ArticleBiotechnology progress
In silico model-based inference: a contemporary approach for hypothesis testing in network biology.
Article in Biotechnology progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Head-to-Head Comparison of CCN4, DNMT3A, PTPN11, and SPARC as Suppressors of Anti-tumor Immunity.Cellular and molecular bioengineering · 2023Article
- Multimodal network dynamics underpinning working memory.Nature communications · 2020Article
- Interleukin-12 elicits a non-canonical response in B16 melanoma cells to enhance survival.Cell communication and signaling : CCS · 2020Article
- Cell Communication Network Factor 4 (CCN4/WISP1) Shifts Melanoma Cells from a Fragile Proliferative State to a Resilient Metastatic State.Cellular and molecular bioengineering · 2020Article
- An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer.BMC cancer · 2020Article
- BioNetGen 2.2: advances in rule-based modeling.Bioinformatics (Oxford, England) · 2016Article
- Inferring the Impact of Regulatory Mechanisms that Underpin CD8+ T Cell Control of B16 Tumor GrowthFrontiers in pharmacology · 2016Article
- Interlocked positive and negative feedback network motifs regulate β-catenin activity in the adherens junction pathway.Molecular biology of the cell · 2015Article
- In silico model-based inference: an emerging approach for inverse problems in engineering better medicines.Current opinion in chemical engineering · 2015Article
- CD8(+) T cell response to adenovirus vaccination and subsequent suppression of tumor growth: modeling, simulation and analysis.BMC systems biology · 2015Article
- Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study.Journal for immunotherapy of cancer · 2015Review
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.