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市场调查报告书
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计算生物学市场 - 2018-2028 年全球产业规模、份额、趋势、机会和预测，按应用、工具、服务、最终用户、地区、竞争预测和机会细分，2018-2028F

Computational Biology Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028 Segmented By Application, By Tool, By Service, By End User, By Region, By Competition Forecast & Opportunities, 2018-2028F
出版日期: | 出版商: TechSci Research | 英文 190 Pages | 商品交期: 2-3个工作天内

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简介目录

2022年，全球计算生物学市场估值达到48.9亿美元，预计在预测期内将出现显着增长，预计到2028年复合年增长率(CAGR)为7.49%。全球计算生物学市场涉及利用计算技术（包括演算法、资料分析和数学建模）来理解和审查生物资料。该领域在生命科学的各个领域发挥关键作用，包括基因组学、蛋白质组学、药物发现和个人化医疗。

主要市场驱动因素

生物数据的爆炸性成长

市场概况
预测期 2024-2028
2022 年市场规模 48.9亿美元
2028 年市场规模 75.1亿美元
2023-2028 年复合年增长率 7.49%
成长最快的细分市场 药物发现与疾病建模
最大的市场 北美洲

生物学领域已经进入了一个新时代，其特征是生物资料前所未有的爆炸性增长。从基因组定序到复杂生物系统的研究，产生的资料量和复杂性令人震惊。海量的资料催生了计算生物学领域，该领域利用先进的演算法和资料分析技术来理解这些丰富的资讯。基因组定序一直是生物资料激增的驱动力。 2003 年完成的人类基因组计画标誌着基因组学的一个重要里程碑，但这只是开始。如今，高通量定序技术使得快速且经济高效地对整个基因组进行定序成为可能。这产生了庞大的基因组资料储存库，为遗传学、演化和疾病易感性提供了重要的见解。基因组学只是生物资料爆炸的一个面向。研究基因表现模式的转录组学和专注于蛋白质的蛋白质组学也促进了资料的涌入。研究人员现在可以检查生物体的整个转录组或蛋白质组，从而深入了解基因调控、蛋白质功能和疾病机制。单细胞测序技术将生物学研究提升到了更精细的水平。科学家现在可以分析组织内的单一细胞，而不是研究组织或细胞群。这项技术彻底改变了我们对细胞异质性、组织发育和疾病进展的理解。然而，它会产生大量资料，需要复杂的计算分析。多个组学资料来源（基因组学、转录组学、蛋白质组学、代谢组学等）的整合是全面理解复杂生物系统的强大方法。然而，它使资料量倍增。计算生物学在协调和解释这些综合资料集、实现对生物现象的整体洞察方面发挥关键作用。製药业依靠计算生物学来加速药物发现。透过分析大量化合物资料集及其与生物分子的相互作用，研究人员可以识别潜在的候选药物、预测其功效并优化其特性。这种数据驱动的方法显着减少了将新药推向市场的时间和成本。

基因组学的进展

过去几十年来，基因组学领域取得了显着的进步，彻底改变了我们对遗传学、疾病和生命本身复杂性的理解。这一转变的核心是基因组学和计算生物学之间的协同作用。 2003 年完成的人类基因组计画标誌着基因体学的转捩点。对人类基因组中的所有基因进行绘图和测序是一项巨大的合作努力。这项里程碑式的成就为基因组学革命奠定了基础，促进了高通量 DNA 定序技术的快速发展。新一代定序 (NGS) 技术的出现改变了基因组学的游戏规则。这些仪器可以在短时间内对大量 DNA 进行定序，单次运行即可产生数 TB 的资料。资料输出的指数级增长需要先进的计算工具和专业知识来有效地处理和分析资料。高通量测序的激增导致了基因组资料的爆炸性增长。研究人员现在不仅可以对人类基因组进行定序，还可以对无数其他物种的基因组进行定序，揭示对演化、遗传多样性和疾病遗传基础的重要见解。如此丰富的资料刺激了对计算生物学解决方案提取有意义资讯的需求。经济实惠的直接面向消费者的 DNA 检测的出现使基因组学变得普惠大众。个人现在可以获得他们的遗传讯息，这可以提供对血统、疾病倾向和生活方式建议的见解。人们对个人基因组学日益增长的兴趣产生了对能够分析和解释这些个体基因谱的计算工具的巨大需求。基因组医学利用基因组资料来指导临床决策。它能够识别与疾病相关的基因突变，促进早期诊断，并支持个人化治疗计划。随着基因组医学越来越融入医疗保健系统，计算生物学工具在将基因组资讯转化为可行的见解方面发挥核心作用。传统的基因组技术经常分析细胞群，掩盖组织内的多样性。单细胞基因组学技术现在允许研究人员研究单一细胞，揭示复杂的细胞异质性。这些技术产生巨大的数据集，需要计算方法来揭示复杂的细胞景观。

药物发现与开发

药物发现和计算生物学领域正在经历令人兴奋的融合。随着製药业竞相开发创新药物，计算生物学成为不可或缺的盟友。对治疗从癌症到罕见遗传性疾病等多种疾病的新型药物化合物的需求持续增长。药物发现是一个漫长且资源密集的过程，但它对于改善医疗保健结果和患者的生活品质至关重要。计算生物学透过加速药物开发的各个阶段提供关键支持。计算生物学允许研究人员进行计算机（基于计算机）药物筛选。这种方法涉及模拟潜在药物化合物与目标分子（例如蛋白质或酶）之间的相互作用。透过虚拟筛选数千种化合物，研究人员可以更快、更低成本地识别潜​​在的候选药物。计算生物学在预测药物-标靶相互作用方面发挥关键作用。演算法和机器学习模型分析生物资料，以确定药物分子如何与特定细胞标靶相互作用。这种预测能力显着缩短了药物开发时间并减少了实验失败。一旦确定了潜在的候选药物，计算生物学就有助于优化其特性。研究人员可以修改先导化合物的化学结构，以增强其功效、降低毒性并提高生物利用度。这个迭代过程被称为先导最佳化，在很大程度上依赖计算建模和模拟。了解疾病所涉及的潜在生物学途径对于药物开发至关重要。计算生物学工具透过分析复杂的组学资料来帮助阐明这些途径。这些知识指导研究人员确定关键标靶并开发调节特定生物过程的药物。

协作和跨产业伙伴关係

在当今互联的世界中，协作和伙伴关係是创新和进步的强大催化剂。全球计算生物学市场也不例外，从跨产业合作中受益匪浅。计算生物学领域的合作促进了知识和专业知识的交流。学术机构和研究组织往往拥有前沿的研究成果，而製药公司则带来实际的药物开发经验。当这些实体聚集在一起时，它们将理论见解与现实世界的应用结合，推动该领域的创新。计算生物学的主要挑战之一是获取高品质的生物资料。研究组织和技术公司之间的合作可以提供宝贵的资料资源。例如，公私合作伙伴关係可以使研究人员能够存取大型资料集，使他们能够进行全面分析并开发更准确的模型。协作努力可以汇集人力和财力资源。这种资源协同可以加速研发进程。当多个实体为一个专案做出贡献时，就有可能处理更广泛和复杂的任务，例如大规模基因组研究或药物发现计划。计算生物学本质上涉及多个学科，包括生物学、电脑科学和统计学。合作计画通常涉及来自这些不同背景的研究人员。这种跨学科方法鼓励新的视角和创造性的问题解决，从而带来单一组织内不可能的突破。製药业越来越多地转向计算生物学来进行药物发现。製药公司和计算生物学专家之间的合作可以加快潜在候选药物的识别。跨产业合作伙伴关係促进了计算工具的应用，以预测药物与标靶的相互作用并优化先导化合物。

主要市场挑战

数据复杂性和数量

生物资料的指数成长是一把双面刃。虽然它提供了丰富的信息，但它在资料复杂性和数量方面也提出了重大挑战。处理、储存和分析海量资料集需要强大的运算基础设施和高效的演算法。

资料隐私和安全

生物资料，尤其是基因组信息，非常敏感，受到严格的隐私法规的约束。确保资料隐私同时允许进行有意义的分析是一种微妙的平衡。计算生物学市场必须解决这些问题，以获得公众信任并遵守不断发展的资料保护法。

互通性和标准化

计算生物学工具和平台的资料格式和分析方法通常各不相同。缺乏标准化阻碍了资料共享和协作。建立通用资料标准和可互通的工具对于克服这项挑战至关重要。

熟练劳动力短缺

计算生物学领域需要多学科技能，包括生物学、电脑科学、数学和统计学。这些领域缺乏具备专业知识的专业人士，这使得组织很难找到并留住合格的人才。

主要市场趋势

单细胞组学革命

单细胞测序和组学技术正在迅速发展。这些技术使研究人员能够剖析复杂组织内单一细胞的分子特征。随着单细胞资料分辨率的提高，计算生物学将在分析和解释这些复杂的数据集方面发挥关键作用。期待为单细胞组学分析量身定制的演算法和工具的创新。

空间转录组学

空间转录组学是一个将基因组学与空间资讯结合的新兴领域。它使研究人员能够绘製组织内基因表现的图谱，从而深入了解细胞的空间组织。空间资料分析的计算方法将受到很高的需求，这为研究组织结构和疾病机制提供了新的方法。

多组学整合

整合多个组学资料来源，例如基因组学、转录组学、蛋白质组学和代谢组学，提供生物系统的整体视图。促进多组学资料整合和分析的计算工具的需求量很大，使研究人员能够发现复杂的相互作用和途径。

区块链资料安全

资料安全和隐私在计算生物学中至关重要，特别是在处理敏感的基因组资讯时。区块链技术可望实现安全、透明的资料管理，确保生物资料的完整性和隐私性。期望看到基于区块链的资料安全和可追溯性解决方案。

细分市场洞察

服务洞察

根据服务类别，到 2022 年，合约细分市场将成为全球计算生物学市场的主导者。这可以归因于合约服务与全球提供的内部服务相比的成本效益。合约研究组织 (CRO) 服务提供者与客户密切合作，制定量身定制的计划，从而成为市场成长的催化剂。

最终使用者见解

预计商业部门将成为市场收入的主要贡献者。政府和商业实体对基因工程研发（R＆D）以及创新药物开发的投资增加是导致计算生物学需求增加的重要因素。

例如，2021 年 5 月，世界卫生组织 (WHO) 和瑞士联邦签署了一份谅解备忘录 (MoU)，建立首个 WHO BioHub 设施，作为 WHO BioHub 系统的一部分。该设施位于瑞士施皮茨，是安全接收、定序、储存和製备生物材料以分发给其他实验室的中心。它还在风险评估中发挥着至关重要的作用，并支持全球针对病原体的准备工作。同样，欧盟委员会对「地平线 2020」计画的大量投资旨在消除创新障碍，促进改善公共和私营部门之间的合作，从而促进创新。这些发展预计将促进对计算生物学不断增长的需求，从而推动该细分市场的收入成长。

区域洞察

北美目前在计算生物学市场占据主导地位，预计将在未来几年保持领先地位。尤其是美国，是合成生物学领域的领导者，合成生物学是一门专注于生物系统的设计、操作和重新编程的新兴学科。自 2005 年以来，美国政府一直大力支持计算生物学和合成生物学，为其发展投入了超过 10 亿美元。美国政府在推动计算生物学方面的年平均投资估计约为 1.4 亿美元。

个人化医疗的兴起促进了医疗机构、政府机构和研究人员之间的合作，以加速创造有效的治疗方法。例如，2020年，Summit Biolabs Inc.与科罗拉多个人化医疗中心（CCPM）建立了全面的策略合作伙伴关係，进行唾液液体活检测试的研究、开发和商业化，以用于癌症的早期检测、新冠病毒的诊断。19. 其他病毒感染。同样，2020 年4 月，HealthCare Global Enterprises 和Strand Life Sciences 推出了StrandAdvantage500，这是一种基于下一代定序(NGS) 的检测方法，可在统一的工作流程中评估从患者肿瘤中提取的DNA 和RNA中与癌症相关的遗传改变。此外，2021年7月，Indivumed GmbH推出了“travel”，这是一个专为肿瘤学和精准医学设计的创新人工智慧发现平台。该平台将 IndivuType 广泛的多组学资料与复杂的疾病模型、高度先进的自动化机器学习工具以及一整套先进的分析功能相结合。

美国的整体计算生物学市场预计在未来几年将大幅成长，这主要是由于在药物开发方面的大量投资，这是全球最高的。

目录

第 1 章：产品概述

  • 市场定义
  • 市场范围
    • 涵盖的市场
    • 考虑学习的年份
    • 主要市场区隔

第 2 章：研究方法

  • 研究目的
  • 基线方法
  • 主要产业伙伴
  • 主要协会和二手资料来源
  • 预测方法
  • 数据三角测量与验证
  • 假设和限制

第 3 章：执行摘要

  • 市场概况
  • 主要市场细分概述
  • 主要市场参与者概述
  • 重点地区/国家概况
  • 市场驱动因素、挑战、趋势概述

第 4 章：客户之声

第 5 章：全球计算生物学市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按应用（细胞和生物模拟、药物发现和疾病建模、临床前药物开发、临床试验、人体模拟软体）
    • 依工具（资料库、基础设施（硬体）、分析软体和服务）
    • 按服务（内部、联络）
    • 按最终用户（学术界、工业界和商业界）
    • 按地区
    • 按公司划分 (2022)
  • 产品市场地图
    • 按应用
    • 按工具
    • 按服务
    • 按最终用户
    • 按地区

第 6 章：北美计算生物学市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按应用（细胞和生物模拟、药物发现和疾病建模、临床前药物开发、临床试验、人体模拟软体）
    • 依工具（资料库、基础设施（硬体）、分析软体和服务）
    • 按服务（内部、联络）
    • 按最终用户（学术界、工业界和商业界）
    • 按国家/地区
  • 北美：国家分析
    • 美国
    • 加拿大
    • 墨西哥

第 7 章：欧洲计算生物学市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按应用（细胞和生物模拟、药物发现和疾病建模、临床前药物开发、临床试验、人体模拟软体）
    • 依工具（资料库、基础设施（硬体）、分析软体和服务）
    • 按服务（内部、联络）
    • 按最终用户（学术界、工业界和商业界）
    • 按国家/地区
  • 欧洲：国家分析
    • 德国
    • 英国
    • 法国
    • 义大利
    • 西班牙

第 8 章：亚太计算生物学市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按应用（细胞和生物模拟、药物发现和疾病建模、临床前药物开发、临床试验、人体模拟软体）
    • 依工具（资料库、基础设施（硬体）、分析软体和服务）
    • 按服务（内部、联络）
    • 按最终用户（学术界、工业界和商业界）
    • 按国家/地区
  • 亚太地区：国家分析
    • 中国
    • 日本
    • 印度
    • 澳洲
    • 韩国

第 9 章：南美洲计算生物学市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按应用（细胞和生物模拟、药物发现和疾病建模、临床前药物开发、临床试验、人体模拟软体）
    • 依工具（资料库、基础设施（硬体）、分析软体和服务）
    • 按服务（内部、联络）
    • 按最终用户（学术界、工业界和商业界）
    • 按国家/地区
  • 南美洲：国家分析
    • 巴西
    • 阿根廷
    • 哥伦比亚

第 10 章：中东和非洲计算生物学市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按应用（细胞和生物模拟、药物发现和疾病建模、临床前药物开发、临床试验、人体模拟软体）
    • 依工具（资料库、基础设施（硬体）、分析软体和服务）
    • 按服务（内部、联络）
    • 按最终用户（学术界、工业界和商业界）
    • 按国家/地区
  • MEA：国家分析
    • 南非计算生物学
    • 沙乌地阿拉伯计算生物学
    • 阿联酋运算生物学
    • 科威特计算生物学

第 11 章：市场动态

  • 司机
  • 挑战

第 12 章：市场趋势与发展

  • 近期发展
  • 併购
  • 产品发布

第 13 章：波特的五力分析

  • 产业竞争
  • 新进入者的潜力
  • 供应商的力量
  • 客户的力量
  • 替代产品的威胁

第14章：竞争格局

  • 商业概览
  • 产品供应
  • 最近的发展
  • 财务（据报导）
  • 主要人员
  • SWOT分析
    • Dassault Systemes SE
    • Certara Inc
    • Chemical Computing Group ULC
    • Compugen Ltd
    • Rosa & Co. LLC
    • GeneData AG
    • Insilico Biotechnology AG
    • Instem PLC
    • Strand Life Sciences Pvt Ltd
    • Schrodinger Inc

第 15 章：策略建议

第 16 章：关于我们与免责声明

简介目录
Product Code: 16136

In 2022, the Global Computational Biology Market reached a valuation of USD 4.89 billion and is expected to experience significant growth in the projected period, with an anticipated Compound Annual Growth Rate (CAGR) of 7.49% through 2028. The Global Computational Biology Market pertains to the utilization of computational techniques, which encompass algorithms, data analysis, and mathematical modeling, to comprehend and scrutinize biological data. This field plays a pivotal role across various domains of life sciences, encompassing genomics, proteomics, drug discovery, and personalized medicine.

Key Market Drivers

Explosion in Biological Data

Market Overview
Forecast Period2024-2028
Market Size 2022USD 4.89 Billion
Market Size 2028USD 7.51 Billion
CAGR 2023-20287.49%
Fastest Growing SegmentDrug Discovery and Disease Modelling
Largest MarketNorth America

The field of biology has entered a new era, one characterized by an unprecedented explosion in biological data. From the sequencing of genomes to the study of complex biological systems, the volume and complexity of data being generated are staggering. This deluge of data has given rise to the field of computational biology, which utilizes advanced algorithms and data analysis techniques to make sense of this wealth of information. The sequencing of genomes has been a driving force behind the surge in biological data. The Human Genome Project, completed in 2003, marked a significant milestone in genomics, but it was just the beginning. Today, high-throughput sequencing technologies have made it possible to rapidly and cost-effectively sequence entire genomes. This has led to a vast repository of genomic data, providing critical insights into genetics, evolution, and disease susceptibility. Genomics is just one facet of the biological data explosion. Transcriptomics, which studies gene expression patterns, and proteomics, which focuses on proteins, have also contributed to the data influx. Researchers can now examine the entire transcriptome or proteome of an organism, offering insights into gene regulation, protein function, and disease mechanisms. Single-cell sequencing technologies have taken biological research to a finer level of granularity. Instead of studying tissues or populations of cells, scientists can now analyze individual cells within a tissue. This technology has revolutionized our understanding of cellular heterogeneity, tissue development, and disease progression. However, it generates massive amounts of data that require sophisticated computational analysis. The integration of multiple omics data sources (genomics, transcriptomics, proteomics, metabolomics, etc.) is a powerful approach for understanding complex biological systems comprehensively. However, it multiplies the volume of data exponentially. Computational biology plays a pivotal role in harmonizing and interpreting these integrated datasets, enabling holistic insights into biological phenomena. The pharmaceutical industry relies on computational biology to accelerate drug discovery. By analyzing vast datasets of chemical compounds and their interactions with biological molecules, researchers can identify potential drug candidates, predict their efficacy, and optimize their properties. This data-driven approach significantly reduces the time and cost of bringing new drugs to market.

Advancements in Genomics

The field of genomics has witnessed remarkable advancements over the past few decades, revolutionizing our understanding of genetics, diseases, and the intricacies of life itself. At the heart of this transformation is the synergy between genomics and computational biology. The Human Genome Project, completed in 2003, marked a turning point in genomics. It was a massive collaborative effort to map and sequence all the genes in the human genome. This monumental achievement set the stage for a genomics revolution, catalyzing the rapid development of high-throughput DNA sequencing technologies. Next-generation sequencing (NGS) technologies emerged as game-changers in genomics. These instruments can sequence vast quantities of DNA in a short time, generating terabytes of data in a single run. This exponential increase in data output necessitated advanced computational tools and expertise to process and analyze the data efficiently. The proliferation of high-throughput sequencing has led to an explosion of genomic data. Researchers can now sequence not only human genomes but also the genomes of countless other species, uncovering critical insights into evolution, genetic diversity, and the genetic basis of diseases. This abundance of data fuels the demand for computational biology solutions to extract meaningful information. The advent of affordable direct-to-consumer DNA testing has made genomics accessible to the masses. Individuals can now obtain their genetic information, which can provide insights into ancestry, disease predispositions, and lifestyle recommendations. This growing interest in personal genomics generates a significant need for computational tools that can analyze and interpret these individual genetic profiles. Genomic medicine leverages genomic data to guide clinical decision-making. It enables the identification of genetic mutations linked to diseases, facilitates early diagnosis, and supports personalized treatment plans. As genomic medicine becomes more integrated into healthcare systems, computational biology tools play a central role in translating genomic information into actionable insights. Traditional genomic techniques often analyze populations of cells, masking the diversity within tissues. Single-cell genomics technologies now allow researchers to study individual cells, unveiling intricate cellular heterogeneity. These techniques generate immense datasets, necessitating computational methods to unravel the complex cellular landscapes.

Drug Discovery and Development

The realms of drug discovery and computational biology are experiencing an exciting convergence. As the pharmaceutical industry races to develop innovative drugs, computational biology has emerged as an indispensable ally. The need for novel pharmaceutical compounds to treat a wide range of diseases, from cancer to rare genetic disorders, continues to grow. Drug discovery is a lengthy and resource-intensive process, but it's essential for improving healthcare outcomes and patient quality of life. Computational biology provides crucial support by accelerating various stages of drug development. Computational biology allows researchers to conduct in-silico (computer-based) drug screening. This approach involves simulating the interaction between potential drug compounds and target molecules, such as proteins or enzymes. By virtually screening thousands of compounds, researchers can identify potential drug candidates faster and with lower costs. Computational biology plays a pivotal role in predicting drug-target interactions. Algorithms and machine learning models analyze biological data to determine how a drug molecule will interact with specific cellular targets. This predictive capability significantly shortens the drug development timeline and reduces experimental failures. Once potential drug candidates are identified, computational biology aids in optimizing their properties. Researchers can modify the chemical structure of lead compounds to enhance their efficacy, reduce toxicity, and improve bioavailability. This iterative process, known as lead optimization, relies heavily on computational modeling and simulations. Understanding the underlying biological pathways involved in diseases is critical for drug development. Computational biology tools help elucidate these pathways by analyzing complex omics data. This knowledge guides researchers in identifying key targets and developing drugs that modulate specific biological processes.

Collaboration and Cross-Industry Partnerships

In today's interconnected world, collaboration and partnerships are powerful catalysts for innovation and progress. The Global Computational Biology Market is no exception, benefiting significantly from cross-industry collaborations. Collaborations in the field of computational biology facilitate the exchange of knowledge and expertise. Academic institutions and research organizations often possess cutting-edge research findings, while pharmaceutical companies bring practical drug development experience. When these entities come together, they combine theoretical insights with real-world applications, driving innovation in the field. One of the primary challenges in computational biology is access to high-quality biological data. Collaboration between research organizations and technology firms can provide valuable data resources. Public-private partnerships, for example, can make large datasets accessible to researchers, enabling them to conduct comprehensive analyses and develop more accurate models. Collaborative efforts allow for the pooling of resources, both human and financial. This resource synergy can accelerate research and development processes. When multiple entities contribute to a project, it becomes possible to tackle more extensive and complex tasks, such as large-scale genomic studies or drug discovery initiatives. Computational biology inherently involves multiple disciplines, including biology, computer science, and statistics. Collaborative projects often involve researchers from these diverse backgrounds. This interdisciplinary approach encourages fresh perspectives and creative problem-solving, leading to breakthroughs that might not have been possible within a single organization. The pharmaceutical industry is increasingly turning to computational biology for drug discovery. Collaborations between pharmaceutical companies and computational biology experts can expedite the identification of potential drug candidates. Cross-industry partnerships facilitate the application of computational tools to predict drug-target interactions and optimize lead compounds.

Key Market Challenges

Data Complexity and Volume

The exponential growth of biological data is a double-edged sword. While it provides a wealth of information, it also presents a significant challenge in terms of data complexity and volume. Handling, storing, and analyzing massive datasets require robust computational infrastructure and efficient algorithms.

Data Privacy and Security

Biological data, especially genomic information, is sensitive and subject to strict privacy regulations. Ensuring data privacy while allowing for meaningful analysis is a delicate balance. The computational biology market must address these concerns to gain public trust and comply with evolving data protection laws.

Interoperability and Standardization

Computational biology tools and platforms often vary in their data formats and analysis methods. This lack of standardization hinders data sharing and collaboration. Establishing common data standards and interoperable tools is essential to overcome this challenge.

Shortage of Skilled Workforce

The field of computational biology requires a multidisciplinary skill set, encompassing biology, computer science, mathematics, and statistics. There is a shortage of professionals with expertise in these areas, making it challenging for organizations to find and retain qualified talent.

Key Market Trends

Single-Cell Omics Revolution

Single-cell sequencing and omics technologies are rapidly gaining momentum. These techniques allow researchers to dissect the molecular profiles of individual cells within complex tissues. As the resolution of single-cell data improves, computational biology will play a critical role in analyzing and interpreting these intricate datasets. Expect innovations in algorithms and tools tailored for single-cell omics analysis.

Spatial Transcriptomics

Spatial transcriptomics is an emerging field that combines genomics with spatial information. It enables researchers to map gene expression within tissues, providing insights into the spatial organization of cells. Computational methods for spatial data analysis will be in high demand, offering new ways to study tissue architecture and disease mechanisms.

Multi-Omics Integration

Integrating multiple omics data sources, such as genomics, transcriptomics, proteomics, and metabolomics, provides a holistic view of biological systems. Computational tools that facilitate the integration and analysis of multi-omics data will be in high demand, enabling researchers to uncover intricate interactions and pathways.

Blockchain for Data Security

Data security and privacy are paramount in computational biology, particularly when handling sensitive genomic information. Blockchain technology holds promise for secure and transparent data management, ensuring the integrity and privacy of biological data. Expect to see blockchain-based solutions for data security and traceability.

Segmental Insights

Service Insights

Based on the category of Service, the Contract segment emerged as the dominant player in the global market for computational biology in 2022. This can be attributed to the cost-effectiveness of contract services compared to the in-house services offered globally. Providers of Contract Research Organization (CRO) services collaborate closely with clients to create tailored plans, thereby acting as a catalyst for market growth.

On the other hand, the in-house segment is projected to experience the most rapid growth. In-house services grant companies' greater control over their internal operations, as they directly employ these services. This approach offers advantages such as cost savings and time efficiency, contributing to its accelerated growth.

End User Insights

The commercial sector is anticipated to be the primary contributor to market revenue. Increased investments in Research and Development (R&D) in genetic engineering and the development of innovative medicines by both government and commercial entities are significant factors contributing to the heightened demand for computational biology.

As an example, in May 2021, the World Health Organization (WHO) and the Swiss Confederation inked a Memorandum of Understanding (MoU) to establish the inaugural WHO BioHub Facility as part of the WHO BioHub System. Situated in Spiez, Switzerland, this facility serves as a hub for the secure reception, sequencing, storage, and preparation of biological materials for distribution to other laboratories. It also plays a crucial role in risk assessments and supports global preparedness against pathogens. Similarly, substantial investments from the European Commission into the Horizon 2020 program aim to eliminate innovation barriers and promote improved collaboration between the public and private sectors, fostering innovation. These developments are expected to bolster the rising demand for computational biology, consequently driving revenue growth in this market segment.

Regional Insights

North America presently holds the dominant position in the computational biology market and is expected to maintain its leadership for several more years. The United States, in particular, stands as the frontrunner in the field of synthetic biology, which is an emerging discipline focused on the design, manipulation, and reprogramming of biological systems. The U.S. government has been a substantial supporter of computational biology and synthetic biology since 2005, channeling over USD 1 billion toward their development. The annual average investment by the U.S. government in advancing computational biology is estimated at approximately USD 140 million.

The rise of personalized medicine has fostered collaborative initiatives among medical institutions, government bodies, and researchers to expedite the creation of effective treatments. For instance, in 2020, Summit Biolabs Inc. and the Colorado Center for Personalized Medicine (CCPM) established a comprehensive strategic partnership to conduct research, development, and commercialization of saliva liquid-biopsy tests for the early detection of cancer, diagnosis of COVID-19, and other viral infections. Similarly, in April 2020, HealthCare Global Enterprises and Strand Life Sciences introduced the StrandAdvantage500, a Next-Generation Sequencing (NGS) based assay that assesses cancer-related genetic alterations in DNA and RNA extracted from a patient's tumor in a unified workflow. Furthermore, in July 2021, Indivumed GmbH launched "travel," an innovative AI discovery platform designed for oncology and precision medicine. This platform combines IndivuType's extensive multi-omics data with sophisticated disease models, highly advanced automated Machine Learning tools, and a comprehensive suite of advanced analytical capabilities.

The overall computational biology market in the United States is poised for substantial growth in the coming years, primarily due to the significant investments made in drug development, which are the highest worldwide.

Key Market Players

  • Dassault Systemes SE
  • Certara Inc
  • Chemical Computing Group ULC
  • Compugen Ltd
  • Rosa & Co. LLC
  • GeneData AG
  • Insilico Biotechnology AG
  • Instem PLC
  • Strand Life Sciences Pvt Ltd
  • Schrodinger Inc

Report Scope:

In this report, the Global Computational Biology Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Computational Biology Market, By Application:

  • Cellular and Biological Simulation
  • Drug Discovery and Disease Modelling
  • Preclinical Drug Development
  • Clinical Trials
  • Human Body Simulation Software

Computational Biology Market, By Tool:

  • Databases
  • Infrastructure (Hardware)
  • Analysis Software and Services

Computational Biology Market, By Service:

  • In-house
  • Contact

Computational Biology Market, By End User:

  • Academics
  • Industry and Commercials

Computational Biology Market, By Region:

  • North America
  • United States
  • Canada
  • Mexico
  • Europe
  • Germany
  • United Kingdom
  • France
  • Italy
  • Spain
  • Asia-Pacific
  • China
  • Japan
  • India
  • Australia
  • South Korea
  • South America
  • Brazil
  • Argentina
  • Colombia
  • Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Kuwait

Competitive Landscape

  • Company Profiles: Detailed analysis of the major companies present in the Global Computational Biology Market.

Available Customizations:

  • Global Computational Biology market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

  • 3.1. Overview of the Market
  • 3.2. Overview of Key Market Segmentations
  • 3.3. Overview of Key Market Players
  • 3.4. Overview of Key Regions/Countries
  • 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Computational Biology Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 5.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 5.2.3. By Service (In-house, Contact)
    • 5.2.4. By End User (Academics, Industry and Commercials)
    • 5.2.5. By Region
    • 5.2.6. By Company (2022)
  • 5.3. Product Market Map
    • 5.3.1. By Application
    • 5.3.2. By Tool
    • 5.3.3. By Service
    • 5.3.4. By End User
    • 5.3.5. By Region

6. North America Computational Biology Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 6.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 6.2.3. By Service (In-house, Contact)
    • 6.2.4. By End User (Academics, Industry and Commercials)
    • 6.2.5. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Computational Biology Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Application
        • 6.3.1.2.2. By Tool
        • 6.3.1.2.3. By Service
        • 6.3.1.2.4. By End User
    • 6.3.2. Canada Computational Biology Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Application
        • 6.3.2.2.2. By Tool
        • 6.3.2.2.3. By Service
        • 6.3.2.2.4. By End User
    • 6.3.3. Mexico Computational Biology Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Application
        • 6.3.3.2.2. By Tool
        • 6.3.3.2.3. By Service
        • 6.3.3.2.4. By End User

7. Europe Computational Biology Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 7.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 7.2.3. By Service (In-house, Contact)
    • 7.2.4. By End User (Academics, Industry and Commercials)
    • 7.2.5. By Country
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany Computational Biology Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Application
        • 7.3.1.2.2. By Tool
        • 7.3.1.2.3. By Service
        • 7.3.1.2.4. By End User
    • 7.3.2. United Kingdom Computational Biology Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Application
        • 7.3.2.2.2. By Tool
        • 7.3.2.2.3. By Service
        • 7.3.2.2.4. By End User
    • 7.3.3. France Computational Biology Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Application
        • 7.3.3.2.2. By Tool
        • 7.3.3.2.3. By Service
        • 7.3.3.2.4. By End User
    • 7.3.4. Italy Computational Biology Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Application
        • 7.3.4.2.2. By Tool
        • 7.3.4.2.3. By Service
        • 7.3.4.2.4. By End User
    • 7.3.5. Spain Computational Biology Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Application
        • 7.3.5.2.2. By Tool
        • 7.3.5.2.3. By Service
        • 7.3.5.2.4. By End User

8. Asia-Pacific Computational Biology Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 8.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 8.2.3. By Service (In-house, Contact)
    • 8.2.4. By End User (Academics, Industry and Commercials)
    • 8.2.5. By Country
  • 8.3. Asia-Pacific: Country Analysis
    • 8.3.1. China Computational Biology Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Application
        • 8.3.1.2.2. By Tool
        • 8.3.1.2.3. By Service
        • 8.3.1.2.4. By End User
    • 8.3.2. Japan Computational Biology Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Application
        • 8.3.2.2.2. By Tool
        • 8.3.2.2.3. By Service
        • 8.3.2.2.4. By End User
    • 8.3.3. India Computational Biology Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Application
        • 8.3.3.2.2. By Tool
        • 8.3.3.2.3. By Service
        • 8.3.3.2.4. By End User
    • 8.3.4. Australia Computational Biology Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Application
        • 8.3.4.2.2. By Tool
        • 8.3.4.2.3. By Service
        • 8.3.4.2.4. By End User
    • 8.3.5. South Korea Computational Biology Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Application
        • 8.3.5.2.2. By Tool
        • 8.3.5.2.3. By Service
        • 8.3.5.2.4. By End User

9. South America Computational Biology Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 9.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 9.2.3. By Service (In-house, Contact)
    • 9.2.4. By End User (Academics, Industry and Commercials)
    • 9.2.5. By Country
  • 9.3. South America: Country Analysis
    • 9.3.1. Brazil Computational Biology Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Application
        • 9.3.1.2.2. By Tool
        • 9.3.1.2.3. By Service
        • 9.3.1.2.4. By End User
    • 9.3.2. Argentina Computational Biology Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Application
        • 9.3.2.2.2. By Tool
        • 9.3.2.2.3. By Service
        • 9.3.2.2.4. By End User
    • 9.3.3. Colombia Computational Biology Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Application
        • 9.3.3.2.2. By Tool
        • 9.3.3.2.3. By Service
        • 9.3.3.2.4. By End User

10. Middle East and Africa Computational Biology Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 10.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 10.2.3. By Service (In-house, Contact)
    • 10.2.4. By End User (Academics, Industry and Commercials)
    • 10.2.5. By Country
  • 10.3. MEA: Country Analysis
    • 10.3.1. South Africa Computational Biology Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Application
        • 10.3.1.2.2. By Tool
        • 10.3.1.2.3. By Service
        • 10.3.1.2.4. By End User
    • 10.3.2. Saudi Arabia Computational Biology Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Application
        • 10.3.2.2.2. By Tool
        • 10.3.2.2.3. By Service
        • 10.3.2.2.4. By End User
    • 10.3.3. UAE Computational Biology Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Application
        • 10.3.3.2.2. By Tool
        • 10.3.3.2.3. By Service
        • 10.3.3.2.4. By End User
    • 10.3.4. Kuwait Computational Biology Market Outlook
      • 10.3.4.1. Market Size & Forecast
        • 10.3.4.1.1. By Value
      • 10.3.4.2. Market Share & Forecast
        • 10.3.4.2.1. By Application
        • 10.3.4.2.2. By Tool
        • 10.3.4.2.3. By Service
        • 10.3.4.2.4. By End User

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenges

12. Market Trends & Developments

  • 12.1. Recent Development
  • 12.2. Mergers & Acquisitions
  • 12.3. Product Launches

13. Porter's Five Forces Analysis

  • 13.1. Competition in the Industry
  • 13.2. Potential of New Entrants
  • 13.3. Power of Suppliers
  • 13.4. Power of Customers
  • 13.5. Threat of Substitute Products

14. Competitive Landscape

  • 14.1. Business Overview
  • 14.2. Product Offerings
  • 14.3. Recent Developments
  • 14.4. Financials (As Reported)
  • 14.5. Key Personnel
  • 14.6. SWOT Analysis
    • 14.6.1. Dassault Systemes SE
    • 14.6.2. Certara Inc
    • 14.6.3. Chemical Computing Group ULC
    • 14.6.4. Compugen Ltd
    • 14.6.5. Rosa & Co. LLC
    • 14.6.6. GeneData AG
    • 14.6.7. Insilico Biotechnology AG
    • 14.6.8. Instem PLC
    • 14.6.9. Strand Life Sciences Pvt Ltd
    • 14.6.10. Schrodinger Inc

15. Strategic Recommendations

16. About Us & Disclaimer