector is not only enhancing the precision of biological research but also accelerating the pace of innovation. AI algorithms are being employed to analyze complex biological data sets, facilitating the discovery of new biological pathways and the development of novel therapeutic strategies. This synergy between AI and biotechnology is paving the way for groundbreaking applications in personalized medicine, where treatments can be tailored to individual genetic profiles, thus improving efficacy and reducing adverse effects. As AI continues to evolve, its role in biotechnology is expected to expand, offering unprecedented opportunities for scientific breakthroughs and the development of sustainable solutions.

Digital Cell-Bank Management SaaS Market Outlook

According to our latest research, the global Digital Cell-Bank Management SaaS market size reached USD 1.42 billion in 2024, exhibiting robust momentum driven by the increasing digitization of life sciences workflows and the urgent demand for secure, scalable, and compliant cell-bank management solutions. The market is projected to grow at a CAGR of 15.7% from 2025 to 2033, reaching approximately USD 5.46 billion by 2033. This impressive growth trajectory is primarily fueled by the rising adoption of cloud-based platforms, the expansion of biopharmaceutical R&D, and the intensifying focus on data integrity and regulatory compliance across global healthcare and research sectors.

One of the primary growth drivers in the Digital Cell-Bank Management SaaS market is the accelerating digital transformation within the biopharmaceutical and healthcare industries. With the proliferation of advanced therapies, including cell and gene therapies, organizations are increasingly seeking robust digital platforms to manage vast and complex cell-bank repositories. These platforms streamline sample tracking, ensure data traceability, and facilitate regulatory compliance, which is critical as regulatory bodies worldwide tighten oversight on biological sample management. The integration of artificial intelligence and machine learning within SaaS solutions further enhances predictive analytics and automation, reducing manual errors and operational costs. This trend is expected to deepen as organizations prioritize efficiency, data security, and scalability in their cell-bank management operations.

Another significant growth factor is the surge in collaborative research activities and the globalization of clinical trials. Academic institutions, contract research organizations (CROs), and pharmaceutical companies are increasingly collaborating across borders, necessitating centralized, cloud-based cell-bank management solutions. Digital SaaS platforms enable real-time data sharing, secure access control, and seamless integration with other laboratory information management systems (LIMS). These capabilities are particularly crucial as research consortia and multinational projects demand standardized, interoperable, and auditable solutions. Moreover, the COVID-19 pandemic underscored the need for remote access to critical biological data, further catalyzing the shift toward SaaS-based digital cell-bank management.

The growing complexity of cell lines and the diversification of cell types being utilized in research and therapeutic development also contribute to market expansion. As biopharmaceutical pipelines increasingly incorporate mammalian, microbial, insect, and plant cells, organizations require flexible and customizable SaaS platforms capable of accommodating diverse workflows and regulatory requirements. Additionally, the rising prevalence of personalized medicine and regenerative therapies has amplified the need for secure, long-term storage and retrieval of cell lines and associated metadata. This evolution in scientific research and clinical applications is expected to sustain high demand for advanced digital cell-bank management solutions in the coming years.

From a regional perspective, North America currently dominates the Digital Cell-Bank Management SaaS market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The strong presence of leading biopharmaceutical companies, advanced healthcare infrastructure, and stringent regulatory frameworks in North America have fostered early adoption of SaaS-based cell-bank management solutions. Europe is also witnessing significant growth, propelled by government initiatives supporting biotech innovation and digital health. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by expanding R&D investments, the rise of contract manufacturing, and increasing collaborations between local and global research entities. This regional diversification is expected to intensify as global players expand their footprint in emerging markets with tailored SaaS offerings.

Component Analysis

Within the Digital Cell-Bank Management SaaS market, the component segment is bifurcated into Software and Services, both of which play integral roles in shaping the market’s growth trajectory. The software segment encompasses core SaaS platforms designed for digital cell-bank man