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Treloar NJ, Braniff N, Ingalls B, Barnes CP. Deep reinforcement learning for optimal experimental design in biology. PLoS Comput Biol 2022; 18:e1010695. [PMID: 36409776 PMCID: PMC9721483 DOI: 10.1371/journal.pcbi.1010695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 12/05/2022] [Accepted: 10/31/2022] [Indexed: 11/22/2022] Open
Abstract
The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.
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Affiliation(s)
- Neythen J. Treloar
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Nathan Braniff
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada
| | - Brian Ingalls
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada
| | - Chris P. Barnes
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
- UCL Genetics Institute, University College London, London, United Kingdom
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Abroshan H, Kwak HS, An Y, Brown C, Chandrasekaran A, Winget P, Halls MD. Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics. Front Chem 2022; 9:800371. [PMID: 35111731 PMCID: PMC8802167 DOI: 10.3389/fchem.2021.800371] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 12/20/2021] [Indexed: 01/26/2023] Open
Abstract
Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidates for organic electronics. However, building reliable predictive ML models requires creating and managing a high volume of data that adequately address the complexity of materials' chemical space. In this regard, active learning (AL) has emerged as a powerful strategy to efficiently navigate the search space by prioritizing the decision-making process for unexplored data. This approach allows a more systematic mechanism to identify promising candidates by minimizing the number of computations required to explore an extensive materials library with diverse variables and parameters. In this paper, we applied a workflow of AL that accounts for multiple optoelectronic parameters to identify materials candidates for hole-transport layers (HTL) in OLEDs. Results of this work pave the way for efficient screening of materials for organic electronics with superior efficiencies before laborious simulations, synthesis, and device fabrication.
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Affiliation(s)
| | | | - Yuling An
- Schrödinger, Inc., New York, NY, United States
| | | | | | - Paul Winget
- Schrödinger, Inc., New York, NY, United States
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Srinivasan S, Cluett WR, Mahadevan R. A scalable method for parameter identification in kinetic models of metabolism using steady-state data. Bioinformatics 2020; 35:5216-5225. [PMID: 31197317 DOI: 10.1093/bioinformatics/btz445] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/26/2019] [Accepted: 06/05/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data require the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity. Additionally, a priori experimental design, a necessity to obtain informative data for parameter estimation, also does not account for using steady-state data to estimate parameters in kinetic models. RESULTS Here, we present a scalable methodology to structurally identify parameters for each flux in a kinetic model of metabolism based on the availability of steady-state data. In doing so, we also address the issue of determining the number and nature of experiments for generating steady-state data to estimate these parameters. By using a small metabolic network as an example, we show that most parameters in fluxes expressed by mechanistic enzyme kinetic rate laws can be identified using steady-state data, and the steady-state data required for their estimation can be obtained from selective experiments involving both substrate and enzyme level perturbations. The methodology can be used in combination with other identifiability and experimental design algorithms that use dynamic data to determine the most informative experiments requiring the least resources to perform. AVAILABILITY AND IMPLEMENTATION https://github.com/LMSE/ident. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shyam Srinivasan
- Department of Chemical Engineering and Applied Chemistry, 200 College Street, University of Toronto, Toronto, ON, M5S3E5, Canada
| | - William R Cluett
- Department of Chemical Engineering and Applied Chemistry, 200 College Street, University of Toronto, Toronto, ON, M5S3E5, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, 200 College Street, University of Toronto, Toronto, ON, M5S3E5, Canada.,Institute of Biomaterials and Biomedical Engineering, 164 College Street, University of Toronto, Toronto, ON, M5S 3G9, Canada
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Pratapa A, Adames N, Kraikivski P, Franzese N, Tyson JJ, Peccoud J, Murali TM. CrossPlan: systematic planning of genetic crosses to validate mathematical models. Bioinformatics 2019; 34:2237-2244. [PMID: 29432533 DOI: 10.1093/bioinformatics/bty072] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 02/07/2018] [Indexed: 12/27/2022] Open
Abstract
Motivation Mathematical models of cellular processes can systematically predict the phenotypes of novel combinations of multi-gene mutations. Searching for informative predictions and prioritizing them for experimental validation is challenging since the number of possible combinations grows exponentially in the number of mutations. Moreover, keeping track of the crosses needed to make new mutants and planning sequences of experiments is unmanageable when the experimenter is deluged by hundreds of potentially informative predictions to test. Results We present CrossPlan, a novel methodology for systematically planning genetic crosses to make a set of target mutants from a set of source mutants. We base our approach on a generic experimental workflow used in performing genetic crosses in budding yeast. We prove that the CrossPlan problem is NP-complete. We develop an integer-linear-program (ILP) to maximize the number of target mutants that we can make under certain experimental constraints. We apply our method to a comprehensive mathematical model of the protein regulatory network controlling cell division in budding yeast. We also extend our solution to incorporate other experimental conditions such as a delay factor that decides the availability of a mutant and genetic markers to confirm gene deletions. The experimental flow that underlies our work is quite generic and our ILP-based algorithm is easy to modify. Hence, our framework should be relevant in plant and animal systems as well. Availability and implementation CrossPlan code is freely available under GNU General Public Licence v3.0 at https://github.com/Murali-group/crossplan. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Aditya Pratapa
- Department of Computer Science, Virginia Tech, Blacksburg, USA
| | - Neil Adames
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, USA
| | - Pavel Kraikivski
- Department of Biological Sciences, Virginia Tech, Blacksburg, USA
| | | | - John J Tyson
- Department of Biological Sciences, Virginia Tech, Blacksburg, USA
| | - Jean Peccoud
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, USA
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, USA
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Jeong JE, Qiu P. Quantifying the relative importance of experimental data points in parameter estimation. BMC SYSTEMS BIOLOGY 2018; 12:103. [PMID: 30463558 PMCID: PMC6249737 DOI: 10.1186/s12918-018-0622-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Ordinary differential equations (ODEs) are often used to understand biological processes. Since ODE-based models usually contain many unknown parameters, parameter estimation is an important step toward deeper understanding of the process. Parameter estimation is often formulated as a least squares optimization problem, where all experimental data points are considered as equally important. However, this equal-weight formulation ignores the possibility of existence of relative importance among different data points, and may lead to misleading parameter estimation results. Therefore, we propose to introduce weights to account for the relative importance of different data points when formulating the least squares optimization problem. Each weight is defined by the uncertainty of one data point given the other data points. If one data point can be accurately inferred given the other data, the uncertainty of this data point is low and the importance of this data point is low. Whereas, if inferring one data point from the other data is almost impossible, it contains a huge uncertainty and carries more information for estimating parameters. RESULTS G1/S transition model with 6 parameters and 12 parameters, and MAPK module with 14 parameters were used to test the weighted formulation. In each case, evenly spaced experimental data points were used. Weights calculated in these models showed similar patterns: high weights for data points in dynamic regions and low weights for data points in flat regions. We developed a sampling algorithm to evaluate the weighted formulation, and demonstrated that the weighted formulation reduced the redundancy in the data. For G1/S transition model with 12 parameters, we examined unevenly spaced experimental data points, strategically sampled to have more measurement points where the weights were relatively high, and fewer measurement points where the weights were relatively low. This analysis showed that the proposed weights can be used for designing measurement time points. CONCLUSIONS Giving a different weight to each data point according to its relative importance compared to other data points is an effective method for improving robustness of parameter estimation by reducing the redundancy in the experimental data.
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Affiliation(s)
- Jenny E. Jeong
- Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332 GA USA
| | - Peng Qiu
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, 30332 GA USA
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Abstract
SIGNIFICANCE Reductionist studies have contributed greatly to our understanding of the basic biology of aging in recent years but we still do not understand fundamental mechanisms for many identified drugs and pathways. Use of systems approaches will help us move forward in our understanding of aging. Recent Advances: Recent work described here has illustrated the power of systems biology to inform our understanding of aging through the study of (i) diet restriction, (ii) neurodegenerative disease, and (iii) biomarkers of aging. CRITICAL ISSUES Although we do not understand all of the individual genes and pathways that affect aging, as we continue to uncover more of them, we have now also begun to synthesize existing data using systems-level approaches, often to great effect. The three examples noted here all benefit from computational approaches that were unknown a few years ago, and from biological insights gleaned from multiple model systems, from aging laboratories as well as many other areas of biology. FUTURE DIRECTIONS Many new technologies, such as single-cell sequencing, advances in epigenetics beyond the methylome (specifically, assay for transposase-accessible chromatin with high throughput sequencing ), and multiomic network studies, will increase the reach of systems biologists. This suggests that approaches similar to those described here will continue to lead to striking findings, and to interventions that may allow us to delay some of the many age-associated diseases in humans; perhaps sooner that we expect. Antioxid. Redox Signal. 29, 973-984.
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Affiliation(s)
| | - Daniel E L Promislow
- 2 Department of Pathology, University of Washington , Seattle, Washington.,3 Department of Biology, University of Washington , Seattle, Washington
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Wang Z, Gudibanda A, Ugwuowo U, Trail F, Townsend JP. Using evolutionary genomics, transcriptomics, and systems biology to reveal gene networks underlying fungal development. FUNGAL BIOL REV 2018. [DOI: 10.1016/j.fbr.2018.02.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Scutari M, Auconi P, Caldarelli G, Franchi L. Bayesian Networks Analysis of Malocclusion Data. Sci Rep 2017; 7:15236. [PMID: 29127377 PMCID: PMC5681542 DOI: 10.1038/s41598-017-15293-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 05/22/2017] [Indexed: 12/03/2022] Open
Abstract
In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start from a sample of 143 patients characterised through a series of a maximum of 21 different craniofacial features. We estimate a network model from these data and we test its consistency by verifying some commonly accepted hypotheses on the evolution of these disharmonies by means of Bayesian statistics. We show that untreated subjects develop different Class III craniofacial growth patterns as compared to patients submitted to orthodontic treatment with rapid maxillary expansion and facemask therapy. Among treated patients the CoA segment (the maxillary length) and the ANB angle (the antero-posterior relation of the maxilla to the mandible) seem to be the skeletal subspaces that receive the main effect of the treatment.
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Affiliation(s)
- Marco Scutari
- Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK
| | | | - Guido Caldarelli
- IMT School for Advanced Studies, Piazza San Francesco 19, 55100, Lucca, Italy.
- Istituto dei Sistemi Complessi CNR, Unità Sapienza, Dip. Fisica, P.le A. Moro 2, 00185, Rome, Italy.
- London Institute for Mathematical Sciences, 35a South St, Mayfair, London, W1K 2XF, UK.
| | - Lorenzo Franchi
- Dipartimento di Chirurgia e Medicina Traslazionale, Università degli Studi di Firenze, Firenze, Italy
- Thomas M. Graber Visiting Scholar, Department of Orthodontics and Pediatric Dentistry, School of Dentistry, The University of Michigan, Ann Arbor, MI, USA
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Wang J, Park E. Active learning for penalized logistic regression via sequential experimental design. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.10.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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White A, Tolman M, Thames HD, Withers HR, Mason KA, Transtrum MK. The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems. PLoS Comput Biol 2016; 12:e1005227. [PMID: 27923060 PMCID: PMC5140062 DOI: 10.1371/journal.pcbi.1005227] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Accepted: 10/27/2016] [Indexed: 12/15/2022] Open
Abstract
We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a model’s discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system–a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model. Sloppy models are often unidentifiable, i.e., characterized by many parameters that are poorly constrained by experimental data. Many models of complex biological systems are sloppy, which has prompted considerable debate about the identifiability of parameters and methods of selecting optimal experiments to infer parameter values. We explore how the approximate nature of models affects the prospect for accurate parameter estimates and model predictivity in sloppy models when using optimal experimental design. We find that sloppy models may no longer give a good fit to data generated from “optimal” experiments. In this case, the model has much less predictive power than it did before optimal experimental selection. We use a simple hyper-model of model error to quantify the model’s discrepancy from the physical system and discuss the potential limits of accurate parameter estimation in sloppy systems.
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Affiliation(s)
- Andrew White
- Department of Physics & Astronomy, Brigham Young University, Provo, Utah, United States of America
| | - Malachi Tolman
- Department of Physics & Astronomy, Brigham Young University, Provo, Utah, United States of America
| | - Howard D. Thames
- Department of Biostatistics, UT MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Experimental Radiation Oncology, UT MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Hubert Rodney Withers
- Department of Experimental Radiation Oncology, UT MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Kathy A. Mason
- Department of Experimental Radiation Oncology, UT MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Mark K. Transtrum
- Department of Physics & Astronomy, Brigham Young University, Provo, Utah, United States of America
- * E-mail:
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Sadowski MI, Grant C, Fell TS. Harnessing QbD, Programming Languages, and Automation for Reproducible Biology. Trends Biotechnol 2015; 34:214-227. [PMID: 26708960 DOI: 10.1016/j.tibtech.2015.11.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 11/16/2015] [Accepted: 11/19/2015] [Indexed: 12/18/2022]
Abstract
Building robust manufacturing processes from biological components is a task that is highly complex and requires sophisticated tools to describe processes, inputs, and measurements and administrate management of knowledge, data, and materials. We argue that for bioengineering to fully access biological potential, it will require application of statistically designed experiments to derive detailed empirical models of underlying systems. This requires execution of large-scale structured experimentation for which laboratory automation is necessary. This requires development of expressive, high-level languages that allow reusability of protocols, characterization of their reliability, and a change in focus from implementation details to functional properties. We review recent developments in these areas and identify what we believe is an exciting trend that promises to revolutionize biotechnology.
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Affiliation(s)
- Michael I Sadowski
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | - Chris Grant
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | - Tim S Fell
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK.
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Maciejewski M, Wassermann AM, Glick M, Lounkine E. Experimental design strategy: weak reinforcement leads to increased hit rates and enhanced chemical diversity. J Chem Inf Model 2015; 55:956-62. [PMID: 25915687 DOI: 10.1021/acs.jcim.5b00054] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
High Throughput Screening (HTS) is a common approach in life sciences to discover chemical matter that modulates a biological target or phenotype. However, low assay throughput, reagents cost, or a flowchart that can deal with only a limited number of hits may impair screening large numbers of compounds. In this case, a subset of compounds is assayed, and in silico models are utilized to aid in iterative screening design, usually to expand around the found hits and enrich subsequent rounds for relevant chemical matter. However, this may lead to an overly narrow focus, and the diversity of compounds sampled in subsequent iterations may suffer. Active learning has been recently successfully applied in drug discovery with the goal of sampling diverse chemical space to improve model performance. Here we introduce a robust and straightforward iterative screening protocol based on naı̈ve Bayes models. Instead of following up on the compounds with the highest scores in the in silico model, we pursue compounds with very low but positive values. This includes unique chemotypes of weakly active compounds that enhance the applicability domain of the model and increase the cumulative hit rates. We show in a retrospective application to 81 Novartis assays that this protocol leads to consistently higher compound and scaffold hit rates compared to a standard expansion around hits or an active learning approach. We recommend using the weak reinforcement strategy introduced herein for iterative screening workflows.
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Affiliation(s)
- Mateusz Maciejewski
- †Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Anne Mai Wassermann
- †Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Meir Glick
- †Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Eugen Lounkine
- †Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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