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Leveraging mathematical models to improve the statistical robustness of cancer immunotherapy trials
IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2025-01-11 DOI: 10.1016/j.coisb.2024.100540
Jeroen H.A. Creemers , Johannes Textor
Cancer immunotherapy is an important application area for mathematical modeling. Current modeling studies have a range of ambitious goals from dose optimization to creating “digital twins” of individual cancer patients for treatment response prediction. Here we focus on a humbler, but nonetheless important, goal: aiding with the planning and design of clinical trials. Cancer immunotherapy trials can be hard to design due to heterogeneous and time-varying treatment effects. While clinical statisticians already use computer simulations, these rarely integrate explicit pathophysiological mechanisms, such as cancer-immune interactions, to specifically adapt the design to the treatment. Encouraged by rapid progress in mathematical modeling, we here propose an “in-silico-first” approach–already common in industry–where doctors, statisticians, and modelers build knowledge-based mathematical models to examine and refine the statistical design of clinical trials. Ultimately, we hope that this collaborative effort will lead to more robust designs of future clinical trials, resulting in improved success rates.
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引用次数: 0
Calcium-mediated mitochondrial energy deficiency in Parkinson's and Alzheimer's diseases: Insights from computational modelling
IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2025-01-10 DOI: 10.1016/j.coisb.2024.100539
Valérie Voorsluijs , Alexander Skupin
Alzheimer's and Parkinson's diseases are the most prevalent neurodegenerative disorders worldwide and are characterised by progressive cognitive and functional impairments caused by neuronal loss. Energy deficiency is a predominant hallmark of their pathophysiology and plays a central role in the development of the disease, notably by mitochondrial dysfunction enhancing protein aggregation and oxidative stress which trigger subsequently immune responses and neuronal loss. Quantifying this energetic deficiency and identifying specific causative mechanisms from the complex network of interacting metabolic and regulatory pathways at play is rather challenging, where integrative mathematical modelling represents a powerful tool to support these investigations. Here, we review the latest developments in integrative modelling in brain bioenergetics in relation to Alzheimer's and Parkinson's diseases where we focus on the regulatory role of Ca2+ signalling. Finally, we discuss recent challenges and future directions to improve the current understanding of the energy-deficiency theory of neurodegeneration.
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引用次数: 0
Editorial Board Page 编委会页面
IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2024-12-01 DOI: 10.1016/S2452-3100(24)00031-3
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引用次数: 0
Untangling cell–cell communication networks and on-treatment response in immunotherapy
IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2024-11-19 DOI: 10.1016/j.coisb.2024.100534
Lisa Maria Steinheuer , Niklas Klümper , Tobias Bald , Kevin Thurley
Immunotherapies have shown efficacy in improving autoimmune conditions such as rheumatoid arthritis and are now widely established for various cancer entities. Nevertheless, predicting patient outcomes prior to therapy remains very challenging, likely attributable to the diversity and complex, interactive dynamics of immune cells. Recent advancements in statistical analysis as well as machine learning and mathematical modeling techniques have provided insights into immune-cell regulation and tumor-immune dynamics. Here, we discuss recent developments in this field, with the aim of deriving a path to improvements in treatment biomarker identification and adverse effect prediction. Deriving a quantitative understanding of the complex interactions among immune cell subpopulations holds promise for optimizing treatment strategies in numerous health conditions from chronic inflammation to cancer.
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引用次数: 0
From regulation of cell fate decisions towards patient-specific treatments, insights from mechanistic models of signalling pathways 从细胞命运决定的调控到针对患者的治疗，信号通路机理模型的启示
IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2024-08-28 DOI: 10.1016/j.coisb.2024.100533
Mareike Simon , Fabian Konrath , Jana Wolf

Cell fate decisions are tightly regulated by complex signalling networks. Disturbed signalling through these networks is prominent in disease development. To elucidate pathway contributions and effects of alterations to the regulation of proliferation, quiescence, senescence, and apoptosis, computational modelling has been essential. Modelling heterogeneity on different scales was shown to be important for cell fate prediction. In recent years, personalised models capturing signalling and cell fate decisions have been developed. Of special interest is the application of these models to predict the response to drugs. In this review, we highlight examples of mathematical models of signalling pathways that regulate disease-relevant cell fate decisions on the path to develop individualised patient models for optimal treatment prediction.

细胞命运的决定受到复杂信号网络的严格调控。通过这些网络进行的信号传导紊乱在疾病发展中十分突出。要阐明增殖、静止、衰老和凋亡调控途径的贡献和改变的影响，必须进行计算建模。不同尺度的异质性建模对于细胞命运预测非常重要。近年来，捕捉信号和细胞命运决定的个性化模型已经开发出来。这些模型在预测药物反应方面的应用尤其引人关注。在这篇综述中，我们将重点介绍调控与疾病相关的细胞命运决定的信号通路数学模型的实例，这些数学模型正朝着开发用于最佳治疗预测的患者个体化模型的方向发展。
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引用次数: 0
Editorial overview: Systems biology of ecological interactions across scales 编辑综述：跨尺度生态相互作用的系统生物学
IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2024-08-02 DOI: 10.1016/j.coisb.2024.100532
Edo Kussell, Nobuto Takeuchi
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引用次数: 0
A critical review of multiscale modeling for predictive understanding of cancer cell metabolism 多尺度建模用于预测性了解癌细胞代谢的重要综述
IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2024-07-20 DOI: 10.1016/j.coisb.2024.100531
Marco Vanoni , Pasquale Palumbo , Stefano Busti , Lilia Alberghina

Metabolism, whose reprogramming is an established cancer hallmark, promotes growth and proliferation in cancer cells. Genome-wide metabolic models are becoming increasingly capable of describing cancer growth. Multiscale models may allow the capture of other relevant features of cancer cells and their relationship with the tumor microenvironment. The merging of multiscale metabolic modeling and artificial intelligence can lead to a paradigm shift in oncology, possibly leading to patient-specific personalized digital twins.

新陈代谢的重编程是癌症的既定标志，它促进了癌细胞的生长和增殖。全基因组代谢模型越来越能够描述癌症的生长。多尺度模型可捕捉癌细胞的其他相关特征及其与肿瘤微环境的关系。多尺度代谢模型与人工智能的结合可带来肿瘤学的范式转变，并有可能产生针对特定患者的个性化数字双胞胎。
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引用次数: 0
Network modeling approaches for metabolic diseases and diabetes 代谢性疾病和糖尿病的网络建模方法
IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2024-06-21 DOI: 10.1016/j.coisb.2024.100530
Apurva Badkas , Maria Pires Pacheco , Thomas Sauter

Metabolic diseases (MD) are amenable to network-based modeling frameworks, given the systemic perturbations induced by disrupted molecular mechanisms. We present here a brief overview of network modeling methods applied to inborn errors of metabolism (IEM), systemic metabolic conditions (mainly diabetes), and metabolism-related inflammation and autoimmune disorders. Clinical diagnosis and identification of causal agents in IEMs and uncovering the multifactorial mechanisms underlying the development of diabetes and other systemic metabolic diseases are the main challenges being addressed. The review also highlights some of the studies undertaken to investigate the role of the gut microbiome in MD, especially in diabetes. While the network frameworks employed in different modeling approaches have provided novel insights, some technique-specific limitations and overall gaps in general research trends need further attention.

代谢性疾病（MD）因其分子机制紊乱而引起的系统性扰动，适合采用基于网络的建模框架。我们在此简要概述了应用于先天性代谢错误（IEM）、全身性代谢疾病（主要是糖尿病）以及与代谢相关的炎症和自身免疫性疾病的网络建模方法。先天性代谢畸形的临床诊断和病因鉴定，以及揭示糖尿病和其他系统性代谢疾病的多因素发病机制是目前面临的主要挑战。本综述还重点介绍了为调查肠道微生物组在糖尿病（尤其是糖尿病）中的作用而开展的一些研究。虽然不同建模方法所采用的网络框架提供了新颖的见解，但一些特定技术的局限性和总体研究趋势的差距需要进一步关注。
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引用次数: 0
Editorial Board Page 编辑委员会页面
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2024-06-01 DOI: 10.1016/S2452-3100(24)00022-2
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引用次数: 0
Computational systems biology of cellular processes in the human lymph node 人体淋巴结细胞过程的计算系统生物学
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Opinion in Systems Biology
Pub Date : 2024-06-01 DOI: 10.1016/j.coisb.2024.100518
Sonja Scharf , Jörg Ackermann , Patrick Wurzel , Martin-Leo Hansmann , Ina Koch

The human immune system is determined by the functionality of the human lymph node. With the use of high-throughput techniques in clinical diagnostics, a large number of data is currently collected. The new data on the spatiotemporal organization of cells offer new possibilities to build a mathematical model of the human lymph node - a virtual lymph node. The virtual lymph node can be applied to simulate drug responses and may be used in clinical diagnosis. Here, we review mathematical models of the human lymph node from the viewpoint of cellular processes. Starting with classical methods, such as systems of differential equations, we discuss the values of different levels of abstraction and methods in the range of artificial intelligence techniques formalism.

人体淋巴结的功能决定了人体的免疫系统。随着高通量技术在临床诊断中的应用，目前已收集到大量数据。有关细胞时空组织的新数据为建立人体淋巴结的数学模型--虚拟淋巴结--提供了新的可能性。虚拟淋巴结可用于模拟药物反应，也可用于临床诊断。在此，我们从细胞过程的角度回顾人体淋巴结的数学模型。从微分方程系统等经典方法开始，我们讨论了人工智能技术形式主义范围内不同抽象程度和方法的价值。
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