![]() Such models ( Bouhaddou et al., 2018 Fröhlich et al., 2018 Münzner et al., 2019) are usually small in scale: tens of equations and 10s–100s of model species ( Figure 1). The popular mechanistic models are sets of detailed equations describing curated knowledge of what is happening within the cells. To understand the underlying biological facts, analysis of the wealth of the aforementioned big datasets should become more practical and go beyond context-dependent and scope-limited biological events.īuilding computational models that explain and predict such highly heterogenous and complex cellular responses is no easy task. The knowledge base of these databases includes genomics, proteomics, epigenomics, and clinical information ( Barrett et al., 2012 Uhlen et al., 2015 Subramanian et al., 2017 Hoadley et al., 2018 Wishart et al., 2018 Nusinow et al., 2020). Owing to the advances in wet-lab experimental techniques and tools, “Big Data” repositories become more prominent each year. The models guide experimental hypothesis generation, and experimental observations enable fine-tuning computational models to understand the biological phenomena. This burden might be reduced by understanding the underlying molecular mechanisms with the help of computational models ( Yu et al., 2018 Saez-Rodriguez and Blüthgen, 2020).Ĭomputational tools and models are becoming indispensable in medical research, where a cycle of experimentation and computation is used to learn about and test new hypotheses. The traditional drug discovery pipeline is comprised of extensive trial-and-error experiments, testing thousands of chemicals, refining their structure for safety and toxicity, and administering years of clinical trials. Thus, the need for personalized interventions to block tumor growth is high. The molecular signaling mechanisms of cancer cells are highly heterogenous, leading to treatment resistance and recurrence. This work is a template for combining big-data-inferred interactions with mechanistic models, which could be more broadly applicable for building multi-scale precision medicine and whole cell models. As a test case, we focused on incorporating novel IFNγ/PD-L1 related associations into a large-scale mechanistic model for cell proliferation and death to better recapitulate the recently released NIH LINCS Consortium MCF10A dataset and enable description of the cellular response to checkpoint inhibitor immunotherapies. These interactions are used as a basis for new reactions in mechanistic models. ![]() In this proposed MEMMAL (MEchanistic Modeling with MAchine Learning) framework, machine learning/statistical models built using omics datasets provide predictions for new interactions between genes and proteins where there is physicochemical uncertainty. Here, we explore combining machine learned associations with mechanistic models to develop computational models that could more fully represent cellular behavior. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Some models are mechanistic with detailed equations describing known (or supposed) physicochemical processes, while some are statistical or machine learning-based approaches, that explain datasets but have no mechanistic or causal guarantees. ![]() 2Department of Bioengineering, Clemson University, Clemson, SC, United StatesĬomputational models that can explain and predict complex sub-cellular, cellular, and tissue-level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine).1Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, United States. ![]()
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