22 papers · ranked by Valyu relevance
Yaron Ilan, Jacques Demongeot
Title: Simple Summary Uncertainty in biology refers to situations in which information is imperfect or unknown. Variability is measured by the frequency distribution of observed data, allowing for an understanding of the fundamental principles influencing diversity across different levels of biological organization.…
Shou‐Li Li, Joseph Keller, Michael C. Runge, Katriona Shea
1. The management of biological invasions is a worldwide conservation priority. Unfortunately, decision-making on optimal invasion management can be impeded by lack of information about the biological processes that determine invader success (i.e. biological uncertainty) or by uncertainty about the effectiveness of…
Yaron Ilan, Youssef Daali, Enrico Capobianco
Different disciplines are developing various methods for determining and dealing with uncertainties in complex systems. The constrained disorder principle (CDP) accounts for the randomness, variability, and uncertainty that characterize biological systems and are essential for their proper function. Per the CDP…
Iain G. Johnston, Benjamin C. Rickett, Nick S. Jones
'Back-of-the-envelope' or 'rule-of-thumb' calculations involving rough estimates of quantities play a central scientific role in developing intuition about the structure and behaviour of physical systems, for example in so-called 'Fermi problems' in the physical sciences. Such calculations can be used to powerfully and…
Anna Deneer, Jaap Molenaar, Christian Fleck
Uncertainty is ubiquitous in biological systems. These uncertainties can be the result of lack of knowledge or due to a lack of appropriate data. Additionally, the natural variability of biological systems caused by intrinsic noise, e.g. in stochastic gene expression, leads to uncertainties. With the help of numerical…
Anna Deneer, Jaap Molenaar, Christian Fleck
Uncertainty is ubiquitous in biological systems. For example, since gene expression is intrinsically governed by noise, nature shows a fascinating degree of variability. If we want to use a model to predict the behaviour of such an intrinsically stochastic system, we have to cope with the fact that the model parameters…
Erik Hoel, Brennan Klein, Anshuman Swain, Ross Grebenow + 1 more
The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein-protein interactome networks remain black boxes. One consequence of this black-box…
Yonghyun Song, Changbong Hyeon
We review the trade-offs between speed, fluctuations, and thermodynamic cost involved with biological processes in nonequilibrium states, and discuss how optimal these processes are in light of the universal bound set by the thermodynamic uncertainty relation (TUR). The values of the uncertainty product Q of TUR, which…
Hamda Ajmal, Michael C. Madden, Catherine Enright
Mathematical modeling with Ordinary Differential Equations (ODEs) has proven to be extremely successful in a variety of fields, including biology. However, these models are completely deterministic given a certain set of initial conditions. We convert mathematical ODE models of three benchmark biological systems to…
Fanny Petibon, Ewa A. Czyż, Giulia Ghielmetti, Andreas Hueni + 3 more
The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a…
H. T. McGovern, Alexander De Foe, Hannah Biddell, Pantelis Leptourgos + 3 more
'Pantelis Leptourgos' 'Philip Corlett' 'Kavindu Bandara' 'Brendan T. Hutchinson'] Generalized anxiety disorder is among the world’s most prevalent psychiatric disorders and often manifests as persistent and difficult to control apprehension. Despite its prevalence, there is no integrative, formal model of how anxiety…
Nathaniel J. Linden, Boris Kramer, Padmini Rangamani
Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and ‘-omics’…
Wonseok Hwang, Changbong Hyeon
Molecular motors play key roles in organizing the interior of cells. An ideal motor would translocate cargos with a high speed and a minimal error in transport distance (or time), while consuming minimal amount of energy. The travel distance and its variance of motor are, however, constrained by energy consumption, the…
Eszter Lakatos, Michael P.H. Stumpf
Controlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently non-linear. We present an approach to studying the impact of control measures on motifs of molecular interactions, that…
Luis Pedro García-Pintos
I derive uncertainty relations that bound the rate of evolutionary processes driven by natural selection, mutations, or by genetic drift. These rate limits imply that the variability –or statistical uncertainty– in a population allows for faster evolutionary rates. In particular, the variability of a given quantitative…
André Chalom, Paulo Inácio Prado
The correct use and interpretation of models depends on several steps, two of which being the calibration by parameter estimation and the analysis of uncertainty. In the biological literature, these steps are seldom discussed together, but they can be seen as fitting pieces of the same puzzle. In particular, analytical…
Anna Matuszyńska, Oliver Ebenhöh, Matias D. Zurbriggen, Daniel C. Ducat + 1 more
Synthetic biology designs and constructs new biological parts, devices and systems with predetermined functionalities. With the unlimited ability to synthesise any DNA and RNA and transfer it to almost any organism, we are at the dawn of a new era in which biology is being recreated in ways never before possible. It…
Authors not listed
This conceptual framework proposes an out-of-the-box approach to innovate non-biological drugs that surpass biologics by 200-fold in efficacy and safety for cancer treatment. Integrating advanced paradigms from physics, chemistry, medicine, biology, engineering, and materials science, we delineate a multi-dimensional…
Authors not listed
Protein-ligand interaction prediction with proteochemometric (PCM) models can provide valuable insights during early drug discovery and chemical safety assessment. These models have benefitted from the large amount of data available in bioactivity databases. However, an issue that is often overlooked when using this…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Yalin Li, John Trimmer, Steven Hand, Xinyi Zhang + 6 more
The pursuit of sustainability has catalyzed broad investment in the research, development, and deployment (RD&D) of innovative water, sanitation, and resource recovery technologies, yet the lack of transparent and agile methodologies to navigate the expansive landscape of technology development pathways remains a…
Authors not listed
The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…