Biological uncertainty

Zbilut, Joseph P.; Giuliani, Alessandro

doi:10.1007/s12064-008-0026-z

Cited by 12 publications

(7 citation statements)

References 14 publications

(9 reference statements)

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“…In physics, the Heisenberg uncertainty principle places limits on the precision in determining pairs of values for a single particle, such as position and momentum. The notion of uncertainty in biology is quite different (21) and there is, as yet, no biological uncertainty principle, although the well-known inability to determine the exact nucleotide sequence of a cell's genome due to experimental errors has been proposed as a biological one (22). In biology, of much greater importance is understanding how cells function, which immediately raises the question we have addressed here of what level of complexity can possibly be simulated on a computer.…”

Section: Discussionmentioning

confidence: 99%

Estimating computational limits on theoretical descriptions of biological cells

Netz

Eaton

2021

Proc. Natl. Acad. Sci. U.S.A.

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There has been much success recently in theoretically simulating parts of complex biological systems on the molecular level, with the goal of first-principles modeling of whole cells. However, there is the question of whether such simulations can be performed because of the enormous complexity of cells. We establish approximate equations to estimate computation times required to simulate highly simplified models of cells by either molecular dynamics calculations or by solving molecular kinetic equations. Our equations place limits on the complexity of cells that can be theoretically understood with these two methods and provide a first step in developing what can be considered biological uncertainty relations for molecular models of cells. While a molecular kinetics description of the genetically simplest bacterial cell may indeed soon be possible, neither theoretical description for a multicellular system, such as the human brain, will be possible for many decades and may never be possible even with quantum computing.

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Section: Discussionmentioning

confidence: 99%

Estimating computational limits on theoretical descriptions of biological cells

Netz

Eaton

2021

Proc. Natl. Acad. Sci. U.S.A.

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“…Moreover, in this field the issue of how conceiving of uncertainty is not only related to the debate on the statistical tools used in medical research, it is also related to the even more basic assumptions that one has about what role uncertainty plays in biology, and so about what are the very basic principles of biology (Longo, Montévil, Sonnenschein, Soto 2015;Longo manuscript;Zbilut, Giuliani 2008). We cannot fully address this topic here, but it may be interesting to look at cancer research in order to show that: 1) the way we use our statistical tools in medicine cannot be neutral with respect to our more basic theoretical commitments; 2) the more adequate way to account for how one chooses some basic theoretical commitments rather than some rival ones is by describing that choice in terms of plausibility.…”

Section: Uncertainty and Cancer Researchmentioning

confidence: 99%

Evidence amalgamation, plausibility, and cancer research

Bertolaso

Sterpetti

2017

Synthese

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Cancer research is experiencing 'paradigm instability', since there are two rival theories of carcinogenesis which confront themselves, namely the Somatic Mutation Theory and the Tissue Organization Field Theory. Despite this theoretical uncertainty, a huge quantity of data is available thanks to the improvement of genome sequencing techniques. Some authors think that the development of new statistical tools will be able to overcome the lack of a shared theoretical perspective on cancer by amalgamating as many data as possible. We think instead that a deeper understanding of cancer can be achieved by means of more theoretical work, rather than by merely accumulating more data. To support our thesis, we introduce the analytic view of theory development, which rests on the concept of plausibility, and make clear in what sense plausibility and probability are distinct concepts. Then, the concept of plausibility is used to point out the ineliminable role played by the epistemic subject in the development of statistical tools and in the process of theory assessment. We then move to address a central issue in cancer research, namely the relevance of computational tools developed by bioinformaticists to detect driver mutations in the debate between the two main rival theories of carcinogenesis. Finally, we briefly extend our considerations on the role that plausibility plays in evidence amalgamation from cancer research to the more general issue of the divergences between frequentists and Bayesians in the philosophy of medicine and statistics. We argue that taking into account plausibility-based considerations can lead to clarify some epistemological shortcomings that afflict both these perspectives. probability and the concept of randomness concludes this part (section 2.8). In the second part of the paper (section 3), after having briefly illustrated the main rival conceptions of carcinogenesis (section 3.1), the notion of personalized cancer medicine (section 3.2), and the concept of driver mutations (section 3.3), we address some issues in cancer research to test the adequacy and fertility of the theoretical framework presented in the first part. More precisely, we focus on some of the computational tools that have been developed by bioinformaticists for searching driver mutations in cancer specimens (sections 3.4. and 3.5), in order to highlight the role played by plausibility-based considerations in the development of statistical tools and in the assessment of theoretical hypotheses (section 3.6). Finally, in the third part, we put the conclusions that can be drawn from our analysis in a broader context (section 4). We think that our proposal may be of use to address a more general issue, which characterizes both the philosophy of medicine and the philosophy of statistics, and which is also crucial for the epistemological investigations of cancer research, namely the confrontation between frequentists and Bayesians on what is the more adequate way to conceive of evidence amalgamation (sections 4.1, 4.2, and 4.3).

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“…At present, however, data scarcity and uncertainty are impediments to unambiguous ENMs hazard identification. 9 At the same time, given the rapid growth of nanotechnology in terms of the volume and different types of ENMs, there is a need for accelerated approaches to toxicity screening. In this regard, the combination of technologies for assay miniaturization (e.g., 384/ 1536 well plates) using single cell lines and simple organisms (e.g., zebrafish 10 ), laboratory automation and robotic equipment enables high throughput screening (HTS 11 ) testing of nanoparticles toxicity in biological systems.…”

Section: Nanoparticle Hazard Identificationmentioning

confidence: 99%

In Silico Analysis of Nanomaterials Hazard and Risk

et al. 2012

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Because a variety of human-related activities, engineer-ed nanoparticles (ENMs) may be released to various environmental media and may cross environmental boundaries, and thus will be found in most media. Therefore, the potential environmental impacts of ENMs must be assessed from a multimedia perspective and with an integrated risk management approach that considers rapid developments and increasing use of new nanomaterials. Accordingly, this Account presents a rational process for the integration of in silico ENM toxicity and fate and transport analyses for environmental impact assessment. This approach requires knowledge of ENM toxicity and environmental exposure concentrations. Considering the large number of current different types of ENMs and that those numbers are likely to increase, there is an urgent need to accelerate the evaluation of their toxicity and the assessment of their potential distribution in the environment. Developments in high throughput screening (HTS) are now enabling the rapid generation of large data sets for ENM toxicity assessment. However, these analyses require the establishment of reliable toxicity metrics, especially when HTS includes data from multiple assays, cell lines, or organisms. Establishing toxicity metrics with HTS data requires advanced data processing techniques in order to clearly identify significant biological effects associated with exposure to ENMs. HTS data can form the basis for developing and validating in silico toxicity models (e.g., quantitative structure-activity relationships) and for generating data-driven hypotheses to aid in establishing and/or validating possible toxicity mechanisms. To correlate the toxicity of ENMs with their physicochemical properties, researchers will need to develop quantitative structure-activity relationships for nanomaterials (i.e., nano-SARs). However, as nano-SARs are applied in regulatory applications, researchers must consider their applicability and the acceptance level of false positive relative to false negative predictions and the reliability of toxicity data. To establish the environmental impact of ENMs identified as toxic, researchers will need to estimate the potential level of environmental exposure concentration of ENMs in the various media such as air, water, soil, and vegetation. When environmental monitoring data are not available, models of ENMs fate and transport (at various levels of complexity) serve as alternative approaches for estimating exposure concentrations. Risk management decisions regarding the manufacturing, use, and environmental regulations of ENMs would clearly benefit from both the assessment of potential ENMs exposure concentrations and suitable toxicity metrics. The decision process should consider the totality of available information: quantitative and qualitative data and the analysis of nanomaterials toxicity, and fate and transport behavior in the environment. Effective decision-making to address the potential impacts of nanomaterials will require considerations of the relevant environ...

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Biological uncertainty

Cited by 12 publications

References 14 publications

Estimating computational limits on theoretical descriptions of biological cells

Estimating computational limits on theoretical descriptions of biological cells

Evidence amalgamation, plausibility, and cancer research

In Silico Analysis of Nanomaterials Hazard and Risk

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