Understanding the behaviour of almost any biological object is a fundamentally multiscale problem — a challenge that biophysicists have been increasingly embracing, building on two centuries of biophysical studies at a variety of length scales

A. V. Hill

Alba Diz-Muñoz

André E. X. Brown

D Grün

Ewa K. Paluch

GH Koenderink

JD Watson

Joe Chin-Hun Kuo

O Stegle

Ofer Feinerman

OS Andersen

R Keller

Xavier Trepat

Paluch, EK

UCL Discovery

	 1	Biophysics	across	time	and	space		Ewa	K.	Paluch	MRC	Laboratory	for	Molecular	Cell	Biology,	University	College	London	Institute	for	the	Physics	of	Living	Systems,	University	College	London	Physiology,	Development	and	Neuroscience,	University	of	Cambridge		e.paluch@ucl.ac.uk		Understanding	the	behaviour	of	almost	any	biological	object	is	a	fundamentally	multiscale	problem	—	a	challenge	that	biophysicists	has	been	increasingly	embracing,	building	on	two	centuries	of	biophysical	studies	at	a	variety	of	length	scales.		Until	recently,	biophysics	was	generally	associated	with	studies	of	protein	folding	and	ion	channels.	And	with	good	reason:	when	the	field	underwent	a	tremendous	expansion	in	the	second	half	of	the	20th	century,	many	of	its	key	findings	concerned	the	physical	mechanisms	behind	molecular	structures	and	assemblies.	These	successes	were	a	boon	for	structural	and	molecular	biology,	but	ultimately	narrowed	the	focus	of	what	was	then	a	blossoming	field.	Now,	research	in	biophysics	is	returning	to	the	bigger	picture	spanning	multiple	length	scales	—	proving	that	physics	has	just	as	much	to	say	about	the	living	as	it	does	inanimate	matter.		Historically,	biophysics	emerged	between	the	18th	and	the	19th	centuries	as	part	of	the	study	of	animal	physiology.	Luigi	Galvani,	who	laid	the	foundations	of	electrophysiology	by	studying	the	effect	of	electricity	on	frog	leg	muscle	movements	in	the	late	18th	century,	could	be	qualified	as	an	early	biophysicist.	But	the	beginning	of	biophysics	is	usually	attributed	to	a	group	of	physiologists	working	around	Johannes	Müller	in	Berlin	in	the	1840s,	who	established	physiology	as	an	experiment-driven	discipline	founded	in	chemistry	and	physics.	Their	work	went	against	the	prevailing	view	that	animals	are	governed	by	different	laws	than	those	describing	inanimate	objects	1.	Instead,	they	promoted		 2	the	notion	that	living	matter	is,	just	like	inanimate	matter,	governed	by	the	laws	of	physics	—	a	view	revisited	70	years	later	by	D’Arcy	Thompson	in	the	context	of	morphogenesis		2.			Müller’s	circle,	sometimes	called	the	Berlin	school	of	physiology,	included	scientists	famous	for	their	contributions	to	physics,	such	as	Hermann	von	Helmholtz,	and	generated	important	insight	into	—	among	other	things	—	electrophysiology,	the	physics	of	muscles	and	nerves,	and	the	physiology	of	vision.	What	can	be	considered	the	first	biophysics	textbook,	discussing	the	physics	of	physiology,	was	published	in	1856	by	Adolf	Fick	under	the	title	“Medical	Physics”.		The	actual	term	biophysics	is	attributed	to	Karl	Pearson,	who	in	“the	Grammar	of	Science”	in	1892	noted	that	this	branch	of	science	was	still	underdeveloped,	but	“not	improbably	[had]	an	important	future”.	The	popularity	of	biophysics	rose	in	the	first	half	of	the	20th	century.	While	neuro-	and	muscle	physiology	continued	to	develop	1,	early	embryologists	increasingly	approached	the	question	of	morphogenesis,	or	acquisition	of	shape,	in	development	as	a	question	of	mechanics	3.	In	1917,	D’Arcy	Thompson	published	his	famous	essay	“On	Growth	and	Form”,	in	which	he	argued	that	physics	must	guide	all	studies	of	shape,	growth	and	pattern	formation	in	biology	2.	These	developments	led	to	the	creation	of	departments	dedicated	to	biophysics	4	and	culminated	in	the	foundation	of	the	Biophysical	Society	as	an	offspring	of	the	American	Physiological	Society	in	1957.		Over	the	course	of	the	20th	century,	the	focus	of	biophysics	progressively	shifted	from	the	macroscale	to	the	nanoscale.		This	transition	was	initiated	by	a	growing	interest	in	the	physical	basis	of	genes,	famously	discussed	by	the	physicist	Erwin	Schrödinger	in	his	essay	“What	is	Life”	5.	The	subsequent	elucidation	of	DNA	structure	in	1953	by	James	Watson	and	Francis	Crick,	based	on	X-ray	crystallography	images	by	Rosalind	Franklin	6,	is	arguably	the	most	famous	advance	of	structural	biophysics.	It	motivated	innumerable	studies	of	protein	structures	that,	for	a	while,	integrated	biophysics	within	molecular	biology.		 3		In	the	last	couple	decades,	biophysics	has	expanded	from	largely	reductionist	approaches	—	at	the	scale	of	organisms	or	molecules	—	to	the	realm	of	what	is	often	called	systems	biology.	This	evolution	stemmed	from	a	growing	interest	in	the	emergent	properties	of	biological	systems	–	cells,	tissues,	organisms	-	displaying	cooperative	behaviours	that	cannot	be	directly	extrapolated	from	the	properties	of	their	components.	Molecular	interactions	at	the	sub-nanometre	scale	control	molecular	assemblies	on	a	scale	spanning	tens	of	nanometres,	which	determine	the	properties	of	cells	measuring	tens	of	micrometres.	Yet	detailed	knowledge	of	the	underlying	processes	on	one	scale	is	not	necessarily	sufficient	to	understand	behaviours	on	another.		This	is	evident	in	the	fact	that	a	cell’s	shape,	mechanics,	or	signalling	behaviour	cannot	be	divined	from	nanoscale	dynamics	in	the	molecularly	crowded	environment	of	the	cell	surface	(Refs	Kuo	and	Diz-Munoz	in	this	issue).	Similarly,	although	tissue	behaviour	is	controlled	by	cell	dynamics	and	interactions,	it	cannot	be	directly	predicted	from	cellular	properties	or	from	the	molecular	principles	that	control	them	(Ref	Trepat	in	this	issue).	The	same	problem	exists	at	a	higher	scale,	in	investigations	of	collective	behaviour	in	groups	of	animals	(Ref	Gov	in	this	issue).			Tools	from	statistical	physics	have	been	invaluable	in	providing	a	conceptual	framework	to	understand	emergent	multiscale	properties	in	biology	(Refs	Kuo,	Trepat,	Gov	in	this	issue).	Emergent	behaviours	in	a	complex	system	can	often	be	described	in	the	form	of	phase	diagrams,	as	a	function	of	variables	describing	specific	properties	of	the	system	components	(Ref	Trepat	in	this	issue).	Such	approaches	are	powerful	because	they	help	identify	key	mesoscale	principles	governing	the	collective	behaviour.	Transitions	in	behaviour	—	be	it	a	cell’s	state,	the	shape	of	a	tissue	or	the	directional	polarization	of	a	group	of	ants	—	can	then	be	described	in	the	rich	physical	framework	of	phase	transitions.			One	particular	biological	complexity	that	is	rarely	addressed	is	the	presence	of	multiple	feedbacks	between	scales.	The	nanoscale	organization	of	cytoskeletal		 4	networks	determines	cell	mechanics	and	shape	(reviewed	in	7),	but	the	binding	affinities	of	multiple	cytoskeleton	components	are	force	sensitive.	Some	cytoskeletal	cross-linkers	form	catch	bonds,	meaning	their	attachment	is	enhanced	by	mechanical	load,	while	others	form	slip	bonds	that	release	under	load.	In	this	way,	a	cell’s	mechanics	directly	affects	the	organization	of	the	cytoskeletal	networks	that	govern	it.	At	a	more	complex	level,	cell	mechanics	influences	cellular	signalling,	with	many	cell	types	responding	to,	for	example,	the	stiffness	of	their	environment.	Such	mechanosensing	behaviour	also	feeds	back	on	cytoskeletal	organization,	and	thus	the	resulting	cellular	mechanical	properties.	There	is	at	present	no	theoretical	framework	that	systematically	includes	such	multiscale	feedback	loops	—	they	are	merely	occasionally	added	ad	hoc	when	specific	feedback	mechanisms	are	known.		As	biophysics	has	undergone	this	transition	from	reductionist	studies	on	one	scale	to	multiscale	approaches,	there	have	been	occasional	attempts	to	recast	it	as	‘physical	biology’	in	attempt	to	clarify	the	distinction.	Such	semantic	choices	might	become	less	relevant	as	the	field	expands	into	yet	new	directions.	For	instance,	biophysicists	have	recently	started	exploring	quantitative	classifications	of	animal	behaviour	(Ref	Brown	in	this	issue).	A	predictive	theory	of	behaviour	has	so	far	proven	elusive,	mostly	because	its	connection	to	the	genetic	and	physiological	variables	that	control	it	is	at	best	poorly	understood.	Although	they	do	not	provide	a	predictive	framework,	quantitative	classifications	based	on	dimensionality-reduction	techniques	are	starting	to	unveil	the	behavioural	state	space,	laying	the	ground	for	formal	theory	(Ref	Brown	in	this	issue).			Similar	classifications	using	dimensionality	reduction	have	been	used	extensively	in	recent	years	to	describe	changes	in	gene	expression	landscapes	during	cellular	state	changes	8.	There,	statistical	physics	approaches	are	being	developed	using,	for	example,	transcriptome	entropy	to	describe	cellular	states	9.	How	much	predictive	power	such	approaches	will	have	to	understand	biological	fate	changes	is	an	exciting	question	for	future	research.	Going	one	step	further,	it	remains	to	be	seen	whether	statistical	physics	is	the	right	framework	to	model		 5	the	cross-talk	between	genetic	networks,	animal	behaviour	and	evolution.	It	is	also	possible	that	novel	biophysical	approaches	will	be	needed	to	address	these	highly	complex	questions	that	define	living	systems.				Acknowledgements:	EKP	thanks	Kevin	Chalut	for	critical	reading	of	the	manuscript,	and		acknowledges	funding	from	the	Medical	Research	Council	UK	(MRC	Programme	Award	MC_UU_12018/5)	and	the	European	Research	Council	(Starting	Grant	311637-MorphoCorDiv).		References		1	 Andersen,	O.	S.	Introduction	to	Biophysics	Week:	What	is	Biophysics?	Biophys	J	110,	E01-03	(2016).	2	 Thompson,	D.	A.	W.	On	Growth	and	Form.		(Cambridge	University	Press,	1917).	3	 Keller,	R.	Developmental	biology.	Physical	biology	returns	to	morphogenesis.	Science	338,	201-203	(2012).	4	 Hill,	A.	Why	Biophysics?	Science	124,	1233-1237	(1956).	5	 Schrödinger,	E.	What	is	Life?	,		(Cambridge	University	Press,	1944).	6	 Watson,	J.	D.	&	Crick,	F.	H.	Molecular	structure	of	nucleic	acids;	a	structure	for	deoxyribose	nucleic	acid.	Nature	171,	737-738	(1953).	7	 Koenderink,	G.	H.	&	Paluch,	E.	K.	Architecture	shapes	contractility	in	actomyosin	networks.	Curr	Op	Cell	Biol	50,	79-85	(2018).	8	 Stegle,	O.,	Teichmann,	S.	A.	&	Marioni,	J.	C.	Computational	and	analytical	challenges	in	single-cell	transcriptomics.	Nature	reviews.	Genetics	16,	133-145	(2015).	9	 Grun,	D.	et	al.	De	Novo	Prediction	of	Stem	Cell	Identity	using	Single-Cell	Transcriptome	Data.	Cell	stem	cell	19,	266-277	(2016).		

Biophysics across time and space

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Biophysics across time and space

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