How much have we advanced, current situation, and how far can we go with growth models?

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How much have we advanced, current situation, and how far can we go with growth models?

Jaap van Milgen¹, Galyna Dukhta², and Veronika Halas²

1INRA

2Kaposvár University

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Preliminary remark

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Outline

Nutritional modeling of growth

 Empirical models of growth

 Concepts used in nutritional models

 From models to tools

Towards more mechanistic models?

Future directions

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Age, d

Body weight, kg

Empirical modeling of growth

Strathe et al. (2010). J. Anim. Sci. 88:638-649

0 20 40 60 80 100 120 0

1 2 3 4 5 6 7 8 9

Age, d

Body weight, kg

Age (or time) as a driving force for growth

Gonçalves (2017). PhD thesis, UNESP

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0 20 40 60 80 100 120 0

1 2 3 4 5 6 7 8 9 10

0 20 40 60 80 100 120 140

Age, d

Body weight, kg Daily gain, g/d

Growth can be described by a Gompertz function

Parameters usually used:

• Initial body weight

• Mature body weight

• Shape parameter

Parameters usually used:

• Initial body weight

• Mature body weight

• Shape parameter

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0 5 10 15 20 25 30 35 40 0

1 2 3 4 5 6 7 8 9 10

0 20 40 60 80 100 120 140

Age, d

Body weight, kg Daily gain, g/d

Growth can be described by a Gompertz function

In commercial settings, we rarely have data that allow to accurately

estimate the mature body weight

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0 50 100 150 200 250 300 350 400 450 500 0

5 10 15 20 25 30 35 40 45 50

0 50 100 150 200 250

Age, d

Protein mass, kg Protein deposition, g/dPotential protein deposition can be described by a Gompertz function

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50 70 90 110 130 150 170 0

5 10 15 20 25

0 50 100 150 200 250

Age, d

Protein mass, kg Protein deposition, g/d

Potential protein deposition can be described by a Gompertz function

Precocity

Initial protein mass

( initial body weight)

Mean protein deposition

( average daily gain)

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50 70 90 110 130 150 170 0

5 10 15 20 25

0 50 100 150 200 250

Age, d

Protein mass, kg Protein deposition, g/dPotential protein deposition can be described by a Gompertz function

Precocity

Initial protein mass

( initial body weight)

Mean protein deposition

( average daily gain)

The way animals grow (protein)

in combination with the way animals eat determine the nutrient requirements

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Empirical modeling of growth

Schulin-Zeuthen et al. (2008). Anim. Feed Sci. Technol. 143:314-327

Body weight, kg

Cumulative feed intake, kg

Feed intake as a driving force for growth

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FeedFeed AnimalAnimal

Ad libitum feed intake: push or pull?

(the chicken-or-egg question, even for pigs)

Push: the animal eats and thus it grows Pull: the animal eats because it wants to grow

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Pull Push Explicit control

function* Potential lipid deposition Feed intake Additional issues

and questions Constraints can be imposed on feed intake

(e.g., gut capacity, heat stress)

What controls ad libitum feed intake (DM, AME, NE)?

Consequence The animal eats for net energy Lipid deposition becomes an

« energy sink »

*In addition to a function describing protein deposition

Ad libitum feed intake: push or pull?

(the chicken-or-egg question, even for pigs)

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ash deposition water deposition

body weight gain

Concepts used in nutritional growth models

Whittemore and Fawcett (1974). Anim. Prod. 19:221-231

nutrient intake nutrient

intake

lipid deposition

protein deposition

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lipid deposition maintenance &

physical activity

cost of protein deposition

body weight lean meat % backfat thickness

body lipid body lipid body protein

body protein

endogenous losses amino acid balance phenotypic potential

ME_starch ME_sugars ME_lipids ME_residue

ME_{excess CP}

PD-free NE protein

deposition

DE_residue DE_residue DE_lipids

DE_lipids DE_sugars

DE_sugars DE_starch

DE_starch DE_CP

DE_CP

Feed intake (push)

van Milgen et al. (2008). Anim. Feed Sci. Technol. 143: 387-405

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lipid deposition maintenance &

physical activity

cost of protein deposition

body weight breast meat %

body lipid body lipid feather-free

body protein feather-free body protein

endogenous losses amino acid balance phenotypic potential

AMEn_starch AMEn_sugars AMEn_lipids AMEn_residue

AMEn_{excess CP}

PD-free NE protein

deposition

ME_residue ME_residue ME_lipids

ME_lipids ME_sugars

ME_sugars ME_starch

ME_starch ME_CP

ME_CP

Feed intake (push)

feather protein feather protein

feather loss Dukhta et al. (2017, 2018). PhD thesis, Kaposvár University

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 Digestion

 Absorption

 Maintenance

 Synthesis of non-protein nitrogen compounds

 Transamination

 Amino acid imbalance

 Catabolism due to an excess supply

 (Preferential) catabolism to supply energy

 Inevitable catabolism

 Deposition (but the amino acid composition differs among proteins)

Processes involved in amino acid utilization

Moughan (2008). In: Mathematical modeling in animal nutrition. J. France and E. Kebreab (eds.)

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From a model to a decision support tool

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Halas et al. (2004). Br. J. Nutr. 92:707-723

Towards more mechanistic models

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equilibrium [nutrient]

nutrient fow

• dS/dt = (A – C) x S

• A = k₁ + k₂ exp(-k₃ x time)

• C = k₁ + k₄ exp(-k₅x time) At maturity: A = C = k₁

catabolism anabolism

Homeostatic regulation

(short-term regulation)

Homerhetic regulation

(long-term regulation)

Towards more mechanistic models

Lovatto and Sauvant (2003). J. Anim. Sci. 81:683-696 Rivera-Torres et al. (2011). J. Anim. Sci. 89:3170-3505

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Salway (2017). Metabolism at a glance, 4^th edition. Wiley Blackwell

How far should we go

in further refining our nutritional models?

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lipid

protein starch sugars fiber/VFA

intermediary metabolism

lipid ATP

protein

heat

How far should we go

in further refining our nutritional models?

glycogen

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Energy efficiency of glucose  ATP

cost = 2820/31 = 91.0 kJ/ATP

glucose 31 ATP

1 glucose + 6 O₂  31 ATP + 6 CO₂

(1 glucose = 2820 kJ/mol = 74.2 kJ/g)

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cost = 2820/30 = 94.0 kJ/ATP

glucose 30 ATP glycogen

1 glucose  glycogen  30 ATP

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cost = (2820*6/31)/2 = 272.9 kJ/ATP

glucose

2 lactate +2 ATP 2 lactate

glucose

6 ATP

(6/31 glucose)

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cost = 2820*14/334 = 118.2 kJ/ATP

14 glucose

lipid lipid

334 ATP

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glucose  glutamate  ATP cost = 2820/(29.75) = 94.8 kJ/ATP

ATP

glutamate glucose

glutamate

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direct 91.0 kJ/ATP = 100%

via glycogen (muscle) 97%

via lactate (gluconeogenesis) 33%

via lactate (oxidation) 100%

via lipid 77%

via glutamate 96%

1 glucose + 6 O₂  31 ATP + 6 CO₂

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Nutritional growth models use digestible nutrients as model inputs …

… to predict performance traits of a single animal in a “standard” environment

Modeling biological functions

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understand

predict

control observe

From a co ncep t-dri ven appr oach …

... towa

rds a da ta- driven a

pproach

Our capacity to observe is increasing exponentially

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image analysis serotonin,

cortisol behavior and welfare

feed intake patterns feeding behavior individual feed intake

digestive efficiency metabolic efficiency

transcriptomics proteomics

metabolomics digestibility markers

gut health microbiota

Our capacity to observe is increasing exponentially

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1979 1984 1999

“one may even venture the opinion that modeling is now

a mature discipline”

“tremendous progress has been made, but we still

have a long way to go”

“attempting to bring it to childhood”

Modeling: where do we come from?

(Lee Baldwin, 1999)

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Conclusions

 We still have a long way to go to understand the full story of nutrition and metabolism

 Tool development is an essential element in model uptake

 Monitoring/phenotyping/big data will bring new life and new challenges to nutritional modeling

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€10 M

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Industry

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Industry

Partners

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The Feed-a-Gene Project has received funding from the European Union’s H2020 Programme under grant agreement no 633531

Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems

(www.feed-a-gene.eu)