Open problems in mathematical biology

被引:8
作者
Vittadello, Sean T. [1 ,2 ]
Stumpf, Michael P. H. [1 ,2 ,3 ]
机构
[1] Univ Melbourne, Melbourne Integrat Genom, Melbourne, Australia
[2] Univ Melbourne, Sch Biosci, Melbourne, Australia
[3] Univ Melbourne, Sch Math & Stat, Melbourne, Australia
关键词
Systems modelling; Model selection; Multi-scale modelling; Automated model development; MODEL SELECTION; CONCEPTUAL BIOLOGY; SYSTEMS; EMERGENCE; ROBUSTNESS; SENSITIVITY; CHALLENGES; EVOLUTION; FRAMEWORK; DYNAMICS;
D O I
10.1016/j.mbs.2022.108926
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Biology is data-rich, and it is equally rich in concepts and hypotheses. Part of trying to understand biological processes and systems is therefore to confront our ideas and hypotheses with data using statistical methods to determine the extent to which our hypotheses agree with reality. But doing so in a systematic way is becoming increasingly challenging as our hypotheses become more detailed, and our data becomes more complex. Mathematical methods are therefore gaining in importance across the life-and biomedical sciences. Mathematical models allow us to test our understanding, make testable predictions about future behaviour, and gain insights into how we can control the behaviour of biological systems. It has been argued that mathematical methods can be of great benefit to biologists to make sense of data. But mathematics and mathematicians are set to benefit equally from considering the often bewildering complexity inherent to living systems. Here we present a small selection of open problems and challenges in mathematical biology. We have chosen these open problems because they are of both biological and mathematical interest.
引用
收藏
页数:11
相关论文
共 119 条
[1]   Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms [J].
AlQuraishi, Mohammed ;
Sorger, Peter K. .
NATURE METHODS, 2021, 18 (10) :1169-1180
[2]  
Anderson Philip W., 2011, ANDERSON MORE DIFFER
[3]   MORE IS DIFFERENT - BROKEN SYMMETRY AND NATURE OF HIERARCHICAL STRUCTURE OF SCIENCE [J].
ANDERSON, PW .
SCIENCE, 1972, 177 (4047) :393-&
[4]   Memory improves precision of cell sensing in fluctuating environments [J].
Aquino, Gerardo ;
Tweedy, Luke ;
Heinrich, Doris ;
Endres, Robert G. .
SCIENTIFIC REPORTS, 2014, 4
[5]   The topological requirements for robust perfect adaptation in networks of any size [J].
Araujo, Robyn P. ;
Liotta, Lance A. .
NATURE COMMUNICATIONS, 2018, 9
[6]  
Arnold V.I., 1992, CATASTROPHE THEORY, V3rd
[7]  
Attard P., 2012, Non-Equilibrium Thermodynamics and Statistical Mechanics: Foundations and Applications
[8]   How to deal with parameters for whole-cell modelling [J].
Babtie, Ann C. ;
Stumpf, Michael P. H. .
JOURNAL OF THE ROYAL SOCIETY INTERFACE, 2017, 14 (133)
[9]   Topological sensitivity analysis for systems biology [J].
Babtie, Ann C. ;
Kirk, Paul ;
Stumpf, Michael P. H. .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2014, 111 (52) :18507-18512
[10]   Mechanistic models versus machine learning, a fight worth fighting for the biological community? [J].
Baker, Ruth E. ;
Pena, Jose-Maria ;
Jayamohan, Jayaratnam ;
Jerusalem, Antoine .
BIOLOGY LETTERS, 2018, 14 (05)