Analysis of tox & deg data
Bas Kooijman
Dept theoretical biology
Vrije Universiteit Amsterdam
Bas@bio.vu.nl
http://www.bio.vu.nl/thb
Maarssen, 2004/10/21
Contents
Introduction
• DEB theory
• DEB laboratory
• Effects of toxicants
sublethal effects
tumour induction & growth
lethal effects
extrapolation
• Biodegradation
microbial flocs
co-metabolism
adaptation
• Foundation Biomass
imbedding
modes of operation
Maarssen, 2004/10/21
Dynamic Energy Budget theory
for metabolic organization
• links levels of organization
molecules, cells, individuals, populations, ecosystems
scales in space and time: scale separation
• interplay between biology, mathematics,
physics, chemistry, earth system sciences
• framework of general systems theory
• quantitative; first principles only
equivalent of theoretical physics
• fundamental to biology; many practical applications
(bio)production, medicine, (eco)toxicity, climate change
Space-time scales
space
Each process has its characteristic domain of space-time scales
system earth
ecosystem
population
individual
cell
molecule
When changing the space-time scale,
new processes will become important
other will become less important
Individuals are special because of
straightforward energy/mass balances
time
Some DEB pillars
• life cycle perspective of individual as primary target
embryo, juvenile, adult (levels in metabolic organization)
• life as coupled chemical transformations (reserve & structure)
• time, energy & mass balances
• surface area/ volume relationships (spatial structure & transport)
• homeostasis (stoichiometric constraints via Synthesizing Units)
• syntrophy (basis for symbioses, evolutionary perspective)
• intensive/extensive parameters: body size scaling
Basic DEB scheme
food
feeding
defecation
faeces
assimilation
reserve
somatic
maintenance
growth
structure

1-
maturity
maintenance
maturation
reproduction
maturity
offspring
Electronic DEB laboratory
http://www.bio.vu.nl/thb/deb/deblab/ (free download site)
DEBtool for research applications
open source (Octave, Matlab)
covers full range of DEB research (fundamental + applied)
advanced regression routines for simultaneous model fitting
DEBtox for routine ecotoxicity applications
load module
DEBtox
Present tasks:
analysis of bioassays on survival, body growth, reproduction, population growth
NEC (including profile likelihood), ECx-time curves
OECD/ISO report on analysis of toxicity data
NOEC methods: not recommended, for historic continuity only
ECx methods: fixed exposure times only, descriptive
Biology-based methods: DEBtox; process-based
OECD-meeting Braunschweig 1996: stimulate exposure-explicit regression methods
DEBtox: only exposure time-explicit method presently available
Near-future extensions:
biodegradation models, multi-sample analysis, population consequences
profile likelihoods for more parameters (elimination rate, toxicity parameters)
Future extensions:
more bioassays, sensitivity-variations, ecosystem effects, predictions based on physical chemistry
mixture toxicity, coupling to exposure models, implementation in environmental risk assessment
Concentration ranges of chemicals
• too little
def: variations in concentration come with variations in effects
• enough
def: variations in concentration within this range hardly affect
physiological behaviour of individuals
• too much
def: variations in concentration come with variations in effects
e.g. water concentration can be too much even for fish
no basic difference between toxic and non-toxic chemicals
“too little” and “enough” can have zero range for some chemicals
Implication: lower & upper NEC for each compound
Effects on organisms
• Process-based perspective on disturbances
chemicals, temperature, parasites, noise
exposure-time explicit methods (response surface)
• Primary target: individuals
some effects at sub-organism level can be compensated (NEC)
• Effects on populations derived from individuals
energy budget basic to population dynamics
• Parameters of budget model individual specific
and (partly) under genetic control
Models for toxic effects
Three model components:
• kinetics
external concentration  internal concentration
example: one-compartment kinetics
• change in target parameter(s)
internal concentration  value of target parameter(s)
example: linear relationship
• physiology
value of parameter  endpoint (survival, reproduction)
example: DEB model
Modes of action of toxicants
 assimilation
food
 maintenance costs
defecation
feeding
faeces

 growth costs
assimilation
 reproduction costs
reserve
somatic
maintenance

maint
1-
7

growth

structure
u

tumour
6
 hazard to embryo
maturity
maintenance
maturation
reproduction
maturity
offspring

6
tumour induction
7
endocr. disruption
8
lethal effects:
hazard rate
Mode of action affects
translation to pop level
Toxic effect on survival
Effect of Dieldrin on
survival of Poecilia
One-compartment kinetics
Hazard rate is linear in
internal concentration
killing rate 0.038 l g-1 d-1
elimination rate 0.712 d-1
NEC 4.49 g l-1
DEB-based effects on body growth
Indirect effects
indicator: effects on ultimate size at constant food
• decrease of assimilation rate (food intake, digestion)
• increase of specific maintenance costs
Direct effects
indicator: no effects on ultimate size at constant food
• increase of costs for synthesis of biomass (structural)
weight1/3, mg1/3
Effect on assimilation
time, d
CuCl2 mg/kg
Data from Klok & de Roos 1996
NEC = 4.45 mg CuCl2 /kg on Lumbricus rubellus
DEB-based effects on reproduction
Indirect effects
indicator: effects on onset of reproduction
• decrease of assimilation rate (food intake, digestion)
• increase of specific maintenance costs
• increase of costs for synthesis of biomass (structural)
Direct effects
indicator: no effects on onset of reproduction
• increase of costs for the synthesis of offspring
• decrease of survival probability at birth
Direct effect on reproduction
cum. # young/female
0
g Cd/l
0.2
0.4
0.8
1
2
time, d
Effect on hazard
NEC = 0.023 g Cd/l
Effects on populations
At constant food density:
At variable food density:
individual-based modelling of populations
requires modelling of resources
Population effects
can depend on food density
3,4-dichloroaniline
direct effect on reproduction
potassium metavanadate
effect on maintenance
Population growth of rotifer Brachionus rubens at 20˚C
for different algal concentrations
Maintenance first
Chlorella-fed batch cultures of Daphnia magna, 20°C
300
200
neonates at 0 d: 10
winter eggs at 37 d:
0, 0, 1, 3, 1, 38
number of daphnids
400
cells.day-1
300
200
max number of daphnids
30106
Kooijman, 1985 Toxicity at population level.
In: Cairns, J. (ed) Multispecies toxicity testing.
Pergamon Press, New York, pp 143 - 164
Maitenance requirements:
6 cells.sec-1.daphnid-1
100
100
106 cells.day-1
0
0
8 11
15 18 21 24 28 30 32 35 37
time, d
6 12 30
60
120
Food intake at carrying capacity
103 cells/daphnid.d
103 cells/daphnid.d
metavanadate
log mg V/l
potassium dichromate
log mg K2Cr2O7/l
sodium bromide
log mg Br/l
9-aminoacridine
log mg AA/l
2,6-dimethylquinoline
log mg DMQ/l
colchicine
log mg Col/l
Advantages of DEBtox method
• effective use of all data
smaller number of parameters per data-point
reduction of required test animals
simultaneous use of data on multiple end points
• more informative
standard statistics (NOEC, ECx, slope) can be calculated from
new ones (NEC, tolerance conc., elimination rate), but not vice versa
• process-based characterizations of effect
independent of exposure time
allows NEC estimates for risk assessments to replace NOEC
• tight link of toxicity with pharmacology/physiology/ecology
• extrapolations are facilitated
acute  chronic; individual  population; lab  field
one species  other species; one chemical  other chemicals
Biodegradation
Uptake of substrates is core-element of DEB theory
Special issues
• microbes typically grow in flocs
this limits access to substrate by several orders of magnitude
• adaptation to new substrates
short term: by expression of genes for this substrate
long term: by change in species-composition
• co-metabolism
uptake of new substrate can depend on that of other substrates
• co-limitation (e.g. by nutrients such as N-compounds)
this is a core-element of DEB theory
Yield vs growth
1/yield, mmol glucose/ mg cells
Streptococcus bovis, Russell & Baldwin (1979)
Marr-Pirt
(no reserve)
DEB
spec growth rate
yield
1/spec growth rate, 1/h
Russell & Cook (1995): this is evidence for down-regulation
of maintenance at low growth rates
DEB theory: high reserve density gives high growth rates
structure requires maintenance, reserves not
Growth of microbial flocs
Microbes in sewage treatment plants grow in flocs
< 10% of microbes is metabolic active
Growth limited by transport of substrate into the floc
• core starves to death
substrate  living + dead biomass (detritus)
• flocculated growth rate rF
<< suspension growth rate r
(upto factor 1000)
2 extra parameters:
• size at which floc destabilizes
• penetration rate of substrate into floc
Brandt & Kooijman 2000 Two parameters account for the flocculated
growth of microbes in biodegradation assays. Biotech & Bioeng 70: 677-684
Co-metabolism
Co-metabolic degradation of
3-chloroaniline by Rhodococcus
with glucose as primary substrate
Data from Schukat et al, 1983
Brandt et al, 2003
Water Research
37, 4843-4854
acetate
cells
Substrate conc., mM
biomass conc., OD433
Diauxic growth
oxalate
Adaptation to
different substrates
is controlled by:
enzyme turnover
0.15 h-1
preference ratio
0.5
time, h
Growth of acetate-adapted Pseudomonas oxalaticus OX1
data from Dijkhuizen et al 1980
SU-based DEB curves fitted by Bernd Brandt
Brandt et al, 2004
Water Research
38, 1003-1013
Netherlands Center for
Environmental Modeling
Members NCEM: http://www.ncem.nl
• Foundation for Biomathematical Assessments
Biomass (Vrije Universiteit, Amsterdam)
Bas Kooijman
VUA
effects of toxicants on organisms, bio-degradation
• Radboud Center for Environmental Modelling
Dik van de Meent
RCEM (Radboud Univ. Nijmegen)
RUN +
emission, transport & transformation of chemicals
RIVM Bilthoven
• Dept Indust. Ecol.; Inst. Environ. Sciences
IE-MCL (Leiden University)
life-cycle studies for chemicals and products
Aims:
• collaboration
• coordinated research acquisition
Gjalt Huppes
LU
Aims of Biomass
• stimulation interaction between dept Theoretical Biology (TB-VU),
and companies, governmental institutions
modelling, data analysis, computational sciences, advice on setup of experim
• offering talented scientists opportunities to contribute in this interac
stimulate talented students to specialize in research areas of the foundation
• development of applications of Dynamic Energy Budget (DEB) the
ecotoxicology, risk assessment, nutrition, medical biology and biotechnology
• organizing specialized courses
research areas of foundation, application of math. & computer science in biol
Modes of operation
• person-oriented research acquisition
partners hire scientists on part-time basis (restrictions on info-flux)
employed by & detached at the foundation (Amsterdam)
partner controls work package for specified amount of time
• project-oriented research acquisition
clients sponsor projects by PhD students or postdocs who are
part-time (80%) employed by VU (NOW, STW, EU projects)
temporarily supplemented by the foundation on project basis
• support for selected students
financial support for explicit commitments
excellent study results (full focus on study)
specialization in mathematical biology
• information supply: courses
generation of resources, stimulation of research
Benefits for partners
who support foundation staff
• hire highly skilled modelers on part-time basis
and use them for data analysis, advice on experimental research
• benefit from a team that supports these specialists in
fundamental research, data analysis, statistics, computer science
• contact & train talented students via traineeships
who may become future employees
• access to & influence on new developments in DEB applications
& fundamental research that generates these applications
Benefits for clients
who sponsor projects
• solve a particular problem using knowledge of experts
who are supported by TB-VU and foundation staff
and who participate in the Netherlands Center for Environmental Modeling
and have assistance from students with traineeships
• get into contact with talented young scientists and students
who may become future employees
Permanent foundation
staff members
First permanent staff member: Tjalling Jager (0.8 fte)
he has developed EUSES at RIVM
Analysis of ecotoxicity & biodegradation data,
such as those from standardized tests (ISO/OECD)
toxico-kinetic & effect data
Prediction of transport & fate of chemicals in the environment
possible effect scenarios in collaboration with RCEM in NCEM
life cycle studies in collaboration with IE-CML in NCEM
Aim: second permanent staff member in same field
Future developments: food production/conservation, biotechnology
Estimated financial costs
Yearly costs in k-euro:
salary 80%
auditor
room(14m2)
exploitation
total
faculty 9%
69 = 86.25  0.8
1
4
10
84
financial admin 4%
computer service 3%
personnel service 1%
general 1% (incl use library)
guidance by TB-VU staff 10%
total 19%
104 = 84/ (1 - 0.19)
Yearly effective hours:
1280 = 1600  0.8
background research 50%
contracted = 640 h/a
costs per hour (excl vat):
104/ 640 = 163 euro/h
2 partners = 320 h/partner
= 52 k-euro/(partner  a)
Yearly index 3%
PM: liability insurance