More Like this

1. Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints

   https://doi.org/10.1093/bioinformatics/bty446  

   Chan, Siu H. J. ; Wang, Lin ; Dash, Satyakam ; Maranas, Costas D. ; Wren, ed., Jonathan ( June 2018 , Bioinformatics)

   Genome-scale metabolic network models and constraint-based modeling techniques have become important tools for analyzing cellular metabolism. Thermodynamically infeasible cycles (TICs) causing unbounded metabolic flux ranges are often encountered. TICs satisfy the mass balance and directionality constraints but violate the second law of thermodynamics. Current practices involve implementing additional constraints to ensure not only optimal but also loopless flux distributions. However, the mixed integer linear programming problems required to solve become computationally intractable for genome-scale metabolic models.

   We aimed to identify the fewest needed constraints sufficient for optimality under the loopless requirement. We found that loopless constraints are required only for the reactions that share elementary flux modes representing TICs with reactions that are part of the objective function. We put forth the concept of localized loopless constraints (LLCs) to enforce this minimal required set of loopless constraints. By combining with a novel procedure for minimal null-space calculation, the computational time for loopless flux variability analysis (ll-FVA) is reduced by a factor of 10–150 compared to the original loopless constraints and by 4–20 times compared to the current fastest method Fast-SNP with the percent improvement increasing with model size. Importantly, LLCs offer a scalable strategy for loopless flux calculations for multi-compartment/multi-organism models of large sizes, for example, shortening the CPU time for ll-FVA from 35 h to less than 2 h for a model with more than104 reactions.

   Matlab functions are available in the Supplementary Material or at https://github.com/maranasgroup/lll-FVA

   Supplementary data are available at Bioinformatics online.

    

   more » « less
2. DaTeR: error-correcting phylogenetic chronograms using relative time constraints

   https://doi.org/10.1093/bioinformatics/btad084  

   Mondal, Abhijit ; Rangel, L. Thiberio ; Payette, Jack G. ; Fournier, Gregory P. ; Bansal, Mukul S. ; Schwartz, ed., Russell ( February 2023 , Bioinformatics)

   A chronogram is a dated phylogenetic tree whose branch lengths have been scaled to represent time. Such chronograms are computed based on available date estimates (e.g. from dated fossils), which provide absolute time constraints for one or more nodes of an input undated phylogeny, coupled with an appropriate underlying model for evolutionary rates variation along the branches of the phylogeny. However, traditional methods for phylogenetic dating cannot take into account relative time constraints, such as those provided by inferred horizontal transfer events. In many cases, chronograms computed using only absolute time constraints are inconsistent with known relative time constraints.

   In this work, we introduce a new approach, Dating Trees using Relative constraints (DaTeR), for phylogenetic dating that can take into account both absolute and relative time constraints. The key idea is to use existing Bayesian approaches for phylogenetic dating to sample posterior chronograms satisfying desired absolute time constraints, minimally adjust or ‘error-correct’ these sampled chronograms to satisfy all given relative time constraints, and aggregate across all error-corrected chronograms. DaTeR uses a constrained optimization framework for the error-correction step, finding minimal deviations from previously assigned dates or branch lengths. We applied DaTeR to a biological dataset of 170 Cyanobacterial taxa and a reliable set of 24 transfer-based relative constraints, under six different molecular dating models. Our extensive analysis of this dataset demonstrates that DaTeR is both highly effective and scalable and that its application can significantly improve estimated chronograms.

   Freely available from https://compbio.engr.uconn.edu/software/dater/

   Supplementary data are available at Bioinformatics online.

    

   more » « less
3. How barriers become invisible: Children are less sensitive to constraints that are stable over time

   https://doi.org/10.1111/desc.13496  

   Amemiya, Jamie ; Heyman, Gail D ; Walker, Caren M ( July 2024 , Developmental Science)

   When making inferences about the mental lives of others (e.g., others’ preferences), it is critical to consider the extent to which the choices we observe are constrained. Prior research on the development of this tendency indicates a contradictory pattern: Children show remarkable sensitivity to constraints in traditional experimental paradigms, yet often fail to consider real‐world constraints and privilege inherent causes instead. We propose that one explanation for this discrepancy may be that real‐world constraints are often stable over time and lose their salience. The present research tested whether children (N = 133, 5‐ to 12‐year‐old mostly US children; 55% female, 45% male) becomelesssensitive to an actor's constraints after first observing two constrained actors (Stable condition) versus after first observing two actors in contexts with greater choice (Not Stable condition). We crossed thestabilityof the constraint with thetypeof constraint: either the constraint was deterministic such that there was only one option available (No Other Option constraint) or, in line with many real‐world constraints, the constraint was probabilistic such that therewasanother option, but it was difficult to access (Hard to Access constraint). Results indicated that children in the Stable condition became less sensitive to the probabilistic Hard to Access constraint across trials. Notably, we also found that children's sensitivity to constraints was enhanced in the Not Stable condition regardless of whether the constraint was probabilistic or deterministic. We discuss implications for children's sensitivity to real‐world constraints.

   This research addresses the apparent contradiction that children are sensitive to constraints in experimental paradigms but are ofteninsensitiveto constraints in the real world.

   One explanation for this discrepancy is that constraints in the real world tend to be stable over time and may lose their salience.

   When probabilistic constraints (i.e., when a second option is available but hard to access) are stable, children become de‐sensitized to constraints across trials.

   First observing contexts with greater choice increases children's sensitivity to both probabilistic and deterministic constraints.

    

   more » « less
4. Causal Constraints in the Life and Social Sciences

   https://doi.org/10.1017/psa.2023.165  

   Ross, Lauren N. ( December 2023 , Philosophy of Science)

   This paper examines constraints and their role in scientific explanation. Common views in the philosophical literature suggest that constraints are non-causal and that they provide non-causal explanations. While much of this work focuses on examples from physics, this paper explores constraints from other fields, including neuroscience, physiology, and the social sciences. I argue that these cases involve constraints that are causal and that provide a unique type of causal explanation. This paper clarifies what it means for a factor to be a constraint, when such constraints are causal, and how they figure in scientific explanation.

    

   more » « less
5. Simulating domain architecture evolution

   https://doi.org/10.1093/bioinformatics/btac242  

   Cui, Xiaoyue ; Xue, Yifan ; McCormack, Collin ; Garces, Alejandro ; Rachman, Thomas_W ; Yi, Yang ; Stolzer, Maureen ; Durand, Dannie ( June 2022 , Bioinformatics)

   Simulation is an essential technique for generating biomolecular data with a ‘known’ history for use in validating phylogenetic inference and other evolutionary methods. On longer time scales, simulation supports investigations of equilibrium behavior and provides a formal framework for testing competing evolutionary hypotheses. Twenty years of molecular evolution research have produced a rich repertoire of simulation methods. However, current models do not capture the stringent constraints acting on the domain insertions, duplications, and deletions by which multidomain architectures evolve. Although these processes have the potential to generate any combination of domains, only a tiny fraction of possible domain combinations are observed in nature. Modeling these stringent constraints on domain order and co-occurrence is a fundamental challenge in domain architecture simulation that does not arise with sequence and gene family simulation.

   Here, we introduce a stochastic model of domain architecture evolution to simulate evolutionary trajectories that reflect the constraints on domain order and co-occurrence observed in nature. This framework is implemented in a novel domain architecture simulator, DomArchov, using the Metropolis–Hastings algorithm with data-driven transition probabilities. The use of a data-driven event module enables quick and easy redeployment of the simulator for use in different taxonomic and protein function contexts. Using empirical evaluation with metazoan datasets, we demonstrate that domain architectures simulated by DomArchov recapitulate properties of genuine domain architectures that reflect the constraints on domain order and adjacency seen in nature. This work expands the realm of evolutionary processes that are amenable to simulation.

   DomArchov is written in Python 3 and is available at http://www.cs.cmu.edu/~durand/DomArchov. The data underlying this article are available via the same link.

   Supplementary data are available at Bioinformatics online.

    

   more » « less